Sentiment analysis of GO-JEK services quality using Multi-Label Classification

One of the most popular online transportation providers in Indonesia is GO-JEK. At the first establishment, GO-JEK is only an online taxi motorbike service (in Bahasa Indonesia: ojek) that transforms from a conventional taxi motorbike. After several years GO-JEK began to develop more services, like GO-FOOD, GO-SEND, GO-CAR, GO-MART, GO-RIDE, GO-PAY, GO-TIX, GO-BOX, and GO-MED. As the GO-JEK services develop into more categories, it is more challenging to automatically analyze the sentiment polarity for each category of services category. An ordinary classification algorithm or single-label classification is concerned with learning from a set of examples associated with a single label classification. However, in this case, we want to classify GO-JEK services based on two-class targets, which are GO-JEK service categories and polarity sentiment classification. This research methodology contains Dataset Preparation, Feature Selection, Basic text mining process, train, and split dataset, and Classification. We implemented two classification methods: Multi-Label Classification and a simultaneous classification using the Random Forest Algorithm as a comparison. Based on this dataset, the most mentioned GO-JEK service is GO-FOOD followed by GO-SEND, GO-RIDE, GO-CAR, and GO-MART. Based on the service category, GO-FOOD gets the most positive reviews following by GO-RIDE and GOJEK 90. Some service categories like GO-SEND and GO-MART get more negative reviews than positive reviews. The accuracy of the Multi-label classification method raised 76%. Simultaneous classification accuracy using the Random Forest algorithm produce for service category classification yields 97% and only 78% for sentiment polarity classification. We can see that both algorithm, multi-label classification, and Random Forest algorithm, yields almost the same classification accuracy for polarity sentiment classification. We can conclude that the imperfect accuracy in polarity sentiment classification is related to the difficulty of identifying the most suitable polarity for each tweet. Many sarcasm tweets and slang words that cannot be identified.

Introduction
The development of Indonesian transportation has entered a disruptive model. Online transportation, such as Taxi Online, becomes more popular because customers can get many conveniences and ease in one click away from their smartphone. Furthermore, users can search for the most suitable transportation services in their need based on reviews of other customers from the application or social media. Many people commonly use social media to share their opinion and comments. Many companies utilized these reviews as feedback to increase their services and the quality of their products. One of the most popular online transportation providers in Indonesia is GO-JEK. At the first establishment, GO-JEK is only an online taxi motorbike service (in Bahasa Indonesia: ojek) that transforms from a conventional taxi motorbike. After several years GO-JEK began to develop more services, like GO-FOOD (buying foods services), GO-SEND (shipping goods in the city), GO-CAR (car taxi online), GO-MART (shopping services), GO-RIDE (motorbike taxi ), GO-PAY (virtual money that can be used to pay GO-JEK services transaction), GO-TIX (buying cinema movie services), GO-BOX (moving house services) and GO-MED (buying medicine services) [1]. GO-JEK has an official Twitter account, namely @gojekindonesia. The purpose of this account is to accommodate the reviews and comments from their customers, particularly about GO-JEK services. Based on these reviews, companies can analyze a customer's opinion to improve their products and services. Opinion analysis or sentiment analysis is contextual mining of text to identify and classify opinions or sentiment in the source text, whether underlying positive, neutral or negative. Many previous studies analyze the sentiment with machine learning approaches. As the GO-JEK services develop into more categories, it is more challenging to analyze each services category's sentiment polarity. An ordinary classification algorithm or singlelabel classification like Support Vector Machine (SVM), Naïve Bayes, and Random Forest Classifier are concerned with learning from a set of examples associated with a single label classification. However, in this case, we want to classify GO-JEK services based two-class target, which is GO-JEK service categories and polarity sentiment classification for each GOJEK services categories. One solution we can implement a single label classification algorithm with two simultaneous steps. We classify the service categories and follow the next step, a classification based on the polarity sentiment. Another solution we can implement a multi-label classification algorithm. Multi-label classification is an algorithm to classify a set of examples related to more than one class. The aim is to predict multiple output variables for each input instance. This differs from the binary or multi-class classification, which involves only a single target variable [2]. This study aims to classify GO-JEK reviews into its service category and analyze them into positives, negatives, and neutral polarity for each category of the services. We implemented a multi-label or multi-output classification algorithm using the Scikit-learn classifier platform [3] [4]. Scikit-learn is an integrated module for Python, especially for machine learning algorithms [3]. In this study, we also implemented simultaneous steps classification of a single label classification algorithm, which is a Random Forest algorithm as a comparison. The paper is organized as follows. Related works are given in Section 2. The methodology is described in Section 3. Section 4 presents the Result and Discussion. The Conclusions are presented in Section 5.

Related Work
Some of the previous studies implemented multi-label classification for sentiment analysis, like [5][6] [7]. The study by [5] has three main components, which are text segmentation, feature extraction, and multi-label classification. The study by [5] utilized three different dictionaries and implemented a bag of words for feature representation. The study by [6] proposed a methodology for expanding a multi-label emotion lexicon based on a collection of un-labeled tweets using multi-label classification. The study by [7] proposed the emotion concentration to reduce the issue of noisy training documents and identify the same word's emotional senses with exploited topic models. Our work classifies the dataset only has one polarity sentiment among three classes, positive, neutral or negative. These studies observed that the dataset could have another sentiment class like "happy" or "sad." Another differentiation with our study is the observed language. Our study observed Bahasa Indonesia that have a different pre-processing task with English, especially in the stemming and normalization stage. Some previous studies observed Bahasa Indonesia like the study by [8] [9].

Methodology
The methodology for mining and classify the reviews about GO-JEK services quality contains several processes. The first process is dataset preparation and following by another process. More detail about this methodology can be seen in general architecture in Figure 1

Dataset Preparation
There are two subprocesses in preparing the dataset stage: the Crawling Data and Class Labeling process. Crawling Data is a process to collect the data. We collected the user's review of several GO-JEK categories from Twitter. These reviews or the post on Twitter also known by "tweet". We choose Twitter because Indonesia, as one of the countries that have the most users using Twitter. Moreover, Twitter as one of the social media that provide Application Programming Interface (API) that make researchers easy to crawl the data. We crawled or collected about 1500 review tweets that mention @Gojekindonesia. The next step is the Class Labeling process. Class Labeling is a process to label the tweets into two suitable classes or categories. The first label is to classify the tweets based on the GO-JEK service category. This study only took six GO-JEK service categories: GO-CAR, GO-SEND, GO-MART, GO-FOOD, GO-RIDE, and GO-PAY. We also classify the tweets that do not mention specific GO-JEK services but contain sentiment polarity to general GO-JEK services into class GOJEK. In this process, we implemented a string matching method to do this labeling process automatically. The label of these service categories is based on the keyword that mentions the tweets' service category. We also consider the possibilities of the typo from the customers to mention these categories. For example, "gocar", "go-car" and "go car" mean the same, that the users are talking about Go-Car Services. The other labeled process is to classify Twitter based on their sentiment polarity, positive, neutral, and negative. In this process, we utilized a human annotator and did this process manually.

Feature Selection
Feature Selection is a process to prepare the dataset, so machine learning approaches can further process it. Generally, machine learning can only process the data as input in number or vector form. Therefore, we need to pre-process the dataset from the previous stage that is still in text form. Several stages in this process are Data Cleaning, Case Folding, Tokenization, and Stop Word Removal. Data Cleaning is a process to remove noise or unwanted terms or symbols from the dataset. In this process, we remove all the symbols and numbers like "@", "RT", "#" and emoticon. We also remove URL addresses and punctuations. The next step is Case Folding, or converting all the text in the dataset into lower case. The next process is Tokenization; this process is a process to split the Twitter sentences into words or terms. Stop Word Removal process is a process to remove all the vocabularies in the dataset that include in stop-words vocabularies. Stop-words vocabularies are words that are claimed as not important words or does not have much effect in a language. In this study, we implemented Tala Vocabularies [10] as stop-words vocabularies for Bahasa Indonesia.

Convert Negation
Convert negation is a process to identify all negative terms that appear in front of a word. The purpose of this identification is to convert the polarity of the word into its opposite. Because all the negative terms like "bukan", "tidak", "enggak", "ga", "jangan", "nggak", "tak", and "gak" (in English: "Not") that appears before a word will change the polarity value of the word, in this stage, we convert this sequence word into the antonym of the adjective word. For example, the sequence word "tidak bagus" (in English: not good), we will replace "tidak bagus" into "jelek" (in English: bad). An example of this process can be seen in Table 1.

Stemming
Stemming is a process to get a root base of a word by removing all the words' affixes. This process aims to minimize the mistake in identifying the keywords from the general word vocabularies. We implemented Nazief Adriani algorithm [11], which is one of the best stemming algorithms for Bahasa Indonesia. We utilized a dictionary to check general words in Bahasa Indonesia; this dictionary contains 29932 words. We get this dictionary by downloading it from Bahasa Indonesia dictionary on kateglo.com [12].

Normalization
Normalization identifies informal words in the dataset and converts these words into standard language form. Usually, informal words are called as slang words. Many slang words appeared on an informal platform like social media. Moreover, one of the challenges of analyzing social media is too many slang words in it. We can not analyze this kind of words because they are out of the vocabulary and have a higher tendency in ambiguity interpretation. For example, the word "betul" (in English : True), sometimes in slang words will be written as "btl", "betol", "bnr" "tul" etc. This problem can be solved by replacing the informal words with a formal word with the same context. Besides normalizing the slang words, in this study, we also normalize the acronym and abbreviation. The excerpt of this process can be seen in Table 2.

Data Representation
Data representation is a process to convert text data from feature selection into numbers or vectors. This process is also known as the word weighting process. In this study, we implemented a term frequency method. All terms or words are represented based on how many times they appear on a tweet.

Train and Split Dataset
Train and split test a utility to create training and testing sets from a single data set randomly. We implemented utility from Scikit Learn for Python. This study split the dataset 70% as a training set and 30% tweets as a testing set.

Classification
In this stage, we implemented Multi-Label classification and Random Forest Classification as a comparison. We implemented MultiOutputClassifier Algorithm from Scikit Learn that predicts multiple variables of output from data input. This algorithm is one of the Multioutput-multiclass classification and multi-task classification algorithms modules in Scikit Learn. Multioutput-multiclass classification and multi-task classification means that a single estimator has to handle several common classification tasks. This is both a generalization of the multilabel classification task, which only considers binary classification, and a generalization of the multi-class classification task [2]. This method is also known as multi-label learning, multi-dimensional, or multi-objective [13]. Multi-output models can produce multiple outputs to assign to multiple target variables y for each test instance [2]. For instance, an example of fruit classification, a sample data could be labeled as "pear"; and "yellow" or "green" for another output. This example is similar to our study case, which classifies the GO-JEK service categories and sentiment polarity for each category. This is the simultaneous prediction of multiple target variables using the same set of predictive variables. The prediction task is called a multi-label classification because the target variables are binary and not real-valued variables [14].
As a comparison, we also implemented a simultaneous process classification using Random Forest Classifier. We choose Random Forest because this algorithm easy to use, and many previous studies showed this algorithm generates a great result. First, the system will predict the GOJEK service category, and then the system will predict the polarity sentiment for each category.

Result and Discussion
In this study, the Preparing Dataset stage is a process to yield a collection of tweets with two label classification, which is based on GO-JEK service category and based on sentiment polarity category. The crawling process yields a tweets dataset that contains 370 positive reviews, 370 neutral reviews, 560 negative reviews, and 200 unrelated tweets to GO-JEK. To make the data balance; therefore, as many as 190 negatives, reviews were randomly excluded from the dataset. We also exclude 200 unrelated tweets because not related to Go-JEK quality services reviews. After this process, the number of twitters in this dataset is 1110 tweets. Based on this dataset, the most mentioned GO-JEK service is GO-FOOD, which are 267 tweets, followed by GO-SEND are 266 tweets, GO-RIDE is 236 tweets, GO-CAR is 202 tweets, and GO-MART are 20 tweets. The percentages of the distribution of these service categories can be seen in Figure 2. Based on the service category, GO-FOOD gets the most positive reviews, which are 125 tweets following by GO-RIDE 100 tweets and GOJEK 90 tweets. Some service categories like GO-SEND and GO-MART get more negative reviews than positive reviews. The distribution of sentiment polarity for each service category can be seen in Figure 3. Feature selection is a process to extract the most important feature from the dataset. In this study, we implemented a Bag of Words (BoW) feature method. We split all the twitters from the dataset into words by words. We implemented some basic text mining like case folding, stemming, normalization, and stop word removal from reducing the dimension. The excerpt of this stage can be seen in Table 3.    6 We split the dataset into 70% training and 30% for the testing stage. The accuracy of the Multi-label classification method is 76% for testing accuracy. Simultaneous classification accuracy using the Random Forest algorithm produces for service category classification yields 94% for testing accuracy. For polarity, sentiment classification yields 78% for testing accuracy. We can see that both algorithms, multi-label classification, and Random Forest algorithm yields almost the same classification accuracy. The confusion matrix of Random Forest accuracy can be seen in Figure 4 and Figure 5. Based on Figure 5, we can see that almost all category classification has high accuracy.    Figure 5 shows the confusion matrix of polarity sentiment classification. We can see that some of the predicted classifications are different from actual values. The best prediction is for negative polarity sentiment classification, which has correct 74 predictions of 92 data testing. The worst prediction is the Neutral prediction class, which has only 78 correct 103 data testing predictions. The poor accuracy in polarity sentiment classification is related to the difficulty of identifying the most suitable polarity for each tweet-many sarcasm tweets and slang words that can not be identified.

Conclusion
This study aims to analyze the reviews about GO-JEK services on Twitter and classify them into positives, negatives, and neutral polarity for each category of the services. We implemented a multi-label or multi-output classification algorithm using the Scikit-learn classifier platform. In this study, we also implemented simultaneous steps classification of a single label classification algorithm, which is the Random Forest algorithm as a comparison. The preparing Dataset stage is a process to yield a collection of tweets with two label classification, which is the GO-JEK service category and polarity sentiment category. Based on the dataset, the most mentioned GO-JEK service is GO-FOOD, followed by GO-SEND, GO-RIDE, GO-CAR, and GO-MART. Based on the service category, GO-FOOD gets the most positive reviews following by GO-RIDE and GOJEK. Some service categories like GO-SEND and GO-MART get more negative reviews than positive reviews. The accuracy for Multi-label classification is 76% for testing accuracy. Simultaneous classification accuracy using the Random Forest algorithm produces for service category classification yields 94% for testing accuracy. For polarity, sentiment classification yields 78% for testing accuracy. We can see that both algorithms, multi-label classification, and Random Forest algorithm yields almost the same classification accuracy. The reduced accuracy in polarity sentiment classification is related to the difficulty of identifying the most suitable polarity for each tweet. Many sarcasm tweets and slang words that can not be identified.


Train the Neural Network by Abstract Images

Like the textbook for students’ learning, the training data plays a significant role in the network’s training. In most cases, people intend to use big-data to train the network, which leads to two problems. Firstly, the knowledge learned by the network is out of control. Secondly, the space occupation of big-data is huge. In this paper, we use the concepts-based knowledge visualization [33] to visualize the knowledge learned by the model. Based on the observation results and information theory, we make three conjectures about the key information provided by the dataset. Finally, we use experiments to prove that the artificial abstracted data can be used in networks’ training, which can solve the problem mentioned above. The experiment is designed based on Mask-RCNN, which is used to detect and classify three typical human poses on the construction site.

Introduction
Since the convolutional neural network was proposed ( [14]- [17]), the technique of image recognition ushered in explosive development. However, some questions need to solve when we apply the neural network. Data dependence is one of the most serious problems. Compared with traditional machine learning methods, deep learning depends on the training data because it requires the data to reflect the underlying patterns of the data [24].
People usually intend to use big-data to solve this problem. However, big-data cannot solve everything. In [18], the author points out that with the increasing of the data scale, the accuracy of classification is increasing, but the robustness of the model is decreasing at the same time. Moreover, the accuracy improvement has an upper bound as mentioned in [13]. More important is that the noise is inevitable in a huge dataset, including irrelevant information [25], [32], error labels [23] etc. It can cause unpredictable impacts potentially. Finally, the most significant shortcoming of big-data is the great space occupation and computing cost in training.
As a model with similar working mechanisms of the human brain, rethinking the human learning process might be the best way to break the current dilemma. Teachers intend to use few representative and simplified samples in education but not many real instances to show one object's feature. The educator will take a lot of time to write the most important tool in students learning, the textbook. It is very inspiring to us. Can we use a well-designed dataset with abstract images to train the model?

Related works
To solve the low sampling efficiency and avoid security risks in the real world training, people use simulated data to train the model, called simulation to real transfer (sim2real). Among the methods of sim2real, two kinds of methods are similar to our ideas, domain adaption, and domain randomization. The purpose of the former is to map the data of different source domains and target domains into a feature space, and minimize the distance between them in the space, like [2], [3], [7], [8]. And the domain randomization intends to learn the random combinations of various characteristic variables or features, like [27], [30]. Different from our ideas, synthetic data generation requirements in these two kinds of methods are similar to the real. Essentially, they are solutions for the lack of data in networks' training.
On the contrary, in our work, we actively filter some of the real-world information. We intended to verify if the abstract data can be used in the network's training or not. Like the educator editing the textbook's content, we want to use data with a more effective form to train the network. In a word, the methods related to sim2real intend to use features to generate simulated images, whereas our work is to minimize the information provided by the image of the real world without affecting the network's final performance.

Motivation
Training the network with abstract data has the following advantages. First, It can eliminate the "shortcut" in the dataset and avoid networks' cheating in learning. Secondly, the abstract data takes up less space than the original data and can accelerate the network's training.

Networks' cheating
In most cases, the trained net-work's performance is the only measure in the evaluation of the training process. However, as the author mentioned in [9], some of the information in the dataset can be the "shortcut" to complete tasks, which leads to the training's failure. The model uses a simple "shortcut" to complete a complex learning task called Clever Hans Effect. In practice, this phenomenon has been observed in the early version of BERT [5] when it completes the argument reasoning comprehension task. In [22], the author shows that the model does not understand the task. It makes the judgment just based on the statistical feature of the dataset. In [10], the author points out that in some cases, the CNNbased model classifies the image just depending on the texture unexpectedly. In other words, the CNN is misled by the training dataset, it does find the best way to complete the task, but that is not what we want it to learn. In [20], the author points out that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the datasets.
In the examples above, the network makes the correct judgment based on a hidden trick but not the logic we want it to learn. Even though we can visualize some of the knowledge learned by networks as [33]- [36] show, we still cannot impact their learning. Therefore, to eliminate the "shortcut" hidden in the dataset, control the information provided by the training data is the last option. Using well-designed abstract data can purify the knowledge, speed up the training, and control the information learned by the neural network.

Our Contribution:
This work verifies that the abstract images can also be used in the network's training. Using the data with reasonable abstraction will not affect the performance of the model after training. It can reduce the size of the dataset and avoid models' cheating in the learning process effectively.

Network cheating in human pose detection
Because the learning process is out of control, the network's cheating (Clever Hans Effect) is inevitable. This section demonstrates an example of a network's cheating behavior to show the importance of eliminating the "shortcut" hidden in the dataset.

Introduction of the Model
To ensure workers' health and avoid labor injury caused by long working hours, net-works for human pose detection are wildly used to monitor worker movements. Based on Mask-RCNN [12], we design a network to monitor the pose of human to prevent work-related musculoskeletal disorders. [28] shows a study of occupational mobility in a cohort of construction workers. It shows that disorders of the back and spine are one of the major causes of early retirement. Therefore, this model is designed to recognize three main poses related to workers' back and spine on the construction site, standing, bending, and squatting (see [31]). Because there are only three kinds of the pose, to simplify the computing, we do not use the network to extract human skeleton information but identify the region with a human directly, as Fig. 2 shows. VGG Image Annotator (VIA) [6] is used to mark the label.

Clues of the Network's Cheating
The model is trained by the real data (see Fig. 1(a)), and its mAP (IoU = 50%) can get to 89% on the similar data (test dataset of workers). However, when we use another test case of workers, the mAP decreases to 71% rapidly, which is caused by the accuracy decrease in classification but not the detection. This phenomenon catches our attention. After analyzing the dataset, we find some clues about the network's cheating. On the construction site, workers with different types of work are usually with different types of cloth. In Fig. 3, we present two typical work on the construction site, quality supervisor and concrete pouring worker. For the clothing, the former wear usual clothes, and the latter are required to wear the special vest for their safety. Meanwhile, the former usually stands and plays the role of a supervisor in most cases. And the latter often needs to bend or squat because of their work. There is a "shortcut", classifying the pose of human by their cloth.
To verify our hypothesis, we create a special dataset based on the original dataset shown in Fig. 10(a). In the training dataset,  We replace all the "bend" samples with the same amount of designed images as Fig. 4 shows. Unlike the original images, the worker is always with an orange fluorescent vest and yellow helmet in this group of pictures.
After training, we use it to identify another group of designed images (240 images) as Fig. 5 shows. In this group, the worker with the same equipment, but all their pose is "squat".
As a result, there are 63% samples are classified as "bend", 30% are detected as "squat", 3% are classified as "stand", and 4% are not detected. This result indicates that to get a trustworthy model based on neural network, control what is learned by the network is necessary. However, there is no effective way for us to control the learning process of the network. Controlling the information provided by the dataset is the last way we can choose. In the following part of this paper, we demonstrate our work in this aspect.

Basis of abstraction
To eliminate the "shortcut" in the dataset, we try to minimize the quantity of information in the input image on the premise of not affecting model training as much as possible. To generate the abstract data, we need to know what kind of information is useful.
One of the abstract basis is based on the entropy analysis method. In [11], [21], the author proposes a method to quantify the input information that is encoded in a specific intermediate layer of a DNN. The entropy measures how much input information is neglected when the DNN extracted the feature of this layer, and we can use low entropy part to visualize the region with more information. The information discarding is formulated as the conditional entropy ( ) of the input, given the intermediate-layer feature = ( ), as Eq. 1 shows ( ) s. t. ∀x ∈ , ( ) − * ≤ .  where = log + log 2 . This entropy is called 1d entropy when is the entropy of one pixel. For a colorful image as the left one shown in Fig. 6, the entropy is high, which can provide more features. However, for a binarized image, the entropy is much lower, which can provide fewer features. Therefore, for the images with the same content, the binarized image contains less information than the colorful one (Cor. 1) as Fig. 6 shows.
(a) Simple Boundary (b) Complex Boundary Fig. 7 Comparing with the shape with complex boundary, the shape with simple boundary contains less information.
Corollary 1: for the images with the same content, the binarized image contains less information than the colorful one.
One step further, we can expand the definition of Eq. 2 by the concept of the "super pixel". Here, the pixel is not a point but a set contains one pixel and its neighbors, which can be defined by the radium. We can use a tuple ( , ) to briefly describe a super pixel, where is the pixel value of the center pixel, and is the average value of all its neighbors. For Eq. 2, if the object is super pixel, the result is called 2d entropy. Based on the definition, we can infer that for two images with the same content, the simple boundary contains less information than the complex one (Cor. 2). For example, in Fig. 7, we use the box with four pixels to sample the shape. For an image with simple boundary, like the left one in Fig. 7, We can get nine kinds of samples from it. For an image with complex boundary, like the right one in Fig. 7, we can get ten kinds of samples from it.
Corollary 2: For the image with the same content, the simple boundary contains less information than the complex one.
Another abstract basis is based on the results of knowledge visualization of CNN. In [34], the author provides a method to visualize the pattern learned by the CNN. We use it to analyze our dataset and draw the heat map of inference score (see Fig. 8). Comparing with the details of the image, the model seems more sensitive to the boundary of the region, which indicates that the information related to the boundary of the region plays an important role in the network's training (Cor. 3).
Corollary 3: The information related to the boundary is significant in networks' training. Fig. 8 The boundary of the region plays a significant role in the network's training.

Multi-level abstraction
Based on corollaries mentioned above, we abstract the samples at five levels as Fig. 1 shows. To measure the information of images, we calculate 1d entropy and use the pixel and its 8-neighborhoods to calculate 2d entropy based on Eq. 2. In our experiments, (H1d, H2d) is used to represent the information contains by the images. Level-0: Level-0 data is the images collected from the construction site directly without changing, whose average entropy is (5.76, 11.85) (see Fig. 1(a)).
Level-1: Based on Cor. 1, removing the information hidden in the colorful pixels can reduce the information provided by the image. And based on Cor. 3, the boundary of the region need to be kept. Therefore, we use the silhouette of the original image as the level-1 abstraction whose average entropy is (0.56, 1.28) (see Fig. 1(b)).
Level-2: Based on Cor. 2, we further simplify the information stored in the boundary. Human pose detection requires a model to represent the human pose, and we want simplify the boundary of the model as much as possible. There are two kinds of components in the stick-man model, as Fig. 9 shows, and its boundary is much simpler than the silhouette (circular border and straight border). Therefore, we use the stick-man to represent the human pose in the images as level-2 abstraction whose average entropy is (0.48, 0.96) (see Fig. 1(c)). Level-1f and level-2f: As Fig. 8 shows, the hat and cloth whose color contrasting with the background are obvious in the heat map of inference score, which means they play a more important role in the classification of the human pose. Moreover, [10] point out that CNN-based models are more interested in the texture. To explore the importance of these features in network training, we add these feature on level-1 and level-2 data to get level-1f and level-2f whose average entropy are (0.95, 2.12) and (0.76, 1.38) (see Fig. 1(d) and Fig. 1(e)).

Network and Dataset
The experiments are based on a personalized Mask-RCNN based on [1]. To make our experiments easily reproducible, we change the default settings of Mask-RCNN provided by [1] as little as possible and use Colab [4] with GPU acceleration as our experiment environment. For the dataset, the details of the distribution are shown in Tab I. Some of the samples of these five datasets are shown in Fig. 7-A. We select pre-trained ResNet-101 as the backbone. There are two image dataset, ImageNet [26] and COCO [19] which are used to pre-train the backbone. The accuracy of the COCO-based model is 89.48%, and the accuracy of the ImageNet-based model is 47.8%. In the following part, to control the variable, all the model's backbone is pre-trained by the COCO.

Experiment and analysis
In the experiment, we train five models from scratch to fine-tuned with the same configuration as Tab. II shows and test their performance on a test dataset of workers. As    12(a) shows, the models' performances trained by level-1 and level-1f are very close to the model trained by the real data (level-0), which means the level-1 based abstraction is effective. Compared with the level-1 data, the model trained by level-1f is a bit worse, which means that the feature cannot help the network improve its performance in this level of abstraction. On the contrary, compared with the level-2, the model trained by level-2f performs better, which means the feature helps the model classify the human pose. We can infer that for a specific kind of feature, its effectiveness could be different in different situation.
As a reference, we test the models' performances on a test dataset based on a dataset with athletes as Fig. 11 shows, and the result is shown in Fig. 12(b). On this dataset, the model's performance trained by level-1 data is almost the same as the model trained by level-0 data, which verifies again for the effectiveness of level-1 abstraction.
We calculate the accuracy difference between the models' performances on the worker dataset and the player dataset. As Tab. III shows. the performances of models trained by datasets with features (level-1f and level-2f) decrease more dramatically, which means this part of information hinder the network's recognition of human pose on the player dataset. Moreover, the accuracy decreases are positively correlated to the information quantity of the dataset. The more information the model learns, the more dramatically accuracy decreases. It indicates that there are some kinds of unknown but essential differences between the two datasets (worker dataset and player dataset), which makes not all the knowledge learned from the datasets based on the former can be used in the classification of the latter. In other aspects, it means the model trained by the abstract data is easier to transfer, which is another advantage of the abstract data. Finally, we compare the scale and time cost of the two datasets with similar effectiveness, level-1 and level-0 as Tab. 4 shows. In these two aspects, the level-1 abstract data has clear advantages.

Summary and discussion
This paper verifies that the data with meaningful abstrac-tion can be used in training. However, there are still some limitations.

Question about the abstract level
In this paper, we use five levels of abstraction (level-0, level-1, level-1f, level-2, and level-2f). Someone may question it because there is no mathematical model for the abstraction process. We do not deny that this is a shortcoming of this paper, although we list the abstract theoretical basis. However, as a preliminary exploration in this field, our paper proves the feasibility of using abstract data to train neural networks, laying the foundation for further exploration in the future.

Question about human pose label
In this paper, we do not use the traditional human pose representation (skeleton information) but use the region with mark directly. Someone would doubt that the result in this paper is not representative of human pose detection and classification. Above all, this strategy satisfies the application's requirement and reduces the computing complexity, which is significant for the potential application platform device. Indeed, this nontraditional strategy might cause some differences between our models' performance and traditional pose detection models. However, this paper's main contribution is not in the human pose detection but the verification 10 of the utilization of training based on abstract data. All models are based on the same strategy. Therefore, the conclusion in this paper is meaningful and trustworthy.
In conclusion, this paper verifies that the data with meaning-ful abstracts can train the network. It has two main advantages. First, it eliminates the "shortcut" hidden in the dataset, which guarantees the training result is trustworthy. Second, it has a clear advantage on the space occupation comparing with the original data. Moreover, our experiments verify the correctness of the visualization method mentioned in [34]. We believe that the well-designed abstract dataset will replace the big-data used in the neural networks' training in the future.


Realism of Simulation Models in Serious Gaming: Two case studies from Urban Water Management Higher Education

For games used in educational contexts, realism, i.e., the degree of congruence between the simulation models used in the games and the real-world systems represented, is an important characteristic for achieving learning goals well. However, in the past, the realism of especially entertainment games has often been identified as insufficient. Thus, this study is investigating the degree of realism provided by current games. To this purpose, two games in the domain urban water management, a subdomain of environmental engineering (EE), are examined. One is ANAWAK, a web-based serious game on water management and climate change. For ANAWAK, an analysis of the simulation model is conducted. Second, the simulation model of the entertainment game Cities: Skylines (CS) is analyzed. In addition, a survey among CS players (N=61) is conducted. Thereby, different degrees of realism in various EE subdomains are revealed. All in all, there are still considerable deficits regarding the degree of realism in the CS simulation model. However, modding as a means of achieving more realistic simulation models is more widely supported than in the past.

Introduction
The domain environmental engineering (EE) addresses the engineered design of the environment and includes several subdomains, such as urban water management, waste management, energy, and transportation. Among key engineering design tools are simulations [1]. Simulation refers to the time-dependent imitation of a real-world system [2]. The underlying simulation model is a purpose-oriented abstraction of the real-world system to be simulated, i.e., only those aspects of the system that are relevant to the purpose are modeled.
Serious Games. Simulations are also applied effectively in educational contexts of engineering domains [3], for example to enable learners to experiment exploratively with simulation models and thus to understand the systems simulated, hence the learning content. Some of the simulations in educational contexts take the form of digital games. Fundamental part of digital games are simulation models. The games aim to motivate learners and thus increase the learners' engagement with the games and thus indirectly with the learning content [4]. For example, in the EE subdomain urban water management, exist many educational simulation games, also referred to as serious games [5][6][7][8].
Realism. The domain-specific content of games is considered very relevant for learning [9]: domain-specific content should contribute to the authenticity of the learning environment [10], and consists, among other things, of the terminology and simulation models used. Furthermore, the domain-specific content of serious games is supposed to be as close to reality as possible [11,12]. In this regard, simulation models are considered to have a driving role in serious games [13,14]. Tashiro and Dunlap [15] regard realism as one of the key dimensions of learning outcomes in educational games, along with engagement. A term used similarly to authenticity of domain-specific content is validity [13]. In serious games with the purpose of learning, one may also refer to learning goal orientation, i.e., the aspects of the systems modelled that are necessary to achieve the learning goals are considered specifically in the simulation models. Accordingly, realism might be defined as the degree of congruence between the simulation models used in the games and the realworld systems to be represented. Thus, in this study, the term realism is used to describe the quality of games whose domain-specific content in the form of simulation models and terminology is oriented as closely as possible to the relevant aspects of the modeled system being addressed in the game. Not considered in this study is visual realism, which is beneficial for the acceptance of what is presented only up to a certain limit [16]. In the literature, this proposition is also known as Uncanny Valley [17], which refers to the declining acceptance of artificial characters above a certain level of realism.
Entertainment Games as Serious Games. In addition to serious games created explicitly for learning purposes, the use of entertainment games in educational contexts is also common, such as Civilization [18] or Roller Coster Tycoon [19]. Among the advantages of using entertainment games as serious games is the nonexistent development effort [20]. At the same time, entertainment games are optimized for being enjoyable, i.e., entertainment games are characterized by high attractiveness. However, even in the case of entertainment games that are used for learning, the game domain-specific content-to a large extent the simulation models-needs to be oriented to learning goals, i.e., the games show a high degree of realism in the previously defined sense. However, in general, simulation models of entertainment games in educational contexts are attributed inaccuracies [21]. The entertainment game studied here, Cities: Skylines, also appears to lack technical accuracy for the domain urban planning [22]. Such inaccuracies of simulation models might need to be considered in the design of learning scenarios, for example, for addressing learning outcomes outside of the game [23]. Thus, the value of entertainment games is also often seen in further effects, e.g., as a trigger for learning processes with further media [24]. For example, Bawa et al. [25] exploit the motivation nurtured from MMOGs for learning with media beyond the MMOG, such as wikis.
The Case of SimCity. The impetus for the study outlined in this article arose from the observation that entertainment games are not necessarily designed with learning goals in mind. More precisely, entertainment games often do not offer the degree of realism necessary to support specific learning goals. This may also be the case with entertainment games that appear to be very realistic at first glance. The case that illustrates these statements is SimCity [26] in relation to the learning objectives of the domain urban water management [27].
In consequence, exemplarily from the perspective of the domain urban water management, thus, the research question arises to what extent the progress in game development in recent years, visible among others in the currently most popular city builder game Cities: Skylines (CS) [28], has led to a higher degree of realism, which is expressed for example by more detailed simulation models that might support a wider range of learning goals than before. This research question yet recognizes that by carefully aligning learning goals and learning scenario using an entertainment game, some domain-specific learning goals may be achievable, and that beyond domain-specific learning objectives, additional higher-order learning goals (e.g., metacognitive learning goals) may be reachable. However, the research questions targets the extent, to which domain-specific learning objectives may be achieved without significant alignment effort.
The remainder of the paper is organized as follows: in the next section the methodology applied is outlined and the section thereafter describes the two games. Section 4 presents the results, which are discussed in Section 5 along with the limitations of this study. Finally, section 6 summarizes the findings.

Method
For an evaluation of the current degree of realism of simulation games in urban water management, the simulation models of two simulation games were analyzed. The selection of the games was guided by representing each of the two poles-, specifically developed serious game and entertainment game-, a specific educational scope was not required, nevertheless, both games are suitable for graduate education use. Therefore, sufficient complexity of the simulation models is reasonable allowing an assessment of the degree of realism provided by the games. One game was a specifically developed serious game (ANAWAK), which was analyzed for providing a basis for comparison. Second, the components related to urban water management in CS were analyzed. In each case, the model analysis was performed by two EE experts. For the analysis of CS, both experts each spent about 20 h of playing time, while ANAWAK's lower complexity required only about 5 h each. Both first independently assessed the games for urban water management components, such as water treatment plants (WTPs), identified parameters of the components selected, and analyzed whether and how they were presumed to be incorporated into the simulation model. In cases where the experts' assessments did not correspond, the experts elaborated in discussions unified assessments. Only in-game observations and evaluation of documentation were exploited for the model analyses; source code or other formal models were not available. The model analyses were supplemented by an online survey of CS players to collect players' assessment of the degree of realism of CS simulation models with respect to EE systems.

Game Descriptions
ANAWAK. ANAWAK is a serious game designed for learning purposes [29]. ANAWAK is the result of a research project on climate adaptation [30]. The learning content of the game originates from the research project and relates to the basics of urban water management in times of climate change. The game, which is unfortunately currently offline, is a web-based game asking the player to take over the water management of a settlement, and to make turn-based decisions that affect the settlement's water supply. On a simplified terrain view ( Fig. 1), the player interactively influences various game components. Current and past states of the game with respect to the subgoals of sustainability assessment (ecology, economy, and society) are available through a menu. At the end of the game, which takes about 20 minutes to play, the result is presented. ANAWAK was chosen as representative for a group of serious games which, departing from the learning goals, were implemented with rather unelaborate aesthetics and rather simple game mechanics and thus,remarkablyaccording to the above definition, show a high degree of realism. Fig. 1. Game scene ANAWAK (Translations added by authors) Cities: Skylines (CS). Since its launch in 2015, CS has been one of the most popular city builder games, which is reputed having a high degree of realism and has also been used in real urban planning contexts, e.g., in transportation planning for the city of Stockholm [31]. CS exhibits a high degree of realism especially in the domain transportation [32]. Actual georeferenced data have also been displayed in CS [33]. Overall, therefore, as a recent game with detailed simulation models, CS appeared to be a candidate for investigating the degree of realism of urban water management domain-specific simulation models in an entertainment city builder game, especially since CS is considered a de facto successor to SimCity.

Simulation Model Analysis of ANAWAK
ANAWAK employs a static component arrangement, i.e., players cannot change the structure of the game scenario. The simulation model of ANAWAK is described with a qualitative representation of how subgoals are influenced by parameters of components. Taken from the game documentation, Table 1 provides a list of urban water management-related components that may be manipulated by players and are controlled by parameters (shown indented). Adjusting the parameters changes the status of the components and affects the subgoals. For example, for the urban drainage component, increasing the amount of swale infiltration has a positive effect on ecology, economy, and society, respectively. In addition to the components shown in the table, there are five weirs usable for controlling water distribution. However, no consistent interdependencies with the assessment subgoals are definable. The dependencies shown in Table 1 define a significant part of the learning goals, for example high water demand (Table 1: 3) is considered negative for the subgoal ecology. The results of the game actions may be gauged from the values of the subgoals on the one hand, but also from the values of read-only parameters of the components on the other hand. Among the components that exclusively feature such read-only parameters are two lakes, whose filling level and water quality may be observed, a forest (parameter: groundwater level) and a wetland (parameter: water quality). Experimentation with the model (manipulation of parameters and subsequent observation of interdependencies) accounts for learning.

Simulation Model Analysis of Cities: Skylines
Since CS is a city builder game, players build cities from components and thus influence the simulation model. CS was examined for components relevant to urban water management based on the authors' gaming experiences and on developerprovided documentation [34]. The following enumeration describes the components identified and introduces discrepancies to reality using the keyword Reality Check.  Pipes. Water pipes supply water and discharge wastewater. Reality Check: Drinking water pipes and wastewater pipes are always positioned simultaneously and therefore on the same route, which is independent of further construction, such as roads. Parameters such as flow rates, pressure or pipe gradients do not exist.  Water Tower. The water tower acts as a groundwater pump, providing a constant flow of 60,000 m 3 /week. If water tower is constructed in an industrial area, polluted water is pumped. Reality Check: There is no connection with precipitation water, groundwater is assumed to be an infinite reservoir.  Water Pumping Station. A water pumping station conveys with constant flow of 120,000 m 3 /week, is placed at a water body and connected to the water network. Reality Check: The capacity of the water body is rather overestimated.  Water Drain Pipe. The water drain pipe is initially the only means of discharging wastewater, it is connected to a water body and discharges untreated wastewater with a flow of 120,000 m 3 /week into the water body. The pollution is visualized after a short time by brown coloration of the water body in the flow direction below the wastewater pipe. The water pumping station therefore is to be built in flow direction above the water drain pipe, otherwise polluted water will be pumped. Reality Check: Water drain pipes are not very common in the western industrialized countries simulated in CS.  Water Treatment Plant. The water treatment plant is unlocked after progressing in the game and cleans a wastewater flow of 16,000 m 3 /week at 85%. Reality Check: No distinction is made between purification process steps, further, the 85% purification performance is given in general and not on specific contaminants.  Rain. CS features rain. Reality Check: Rain has no influence on water management components such as water bodies or groundwater.

CS extensions.
A prominent feature of CS is its support for modding. The software is open for programmatic program changes and is further developed by the developer ("extension packs") as well as by third parties ("mods"). Both, certain extension packs and several mods complement CS from the perspective of urban water management.
The following are examples:  Heating Pipes.
[35] The heating pipes are part of the expansion pack Cities: Snowfall, which adds heating pipes to the water network and interlinks energy management and urban water management.  Stabilization Basin.
[36] The stabilization basin is an alternative to the water treatment plant. The stabilization basin percolates the water and therefore has a low capacity only.  Ground Water Pump.
[37] The pump operates independently of a water body and differs from the water tower not functionally, but in appearance and parameters.  Modular Sewage Treatment Facility. [38] Very close to real water treatment plants are the buildings of this mod, which represent all the main steps of the purification process and is modeled after a real water treatment plant in Denmark.
Reality Check: Again, no specific contaminants in the water are distinguished. Basically, mods and expansion packs provide for a continuous expansion of the CS simulation model, so that currently also significantly more types of water treatment plants are available. In this respect, progress can be seen compared to SimCity. However, the expansions refer to the components themselves, but the descriptive parameters of the components remain unchanged and are the same for all components, such as cost, upkeep, noise pollution, water, electricity consumption and size. This limitation renders developing domain-specific extensions harder.

Questionnaire: CS as a Serious Game in EE
The questionnaire was announced as a survey on CS as a serious game in EE and was available in June 2018 in CS online communities accessible through the homepage of the publisher Paradox. Of the 84 responses, 61 were evaluable. All Likert scales used had 7 points. Demographics. Sex: 91% of participants identified themselves as male, 9% as female. Age: 39% of participants were older than 30 years, 17% were in the 25-30 age group, 19% were 19-24, 24% were 12-18, and 1% were younger than 12. Occupation: 25% were high school students, 20% were college students, 30% were employed and 25 chose Other. Playing Time: on average, participants played CS for 3.6 h (SD = 1.77) per week. EE Affinity: 44% denied an interest in EE. 30% affirmed this question in general and 26% specifically since engaging with CS.

Realism.
To the question "How realistic is CS in terms of accurate representation of real systems?" the participants answered with an average of 4.4 points (SD= 1.43). Participants were also asked about the degree of realism in CS of different subdomains of EE. Transportation (M=4.9, SD=1.46) was rated by far the most realistic. Waste Management (M=4.9, SD=1.46), Wastewater Discharge (M=3.4, SD=1.13), Energy (M=3.3, SD=1.08) and Water Supply (M=3.1, SD=1.14), both subdomains of urban water management, tended to be perceived less realistic. With respect to individual buildings (Fig. 2), the participants gave widely differing ratings from M=5.5 to M=3.9: the most realistic rating was given to the Wind Turbine, which, however, is also not very complex in reality and hence may be easily simulated. The Airport was rated least realistic, a component that is, however, highly complex in reality. In addition, the participants were also asked in an open question about the differences between the buildings shown in CS and the corresponding real buildings. As an example, for the water treatment plant (WTP), a total of ten responses were given. While one answer considered the CS WTP to be very realistic, as it reduces pollution strongly, but not down to drinking water quality, other answers rather saw the differences: sewage is only treated and dumped in the CS WTP, while in the real WTP there might be a complete recycling. There are also different types of WTPs in reality not being available in CS. Further answers point to a too low cleaning performance (4 answers), a too simple simulation model (3 answers) and a quantitatively inaccurate simulation model (2 answers).

Enjoyable Aspects.
In a further question, the participants were asked to indicate the importance of various aspects for the game enjoyment on a Likert scale.  .

Attitudes towards Digital Games as Learning Tools.
The question to what extent participants believe that computer games are generally useful as learning tools scored M=6.3 points (SD=1.00). The question about the extent to which CS is seen as a learning tool scored M=4.3 points (SD=1.34) for the domain EE and M=5.6 (SD=1.34) for other domains in general. EE tends to be seen as less appropriate as other domains, such as urban planning, which was mentioned multiple times in the comments. Table 2 shows the responses to the multiple-choice question about which conceptual statements are taught by playing CS. Again, different emphases in CS are evident. Mods. 79% of the participants indicated using mods. The reasons for using mods were asked in a multiple selection question. A significant proportion (40%) of participants install mods to improve the accuracy of simulation models. These high numbers might be motivated by participants playing CS rather frequently, and on the other hand by participants also being rather serious game-affine. Moreover, 94% would like to improve the gameplay, 64% would like to increase the choice of buildings, 32% would like to increase the quality of the graphics, and 8% have other reasons for using mods.

Discussion
A primary limitation of the study is that it is based on only two games and thus is not representative. Nevertheless, it is to be assumed-and this is aligned with the literature referenced in the introduction-that in most serious games specifically developed, the simulation models show a similar learning goal-oriented approach, and that in most other entertainment games the degree of realism is similarly simplistic. Likewise, there is the restriction that the analysis of CS refers to the initial version without extension packs. However, even a subsequent ad-hoc analysis of the extension packs released to date did not reveal any qualitative discrepancies from the results.
The results of the survey are subject to the caveat that the assessments of realism were not given by experts but by CS players, who might lack the necessary knowledge to provide profound assessments. However, for example, the assessment of the domain transportation as the most realistic is aligned with the literature [32]. Further, in specific assessments, such as the assessment of the realism of buildings, participants could deny an assessment using the option "I do not know". Moreover, the assessments expressed in comments indicated participants in the survey to be quite reflective.
Compared to SimCity, CS does not show any significant progress in the level of detail of the simulation model from an EE perspective. However, due to the game engine used [39], CS offers the possibility to comprehensively access the simulation model via modding. Therefore, building a simulation model with higher degrees of realism is costly, but in general conceivable. The necessary degree of realism depends on the learning goals. It may be sufficient to support qualitative learning goals, as is the case in ANAWAK. However, with the appropriate level of detail in the simulation model, supporting quantitative learning goals is also feasible, albeit effortful.

Conclusions
More than a decade ago, it has been reported that the simulation model of the entertainment game SimCity is optimized for entertainment purposes rather than providing environmental engineering (EE) specific knowledge. Hereby, potential learning goals supported by SimCity in learning contexts are limited. Thus, the aim of this study was to conduct a current review of realism in entertainment games regarding the EE subdomain urban water management. Simulation models of two games, the dedicated serious game ANAWAK and the popular entertainment game Cities: Skylines (CS) were analyzed in relation to urban water management. While ANAWAK is an example of aligning simulation models with learning goals in a simple interactive graphical presentation, the simulation model of CS shows little progress compared to the findings more than a decade ago with regard to realism of domain-specific content. In contrast, progress is evident in the support of extensions to be developed individually, so-called mods, which may be a path to more realism of simulation models. In addition to the model analysis, an online questionnaire was employed to elicit feedback from serious game interested members of the CS community on how realism of the simulation model in CS is perceived in relation to EE and to what extent CS is considered to be an effective learning tool. Findings include that  the realism of the simulations model regarding various EE subdomain differs widely,  players are aware of the limitations of simulation models, and  various domain-specific concepts are nevertheless taught by CS.
Overall, the findings indicate that-when using entertainment games for conveying domain knowledge-matching the learning goals to the games' simulation models is still required. Technological advances have not resulted in simulation models showing significantly more realism, but a more purposeful development of sophisticated mods is supported. Although we have no indication that these findings are limited to the domain of urban water management, the generalizability of the findings to other domains remains to be confirmed through further research.


A Research on Modulation in Digital Communication System

With the development of network and communication technology in modern society, WiFi technology has become an indispensable part of people’s lives. WiFi shows its future application prospects with its wireless portability and multi-user access. This paper briefly introduces the meaning and development of WiFi technology and predicts its future development trend. The communication modulation technology QPSK, 16QAM in WiFi is described in detail, and its characteristics are compared by systemview and MATLAB software. Finally, we summarize the common techniques in WiFi.

Introduction
In recent years, with the development of communication technology, WiFi technology, as the key access mode of Wireless Local Area Network (WLAN), has been widely recognized for its importance and commercial value since its birth for more than 20 years ago. As WiFi technology becomes more widely used, users have higher requirements for data transmission rates. But in the indoor, this is a more complex electromagnetic environment. The existence of multiple effects, frequency selective fading, and other interference sources make it more difficult to achieve high-speed data transmission in the wireless channel than in the wired channel. WLAN needs to adopt appropriate modulation technology to improve the efficiency of WiFi signal transmission and save frequency resources. In the evolution and upgrading of WiFi, QPSK and QAM have been successfully applied as digital modulation technologies of WiFi. Digital modulation is the basis of digital transmission. Through the carrier modulation to transmit digital signals in the channel, carrier expression has three variables, so digital signal modulation technology is divided into amplitude keying ASK, frequency shift keying FSK, and phase shift keying PSK three ways, among which QPSK is one of the most commonly used digital signal modulation methods. The advantages of QPSK modulation are high-frequency spectrum utilization, strong anti-interference, and a good trade-off between frequency band utilization and acceptance of SNR [1]. In 2001, Tarokh et al. made significant progress in constructing complementary sequences with amplitude and phase modulation. They propose a 16-QAM sequence based on the QPSK complementary sequence in the XY plane by using two encoding schemes on QPSK. The relationship between sequence on QPSK and sequence on QAM is found. Thus, the study of complementary sequences is extended to QAM modulation [2]. QAM modulation also has the characteristics of high spectrum utilization and strong noise resistance and can balance the two technical indexes of information transmission and limited bandwidth [3].  [4] IEEE802.11 wireless LAN (WLAN) is a selfmanaged computer LAN (LAN) which can support a high data transmission rate (1-54Mbit/s) and adopts micro-cell and micro-cell structure. There are three key technologies: DSSS, CCK and PBCC, and OFDM. As the spread spectrum signal rate of the DSSS system is much higher than the information rate, the signal transmission bandwidth is much higher than the information bit bandwidth, thus limiting the system frequency band utilization and information transmission rate [5]. At present, spread spectrum modulation technology is becoming mainstream. The orthogonal frequency division multiplexing (OFDM) system is widely used in various wireless communication systems due to its advantages of high transmission rate and high spectrum efficiency [6]. DSSS direct sequence spread spectrum and OFDM orthogonal multi-carrier modulation, the two technologies have their own advantages and disadvantages and play different roles in different situations. There are various modulation technologies in OFDM, including orthogonal amplitude modulation (QAM), a modulation mode of amplitude modulation on two orthogonal carriers. Frequency modulation FSK, frequency shift keying is the use of carrier frequency changes to transmit digital information; Phase modulation PSK, and other technologies. This paper first introduces and compares OFDM and DSSS technologies and then focuses on the simulation, analysis, and comparison of the 16QAM in OFDM [7]. (refers to the superposition of two independent orthogonal 4ASK signals in the QAM modulation mode containing 16 symbols. 4ASK is the signal obtained by using a multilevel signal to diskey the carrier wave. Which is a generalization of the 2 ask system, compared with 2 ask. The advantages of this system lie in the high speed of information transmission) and the technology of QPSK [8]. (quaternary phase modulation is a kind of high spectrum efficiency, strong anti-jamming of the number of modulation mode, QPSK signal of the sine carrier has four possible discrete phase states, each carrier phase carries two binary notation). Their advantages and disadvantages are summarized through simulation, and their application scenarios are summarized.
IEEE802.11 series standard is the most widely used standard in wireless LAN [9]. After decades of development since its launch, it has become more and more mature and widely used. It has become the favorite wireless network. From the development perspective, this paper introduces the IEEE 802.11 series of standards and compares several common standards, IEEE 802.11 and IEEE 802.2.1 LA /b/ G/N. Finally, we will briefly introduce the development of the 802.11 series of WiFi protocols and make predictions for future development [10].

Method
This section describes the principles of the three modulation modes in detail 2.1. QPSK

2.1.1Definition of QPSK
The title is set 17 point Times Bold, flush left, unjustified. The first letter of the title should be capitalized with the rest in lower case. It should not be indented. Leave 28 mm of space above the title and 10 mm after the title.
Definition of QPSK: Four-phase phase shift modulation uses four different phase differences of the carrier to represent the digital input information, which is a quad phase shift keying. QPSK is a phase modulation technology when M=4, which defines four carrier phases, which are respectively 45°, 135°, 225° and 315°. The data input by the modulator is a binary number sequence. To cooperate with the quad carrier phase, the binary data needs to be transformed into quad data. This means that you need to divide every two bits of the binary number sequence into four combinations, namely 00,01,10,11, each called a double bit. Each double-bit symbol is composed of two bits of binary information, each representing one of the four symbols in the quaternary system. Each modulation is QPSK can transmit 2 information bits, which are transmitted through the four phases of the carrier wave. The demodulator determines the information bits sent by the sender according to the constellation and the phase of the received carrier signal. To analyze the π/4 QPSK signal, it can be seen from the phase diagram: When the input digital information is "11" symbol, the modulated carrier is output cos ( When the input digital information is "01" symbol, the modulated carrier is output When the input digital information is "00" symbol, the modulated carrier is output When the input digital information is "10" symbol, the modulated carrier is output QPSK modulation block diagram is as follows: The QPSK signal received by the receiver for a certain symbol can be expressed as: According to the demodulation block diagram:

. Orthogonality and baseband signals
Orthogonal frequency division multiplexing (OFDM) is a kind of multi-carrier modulation (MCM), which uses quadrature subcarrier modulating multichannel signals. Under normal circumstances, the channel bandwidth of a communication system is far greater than the bandwidth required for transmitting one signal, so transmitting only one signal in a channel will cause serious waste of channel resources, and OFDM arises as The Times require.
The principle of OFDM is to divide the channel into multiple sub-channels for data transmission, convert the high-speed sub-carriers into low-speed sub-carriers, and then modulate to each different subchannel for transmission. This transmission method converts the non-flat fading channel into multiple orthogonal flat fading sub-channels, thus eliminating the interference between the channel waveforms. The core of OFDM is to use the orthogonal property of the subcarrier. No matter how the amplitude of the subcarrier signal (function) changes, when multiplied in a fixed period, the integral is equal to 0. For example, suppose that the function f(t) is orthogonal to g(t) from 0 to t, i.e A pair of very simple examples of orthogonal functions, sine (t) and sine (2t), to transmit information, let a modulate in sin(t) and b modulate in sin(2t), and let f(t)=a*sin(t),g(t)=b*sin(2t), and t =2π. There At the receiving end, to obtain the original information A and B, we need to integrate the two subcarriers, respectively, in the form of: Because of the orthogonality of the subcarrier, OFDM modulation can transmit multiple subchannels and multiple subcarriers. Because the frequency interval is very small, the utilization of the channel is improved.
Assuming that there are N frequencies 0 f~1 N f − , where f  is the frequency interval that makes each subcarrier orthogonal to each other, there are Based on the above example, we can also get the baseband signal expression of OFDM: The minus sign here is for the sake of subsequent derivation of signals in the complex frequency domain)  In the above OFDM, there are N subchannels, where the formula of the KTH subcarrier is:

Suppose that the period of a symbol of S (t) is
A subcarrier consists of two orthogonal signals (the orthogonicity of sine and cosine waves is independent of frequency), which can be regarded as an i-Q signal I multiplied by signal cosωt, and Q multiplied by signal -sinωt (ω=2πf).
By superimposing each subcarrier, the signal form of OFDM can be obtained: s k s Then according to the formula of S (t), each subcarrier is modulated by IQ, and the output result of the baseband signal is two IQ signals (one is cos and the other is sin). The modulation mode can be QPSK or QAM.

2.2.4QAM
(Quadrature Amplitude Modulation) is formed by superimposing two independent quadrature 4ASK signals. 4ASK is a signal obtained by de-keying the carrier with a multi-level signal. It is the promotion of the 2ASK system. Compared with 2ASK, the advantage of this system lies in the high information transmission rate.
16QAM is produced by a combination of multi-ary amplitude keying (MASK) and quadrature carrier modulation. There are two ways to generate 16QAM: (1) Quadrature amplitude modulation method, which is composed of two orthogonal four-level amplitude keying signals superimposed; (2) Composite phase shift method: it uses two independent channels The four-phase shift phase keying signal is superimposed. The quadrature amplitude modulation method is used here. The modulation principle is shown in Figure   Figure 5.16QAM modulation diagram In the figure, the serial/parallel converter divides the binary symbol sequence with Rb rate into two channels, and the rate is Rb/2. 2-4 The binary symbol sequence converted to Rb/2 becomes a 4-level signal with a rate of RS=Rb/log216. The 4-level signal is multiplied by the quadrature carrier to complete the quadrature modulation, and the two signals are superimposed to produce a 16QAM signal.
The 16QAM signal is demodulated by the quadrature coherent demodulation method. The demodulator first performs quadrature coherent demodulation on the received 16QAM signal, multiplying one by cosωct and the other by sinωct. Then the high-frequency component generated by the multiplier is filtered through a low-pass filter to obtain a useful signal. The output of the low-pass filter can be sampled and judged to recover a level signal. The demodulation block diagram is shown in Figure   Figure 6.16qam demodulation block diagram

Emulation and Result
Firstly, we use Systemview to simulate the QPSK process to better help us understand the signal modulation and demodulation process in QPSK. The module connection diagram is shown in the figure. Firstly, a random sequence is divided into two channels through serial-parallel conversion. Two-phase modulation is carried out on the in-phase carrier and orthogonal carrier, respectively. The two outputs are superimposed to form a modulation signal. After the modulated signal is transmitted through the channel, it is demodulated in the receiving part. The received signal is multiplied by the orthogonal coherent carrier, sampled, and determined by the low-pass filter to form two PSK signals. One 4PSK signal is synthesized by parallel series conversion. During demodulation, it is multiplied by two orthogonal coherent carriers to form two PSK signals through low-pass filter sampling decision (sampler holder equalizes the waveform amplitude), and one 4PSK signal is synthesized through parallel series conversion (input a series of 01 pulse sequence to select the symbols of the upper and lower branches).
Simulation results and analysis: 1) Input signal and final demodulation output sequence: 11011001110011100100  . Waveform multiplied by the carrier during demodulation 6) Signal after passing through the low-pass filter 7)Add sample and holder to smooth the waveform The experimental simulation results show that software simulation can determine the feasibility of the theoretical modulation and demodulation scheme. By comparison, the two waveforms are completely consistent with the upper and lower branch waveforms after serial, parallel conversion of the input signal, and the input and output signals are also completely consistent. Under the condition of the same bandwidth, QPSK can carry twice the amount of information of traditional PSK modulation. QPSK is used for satellite communication, cable modulation and demodulation, video conference system, cellular telephone, and other digital communication for transmitting the MPEG2 video signal. The following is the simulation run of 16QAM on Systemview. The module connection diagram is shown in the figure. 16QAM (quadrature amplitude modulation) is formed by the superposition of two independent quadrature 4ask signals. 4ask is the signal obtained by keying the carrier with multi-level signals. During demodulation, quadrature coherent demodulation is performed on the received 16QAM signal. Then the high-frequency component generated by the multiplier is filtered through the low-pass filter to obtain the useful signal. The low-pass filter output can recover the level signal through sampling decisions. Input signal QPSK modulates the digital signal by changing the phase information of the modulated signal. Set different The initial phase is used to distinguish different digital symbols, and its demodulation process needs to be carried out through phase information. first A series of random 01 code sequences are generated, and then every two codewords are divided into a group for discrimination, mapping, and drawing Constellation, make the sequence demodulate through Gaussian noise, draw the constellation, and observe the signal-to-noise ratio Impact on symbol transmission. The following is the constellation diagram and bit error rate curve of QPSK. It can be seen from the figure that the signal-to-noise ratio has an impact on the bit error rate. The higher the signal-to-noise ratio, the lower the bit error rate. Therefore, properly improving the signal-tonoise ratio in noisy channels can effectively improve the quality of communication. In addition, it can be seen from the constellation diagram of QPSK that its noise tolerance is also large, so the error rate of information in the process of demodulation will be very low. Still, the utilization of its spectrum is relatively low.
Similarly, we get the constellation and bit error rate curve of 16QAM. As can be seen from the constellation diagram, 16QAM modulates more constellation points, so it has more information capacity. When the bandwidth resources are the same, it will contain more