Lately, development of digital mobile telephone systems has proceeded rapidly. However, there has been a problem that, in land digital mobile telephony, waveforms of reception signals would be considerably distorted due to a number of interference waves which bring about the delay in reception and frequency selective fading which is caused by high speed movement of mobile terminals. Therefore this distortion should be compensated by an equalizer. Maximum likelihood sequence estimation which is performed by the equalizer is one of the most effective equalization methods for obtaining correct transmission data from reception signal waveforms which have been distorted due to such delay characteristics of the transmission lines (channel) as frequency selective fading.
The digital mobile telephone system is described below, referring to FIGS. 6 and 7.
It is expected that the digital mobile telephone system will use the TDMA (Time Division Multiple Access) scheme to efficiently use limited radio frequency bands and ensure easy connection to the ISDN (Integrated Service Digital Network) services which have been implemented for the fixed telecommunications networks. An example of the frame configuration of the TDMA scheme is shown in FIG. 6.
In FIG. 6, one frame comprises six time slots (time segments) Slot 1.about.6. One or two of these time slots are assigned to one subscriber. Time slots Slot 1.about.6 respectively consist of a 28-bit training sequence SYNC for synchronization and training of an equalizer, a 12-bit control information sequence SACCH, a 12-bit adjacent channel identification sequence CDVCC, data part DATA of 260 bits in total, and a 12-bit reserved area RSVD.
The leading position t slot (k) of the time slot assigned to each subscriber is detected by the frame synchronization mechanism. The position to be detected by the frame synchronization mechanism is an estimated leading position Et slot (k) (E means estimation) to be determined by estimation. This estimated leading position Et slot (k) is merely an estimated position and therefore is not always an accurate position, and therefore it may vary depending on compensation by the frame synchronization mechanism.
FIG. 7 is a block diagram showing the configuration of the transmitter and the receiver for digital mobile telephony.
In this transmitter/receiver configuration, the receiver 30 is connected to the output side of the transmitter 10 through the transmission line (channel) 20.
The transmitter 10 is composed of an encoder 11, a transmission low-pass filter (LPF) 12, a modulator 13 and other parts.
The receiver 30 is composed of a demodulator 31, a reception low-pass filter (LPF) 32, a frame synchronization mechanism 33, sampling parts 34, an equalizer 35, a decoder and other parts.
Operations of the transmitter 10 and receiver 30 are described below.
The transmitter 10 converts input data bm to transmission symbols ={x1, x2, . . . , xN} through the encoder 11 controls the band width through the transmission low-pass filter, generates a transmission complex base band signal s(t), modulates the transmission complex base band signal s(t) with a carrier through the modulator 13 and transmits it as signal Sc(t) to the transmission channel 20.
The receiver 30 converts signal rc(t), which has passed through the transmission channel 20, to complex base band signal r(t) through the demodulator 31, and obtains reception complex base band signal y(t) which is band-limited by the reception low-pass filter 32.
Then a leading position (time) Et slot(k) of the time slot (segment) assigned to a subscriber is estimated according to this signal y(t) by the frame synchronization mechanism 33, and the reception signal y(t) is sampled by the sampling part 34 according to the following equation: EQU =[y (Et slot (k)+.tau.1+(n-1) T)](n=1, 2, . . . , N) (1)
where,
Et slot (k): Estimated leading time of the kth assigned time slot (E means estimation.) PA1 .tau.1: Fixed sampling phase PA1 T: Symbol interval time
The characteristic of the transmission channel 20 due to the frequency selective fading is compensated with the sample value sequence of signal y(t) by the equalizer 35, and a transmission symbol is estimated.
Finally, estimated values E ={Exn}(n=1, 2, . . . , N; E means estimation) are decoded by the decoder 36 and estimated values Ebm of transmitted data are obtained.
The following describes the above described maximum likelihood sequence estimation which constitutes the equalizer.
As regards the maximum likelihood sequence estimation, the reference "J. G. Proakis `Digital Communications` (1983) New York: McGraw-Hill, pp. 548-554, pp. 610-648" is known.
As disclosed in the above reference, the maximum likelihood sequence estimation is intended to estimate the transmission symbol sequence ={x1, x2, . . . . , xN} for which the probability (likelihood) of materializing the reception signal sequence ={y1, y2, . . . , yN} with the impulse response h(t), as known when the reception signal sequence ={y1, y2, . . . , yN} in a certain range, is obtained. This maximum likelihood sequence is obtained by, after all, obtaining the symbol sequence ={x1, x2, . . . , xN} in which the following value is maximized ##EQU1## if a white Guassian noise is assumed as the noise of the transmission line.
Equation (2) is efficiently calculated by using the Viterbi algorithm which is known as a decoding method for convolutional codes.
The following briefly describes the principle of the Viterbi algorithm applied to the maximum likelihood estimation, referring to FIG. 8 which shows a model of the transmission path.
The transmission path is assumed in a discrete time model n which the impulse response shown in FIG. 8 is limited. In FIG. 8, numeral 40 denotes a delay element with a symbol interval T, numeral 41 denotes a multiplier, numeral 42 denotes an accumulator and numeral 43 denotes an adder.
={hj}(j=0, . . . , L) is a set of sample values h (t-jT) of the impulse response h(t) at the symbol interval T of the transmission path that includes the transmission channel 20 and the transmission and reception low-pass filters 12 and 32, and the length of the impulse response is (L+1) T. Wn is the noise of the transmission path which is additive white Guassian noise. According to this assumption, equation (2) is as given below. ##EQU2##
Therefore, the sum Jn up to k=n in equation (3) is as given below using the sum J.sub.n-1 up to k=n-1. ##EQU3## In this case, J.sub.n is a value which is proportional to the logarithmic likelihood of the reception signal sequence up to k=1 to n and referred to as the "path-metric" value, and the second term of the right side of equation (4) is a value which is proportional to the logarithmic likelihood as to the state transition (branch) and is referred to as the "branch-metric" value.
On the other hand, the state of the transmission path model shown in FIG. 8 at timing n-1 is given by the following status vector. EQU Sn-1={Xn-1, Xn-2, . . . , Xn-1} (5)
If there are M types of transmission symbols, M.sup.L types of states are available for the transmission path.
Assuming the transition from state S.sub.n-1 at timing n-1 to state Sn at timing n, transitions from M types of states S.sub.n-1 are available for each of M.sup.L types of states S.sub.n. The state transition between these timings is referred to as a "branch" and each of Mn types of paths which follow up the states are referred to as a "path". There are paths from M types of states at the preceding timing for each of M.sup.L types of available states at respective timings.
The Viterbi algorithm is for calculating the path-metric value of equation (4) with respect to M number of available paths in respective states at respective timings to select a path with the largest value. Accordingly, M.sup.L types of paths are left at each timing and the past paths are gradually converged to one path. An estimated value of the transmission symbol sequence is obtained from the path to which the past paths finally converge.
FIG. 9 is a block diagram showing an example of a configuration of the conventional maximum likelihood sequence estimator.
This maximum likelihood sequence estimator consists of a Viterbi algorithm processing part 50 and a transmission path estimating part 60.
Reception signal y(t) is sampled according to equation (1) by the sampling parts when the kth assigned time slot is to be processed, and entered into the Viterbi algorithm processing part 50 and the transmission path estimating part 60.
The transmission path estimating part 60 estimates the impulse response of the transmission path according to an adaptive algorithm such as LMS (Least Mean Square), from the host equipment, not shown. The estimates provide information necessary for the sample value sequence and the component units in the receiver 30 shown in FIG. 7 which correspond to the sample value sequence by using the given training sequence shown in FIG. 7 in the training sequence part of the time slot shown in FIG. 6 of the sample value sequence . In the part of the sample value sequence other than the training sequence part of the time slot shown in FIG. 6, the estimation of the impulse response of the transmission path is continued using the estimated values E of the transmission symbol sequence.
The estimated impulse response values E of the transmission path are entered into the Viterbi algorithm processing part.
The Viterbi algorithm processing part 50 estimates a transmission symbol according to the above described principle by using the estimated impulse response values E h of the transmission path, which have been estimated as the sample value sequence for the reception signal.
However, the conventional maximum sequence estimator carries out simulation under conditions that are applied in advance and fixes the sampling phase at a trade-off point which is determined by checking the bit error rate, and therefore there is a problem that an optimum sampling phase where the bit error rate is minimized under all conditions is not established.
In addition, the conventional maximum likelihood sequence estimator has accompanied a problem that, if the number of interference waves and the delay time differ, the optimum sampling phase which minimizes the bit error rate differs accordingly, and moreover the deviation of the sampling phase from the optimum sampling phase may cause deterioration of the bit error rate and it has been difficult to solve this problem.
An object of the present invention is to provide a maximum likelihood sequence estimator capable of solving the above described problems of the prior art with respect to the point that the optimum sampling phase where the bit error rate is minimized under all conditions is not always established since the sampling phase is fixed and also with respect to the point that, if the number of interference waves and the delay time differ, the optimum sampling phase where the bit error rate is minimized differs, and the deviation of the sampling phase from the optimum sampling may cause deterioration of the bit error rate.