The constantly increasing demand for high data rates requires communication networks, including cellular networks, that can meet this demand. In addition, the networks must meet the demand under the evolving types of wireless communication within networks, such as M2M communication and D2D communication.
M2M communication involves the communication of wireless devices/instruments/sensors transmitting primarily measurement reports, triggers, readings, and event detection messages to a serving base station. The traffic and messages generated/transmitted by these wireless sensors/devices is typically sporadic, mainly uplink in direction, and of short duration in most of the cases. D2D communication involves direct communication between devices/sensors or simply mobile phones. This direct communication takes place in the same spectrum as communication used by network operators for the normal communication between mobile phone and base stations. It is therefore anticipated that this direct D2D communication is going to be somehow supervised by the network. The traffic generated from these D2D devices can be anything ranging from low data rate traffic consisting of small packets to high data rates involving the exchange of lengthy data files.
A challenging question for operators is how to evolve their existing cellular networks so as to meet these different and dynamic requirements for both higher data rates and for increased signaling. In addition, the requirement for efficient energy consumption is constantly increasing. In this respect, a number of directions are possible, such as: i) increasing the density of existing macro base stations in the network, ii) increasing cooperation of macro base stations among each other, or iii) deploying smaller base stations in areas where high data rates are needed within a macro base station grid, such as in a Heterogeneous Network/Deployment, (with requisite increased cooperation between macro and smaller base stations).
The option of building a denser macro base stations grid and probably enhancing the cooperation with macro base stations (hence, using options i) or ii) above) can be a solution for meeting the demand for higher data rates. However, this solution is often not a cost-efficient option, because costs and delays associated with the installation of macro base stations, especially in urban areas, can be significant. The same cost and time disadvantages apply for the deployment of macro base stations in isolated rural areas. In addition, a dense deployment of macro base stations could lead to a significantly high amount of signaling due to frequent handovers between macro base stations for users moving at high speeds.
In this landscape, the solution of deploying small base stations within an already existing macro layer grid is a possible option, such as within a “micro” or “pico” layer of a heterogeneous communication network. The reason is that these small base stations are expected to be more cost-efficient than macro base stations, and their deployment time is shorter as well. The macro layer grid can serve users moving at high speed, or wider geographic areas where the demand for high data rates is not that great. The network grid consisting of small base stations or micro base stations can cater to a high density of users requesting high data rates, or hotspots, as these areas are termed. However, a cost-efficient solution to finding efficiencies and satisfying evolving data demands should serve both the macro layer and the micro layer of the communication network.
To optimize the deployment of small base stations grids within an existing grid of macro base stations, or the deployment of macro base stations in isolated rural area, two main challenges are faced. The first and the most important one is where a high concentration of users or wireless sensors/devices is located. Once these hotspots of mobile phones, sensors, or devices are identified, the question is whether the location of the hotspot is such that the demanded high data rate and high levels of signaling can be met by the existing macro layer. Under a further consideration, and assuming that the deployment of smaller base stations has been in an optimal or almost optimal manner, then there might be cases where it is neither cost efficient nor energy consumption efficient for all the installed macro, smaller base stations, and relays to be constantly active. As an example, consider a group of relay nodes placed in or near a crop field, with the main goal of receiving measurement reports from wireless sensors deployed throughout the field, with the sensors detecting such elements has temperature, humidity, rainfall, and the like. These wireless sensors report only at specific time instants during the day; e.g., only in the morning, or only in the night. Consequently, the constant activity of these relay nodes is not needed, and it is considered inefficient in terms of energy consumption. Therefore, there is the need to have knowledge at the network regarding the number of active terminals/devices/sensors within a given geographic area. Such information would be very useful for the network to provide efficient energy use.
To efficiently activate/deactivate a number of base stations within a geographic area, there is a need to have available information at the network on the activity of User Equipment devices (UE) in the area. Considering that new types of activities are involved in the latest versions of 3GPP LTE, such as M2M and D2D communication types, it is not sufficient to merely have information such as User Equipment (UE) activity times or Discontinuous Reception (DRX) cycles of UEs. More advanced radio statistics are needed for this purpose in addition to new types of information related to these new types of communication.
When determining whether and where to place new base stations in locations where high concentrations of users or wireless sensor/devices is observed, such decisions in the past have been made by use of Global Positioning System (GPS), or other similar UE positioning mechanisms. However a major disadvantage of both GPS and other positioning mechanisms is their known poor performance indoors. Another drawback with these positioning mechanisms is that wireless sensors/devices are expected to be of low complexity and hence cost, and they are required to consume little energy. It is therefore highly questionable whether these devices are going to have GPS functionality embedded. Further, in urban areas, a large number of the wireless sensors/devices are going to be located indoors.
Moreover, there is a difference between the geographic position and the radio link quality experienced by an UE or a wireless sensor; e.g., a given UE might be located geographically close to a serving macro base station, but the quality of its radio link to the serving macro base station can be low. The efficiency of introducing a micro or pica cell within a macro network is strongly dependent on the radio relation, or strength of the link, with the serving macro cell.