JPH11215038A - Adaptive equalizer - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain secure satisfactory equalization characteristics with small storage capacity, even with respect to a high transmission rate and fast fading fluctuations. SOLUTION: Supposing that likelihood is sequentially estimated in series for each of paths reaching the nodes (4), (6), (7) and (3), and the most likely likelihood is selected at an evaluation part 23 among the paths A, B, C and D. Under such conditions, a phanometric is calculated at a calculation part 16 from a channel impulse response estimation value of the path B and a received sample value of a path E, and then is added to the likelihood of the path B to determine the likelihood of the path E. Then a channel impulse response estimation parameter value of the path E and the least mean square(LMS) algorithm are calculated from the channel impulse response estimation parameter value of the path B and the received sample value of the path E. These calculated values are stored in a storage part 22. Similarly, the likelihood and the impulse response estimation parameter value of a path F are calculated and stored in the part 22. Then the likelihood and the impulse response parameter value of the path B are erased. A path is selected through comparison of the values of likelihood among paths A, C, D, E and F to perform similar procedures as mentioned above.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an adaptive equalizer for use in, for example, a mobile communication receiver, and more particularly, to efficient adaptive equalization even in high-speed transmission with a bit rate on the order of megabits. To make it possible.

[0002]

2. Description of the Related Art A feature of mobile communication is that a radio propagation environment is a multipath propagation path. Considering the upstream (mobile station transmission, base station reception) communication channel, the bundle of transmission element waves affected by scattering, diffraction, and reflection around the mobile station is directly
Or, after a further distant reflection, it arrives at the base station. Therefore, the base station receives the transmission signal divided into a plurality of components having different arrival angles. these,
The component of the received wave corresponding to the path (propagation path) of each received wave is composed of the bundle of the above-mentioned transmission elementary waves, and the process (scattering, diffraction, reflection, etc.) for creating this bundle is different for each path. Each path is subject to independent fading.

[0003] When communication is performed in such a mobile communication environment, different phenomena appear in the received signal depending on the signal bandwidth. If the bit rate of the transmitted signal is low and its bandwidth is much smaller than the coherence bandwidth of the channel (the width between adjacent peaks of selective fading), the propagation delay difference of the signal received via each of the above paths is It is sufficiently smaller than the symbol time length of the signal (usually equal to the reciprocal of the symbol rate). In this case, on the receiving side, the same information symbol is received on each path, and no waveform distortion occurs in the received signal due to intersymbol interference.

As the bit rate of the transmitted signal becomes progressively higher and the bandwidth of the signal becomes approximately equal to the coherence bandwidth of the channel, different information symbols are received on each path. In this case, waveform distortion occurs in the received signal due to intersymbol interference of several symbols before and after. As described above, since each path is subjected to independent fading, the intersymbol interference of several symbols before and after this becomes time-varying intersymbol interference that changes with time. Therefore, the equalizer for removing such intersymbol interference has a function of estimating the impulse response of a channel (equal to the complex amplitude of each path arranged at its arrival time) and estimating a transmission sequence. Is required. Maximum likelihood sequence estimation (MLS
E: An algorithm based on the Maximum Likelihood Sequence Estimation theory can be applied. If the intersymbol interference is several symbols before and after, the joint (combination) signal processing of this channel estimation and MLSE can be realized with a realistic processing amount. For details, see, for example, literatures: Fukawa, Suzuki, "Sequential Least Square Form Maximum Likelihood Sequence Estimation (RLS-MLSE)-Application of Maximum Likelihood Estimation Theory to Mobile Radio-", IEICE (B-II), J76-B
-II, No. 4, pp. 202-214, Apr. 199
3

[0005] As the bit rate of the transmitted signal is further increased, the received signal is subject to time-varying intersymbol interference of more symbols. Theoretically, the above-described channel estimation and MLSE joint signal processing can be applied to the equalization of the intersymbol interference. However, the number of states of the Viterbi algorithm used in MLSE increases exponentially with the length of intersymbol interference (equal to the length of storage of the channel).
For example, when binary phase modulation (BPSK) is used as the modulation method, the number of states (corresponding to the 12-path propagation path) becomes 2048 when 11 symbols are stored in the channel, which exceeds the actual processing amount. Would.

As can be seen from the above considerations, it can be said that the technical problems in configuring an adaptive equalizer for high-speed transmission on the order of megabits are sequence estimation and channel estimation corresponding to the sequence. Among these problems, as a means for avoiding the difficulty of sequence estimation, an adaptive equalizer using sequential sequence estimation instead of the above-described maximum likelihood sequence estimation MLSE is described in Matsumoto, “Sequential decoding in high-speed mobile communication. ”1997 IEICE Society Conference B-
5-149 has been proposed.

[0007] Sequential sequence estimation is a method originally developed as a decoding algorithm for convolutional codes having a long storage length, and has been studied for codes having a storage length of 40 to 60 for application and purpose to deep space communication. There is history. Since the process in which intersymbol interference occurs is nothing less than convolution operation on a complex number field, it is apparent that sequential sequence estimation has an affinity for adaptive equalization. The above literature indicates that successive sequences are estimated for BPSK in a 12-path channel, and as a result, good characteristics can be obtained with a realistic processing amount.

[0008]

However, in the above document, the channel estimation is performed only at the beginning of the frame, assuming that the fluctuation of the channel is sufficiently slow as compared with the bit rate (usually, the beginning of the frame). A unique word, which is a known pattern, is transmitted to the part on the transmitting side and the receiving side, and the receiving side performs initial channel estimation by using the known pattern. Is continuously used. Indeed, in high-speed transmission, the fluctuation speed of fading normalized by the symbol length is lower than in low-speed transmission,
The assumption that fluctuations are completely negligible is not realistic. Therefore, the above-described sequential sequence estimation and channel estimation are performed simultaneously,
It is necessary to update the channel estimation algorithm for the sequence candidates obtained as a result of the sequential sequence estimation. However, at present, no such algorithm is known.

[0009]

According to the present invention, an impulse response of a channel is estimated from a received signal sample value sequence, and the likelihood in the sequential sequence estimating means is calculated from the impulse response estimated value and the received signal sample value sequence. Is calculated, sequence estimation is sequentially performed by comparison using the likelihood, and the parameter value used for calculating the impulse response estimation at each node selected by the sequential sequence estimation unit as a candidate is stored in the storage unit. Each time a candidate for successive sequence estimation is updated, the channel impulse response estimation value for the candidate sequence is updated.

As described above, according to the present invention, the sequential sequence estimation and the channel estimation are performed simultaneously, and the parameter values used for the impulse response estimation are updated for the sequence candidates obtained as a result of the sequential sequence estimation using the channel estimation algorithm. Good equalization characteristics can be obtained even if the channel fluctuates due to fading.

[0011]

FIG. 1A shows an example of a functional configuration of the present invention. FIG. 2 shows the flow of the processing. Antenna 11, 1
2 receives signals that have undergone independent fading, and these are respectively converted into reception signal sample value sequences by reception units 13 and 14, and are sent to a channel identification unit 15 and a fanometric calculation unit (likelihood calculation unit) 16. Supplied. When the unique word located at the front of the frame is received, the RL of the impulse response estimation unit 17 in the channel identification unit 15
The channel impulse response is estimated using an S (Recursive Least Square) algorithm unit 18 (S1),
Then, in the information symbol part located at the rear part of the frame, the sequential sequence estimation unit 21 performs the sequential sequence estimation (S2). In the sequence evaluation unit 23, from the path number and the likelihood value in the sequential sequence estimation passed from the storage unit 22,
The most probable path is selected, and the selected path number is passed to the fanometric calculation unit 16 (S3).

The fanometric calculation unit 16 calculates the likelihood of a path obtained by extending the passed path by one branch, and transfers the calculation result to the storage unit 22 (S4). The value of the channel impulse response stored in the storage unit 22 is used for the calculation of the likelihood. After the calculation of the likelihood by the fanometric calculation unit 16, the channel identification unit 15 uses the parameter value for channel impulse response estimation stored in the storage unit 22 by using the channel impulse response estimation algorithm, LMS in this example. The algorithm section 19 updates the impulse response estimation parameter value and passes it to the storage section 22 (S5).

As described above, in the present invention, at each node left as a candidate by the sequential sequence estimation, the parameter value used for calculating the channel impulse response estimation at the next node connected to the node is stored. This process and a specific example of the likelihood calculation process of each path will be described with reference to a tree diagram shown in FIG. 1B. In FIG. 1B, nodes in the sequential sequence estimation are divided into two paths for each node with the node as a root.
B shows a state in which six paths (numbers) A, B, C, D, E, and F have been determined. Path A is a node,
, And path B passes through the nodes,.

It is assumed that the processing of the sequential sequence estimation is advanced and a tree diagram represented by four paths A, B, C, and D is obtained as shown in FIG. 1B. Here, the likelihoods of the paths A, B, C, and D (to compare the likelihoods of paths having different lengths, in this example, the fanometric is used as described above.
F. Xiong, “Sequential Sequence Estimation for C
hannels with Intersymbol Interference of Finite or
Infinite Length ", IEEE Trans. Commun., Vol.
COM-38, no. 6, pp. 795-804,199
0) and the sequence evaluation unit 23, and it is assumed that, for example, B is selected as the most likely path.
In that case, the fanometric calculation unit 16 calculates a fanometric value using the channel impulse response estimation value on the path B from the storage unit 22 and the received signal sample value corresponding to the path E, and calculates the fanometric value of the path B likelihood. Then, the likelihood of the path E is stored in the storage unit 22 while the likelihood of the path E is maintained. After that, the impulse response estimating unit 17 calculates a parameter value for channel impulse response estimation stored in the path B in the storage unit 22;
A channel impulse response estimation parameter value is obtained by a channel estimation algorithm (LMS algorithm) using the received signal sample value corresponding to the path E, and the obtained parameter value is used as a parameter value for estimating the channel impulse response in the path E in the storage unit 22. To be stored.

After calculating the likelihood in the path F in the same manner as in the path E, an operation is performed using a channel impulse response estimation algorithm, and the parameter values for estimating the channel impulse response in the path E are stored in the storage unit 22. Memorize. After obtaining the parameter values of the channel impulse response estimation algorithm on the paths E and F, the receiving unit 1
Since the parameter values for estimating the channel impulse response in the path B are not used in 3 and 14, they are deleted from the storage unit 22. The storage unit 2 stores the parameter values of the channel impulse response estimation algorithm in the paths E and F.
After storing in path 2, likelihood comparison is performed on paths A, C, D, E, and F in sequence evaluation section 23, and the above processing is repeated.

When the most probable path length becomes equal to the number of transmission symbols, the sequence is estimated and output as a transmitted signal sequence. The estimation of the impulse response of the channel is not limited to the least squares method, but may be another method. The number of receiving antennas may be one or three or more.

[0017]

As described above, according to the present invention,
For example, simultaneous sequence estimation and channel impulse response estimation
Channel changes due to fading
However, good equalization characteristics can be obtained. As an example
Jerineck Zigan for the sequential sequence estimation algorithm
Guillot algorithm (Literature: F. Jelinek, “Probabilis
ticInformation Theory ”, McGraw-Hill, 1968)
And the LMS in the channel impulse estimation algorithm
(Lease Mean Squares) algorithm,
Figure of error rate characteristics obtained by computer simulation
3 is shown. In FIG. 3, the vertical axis is the error rate, and the horizontal axis is E
_b/ N₀(E₀Is the received energy of one bit, N ₀Is the unit
Noise power per frequency). The modulation method is BPSK
The information transmission speed is 12 Mbps, and the fading
The maximum Doppler frequency that indicates the speed of movement is 1000 Hz
did. And the frame structure is the first 128 synths.
Bol is a channel with a unique word that is a known pattern.
Estimate the impulse response, followed by the information symbol
1024 symbols are added. Propagation path is at the same level
As the 7-wave Rayleigh fading propagation path of
Successive sequence estimation is performed using a 7-tap equalizer.
Perform the 2-branch diversity shown (2-branch diversity
Derivation of likelihood in the case of using diversity is described in reference: E.I. Kat
z, “Sequential Sequence Estimation for Trellis-Co
ded Modulation on Multipath Fading ISI Channel
s ", IEEE. Commun., vol. COM-43, No. 1
2, pp. 2882-2885, 1995).

In FIG. 3, the plot curve of △ indicates the error rate characteristic of the equalizer that simultaneously performs the sequential sequence estimation and the update of the channel impulse response estimation algorithm according to the present invention. 7 shows an error rate characteristic when the channel impulse response estimation algorithm is not updated. From FIG. 3, it can be confirmed that the error rate characteristic is improved by updating the channel impulse response estimation algorithm at the same time as the sequential sequence estimation, and a good equalization characteristic is obtained.

As described above, according to the present invention, sequential sequence estimation and channel impulse response estimation can be performed simultaneously. In the description of the present invention, the LMS algorithm is used as the channel impulse response estimation algorithm. However, a higher-speed algorithm such as another RLS algorithm can be used. When the LMS algorithm is used, the parameter value to be stored is the channel impulse response itself. However, when the RLS algorithm is used, it goes without saying that other means for storing parameters such as a covariance matrix are required.

[Brief description of the drawings]

FIG. 1A is a diagram showing an example of a functional configuration of an embodiment of the present invention;
FIG. 4B is a diagram showing a flow of processing in the sequential sequence estimation in a tree diagram.

FIG. 2 is a flowchart showing a processing procedure according to the embodiment of the present invention.

FIG. 3 is a diagram showing a simulation result for evaluating the present invention.

Claims (1)

[Claims] Means for sampling a receiver output to obtain a received signal sample value sequence; means for estimating an impulse response of a channel by using the received signal sample value sequence as an input; A means for obtaining likelihood using a sample value sequence; a sequential sequence estimating means for sequentially performing sequence estimation based on the comparison using the likelihood; and an impulse at each node selected by the sequential sequence estimating means as a candidate. Adaptive equalization, comprising: means for storing parameter values used for calculation of response estimation; and means for updating a channel impulse response estimation value for a candidate sequence each time the candidate for the sequential sequence estimation is updated. vessel.