Maximum-likelihood sequence estimator with variable number of states

A maximum-likelihood sequence estimator (MLSE) with a variable number of states. A channel response estimator calculates channel responses from a received signal having a predetermined burst length and a reference signal. A controller checks the latest (L-th) response having a larger power than a predetermined threshold value of the channel responses and determines the L number of effective channel responses having the larger power. A Viterbi equalizer with a variable number of states executes a maximum-likelihood sequence estimation on the basis of the trellis with M.sup.(L-1) states (M is a multi-value number of a modulation signal) using the L number of effective channel responses. The MLSE is operated with the minimum number of states every burst to reduce an average processing amount without degrading characteristics and to achieve a low consumption power of receivers.

BACKGROUND OF THE INVENTION 
The present invention relates to a maximum-likelihood sequence estimator 
(MLSE) with a variable number of states for use in mobile communication or 
the like. 
1. Description of the Related Art 
In conventional radio communication, intersymbol interference due to a 
delayed multipath wave causes degradation of characteristics. In 
particular, in a digital automobile telephone system using a TDMA 
(time-division multiplex access) system, suppression of the intersymbol 
interference has been a large subject and it is necessary to adopt an 
equalizing technique such as a decision-feedback equalizer, a 
maximum-likelihood sequence estimator (MLSE) and the like. Especially, the 
MLSE is called a Viterbi equalizer and its equalizing ability is high. 
Hence, the MLSE is widely used for terminals of the European GSM and the 
North American IS-54. 
FIG. 1 shows a conventional maximum-likelihood sequence estimator for use 
in a burst transmission. In FIG. 1, a received signal having a 
predetermined burst length is stored into a memory 100. The received 
signal concerning a training signal position within the burst is input 
from the memory 100 to a channel response estimator 101. The channel 
response estimator 101 calculates channel responses {hi}.sub.i=1,K from 
the received signal while referring to an input training signal. At this 
time, the number K of the channel responses is previously determined 
according to the maximum delay amount of a multipath wave in the worst 
communication environment. The channel response estimator 101 outputs the 
estimated channel responses {hi}.sub.i=1, K to a Viterbi equalizer 
203.sub.K-1, with a fixed number M.sup.(K-1) of states. The Viterbi 
equalizer 203.sub.K-1 executes a maximum-likelihood sequence estimation to 
output a decision signal. In this case, the number M.sup.(K-1) of states 
is constant. 
In FIG. 2, there is shown another conventional adaptive maximum-likelihood 
sequence estimator. In FIG. 2, a received signal is input to a channel 
response estimator 101. The channel response estimator 101 estimates 
channel responses {hi}.sub.i=1, K from the received signal while referring 
to either a training signal when the training is supplied or a decision 
signal when information transmission is carried out. The channel response 
estimator 101 sends the estimated transmisson line responses 
{hi}.sub.i=1,K to a Viterbi equalizer 203.sub.K-1 with a fixed number 
M.sup.(K-1) of states. The Viterbi equalizer 203.sub.K-1 carries out a 
maximum-likelihood sequence estimation to output a decision signal. In 
this case, the number M.sup.(K-1) of states is constant. 
Usually, complexity of an MLSE is much and thus its reduction becomes a 
large subject. The MLSE is described in detail in Document 1: 
"Maximum-Likelihood Sequence Estimation of Digital Sequences in the 
Presence of Intersymbol Interference" by G. D. Forney, Jr., IEEE Trans. on 
Inform. Theory, Vol. IT-18, No. 3, pp. 363-378, May 1972, and Document 2: 
"Adaptive Maximum-Likelihood Receiver for Carrier-Modulated 
Data-Transmission Systems" by G. Ungerboeck, IEEE Trans. on Commun., Vol. 
COM-22, No. 5, pp. 624-636, May 1974. 
In the MLSE, the complexity is determined by the number of states of the 
state transition trellis of the Viterbi algorithm used in the inside. 
Conventionally, the number of states of the MLSE is determined depending 
on the maximum delay amount of the multipath wave in the worst 
communication environment and thus large complexity is always required, 
resulting in a large load of the signal processing by the MLSE. 
Accordingly, a decision-feedback MLSE has been proposed, wherein the 
number of states of the MLSE is reduced in advance from the number of 
states for the worst environment and the information removed by the 
reduction is supplemented with the information of the survived paths, as 
disclosed in, for example, Document 3: "Delayed Decision-Feedback Sequence 
Estimation" by A. Duel-Hallen and C. Heegard, IEEE Trans. on Commun., Vol. 
37, No. 5, pp. 428-436, May 1989. In this system, although the complexity 
is reduced by the reduction of the number of states, the number of states 
is determined in advance regardless of the states of channels. Hence, in 
the worst environment (in the case of non-minimum phases) that the power 
of a delayed wave having a large delay time becomes relatively larger than 
that of the desired wave by fading or the like, it is inevitable that with 
the reduction of the number of states, the characteristics are degraded. 
In order to reduce a consumption power of receivers, particularly, at 
portable mobile terminals, it has been demanded to develop a reducing 
means of a processing load of an MLSE without degrading characteristics. 
SUMMARY OF THE INVENTION 
Accordingly, it is an object of the present invention to provide a 
maximum-likelihood sequence estimator in view of the foregoing problems of 
the prior art, which is capable of reducing an average load of an 
equalization processing and controlling degradation of characteristics to 
the minimum. 
In accordance with one aspect of the present invention, there is provided a 
maximum-likelihood sequence estimator with a variable number of states for 
use in a burst transmission, comprising first means for estimating channel 
responses every burst; second means for estimating a number of components 
having an effective power among the estimated channel responses; and third 
means for carrying out a maximum-likelihood sequence estimation on the 
basis of a trellis diagram of a number of states designated and the 
estimated channel responses, the number of states being determined every 
burst on the basis of the number of the components having the effective 
power. 
In accordance with another aspect of the present invention, there is 
provided a maximum-likelihood sequence estimator with a variable number of 
states, comprising first means for adaptively estimating channel 
responses; second means for estimating a number of components having an 
effective power among the estimated channel responses; and third means for 
carrying out a maximum-likelihood sequence estimation on the basis of a 
trellis diagram of a number of states designated and the estimated channel 
responses, the number of states being determined at any time on the basis 
of the number of the components having the effective power. 
The third means preferably includes a plurality of maximum-likelihood 
sequence estimator units which are operated on the basis of state 
transition trellis of a different number of states and selects one of the 
maximum-likelihood sequence estimator units according to the number of 
states determined on the basis of the components having the effective 
power to operate the selected maximum-likelihood sequence estimator unit. 
Alternatively, the third means preferably includes a plurality of 
maximum-likelihood sequence estimation algorithm which are operated on the 
basis of state transition trellis of a different number of states and a 
signal processor for reading in the maximum-likelihood sequence estimation 
algorithm to execute the read-in maximum-likelihood sequence estimation 
algorithm and selects one maximum-likelihood sequence estimation algorithm 
according to the number of states determined on the basis of the 
components having the effective power to operate the selected 
maximum-likelihood sequence estimation algorithm. 
The maximum-likelihood sequence estimation can be executed on the basis of 
a Viterbi algorithm. 
The maximum-likelihood sequence estimation can be a decision-feedback 
maximum-likelihood sequence estimation. 
The decision-feedback maximum-likelihood sequence estimation can be 
executed on the basis of a Viterbi algorithm. 
The processing amount of the MLSE can be determined by the number of states 
of the state transition the trellis used in the internal Viterbi 
algorithm. Conventionally, the number of states of the MLSE is determined 
according to the maximum delay amount of a multipath wave in the worst 
communication environment and the obtained number of states is constant. 
Hence, the processing amount is always much. However, the occurrence 
frequency of the worst communication environment is not so many and the 
maximum delay amount of the multipath wave is small in a usual 
environment. In this case, the number of states of the MLSE can be 
determined to be small and thus the processing amount can be reduced. 
In the present invention, the number of the channel responses having the 
effective power every burst corresponding to the burst transmission of the 
TDMA or the like is estimated, and within the burst, the number of states 
of the MLSE is determined according to the number of the estimated channel 
responses. 
Further, in the present invention, the number of the channel responses 
having the effective power is always detected, and the number of states of 
the MLSE is adaptively determined according to the number of the detected 
channel responses, thereby controlling the MLSE so as to be always 
operated at the number of states designated. The present 
maximum-likelihood sequence estimator is adaptable to the continuous 
transmission besides the burst transmission. 
Moreover, in the maximum-likelihood sequence estimator of the present 
invention, a decision-feedback maximum-likelihood sequence estimator with 
a variable number of states while reducing the number of states can be 
adopted for the maximum-likelihood sequence estimator units. That is, the 
number of the channel responses having the effective power is detected at 
any time, and the number of states of the maximum-likelihood sequence 
estimator is determined based on the detected value. And the values of the 
transmission signal candidates against the less part than the maximum 
number of the effective power response particularly determined are 
supplemented from the survived path information. Hence, the number of 
states which is required depending on the channel environment and is 
smaller than the case that the usual maximum-likelihood sequence estimator 
is adopted is prepared, and the maximum-likelihood sequence estimator 
having a small average processing load can be implemented without 
degrading characteristics.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
The present invention will now be described in connection with its 
preferred embodiments with reference to the accompanying drawings, wherein 
like reference characters designate like or corresponding parts throughout 
the views and thus the repeated description thereof can be omitted for 
brevity. 
FIG. 3 shows the first embodiment of the present invention, that is, a 
maximum-likelihood sequence estimator for use in a burst transmission 
according to the present invention. In FIG. 8, a received signal having a 
predetermined burst length is stored into a memory 100. For example, a 
burst signal is composed of a plurality of slots, each slot containing a 
known signal in addition to data on both transmission and receive sides, 
as shown in FIG. 4. The received signal concerning a training signal 
position within the burst is input from the memory 100 to a channel 
response estimator 101. 
The channel response estimator 101 calculates channel responses 
{hi}.sub.i=1,K from the received signal while referring to an input 
training signal. At this time, the number K of the channel responses is 
previously determined according to the maximum delay amount of a multipath 
wave in the worst communication environment. One example of the estimated 
channel responses such as effective responses to be used for a metric 
calculation including a response for the desired signal and noneffective 
responses including the latest response component at the worst environment 
time not to be used for the sequence estimation is shown in FIG. 5A. The 
channel response estimator 101 outputs the estimated channel responses 
{hi}.sub.i=1,K to a controller 102. 
The controller 102 checks and sees the latest response having a larger 
power than a predetermined threshold value from the K number of channel 
responses. When it is assumed that this latest response is the L-th 
response (L.ltoreq.K). the L number of channel responses up to the latest 
response are determined to be the effective channel responses 
{hi}.sub.i=1,L and the number of states of the trellis is determined to 
M.sup.(L-1) (M is a multi-value number of a modulation signal). FIG. 6 
shows a trellis diagram for binary signals having 8 states. In this 
example, the number L of the effective channel responses is four, and the 
number of states 8 (=2.sup.3) is determined by the number of possible 
candidates of the transmission signal sequence for 3 response groups 
except the response for the desired signal. The controller 102 outputs the 
number M.sup.(L-1) of the trellis states and the effective estimated 
channel responses {hi}.sub.i=1,L to a Viterbi 103 with a variable number 
of states. 
The Viterbi equalizer 103 with a variable number of states carries out a 
metric calculation using the L number of effective channel responses to 
execute a maximum-likelihood sequence estimation on the basis of the 
trellis with M.sup.(L-1) states. The Viterbi equalizer 103 outputs a 
decision signal. 
In this embodiment, the MLSE is operated with the minimum number of states 
every burst and thus an average processing amount can be reduced to 
achieve a low consumption power, compared with the conventional 
maximum-likelihood sequence estimator for use in the burst transmission, 
wherein the number of states is always M.sup.(K-1), as shown in FIG. 1. 
FIG. 7 shows one embodiment of the Viterbi equalizer 103 with a variable 
number of states, shown in FIG. 3. In this case, a variation range of the 
number of states is considered to be M.sup.1, . . . , and M.sup.(K-1). The 
Viterbi equalizer 103 is composed of the K number of Viterbi equalizer 
units (MLSE) 203.sub.1 to 203.sub.K-1, with respective fixed numbers 
M.sup.1 to M.sup.(K-1) of states and a pair of input and output selectors. 
In this embodiment, the Viterbi equalizer unit having the number of states 
that the controller 102 indicates is selected every burst and the selected 
Viterbi equalizer unit is operated. In this case, the construction and 
algorithm of the conventional Viterbi equalizer with the fixed number of 
states, disclosed in Document 1 can be used for the Viterbi equalizer 
units in this embodiment and hence the description thereof can be omitted 
for brevity. 
FIG. 8 shows the second embodiment of the present invention, an adaptive 
maximum-likelihood sequence estimator according to the present invention. 
In FIG. 8, a received signal is input to a channel response estimator 101. 
The channel response estimator 101 estimates channel responses 
{hi}.sub.i=1,K from the received signal while referring to either a 
training signal when the training is supplied or a decision signal when 
information transmission is carried out. At this time, the number K of the 
channel responses to be obtained is previously determined according to the 
maximum delay amount of a multipath wave in the worst communication 
environment. 
A controller 102 inputs the K number of channel responses {hi}.sub.i=1,K 
from the channel response estimator 101 and investigates the latest 
response having a larger power than a predetermined threshold value from 
the input channel responses. Assuming that this latest response is 
considered to be the L-th response (L.ltoreq.K), in the controller 102, 
the L number of channel responses up to the latest response are determined 
to be the effective channel responses {hi}.sub.i=1,L and the number of 
trellis states is determined to M.sup.(L-1) (M is a multi-value number of 
a modulation signal). The number M.sup.(L-1) of trellis states is 
transferred to a Viterbi equalizer 103 with a variable number of states 
and a path information interchanger 104. 
When no change is detected in the number of states between the present and 
previous times (assuming that the number of states at the previous time is 
M.sup.(P-1), when P=L), the path information interchanger 104 operates 
nothing. On the other hand, when a change in the number of states is 
detected (when P.noteq.L), the path information interchanger 104 sends a 
path information interchange control signal to the Viterbi equalizer 103 
to instruct so that the Viterbi equalizer 103 may sent a path memory 
content and a path metric content to the path information interchanger 
104. Then, the path information interchanger 104 changes the path memory 
content and the path metric content for the trellis with M.sup.(P-1) 
states into those for the trellis with M.sup.(L-1) states and returns the 
changed information to the Viterbi equalizer 103. 
The Viterbi equalizer 103 calculates branch metrics on the basis of the L 
number of effective estimated channel responses and executes a Viterbi 
algorithm (ACS) on the trellis with M.sup.(L-1) states using the 
calculated branch metrics and the interchanged path memory content and 
path metric content to perform a maximum-likelihood sequence estimation, 
resulting in outputting a decision signal. 
In this embodiment, the MLSE is always operated with the minimum number of 
states and thus an average processing amount can be reduced to achieve a 
low consumption power, compared with the conventional adaptive 
maximum-likelihood sequence estimator, wherein the number of states is 
always M.sup.(K-1), as shown in FIG. 2. 
FIG. 9 shows one embodiment of the Viterbi equalizer 103 with a variable 
number of states, shown in FIG. 8. In this case, the Viterbi equalizer 103 
comprises a signal processor 300 and the K number of Viterbi algorithm 
301.sub.1 to 301.sub.K-1 for respective fixed numbers M.sup.1 to 
M.sup.(K-1) of states, in place of the K number of Viterbi equalizer units 
with the respective fixed numbers of states, as shown in FIG. 7. In this 
embodiment, one Viterbi algorithm having the fixed number of state that 
the controller 102 indicates is selected at any time from the K number of 
Viterbi algorithm 301.sub.1 to 301.sub.K-1 by a selector and the selected 
Viterbi algorithm is read into the signal processor 300 so as to operate 
the signal processor 300. In this case, the construction and algorithm of 
the conventional Viterbi equalizer with the fixed number of states, 
disclosed in Document 2 can be used for the Viterbi equalizer and the 
Viterbi algorithm in this embodiment and hence the description thereof can 
be omitted for brevity. 
FIGS. 10 and 12 show the third and fourth embodiments of the present 
invention, that is, decision-feedback maximum-likelihood sequence 
estimators, having the same constructions as the first and the second 
embodiments shown in FIGS. 3 and 8, respectively, except that a 
decision-feedback Viterbi equalizer 303 with a variable number of states 
is used in place of the Viterbi equalizer 103 with a variable number of 
states and a controller 302 operates different from the controller 102. 
Further, FIGS. 11 and 13 show one embodiments of the decision Viterbi 
equalizer 303 shown in FIGS. 10 and 12, having the same construction as 
those shown in FIGS. 7 and 9, except that the K number of 
decision-feedback Viterbi equalizer units 403.sub.1 to 403.sub.K-1 with 
respective fixed numbers M.sup.1 to M.sup.(K-1) of states and the K number 
of decision-feedback Viterbi algorithm 501.sub.1 to 501.sub.K-1, for 
respective fixed number M.sup.a to M.sup.(K-1) of states are used in place 
of the K number of Viterbi equalizer units 203.sub.1 to 203.sub.K-1 with 
respective fixed numbers M.sup.1 to M.sup.(K-1) of states and the K number 
of Viterbi algorithm 301.sub.1 to 301.sub.K-1 for respective fixed numbers 
M.sup.1 to M.sup.(K-1) of states, respectively. 
Hence, the description of the third and the fourth embodiments of the 
present invention is the same as that of the first and the second 
embodiments described above, except that the decision-feedback Viterbi 
equalizer 303 is employed and the operation of the controller 302 is 
different. Thus, the description of the third and the fourth embodiments 
will be carried out mainly in connection with the construction shown in 
FIG. 12. 
In FIG. 12, a received signal is input to a channel response estimator 101. 
The channel response estimator 101 estimates channel response 
{hi}.sub.i=1,K from the received signal while referring to either a 
training signal when the training is supplied or a decision signal when 
information transmission is carried out. At this time, the number of the 
channel responses to be obtained is determined to K. 
A controller 302 inputs the K number of estimated channel responses 
{hi}.sub.i=1,K from the channel response estimator 101 and picks up an N 
(N.ltoreq.K) number of effective estimated channel responses 
{hi}.sub.i=1,N from the same, which are determined to be used by the 
decision-feedback Viterbi equalizer (DFVE with a variable number of 
states) 303. The controller 302 further investigates the latest response 
having a larger power than a predetermined threshold value from the N 
number of the channel responses, as shown in FIG. 5B. Assuming that this 
latest response is considered to be the L-th response (L.ltoreq.N). the 
controller 302 determines the number of states of the trellis of the DFVE 
303 to be M.sup.(L-1) (M is a multi-value number of a modulation signal) 
and transfers the number M.sup.(L-1) of trellis states to the DFVE 303 
with a variable number of states and a path information interchanger 104. 
For example, at the time when N=4 and L=3 by the binary signals, the 
responses to be used are 4 and the number of states becomes 2.sup.(3-1) 
=4. And the decision-feedback Viterbi equalizer 303 uses a trellis diagram 
shown in FIG. 14. 
In FIG. 14, the 8 states of the trellis shown in FIG. 6 are degenerated 
into 4 states of trellis. For example, the state 00 represents the 
contents of the signal candidates of the latest past two times and 
degenerates two states 000 and 100 shown in FIG. 6 by paying the attention 
the commonness of the signal candidates of the past two times. When N=4, 
in the DFVE 303, the signal candidates against the responses of the used 
four times are required in the branch metric calculation in each state. 
In each state, the four signal candidates are given from the present input 
signal candidate, the two signal candidates determined from the 
degenerated state, and one single candidate supplemented from a tentative 
decision signal value on the survived path to that state. The tentative 
decision signal value on the survived path to that state is either a 
signal 0 when the state 00 is the degenerated of the state 000 or a signal 
1 when the state 00 is the degeneration of the state 100. Which 
degeneration is the state 00 can be known by investigating which of the 
state 00 or 10 the survived path to the state 00 takes at the previous 
time by referring to the path memory. 
When no change is detected in the number of states between the present and 
previous times (assuming that the number of states at the previous time is 
M.sup.(P-1), when P=L), the path information interchanger 104 operates 
nothing. On the other hand, when a change in the number of states is 
detected (when P.noteq.L). the path information interchanger 104 sends a 
path information interchange control signal to the DFVE 303 to instruct so 
that the DFVE 303 may sent a path memory content and a path metric content 
to the path information interchanger 104. Then, the path information 
interchanger 104 changes the path memory content and the path metric 
content for the trellis with M.sup.(P-1) states into those for the trellis 
with M.sup.(L-1) states and returns the changed information to the DFVE 
303. 
In each state, the DFVE 303 calculates branch metrics from the present 
input signal candidate, the (L-1) number of signal candidates determined 
by the state, the (N-L) number of signal candidates supplemented from the 
tentative decision signal values on the survived paths, and the N number 
of the effective estimated channel responses and also executes a Viterbi 
algorithm (ACS) on the trellis with M.sup.(L-1) states using the 
calculated branch metrics and the interchanged path memory content and 
path metric content to execute a maximum-likelihood sequence estimator, 
resulting in outputting a decision signal. 
In the conventional decision-feedback maximum-likelihood sequence 
estimator, though the number of states is a small value, the number of 
states is determined in advance. 
Hence, when the number of state is determined to be small, the degradation 
of characteristics becomes large. On the other hand, when to be large, the 
reduction effect is small and it becomes redundant in the channel 
environment having small delay waves such as town and city areas. 
According to the present invention, the decision-feedback 
maximum-likelihood sequence estimator is always operated with the minimum 
number of states and an average processing amount can be reduced to 
achieve a low consumption power and to control the degradation of 
characteristics to be small. 
FIG. 10 shows the decision-feedback maximum-likelihood sequence estimator 
with a variable number of states for use in a burst transmission according 
to the present invention. In this embodiment, the number of states of the 
DFVE is variable in burst unit. 
Further, in the maximum-likelihood sequence estimator using a 
decision-feedback Viterbi equalizer, the number L of states of the 
maximum-likelihood sequence estimator is determined according to the 
channel environment and the maximum number N of the effective power 
responses can be determined to the value which is obtained by adding a 
fixed value to the number L of the states. 
As described above in detail, in the maximum-likelihood sequence estimator 
of the present invention, the MLSE is always operated with the minimum 
number of states on receiving in its environment. As a result, the average 
processing amount of the MLSE can be reduced without degradation of 
characteristics and a low consumption power of a receiver can be attained. 
While the present invention has been described with reference to the 
particular illustrative embodiments, it is not to be restricted by those 
embodiments but only by the appended claims. It is to be appreciated that 
those skilled in the art can change or modify the embodiments without 
departing from the scope and spirit of the present invention.