Diversity receiver for dispersive channels, combining reliability-weighed signals

At least two receiving branches receive incoming signals corresponding to a transmitted data sequence. Each branch includes an equalizer for producing an estimate of the transmitted data sequence, and providing for each data element a reliability information signal representing a computed probability that the data symbol or value for that element is correct. The receiver selects as a most probable estimate the symbol or value having the highest sum of the reliability information signals from the receive branches. Preferably, for a binary system, each branch provides a single numerical value whose sign identifies the symbol (e.g., 1 or 0), and whose absolute value is its probability. The single numerical values from the branches are simply added to determine the best estimate for that element.

BACKGROUND OF THE INVENTION 
The invention relates to a receiver comprising at least two receive 
branches for receiving an incoming signal corresponding to a transmitted 
data sequence, in which the resulting signals of each receive branch are 
combined in accordance with their receive quality. 
Receivers comprising at least two receive branches, each receive branch 
receiving signals that have equal signal contents, are denoted as 
diversity receivers. In so-called space-diversity arrangements the aerials 
of each receive branch are arranged several wavelengths apart. In 
so-called frequency-diversity arrangements the signals are transmitted and 
received at different frequencies. Since the transmission requirements are 
different for each transmission path or each position with respect to 
frequency, the signals are received with different qualities in the 
separate receive branches. With a sufficiently large distance between the 
aerials in a space-diversity arrangement or with a sufficiently large 
distance with respect to the frequency of the signals in a 
frequency-diversity arrangement, the receive qualities of the individual 
signals in the receive branches are even statistically independent of each 
other. Therefore, by properly processing the signals received in the 
separate receive branches, it is possible to obtain a received signal that 
has better properties (e.g. in respect of signal-to-noise ratio) than any 
of the separately received signals. 
In "Microwave Mobile Communications" by William C. Jakes Jr. the concept of 
maximal ratio combining denotes a processing option of the received 
signals of a diversity receiver, in which each received signal is weighted 
in accordance with the useful signal-to-noise signal ratio and the 
weighted signals are added together to form a single signal. 
With maximal ratio combining as far as this is known for analog signals, 
the phase conditions of the analog signals are to be adapted to each other 
before the signals are added together. If, on the other hand, the received 
signals are based on data signals having so large a data rate that 
intersymbol interference occurs caused by the dispersive channel, the 
adding together of the separately received signals generally leads to a 
deterioration of the total signal. 
SUMMARY OF THE INVENTION 
Therefore, it is an object of the present invention to provide in a most 
simple manner a diversity receiver of the type defined in the opening 
paragraph, so that it is suitable for processing data signals which 
present intersymbol interference. 
This object is achieved in that each receive branch comprises an equalizer 
which produces for each detected one-bit data element a reliability 
information signal and in that the transmitted data sequence is estimated 
on the basis of the reliability information associated with the detected 
data elements of the individual receive branches. 
The equalizer arranged in each receive branch detects data elements 
contained in the separately received signals. The detected data elements 
can then be compared to each other in time by means of the synchronizing 
method. Consequently, it is possible to perform not only a summation of 
the data elements in a correct phase relation but also a summation of the 
fight data elements. The reliability information assigned to individual 
data symbols denotes with what probability the equalizer has estimated for 
a specific data symbol in a data element. A proper combination of the data 
symbols detected by different receive branches for one data element makes 
it possible, when the associated reliability information signals are taken 
into account, to determine a data symbol estimate whose reliability is 
higher than the reliability of the corresponding data symbol estimate in 
each separate receive branch. Compared to other methods according to which 
a pure majority decision or a combination weighted by means of a quality 
estimation performed over several data bits (elements) (e.g. useful 
signal-to-noise signal ratio) is implemented, the combination based on 
instantaneous quality estimation presents the advantage of obtaining a 
further improvement of the error rate. 
The invention is based on the consideration that for deciding on a data 
symbol, an estimated data symbol which is anticipated to have a high 
reliability is to have a distinctly higher weight than one or a plurality 
of other estimated data symbols which are anticipated as less likely to be 
correct. Alternatively, it should also be possible for a plurality of said 
estimated data symbols having average reliability values to have a 
combined reliability exceeding that of a different data symbol having a 
good reliability value. Thus, the decision in favour of a data symbol is, 
in essence, determined by the receive branches having the better receive 
quality. 
Based on the statistically independent variations of the noise signals in 
the different receive branches the instantaneous noise amplitudes for a 
same signal bit (element) are generally different. Since the quality of 
the combination is primarily determined by the branch having a currently 
higher reliability, a quality improvement can be obtained even when the 
field strengths at the receive aerials as well as the power levels of the 
noise produced in the receive input stages are identical. 
In data transmission systems, in which the transmitted digital data are 
binary values (for example, 0 and 1), the equalizers can advantageously be 
devised in such a way that, for representing the data symbol (0 or 1) 
detected estimated for each data element and for representing the 
reliability information assigned to this estimate, only a single numerical 
value is provided. The sign of the numerical value identifies the data 
symbol detected for each data element and the absolute value forms the 
reliability information assigned to this data element. The absolute value 
is also denoted as a reliability coefficient in the following. Preferably, 
the numerical value a(b.sub.i) for an estimated bit i is 
##EQU1## 
where P(b.sub.i) represents the current bit error probability in the 
i.sup.th bit interval and the factor C(b.sub.i), in accordance with the 
binary value of the estimated binary data symbol, may assume the value +1 
(for example, for the binary value 1) or -1 (for example, for the binary 
value 0). 
The combination of binary value and reliability information in this form 
presents the advantage that for determining the estimate for the data 
symbol forming data element, the numerical values determined for that data 
element estimate in the separate receive branches are only to be added 
together. The the sum resulting from this addition of these numerical 
values immediately provides the binary value of this data element without 
any further calculation. In the case where for a further processing a 
reliability information signal or a reliability coefficient is necessary 
for this estimate, it is the absolute value of this sum that can be 
normalized as required in accordance with the number of receive branches 
used. This embodiment is advantageous in that an estimate is obtained with 
the smallest circuitry and the least expenditure. 
The invention will be further described and explained with reference to an 
exemplary embodiment represented in the drawing.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
FIG. 1 shows a binary data receiver which has two separate aerial inputs 
for incoming signals and two separate receive branches having the same 
structure. Each receive branch comprises a HF receive section 2a, 2b, an 
arrangement for producing normal and quadrature components in the baseband 
region 3a, 3b and an equalizer 4a, 4b. 
The equalizers 4a, 4b are devised in such a way that they produce a 
reliability information signal with each detected data symbol forming a 
data element in addition to each detected data symbol. This reliability 
information denotes with what probability the equalizer has decided each 
detected data symbol. An equalizer having such properties will be 
described in the following. 
The equalization of a received signal r(t) is based on a channel model 
which approximately describes the dispersive transmit channel through a 
linear finite transversal filter. FIG. 2 represents such a channel model 
in which the transmission features of the transmit channel are modeled by 
the filter coefficients h.sub.0. . . h.sub.n. During the transmission of a 
binary element b.sub.i and the n binary elements b.sub.i-1 . . . b.sub.i-n 
preceding this binary element b.sub.i, the linear combination of 
C(b.sub.i)*h.sub.0 +C(b.sub..sub.i-1)+. . . C(b.sub.i-n)*h.sub.n is formed 
and additionally superimposed by a noise signal v.sub.i. 
In the receiver attempts are made using this specific channel model, to 
imitate the distortions occurring on the transmit path by means of the 
linear combinations comprising a memory and by means of a transversal 
filter 41 shown in FIG. 6. The imitation of the transmit path is obtained 
by accordingly adjusting the filter coefficients h.sub.0, h.sub.1, . . . 
h.sub.n. The filter coefficients h.sub.0, h.sub.1, . . . , h.sub.n may be 
derived from the sample values of an estimated impulse response of the 
transmit channel. For example, a so-called training sequence which is 
known both to transmitter and receiver and consists of a bit string may be 
used for this purpose. With each reception of a training sequence portion 
of an incoming signals the filter coefficients h.sub.0, h.sub.1, . . . 
h.sub.n are adjusted in such a way that the output signal corresponds in 
the best possible way to the relevant part of the input signal. This 
procedure is generally denoted as channel estimation and described, for 
example, in the article by A. Bayer,: "Correlative and iterative channel 
estimation in adaptive Viterbi equalizers for TDMA mobile radio systems", 
ITC Technical Report 109 for the "Stochastische Modelle und Methoden in 
der Informationstechnik" symposium, April 1989, published in VDE Technical 
Report 107, VDE Verlag, Berlin, pp. 363 to 368. Further references as to 
the literature can be found there. For example, a channel estimator 
arranged in this manner is referenced 45 in the exemplary embodiment. 
For equalization and detection purposes the so-called Viterbi method is 
often used. The equalizer/detector described in this context is also based 
on this method. 
To represent the implemented method, a state diagram will be used 
hereinafter with reference to which usually also the Viterbi method is 
described. The state diagram is a graph depicting in vertical direction 
lines of 2.sup.n nodes. FIG. 3 shows by way of example such a graph for 
n=3. Each node represents one of the combinations that can be made from n 
binary elements. n is in this case the number of binary elements preceding 
a binary element that has just been received, whose influence on the binary 
element to be estimated is to be taken into consideration for the 
equalization n; corresponds to the number of binary elements of the 
channel model as shown in FIG. 2. Each combination of these binary 
elements will be denoted as a state hereinafter. In the state diagram a 
plurality of these lines are arranged in horizontal direction. Each column 
is allocated to a specific sample instant i-3, i-2, i-1, i, i+1. The 
individual binary values (in FIG. 3, 000, . . . , 111) which can be 
assigned to a node are denoted as its state. A state always corresponds to 
a possible allocation of n most recently received digital sample values at 
an instant i to a transmitted bit string. 
In the state diagram always the same state is assigned to each node 
depicted in horizontal direction, while the bit strings allocated to these 
states are shown on the left. The first, i.e. the leftmost binary value of 
a state corresponds to the binary value assigned to the most recently 
received sample value, the next binary value to the assignment to the 
sample value preceding this sample value and so forth. Thus, at instant i 
the first binary value corresponds to the estimate b.sub.i and the last 
binary value to the estimate b.sub.i-n. 
When a new sample value z.sub.i is received, both the binary value 0 and 
the binary value 1 can be assigned thereto. For example, as a result of 
the fact that the binary value 0 is assigned to the most recent sample 
value, the bit string 010 becomes the string 0010 or as a result of the 
fact that a binary value 1 is assigned to the most recent sample value, 
the string becomes 1010, which string 0010 or 1010 can be assigned to the 
transition to the next state. In this manner one comes from state 010 to 
state 001 or 101. As a result of the fact that a binary value 0 or 1 is 
assigned to the sample value there are always only two transitions from 
each state to a state in each state column to its right. 
The state diagram shown in FIG. 3 features by way of arrows any transition 
possible in this manner. For example, the two arrows from node x, whose 
state is assigned the bit string 010 at instant i, show, on the one hand, 
a zero-transition to the node y, which is assigned the state 001 at 
instant i+1 and, on the other hand, a one-transition to the node z, which 
is assigned the state 101 at instant i+1. 
For each transition from one node to the next node the probability with 
which this transition takes place is computed. Combinations of linked 
transitions between nodes of adjacent node columns provide a path. This 
path is equivalent to the reconstructed bit string b.sub.i, b.sub.i-1, . . 
. b.sub..sub.i-n. A multiplicative combination of probabilities of the 
individual transitions in a path produces the overall probability of the 
path. 
For computing the transition probability from one state to another state 
the individual binary values of the binary elements b.sub.i . . . 
b.sub.i-n of a state are used as input parameters c.sub.1 . . . c.sub.n of 
the transversal filter. The first input parameter c.sub.0 always 
corresponds to the binary value of the transition, thus to the binary 
value assigned to the sample value just received. The output value of the 
transversal filter produces in a first approximation and, while discarding 
disturbances as a result of noise signals v.sub.i etc. contained in the 
input signal, the value to be assumed by the sample value when the bit 
string b.sub.i, b.sub.i-1 . . . b.sub.i-n used as an input parameter is 
sent over the path model path and received. When comparing the output 
value z.sub.i to the actual sample value z.sub.i the bit string sent most 
probably may thus be found. 
A large transition probability from one state to a state following in time 
does certainly not sufficiently guarantee that this transition is correct. 
As a result of brief disturbances or signal noise a state transition that 
has actually not taken place may seem the most probable transition. Rather 
correct estimates of the state transitions and thus the estimate of the 
binary value of the digital sample value just received are achieved when 
the overall signal course that has taken place thus far is taken into 
consideration in the form of a calculus of probability of all the state 
transitions that lead to one of the 2.sup.n states of the instant 
concerned. For this purpose, an overall coefficient may be assigned to 
each state, which coefficient, as with the formation of a connection 
probability, is formed by a multiplicative combination of all the 
individual coefficients of the transitions that have led to this state. 
So-called metrics instead of coefficients are known to be used for this 
purpose. The metric may then be calculated from the negative logarithm of 
each coefficient. This is advantageous in that only the metrics are to be 
added together when a link probability is to be computed for which the 
individual coefficients are to be multiplied. For producing the metric in 
the exemplary embodiment the output value z.sub.i of the transversal 
filter 41 is subtracted from the digital sample value z.sub.i in a signal 
evaluation circuit 42 and squared. In this manner a squared distance is 
formed. When assuming Gaussian white noise at the receiver input as the 
single noise source, this squared distance corresponds to the negative 
logarithm of the probability of a state transition. Without much loss of 
accuracy this squared distance is normally also used when the noise signal 
is not Gaussian white noise. In this case the metric is only an approximate 
of the negative logarithm of the probability of a state transition. The 
smaller the squared distance the larger the probability that the received 
sample value has emerged from the bit string used as an input parameter. 
Due to the linear combination of the n last binary elements it is possible 
to form an optimal estimate only after all n binary elements have been 
received. Therefore, the estimate b.sub.i-n is formed after the sample 
value z.sub.i has been received. 
The estimate b.sub.i assigned to the sample value z.sub.i is assigned in 
time to the transitions from the states at instant i to the states at 
instant i+1. 
For forming the estimate b.sub.i-n first all transition probabilities from 
all states i to the next states i+1 are computed in a first step, in which 
next states the binary value 0 was assigned to the transition of the binary 
element b.sub.i-n. The overall metric of the new states to be obtained in 
this manner is temporarily computed from the overall metric of the 
relevant preceding state L at the instant i and from the metric of the 
transition from this preceding state to the next state at the instant i+1. 
In a second step the overall metric of the states at instant i+1, in which 
the binary elements b.sub.i-n corresponded to a binary value 1, is 
computed in similar fashion and thus a one-transition is effected. FIG. 4 
represents the state diagram shown in FIG. 3 depicting only all the paths 
for which all the transitions from the instant i-1=i-3 to the instant 
i+1-n=i-2 were zero-transitions i.e. all the transitions at which the 
binary value 0 was assigned to binary element b.sub.i-3. On the other 
hand, FIG. 5 is a state diagram depicting only the paths in which for the 
transitions from instant i-3 to instant i-2 the binary value 1 was 
assigned to the binary element b.sub.i-3. 
The smallest overall metric is computed on the basis of the overall metrics 
of all the states that have resulted from a zero-transition and on the 
basis of the overall coefficients of all the states that have resulted 
from a one-transition. This is to say, that the path having the smallest 
overall metric is selected from the sub-state diagram of FIG. 4 and the 
sub-state diagram of FIG. 5. These two paths will be denoted hereinafter 
as a zero-minimum path or as a one-minimum path respectively, and the 
overall metrics assigned to these paths will be denoted as a zero-minimum 
overall metric and as a one-minimum overall metric respectively. 
The transition starting from instant i-n, which is assigned to the smaller 
of these two selected minimum overall metrics, then provides the estimate 
b.sub.i-n for the binary element b.sub.i-n sent at instant i-n. 
Each of these two selected minimum overall metrics represents the 
probability with which each selected path represented by the state of each 
selected node in the most favourable conditions can be assigned to the 
estimate b.sub.i-n =0 or the estimate b.sub.i-n =1. These probability 
values can be computed back from the overall metrics. In the case of 
coefficients the individual coefficients are to be divided to obtain 
reliability information that denotes by how many times the selected 
estimate is more probable than its complement. 
The use of metrics simplifies this computation. By subtracting the 
zero-minimum overall metric from the one-minimum overall metric one 
obtains a numerical value whose sign indicates the more probable one of 
the two estimates. A positive sign then indicates that the binary value 1 
as an estimate is more probable than the binary value 0. On the other 
hand, the absolute amount of this value forms the reliability coefficient 
q(b.sub.i-n). 
In a last step the two newly formed overall coefficients pertaining to a 
specific state are compared to each other for each state and the smaller 
of the two values is assigned as a new overall coefficient to the state in 
question. 
For implementing this method the embodiment comprises memory locations in a 
memory module 43, which are arranged in three columns of 2.sup.n memory 
locations each. These columns are referenced first, second or third memory 
location column 431, 432, 433. The address A.sub.n . . . , A.sub.1 of a 
memory location each time corresponds to one of the 2.sup.n states. The 
address of a memory location contains in the first memory location column 
431 the overall metric L assigned to a state. The second column 432 
contains the overall metric L.sub.0 obtained when a binary zero is 
assigned to the sample value just received and the third column of memory 
locations the overall metric L.sub.1 obtained when a binary one is 
assigned to the sample value just received. A selection from each column 
431, 432, 433 is effected by means of control signals from a controller 
40. The control signals are led to appropriate enable inputs E1, E2 and E3 
of the memory module 43. When a memory location is to be driven, the 
controller 40 simultaneously applies its address A.sub.n . . . A.sub.1 as 
an input parameter c.sub.1 . . . c.sub.n to the transversal filter 41. 
The controller gives the first input parameter c.sub.0 as a binary value 0 
for each address formed. In an evaluation circuit 42 the squared distance 
1=(z.sub.i -z.sub.i).sup.2 is formed from the value z.sub.i obtained in 
this manner from the transversal filter 41 and the sample value z.sub.i. 
From this squared distance 1 and the overall metric L stored in the first 
column of memory locations 431 under the associated address an arithmetic 
unit 44 forms a new overall metric L.sub.0 by adding the two values 
together, which overall metric is stored in the second column of memory 
locations 432 under the associated address. The result of the addition is 
applied to the data input D of the memory module 43. The associated read 
or write operations of the memory module 43 are controlled by the 
controller via the read/write input R/W of the memory module 43. 
In an identical manner the binary value 1 is given to the first input 
parameter c.sub.0 and the overall metrics L.sub.1 obtained in this manner 
are stored in the third column of memory locations 433. 
After the control circuit 40 has recalculated all the memory contents of 
the second and third memory location column 432, 433 by passing through 
all the address combinations, the smallest value is selected from the new 
overall metrics L.sub.0 of the second register cells 432 and from the 
overall metrics L.sub.1 of the third register cells 433 by means of 
arithmetic unit 44. These two values are subtracted from each other in 
arithmetic unit 44. As depicted hereinbefore, the sign of the difference 
provides the estimate b.sub.i-n and the absolute amount of the difference 
provides the reliability coefficient q(b.sup.i-n) assigned to this 
estimate. 
After the estimate has been determined, the overall metrics L.sub.1, 
L.sub.0 stored in the second and third memory location columns 432,433 are 
exchanged for the new states. For this purpose the controller first 
generates the addresses 000 and 001. The states 000 and 001 result in the 
state 000 due to a zero-transition as described hereinbefore. The contents 
of the memory locations in the second memory location column 432 under the 
addresses 000 and 001 exactly correspond to a zero transition from these 
two states to the state 000. The contents of these two addressed memory 
locations of the second memory location column 432 are therefore compared 
to each other in arithmetic unit 44 and the smaller of the two values is 
written as a new content under the address 000 in the first memory 
location column 431. The contents of the memory locations addressed under 
000 and 001 of the third memory location column 433 correspond to a 
one-transition to the 100 state. The contents of these two addressed 
memory locations are therefore also compared to each other in the 
arithmetic unit and the smaller of the two values of the overall metrics 
stored in these register cells is stored as a new overall metric under the 
memory location corresponding to the address 100 of the first memory 
location column 431. 
By accordingly driving and comparing the further memory locations, each new 
overall metric of a new state is determined and written under the address 
of the first memory location column allocated to the new state. 
Subsequently, the arrival of a new sample value z.sub.i+1 is waited for 
and the associated estimate b.sub.i+1-n is formed in above-described 
manner. 
In each receive branch the numerical value q.sub.a or q.sub.b is determined 
separately and buffered each in its own storage section RAM.sub.a or 
RAM.sub.b respectively. Since the transmitted data are always combined to 
data blocks, each numerical value q.sub.a or q.sub.b, may be assigned to a 
specific data element of such a data block. Data elements are 
simultaneously read out from the storage section RAM.sub.a or RAM.sub.b in 
pairs and added together in an adder 6 in accordance with their position in 
the data block. The result of the adder 6 is the estimate q which is the 
result of the addition of the two estimates q.sub.a, and q.sub.b of the 
two receive branches A and B. As with the estimates q.sub.a and q.sub.b of 
the two receive branches, the sign also in this case denotes the data 
symbol b of the estimate q, and the absolute value of the estimate q 
denotes the reliability coefficient for these data symbols b. 
If there are more than two receive branches the adder 6 need only be 
provided with a corresponding number of inputs so that the numerical 
values available at the individual inputs are again added together to a 
single numerical value q.