Patent Publication Number: US-6700880-B2

Title: Selection mechanism for signal combining methods

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This Application is a continuation of U.S. patent application Ser. No. 09/414,125 entitled “SELECTION MECHANISM FOR SIGNAL METHODS,” filed Oct. 8, 1999, now U.S. Pat. No. 6,466,558, issued Oct. 15, 2002 to Ling, which is a Continuation-In-Part of U.S. patent application Ser. No. 09/332,857, filed Jun. 14, 1999, now U.S. Pat. No. 6,285,861, issued Sep. 4, 2001, both assigned to the assignee of the present invention. 
    
    
     RELATED FIELD 
     This disclosed method and apparatus relates to wireless communications systems, and more specifically to a selection mechanism for signal combining methods. 
     BACKGROUND 
     In a conventional wireless communications system, a mobile station (cellular telephone, portable computer, etc.) is served by a network of base stations. Such base stations serve as the communication relay station for mobile stations. Accordingly, a mobile station must be in wireless communication with at least one base station whenever the mobile station is turned on in order to communicate with the other components of a communications system. Mobile stations sometimes move out of a region served by one base station and into a region served by another base station. Base stations note this fact, and “hand-off” communication from the first base station to the second. It is common for a mobile station to be in communication with both the first and second base station for periods of time. A mobile station that is in communication with more than one base station is said to be in “soft handoff.” In some cases, a mobile station will be in soft handoff with more than two base stations at any one time. 
     Soft handoff is desirable because it reduces dropped calls. In addition, soft handoff allows the mobile unit to receive the same information from more than one source and to use all of this received information (or energy) to assist in decoding the information that is being transmitted to the mobile station by each base station. Using information transmitted from more than one base station means that the power level that is required from any one base station is reduced. 
     One type of wireless communication system is known as Code Division Multiple Access (CDMA). CDMA systems offer greater capacity than other systems. That is, the number of channels of information that can be communicated concurrently is greater in CDMA systems than in other systems, such as a time division multiple access (TDMA) system or a frequency division multiple access (FDMA) system. 
     In CDMA systems in which both voice and data are being communicated, base stations transmit to as many mobile stations as are in the coverage area for that base station on the same frequency at the same time. In addition, each such base station transmits at the same frequency as every other base station in the network. Signals transmitted to a particular mobile station are distinguishable from signals transmitted to other mobile stations only by the fact that they are transmitted using different codes. In contrast, in a TDMA system, transmissions to a first mobile station are sent during a first period of time and transmissions to a second mobile station are sent during a second, non-overlapping period of time. In an FDMA system, transmissions to a first mobile station are transmitted on a first frequency and transmissions to a second mobile station are transmitted on a second frequency. Because a CDMA receiver can receive more than one channel at a time while tuned to a single frequency, a CDMA receiver can more conveniently perform soft handoff then can a TDMA receiver or an FDMA receiver. 
     While CDMA systems have the advantage of being ideally suited for soft handoff, signals transmitted to a first mobile station using a first code appear as noise to a second mobile station attempting to receive signals transmitted to the second mobile station using a second code. This interference is preferably minimized by making the codes assigned to signals transmitted from a base station orthogonal with codes assigned to all other signals being transmitted by that base station. However, codes used with signals that are transmitted by a first base station cannot be made orthogonal with codes used with signals transmitted by a second base station. Therefore, base stations carefully regulate the amount of power used to transmit signals to mobile stations. The power must be high enough to get the signal through, but the power is preferably no higher than necessary, since additional power appears to other mobile stations as additional interference and reduces the number of mobile stations that can be served. 
     Because conventional CDMA communication systems must handle both voice and data, certain performance requirements must be meet. One such requirement is that the delay between the time the information is transmitted from one end of the communication system until the time the information is received at the other end of the communication system must be relatively short. That is, when two people are talking, any perceptible delay between the time words are spoken at one end of the line and the time those words are heard at the other end of the line would be annoying to both the speaker and the listener. 
     In contrast, many data communication systems can tolerate relatively long delays between the time information is sent and that information is received. CDMA systems have recently been proposed that take advantage of the fact that relatively long delays can be tolerated in systems designed to handle only data. Such systems are referred to herein as high data rate (HDR) systems. In HDR systems, a base station is dedicated to communicating with only one mobile station at any one time. The capacity advantages of CDMA are realized with HDR systems. However, it may be difficult or undesirable to perform soft handoff due to the following reasons. First, the transmissions from a base station in an HDR system are all directed to one mobile station at any particular time. Therefore, while the number of code channels being transmitted from an HDR base station is essentially the same, all of the code channels are intended to be received by one mobile station at any one time. As a result, it is complex to coordinate the times of transmission between two base stations in order to allow soft handoff between the two base stations. Second, in order to perform soft handoff, it is necessary to distribute the same data between more than one base station. This will greatly increase the amount of data to be transferred between base stations, especially for high data rate applications. Third, system capacity increases if the mobile unit can always connect to the best serving base station instead of using soft handoff, assuming that the channel condition is relatively static, as is likely for an HDR system. This is true, since an HDR base station typically transmits at maximum power to allow the best data rate. That is, the rate at which data can be transmitted is directly proportional to the amount of power received. Therefore, in order to maximize the data rate, the maximum power is transmitted. However, this increases the amount of interference a first base station contributes to the signals received by mobile stations attempting to receive signals from a second base station. 
     Accordingly, there is a need for a method and apparatus for reducing the amount of interference that a first base station contributes to mobile stations attempting to receive signals from one or more other base stations. 
     Additionally, if the interference is omnidirectional, such as noise, an optimal signal combining algorithm may degrade receiver performance due to the existence of estimation error. Thus, there is also a need for a selection mechanism for signal combining methods that depends on interference characteristics. 
     SUMMARY OF THE PRESENTLY DISCLOSED METHOD AND APPARATUS 
     The presently disclosed method and apparatus reduces the amount of interference imposed upon a first base station by transmissions of other base stations within the same communication system. The presently disclosed method and apparatus takes into account the fact that soft handoff is not easily implemented or undesirable in some communication systems, such as a high data rate (HDR) communication system in which multiple code channels are being transmitted to one receiving station, such as a mobile station, at a time. That is, in a typical HDR system, each base station is transmitting to only one receiving station at a time. To coordinate the times of transmission and data transfer between two base stations in order to allow soft handoff between two base stations is complex. Moreover, the capacity of the HDR system may increase by not using soft handoff under typical channel conditions. Therefore, the presently disclosed method and apparatus deviates from the traditional approach of performing soft handoff and relies on techniques that use two or more antennas for decreasing the interference between transmissions from a first base station and transmissions from one or more other base stations. 
     In accordance with the presently disclosed method and apparatus, two antennas are used to receive transmissions within a receiving station. A rake receiver is coupled to each antenna. The rake receiver has a plurality of fingers, each finger having the ability to identify and independently decode signals arriving with different propagation delays (i.e., the delay encountered between the time the signal is transmitted and the time the signal is received). By optimally combining the signals that are received by each independent finger of the rake receiver, interference that is correlated between a finger associated with the first antenna and a finger associated with the second antenna can be minimized with respect to the desired signal. Optimum combining requires determination of optimum combining coefficients as follows. 
     Optimum combining coefficients for each of the signals received by the fingers of the rake receiver are determined by first pairing the output from a first finger associated with the first antenna with the output from a second finger associated with the second antenna. The first finger receives the desired signal with essentially the same propagation delay as the second finger. That is, the path of the signal decoded by the first finger and the path of the signal decoded by the second finger differ only because the first finger is associated with the first antenna and the second finger is associated with the second antenna. An autocorrelation matrix is estimated. In one of the presently disclosed methods and apparatus, the autocorrelation matrix is an estimate of the autocorrelation of the received signals. Alternatively, the autocorrelation matrix is an estimate of the autocorrelation of the received noise plus interference. 
     In addition, the cross-correlation between the received signal and the transmitted symbol is estimated by estimating the elements of a fading coefficient vector. Each element of the fading coefficient vector is a fading coefficient associated with one of the signal paths traversed by the signals received by the rake receiver. The fading coefficient vector is preferably estimated based on the pilot bursts received on each finger. 
     The autocorrelation matrix of the noise plus the interference is estimated from the received noise component of each of the signals received by the fingers of the rake receiver. The received noise component for a particular finger is calculated by subtracting the fading coefficient associated with the signal received by that finger from the total signal received by that finger in the pilot bursts. In another of the presently disclosed methods and apparatus, the noise and interference is estimated by subtracting the signal y(m) from the signal y(m+1) one chip later in time (i.e., subtracting adjacent samples). In yet another of the presently disclosed methods and apparatus, the autocorrelation matrix of the noise plus interference R nn  is estimated by subtracting the fading coefficient vector multiplied by the transpose conjugate of the fading coefficient vector, from the autocorrelation matrix R yy  of the received signal y(m). 
     Once the fading coefficient vector and the autocorrelation matrix of the signal received by each pair of fingers has been calculated, these values are used to calculate the optimum combining coefficients. Alternatively, once the fading coefficient vectors and the autocorrelation matrix of the noise plus interference for each pair of fingers have been calculated, they are used to calculate the optimum combining coefficients. 
     Upon combining the signals received by each finger of the rake receiver using the optimum combining coefficients, it is desirable to calculate the signal to interference plus noise ratio of the output from the optimum combiner. This ratio is calculated using the transpose conjugate of the optimum combining coefficients and the fading coefficient vector. The result is a system in which interference from sources other than the source of the desired signal is suppressed with respect to the desired signal to improve decoding. The resulting signal to noise plus interference is calculated to allow a receiving station so equipped to determine the data rate that the channel can support. 
     In another aspect, the present invention is directed to a selection mechanism for signal combining methods that depends on interference characteristics. Accordingly, in one aspect of the invention, a method of computing optimal combining coefficients for a plurality of received signals advantageously comprises the steps of estimating an autocorrelation matrix for the plurality of signals based on received noise and interference associated with the plurality of signals; comparing the magnitudes of off-diagonal elements of the autocorrelation matrix with the magnitudes of diagonal elements of the autocorrelation matrix; setting the off-diagonal elements equal to zero if the magnitudes of the off-diagonal elements are significantly less than the magnitudes of the diagonal elements; and computing a plurality of optimal combining coefficients based on the autocorrelation matrix. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a schematic drawing of a wireless network according to the presently disclosed method and apparatus. 
     FIGS. 2A and 2B taken together are a simplified block diagram of a receiving station of the presently disclosed method and apparatus. 
     FIG. 3 is a simplified block diagram of one optimum combiner of one of the presently disclosed methods and apparatus. 
     FIG. 4A is a functional block diagram of a combiner coefficient processor of one of the presently disclosed methods and apparatus. 
     FIG. 4B is a functional block diagram of a combiner coefficient processor  224 ′ of an alternative method and apparatus. 
     FIG. 5 is an illustration of a data field for which three consecutive fading coefficient estimates are to be averaged and then interpolating between consecutive averages. 
     FIG. 6A is a functional block diagram of a combiner coefficient processor  600  of an alternative method and apparatus. 
     FIG. 6B is a functional block diagram of a combiner coefficient processor  600 ′ in yet another alternative method and apparatus. 
     FIG. 7 is a flow chart illustrating algorithm steps for selecting an optimal signal combining algorithm based on interference characteristics. 
    
    
     DETAILED DESCRIPTION 
     FIG. 1 is a schematic drawing of a wireless network  100  according to the presently disclosed method and apparatus. In one of the presently disclosed methods and apparatus, the wireless network  100  is a code division multiple access (CDMA) high data rate (HDR) system. A first base station  102  has an antenna  104 . The first base station  102  transmits signals that are intended to be received by a receiving station  110 , such as a mobile station, having two antennas  112 ,  114 . The signals that are transmitted by the first base station  102  are shown to traverse two distinct paths from the first base station  102  to the two antennas  112 ,  114  of the receiving station  110 . Accordingly, four desired signals (y 11 , y 12 , y 21 , y 22 ) are received at the receiving station  110  from the base station  102 . Each desired signal is delayed a different amount (i.e., has a different propagation delay) due to the different paths traversed. The first subscript associated with a desired signal is indicative of the antenna that received the signal. The second subscript associated with a desired signal is indicative of the propagation delay encountered by the signal. 
     It should be noted that while y 11 , y 21  do not traverse exactly the same path (i.e., they are received by different antennas as indicated by the first subscript) their delays are essentially equal when contrasted against the delays of y 12  and y 22 . That is, the difference in the propagation delay between the signals y 11  and y 21  will be far less than the difference in the propagation delay between the signal y 11  and either y 12 , or y 22 , since the paths traversed by signals y 12  and y 22  are far longer than the path traversed by signal y 11 . Likewise, the difference in the propagation delay between the signals y 12  and y 22  will be far less than the difference in the propagation delay between the signal y 12  and either y 21 , or y 11 , since the path traversed by signals y 21  and y 11  is far shorter than the path traversed by the signal y 12 . 
     A second signal source also transmits signals being received by the receiving station  110 . For simplicity, the second signal source is described throughout this document as a second base station  108  having an antenna  106 . However, it will be understood by those skilled in the art that the second signal source may be a second antenna associated with the same or another base station, or different portion of the same antenna transmitting from the same base station. However, the signals being transmitted by the second base station  108  are not intended to be received by the receiving station  110 . The base stations  102  and  108  each transmit broadband signals over the same frequency band. Therefore, the signals that are being received by the receiving station  110  from the second base station  108  interfere with the reception by the receiving station  110  of the signals transmitted from the first base station  102 . 
     For ease of understanding, only two base stations  102 ,  108  are shown. However, it will be clear to those skilled in the art that more than two base stations may be transmitting. Furthermore, the receiving station  110  is shown to have only two antennas  112 ,  114 . However, in one of the presently disclosed methods and apparatus, additional antennas may be provided on the receiving station  110 . 
     In the presently disclosed method and apparatus, the receiving station  110  uses the signals received by the two antennas  112 ,  114  to assist in suppressing interference from signal sources that are transmitted from different antennas, or portions of an antenna, than the desired signal. 
     FIGS. 2A and 2B taken together are a simplified block diagram of a receiving station  110  of the presently disclosed method and apparatus. As noted above, incoming signals are received at the receiving station  110  over each of the two antennas  112 ,  114 . The receiving station  110  preferably includes two receiver modules  201 A,  201 B. Each receiver module  201  includes: a radio frequency/intermediate frequency (RF/IF) converter  200 ,  202 ; an analog to digital (A/D) converter  204 ,  206 ; a rake receiver  208 ,  210 , a pilot/data demultiplexer (demux)  212 ,  214 ; and a plurality of Walsh decover modules  216 A,  216 B,  216 C,  216 D,  216 E,  216 F. 
     Each of the two RF/IF converters  200 ,  202  is coupled to an associated one of the two antennas  112 ,  114 . Accordingly, signals received over each of the two antennas  112 ,  114  are coupled to the corresponding radio RF/IF converters  200 ,  202 . 
     Each RF/IF converter  200 ,  202  is coupled to a corresponding one of two A/D converters  204 ,  206 . The A/D converters  204 ,  206  convert the output from the RF/IF converter  200 ,  202  into a digital form. Alternatively, a single A/D converter may be used to convert the received analog signals from both antennas to digital form. Each A/D converter  204 ,  206  is coupled to a corresponding one of two rake receivers  208 ,  210 . 
     Each rake receiver  208 ,  210  is capable of distinguishing between each of the signals that have originated from a desired source base station and that encounter different propagation delays to get to the receiving station  110 . Rake receivers used in CDMA receivers are well known in the art for receiving and discriminating between CDMA signals. Since signals y 11 , y 12 , y 21 , y 22  encounter different delays, a conventional rake receiver is capable of distinguishing between these signals. Each signal y 11 , y 12 , y 21 , y 22  from the desired source (i.e., the base station  102 ) that has a distinguishable delay is assigned to a unique “finger”  213 A,  213 B,  213 C,  213 D,  213 E,  213 F of the rake receiver  208 ,  210 . Each such finger  213  outputs a signal that is despread with a delayed pseudo-random noise (PN) code generated by a PN generator  211 . The PN code output from the generator  211  is delayed by one of a plurality of delay modules  209 A,  209 B,  209 C,  209 D,  209 E,  209 F. The amount of the delay imposed by each delay module  209  is set such that the PN code output from each delay module  209  is synchronized to the PN code with which the signals received from the desired source base station  102  were originally spread, plus the propagation delay encountered in transmission from the base station  102  to the receiving station  110 . 
     It should be noted that the signals transmitted by each base station  102 ,  108  may be spread (i.e., encoded) with the same PN code. However, a substantially different delay is imposed with respect to the start of the PN sequence used to encode the signals from each base station  102 ,  108 . The difference in the delay is substantially greater than the delay between any two signals that would arrive at the receiving station  110  from the same base station  102  over different paths. Therefore, by spreading signals transmitted from different base stations with the same PN code, but with substantially different delays, signals from a first base station  102  can be distinguished from signals from a second base station  108 . Furthermore, signals transmitted from the first base station  102  to the receiving station  110  have a different propagation delay than signals transmitted from the base station  108  to the receiving station  110 . Accordingly, these signals can be distinguished from one another. It should be noted that none of the delay modules  209  are set to promote reception of the signals transmitted by the second base station  108 . 
     In a base station generating signals to be received by one of the presently disclosed methods and apparatus, a pilot signal is time multiplexed with data. In one such base station  102 , the pilot and each data stream are covered (i.e., encoded) with a different Walsh code. The pilot is preferably covered with the Walsh code that has a constant value, making decovering the pilot channel less difficult. During the time when the pilot channel is being transmitted (i.e., the pilot burst), no data is transmitted. Two such pilot bursts occur in each forward link slot. A forward link slot is a predetermined period of time within the signal transmitted from a base station to a receiving station. During the time (i.e., the data field) when the data is being transmitted, the pilot channel is not transmitted. The data is code multiplexed. That is, the data is divided into separate data streams. Each data stream is covered with a different Walsh code. All of the data streams are then transmitted at the same time. For example, a first portion of the data is covered with a first Walsh code, a second portion of data is covered with a second Walsh code, and a third portion of data is covered with a third Walsh code. The first, second, and third portions are then all transmitted by the base station at the same time during the data field. 
     Since the data and pilot are transmitted in time multiplexed format, in one of the presently disclosed methods and apparatus, the receiving station  110  includes one pilot/data demux  212 ,  214  associated with each of the antennas  112 ,  114 . However, a single demux may be provided for demultiplexing the signals received by both of the antennas  112 ,  114 . The output from the first demux  212  is a plurality of pilot streams y p11 (m), y p12 (m), . . . y p1N (m) and a plurality of data streams y d11 (m), y d12 (m), . . . y d1N (m), where y p11 (m) indicates a series of pilot samples, each taken at a time “mT” over the pilot channel received on antenna  1 , with propagation delay  1 , and where y d11 (m) indicates a series of data samples, each taken at a time “mT” over the data channel received on antenna  1 , delay  1 , and where “m” is an integer and “T” is a time equal to one data chip in duration. 
     Each pilot and data stream having the same numeric subscripts is associated with the same finger  213  of the rake receiver  208 ,  210 . Each data stream is coupled to Walsh decover module  216 A,  216 B,  216 C,  216 D,  216 E,  216 F. Each Walsh decover module  216  separates the code channels that were code multiplexed into the data field prior to transmission from the base station  102 . The output from the Walsh decover modules  216 A,  216 B,  216 C,  216 D,  216 E,  216 F are separate data streams that have been decovered, as is well known to those skilled in the art. These pilot and data signals are then coupled to an optimum combining processor  218 . 
     The optimum combining processor  218  shown in FIG. 2B includes three optimum combiners  220 A,  220 B,  220 C and a combiner coefficient processor  224 . It should be noted that each optimum combiner  220  is associated with a corresponding one code channel (i.e., Walsh code used to cover the data transmitted over that code channel). That is, if each the Walsh decover module  216  puts out three data streams (each associated with a different code channel and having been decovered by a different Walsh code), then there will be three optimum combiners  220  that will be used. However, it should be understood that in an alternative method and apparatus, the number of code channels and optimum combiners  220  may differ from the three shown in FIG.  2 B. Furthermore, it should be noted that a single module can perform the functions of more than one optimum combiner. Each optimum combiner  220  is coupled to all of the Walsh decover modules  216 , providing each optimum combiner  220  with the data transmitted on one code channel via multiple paths received over both antennas. The output from each optimum combiner  220  is a stream of data symbols representing the data that modulates the signals transmitted by the base station  102 . Due to the processing by the optimum combining processor  218 , the interference encountered when decoding the symbols is less than would result from a conventional combining of the input to the optimum combiner  220 . That is, the SINR of the signals modulated with the output symbols is greater than the SINR of any of the data streams input to the optimum combiner  220 . Furthermore, the SINR of the output symbols is greater than would result from a conventional combining of the inputs to the optimum combiner  220 . 
     FIG. 3 is a simplified block diagram of one optimum combiner  220   a  in one of the presently disclosed methods and apparatus. Since each optimum combiner  220  is essentially identical, only one such optimum combiner  220 A will be described. The optimum combiner  220 A includes a plurality of two input multiplication modules  302 . The multiplication modules  302  multiply the signal at the first input with the signal at the second input and provide the product at the output of the multiplication module  302 . It should be noted that the multiplication modules  302  may be implemented as a function performed by a programmable device (such as a general purpose processor or a DSP) or as dedicated hardware or programmable logic, or in any other manner that allows the multiplication function to be carried out (such as circuitry or processing functionality within an application specific integrated circuit (ASIC)). 
     The number of multiplication modules  302  in an optimum combiner  220  is preferably equal to the total number of Walsh decover modules  216  in the receiving station  110 . A first input to each multiplication module  302  is coupled to a unique corresponding one of the Walsh decover modules  216 . Accordingly, each data stream received on the one code channel associated with the optimum combiner  220 A is coupled to a first input of the particular two input multiplication module  302 A that is associated with that Walsh decover module  216 A. In the method and apparatus shown in FIGS. 2A and 2B, there are six decover modules  216 A,  216 B,  216 C,  216 D,  216 E,  216 F. Accordingly, six multiplication modules  302 A,  302 B,  302 C,  302 D,  302 E,  302 F are shown in the optimum combiner  220 A of FIG. 3. A second input to each multiplication module  302  is coupled to the combiner coefficient processor  224  by signal line  223 . The combiner coefficient processor  224  calculates optimum combining coefficients (w ij *(m)). The subscript “i” represents the particular antenna associated with a finger  213  and the subscript “j” represents the particular delay encountered by a signal transmitted from a desired source. As noted above, while the paths traversed by signals received by the two antennas  112 ,  114  (such as signals y 11  and y 21  shown in FIG. 1) are not identical due to the fact that the signals are received by different antennas, the same second subscript is used to denote that these signals have encountered essentially equal propagation delays. Similarly, the second subscript of the optimum combining coefficients indicates which propagation delay was encountered by the signal to be multiplied with that optimum combining coefficient. 
     The multiplication performed by the multiplication modules  302  allows each received signal to be weighted and rotated (i.e., the phase and amplitude of the received signals can be adjusted). By rotating the phase, the signal to interference plus noise ratio (SINR) of the combined signal output from the summing module  304  is optimized. That is, the SINR will be the highest possible. Accordingly, the interference caused by undesired signals will be reduced. That is, the power received from a base station  102  attempting to communicate with the receiving station  110  can be maximized with respect to the power received from a base station  108  that is not attempting to communicate with the receiving station  110 . 
     The combiner coefficient processor  224  (shown in FIG. 2B) is coupled to the second input of each multiplication module  302  by signal lines  223 . It should be noted that to reduce the number of lines in FIG. 2B, a single signal line  223  is shown from the combiner coefficient processor  224  to each optimum combiner  220 . However, this line  223  represents a connection over which a plurality of values of wij*(m) (six in the case shown in FIGS. 2B and 3) are provided to each multiplication module  302  in each optimum combiner  220 . These values allow the multiplication modules  302  to optimally adjust the received signals before combining within a summing module  304 . The summing module  304  sums the products to combine each of the received rotated signals. Accordingly, the optimum combiner  220 A performs a dot product operation. The output of the summing module  304  (i.e., the output from the optimum combiner  220 A) is provided as an input sample to a conventional decoding or detection module or a processor that performs conventional decoding or detection functions, such as the error correcting decoder  226 . The output from the summing module  304  can be expressed as: 
     
       
           {tilde over (y)} ( m )= w   H ( m )· y ( m )  (1) 
       
     
     where H denotes the transpose conjugate; y(m)=[y 11 (m), y 12 (m), . . . y ij (m), . . . ] T  is the vector containing the sampled received signal at each rake finger  213  associated with each antenna  112 ,  114  at time mT after Walsh decover; y i,j  is the received signal in the j th  rake finger  213  coupled to the i th  antenna at time mT after Walsh decover; w(m)=[w 11 (m), w 12 (m), . . . w ij (m) . . . ] T  is the vector containing the optimum combining coefficients at time mT. 
     It should be noted that j th  rake finger associated with the first antenna will have the same delay as the j th  rake finger associated with the second antenna. For example, the delay imposed by the delay module  209 B associated with the 2 nd  rake finger  213 B which receives signals from the first antenna  112  will be the same as the delay imposed by the delay module  213 E associated with the 2 nd  rake finger  213 E which receives signals from the second antenna  114 . Accordingly, in the presently disclosed method and apparatus, each delay module  209  associated with the first antenna  112  preferably has a counter-part delay module  209  associated with the second antenna  114 . Each of the modules  209  of such a pair of counter-part delay modules  209  preferably has the same delay. 
     After Walsh decover, the received signal at the j th  rake finger  213  of the i th  antenna can be represented as: 
     
       
           y   ij ( m )= c   ij ( m )· x ( m )+ n   ij ( m )  (2) 
       
     
     where: x(m) is the transmitted symbol at time mT; c ij (m) is the fading coefficient at time mT; and n ij (m) is a complex number representing the thermal noise plus the interference at the j th  rake finger  213  coupled to the i th  antenna at time mT. The fading coefficient c i,j (m) is a complex number representing instantaneous channel gain, at a time “mT,” including the effect of propagation loss, shadowing, and fast fading. In binary phase-shift keying, the symbol x(m) is a value of either +1 or −1. However, in quadrature phase-shift keying, quadrature amplitude modulation, or other such modulation techniques, the symbol x(m) belongs to a modulation constellation. 
     GENERATION OF OPTIMUM COMBINING COEFFICIENTS 
     The following is a detailed description of one disclosed method and apparatus used to determine optimum combining coefficients. FIG. 4A is a functional block diagram of a combiner coefficient processor  224  of one of the presently disclosed methods and apparatus. It should be noted that each of the optimum combiners  220  operates the same. Therefore, for simplicity, the operation of only one such optimum combiner  220  is described. 
     Each of the functions being performed in the modules shown in FIG. 4A may be performed by a programmable device (such as a general purpose processor or a DSP) or as dedicated hardware or programmable logic, or in any other manner that allows the function to be carried out (such as circuitry or processing functionality within an application specific integrated circuit (ASIC)). These functions may be performed by a single module, or by multiple modules. Furthermore, each such module may be physically integrated together with one or more of the other modules or may be physically independent from one or more of the other modules. 
     The combiner coefficient processor  224  is coupled to the two pilot/data demultiplexers  212 ,  214 . The pilot/data multiplexer  212  provides the pilot signals y p1 (m) to the combiner coefficient processor  224 , where y p1 (m)=[y p11 (m), y p12 (m) . . . y p1N (m)]. The pilot/data multiplexer  214  provides the pilot signals y p2 (m) to the combiner coefficient processor  224 , where y p2 (m)=[y p21 (m), y p22 (m) . . . y p2N (m)]. 
     In one of the presently disclosed methods and apparatus, the values of the optimum combining coefficients are calculated as a function of an autocorrelation matrix and the cross-correlation r yx (m) of the received signal y(m) with the transmitted symbols x(m). In one of the presently disclosed methods and apparatus, the autocorrelation matrix is an estimate of the autocorrelation of the received signal. Accordingly, the combiner coefficient processor  224  calculates the optimum combining coefficients as: 
     
       
           w ( m )= R   yy   −1 ( m ) r   yx ( m )  (3) 
       
     
     where: R yy (m) is the autocorrelation matrix of the vector y(m)=[y 11 (m), y 12 (m), . . . y ij (m), . . . ] T  containing the sampled received signal at each rake finger  213  coupled to each antenna at time mT after Walsh decover (i.e. R yy (m)=E[y(m)·y H (m)]); and r yx (m) is the cross-correlation between the vector y(m) and the transmitted symbol x(m) (i.e. r yx =E[y(m)·x*(m)], where E denotes the expectation value as defined in statistical mathematics and x*(m) denotes the complex conjugate of x(m). 
     In an alternative method and apparatus, the values of the optimum combining coefficients are calculated by a combiner coefficient processor  224 ′ (as shown in FIG.  4 B and discussed in detail below) as: 
       w′ ( m )= R   nn   −1 ( m ) r   yx ( m )  (4) 
     where R nn (m) is the autocorrelation matrix of the thermal noise plus interference vector n(m)=[n 11 (m), n 12 (m), . . . n ij (m) . . . ] T  (i.e. R nn (m)=E[n(m)·n H (m)]): and r yx (m) is as defined above. In a system in which pilot symbols are represented by |x|=+1; 
     
       
           r   yx   =E[y ( m ) x *( m )]= c=[c   11 ( m ), c   12 ( m ), . . . , c   ij ( m ) . . . ] T   (4a) 
       
     
     where c ij (m) is the fading coefficient at time mT. 
     The variable w as described in equation (3) and w′ as described in equation (4) only differ by a scalar factor. That is: 
     
       
           w′ =(1 +h ) w   (5) 
       
     
     where: h=c H R nn   −1 c. 
     ESTIMATION OF THE CROSS-CORRELATION r yx    
     The estimations of the cross-correlation r yx  between the received signal y(m) and the transmitted symbol x(m) is determined from the fading coefficient vector c during the pilot bursts in the forward link slot, since the cross-correlation r yx  is equal to the vector c=[c 11 , c 12 , . . . c ij , . . . ] T  of the fading coefficients, as described above. 
     The estimation of c is performed by a fading coefficient estimation module  401  using the pilot bursts of the forward link slot as follows. The fading coefficient module  401  receives each of the pilot signals y p11 (m), y p12 (m), . . . y p1N (m), y p21 (m), y p22 (m), . . . y p2N (m) output from each of the two pilot/data demultiplexers  212 ,  214 . For simplicity sake, FIG. 4A shows a vector y p (m)=y p11 (m), y p12 (m), . . . y p1N (m), y p21 (m), y p22 (m), . . . y p2N (m). In one of the presently described methods and apparatus, the transmitted symbol during the pilot burst is equal to a constant value of one (i.e., x=1). Therefore, each element of the fading coefficient vector c ij (m) can be estimated as:                  c   ^     ij     =       1   M            ∑     m   =   1     M            y   pij          (   m   )                   (   6   )                         
     where ĉ ij  is an estimate of the fading coefficient c ij (m) for the pilot burst at the j th  rake finger of the i th  antenna, y pij (m) is the m th  sample of the received signal in the pilot burst at the j th  rake finger of the i th  antenna, and M is the number of symbols in the pilot burst. 
     The fading coefficient estimate ĉ ij  determined in equation (6) gives an estimate of the cross-correlation r yx , only in the pilot bursts. Therefore, in order to perform coherent detection and determine the optimum combiner coefficients using equations (3), an estimate of the fading coefficient ĉ ij  in the data chips is calculated in a first interpolation module  403 . 
     In one of the presently disclosed methods and apparatus, an estimate of the fading coefficient ĉ ij  in the data chips is made by the linear interpolation module  403  by interpolation between the estimates of two fading coefficients ĉ ij  determined in consecutive pilot bursts. Alternatively, the estimate of the fading coefficient ĉ ij  in the data chips is made by averaging a plurality (e.g., two or three) of fading coefficients ĉ ij  in consecutive pilot bursts in an averaging module  405 . An interpolation is then made between the values of two consecutively calculated averages by the interpolation module  403 . 
     FIG. 5 is an illustration of a data field for which three consecutive fading coefficient estimates are to be averaged and then interpolating between consecutive averages. FIG. 5 shows two forward link slots  500 ,  502 . Each forward link slot  500 ,  502  has two pilot bursts  504 ,  506 ,  508 ,  510 . The fading coefficient for each pilot burst  504 ,  506 ,  508 ,  510  is estimated. A first average fading coefficient estimate c(k) is calculated by taking the sum of the three estimates for the first three consecutive pilot bursts  504 ,  506 ,  508  and dividing by three. Next, a second average fading coefficient c(k+1) is calculated for the three pilot bursts  506 ,  508 ,  510  by adding the fading coefficients for each of the pilot bursts  506 ,  508 ,  510  and dividing by three. A linear interpolation is performed between the first and second average fading coefficients. To estimate the fading coefficient for a portion of data that is a distance a from the pilot burst  506 , the following equation is used: 
     
       
           c ( m )=(1 −a )· c ( k )+ a·c ( k+ 1),0 &lt;a&lt; 1  (7) 
       
     
     The cross-correlation vector r yx  is calculated by repeating this procedure for each rake finger of each antenna. 
     ESTIMATION OF THE AUTOCORRELATION MATRIX R yy    
     The autocorrelation matrix of the received signal can be represented as:                R   yy     =       1   M            ∑     m   =   1     M            y        (   m   )              y   H          (   m   )                     (   8   )                         
     where M is the number of samples used to perform the estimation and y(m)=[y 11 (m), y 12 (m), . . . y 1N1 (m), y 21 (M), y 22 (m) . . . y 2N2 (M)] T  is the vector containing the received signal; y ij (m) is the received signal at the i th  antenna and the j th  rake receiver finger sampled at time mT after Walsh decover; N 1  is the number of rake fingers associated with antenna  1  and N 2  is the number of rake fingers associated with antenna  2 . 
     It can be seen from equation (8) that, in the case in which N 1 =N 2  (i.e., the same number of fingers are used to receive the incoming signal over each of two antennas), R yy  is a 2N×2N matrix, comprising 2×2 sub-matrices. Accordingly, the number of 2×2 sub-matrices is equal to N 2 . 
     Those skilled in the art will recognize that the interference at different rake fingers of each antenna is uncorrelated, due to the differences in the propagation delays of the signals received by different rake fingers. Accordingly, the elements of the autocorrelation matrix R yy  derived from rake fingers having different delays can be assumed to be zero. Only the estimation of 2×2 autocorrelation matrices of the signals of the rake fingers having the same delay (i.e., having j 1 =j 2 ) need be calculated:                     R        (           R     (   1   )           0       ⋯       ⋯       ⋯       0           0         R     (   2   )           0       ⋯       ⋯       ⋮           ⋮                   ⋰                               ⋮           ⋮                                 R     (   s   )                       ⋮           ⋮         ⋯                      ⋯                      ⋯                    ⋰       ⋮           0                                           0         R     (   N   )             )                       
     In the presently disclosed method and apparatus using two antennas, each 2×2 sub-matrix, R (s)  located on the diagonal of the matrix R yy  (as shown above) can be expressed as follows:          R     (   s   )       =         (             1   M            ∑     m   =   1     M                   y     1      s            (   m   )            2                 1   M            ∑     m   =   1     M              y     1      s            (   m   )              y     2      s     *          (   m   )                         1   M            ∑     m   =   1     M              y     1      s     *          (   m   )              y     2      s            (   m   )                     1   M            ∑     m   =   1     M                   y     2      s            (   m   )            2               )        1     ≤   s   ≤   N                     
     wherein y 1s (m) is the signal at time mT received by the first antenna through the s th  rake finger, and y 2s (m) is the signal at time mT received by the second antenna through the s th  rake finger. Each of the zeros shown above in the matrix R represents the assumed value of an off-diagonal 2×2 sub-matrix. 
     In one presently disclosed method and apparatus, the autocorrelation matrix of the received signal is estimated by an R yy  estimation module  407  using the pilot bursts to estimate the value of R yy  in the data chips. However, in an alternative method and apparatus, the R yy  estimation module uses the data chips directly, or both the pilot bursts and the data chips to determine the value of R yy  in the data chips. 
     In one presently disclosed method and apparatus in which pilot bursts are used to estimate R yy  in the data chips, an R yy  interpolation module  411  interpolates R yy  from the values determined in the pilot bursts. In an alternative method and apparatus, the R yy  estimation module  407  is coupled to an R yy  averaging module  409  that calculates an average of two or three R yy  values determined in the pilot bursts. The averages output from the R yy  averaging module  409  are coupled to the interpolation module  411  which interpolates the averages to determine the value of R yy  in the data. The averaging and interpolation performed by the averaging and interpolation modules  409 ,  411  is essentially the same as the averaging and interpolation that is done in the averaging and interpolation modules  405 ,  403 . 
     DETERMINATION OF THE OPTIMUM COMBINING COEFFICIENTS w: 
     The optimum combining coefficients, w are determined by an combining coefficient evaluation module  415  using either equation (3), as provided above. Once R yy  has been estimated by the R yy  estimation module  407  and interpolated to represent R yy  in the data chips, the value of R yy  is coupled to an inversion module  413  which inverts the matrix R yy . Equation (3) is then applied to determine the optimum combining coefficients. 
     In yet another alternative method and apparatus, R yy  is determined from the pilot bursts. Equation (3) is used to determine the optimum combiner coefficients in the pilot bursts. Then linear interpolation is used to determine the optimum combining coefficients in the data chips. 
     The computational complexity for the inversion required in equation (3) is very low due to the zeroing of all of the off-diagonal sub-matrices R (s)  of the matrix R yy . That is, the inversion can be performed without inverting the entire matrix R yy . Rather, the submatrices R (s)  can be separately inverted. 
     The algorithm described above is general and can be applied at a generic M×M autocorrelation matrix, where for the autocorrelation matrix inversion we can use the direct inversion or the well known recursive least square (RLS) algorithm. 
     ESTIMATION OF THE AUTOCORRELATION MATRIX R nn    
     FIG. 4B is a functional block diagram of a combiner coefficient processor  224 ′ of an alternative method and apparatus. As shown in FIG. 4B, the combiner coefficient processor  224 ′ includes essentially the same modules as the combiner coefficient processor  224  shown in FIG.  4 A. However, the processor  224 ′ of FIG. 4B includes an R nn  estimation module  407 ′ which calculates an estimate of an autocorrelation matrix of the noise plus interference R nn (m) instead of the R yy  estimation module  407  of FIG.  4 A. To estimate the autocorrelation matrix of the noise plus interference R nn (m), the pilot bursts are preferably used. A first formula is represented as:                R   nn     =       1   M            ∑     m   =   1     M            n        (   m   )              n   H          (   m   )                     (   9   )                         
     where n(m) is the estimated noise plus interference at the receiver in a sample at time mT, and M is the number of samples used to perform the estimation (i.e. the number of symbols in the pilot burst). Each component of vector n(m) is determined by an R nn (m) estimation module  407 ′ by subtracting the channel gain c ij (m) from the received pilot burst y ij (m) as follows: 
     
       
           n   ij ( m )= y   pij ( m )− c   ij ( m )  (10) 
       
     
     where y pij (m) is the m th  sample of the received signal in the pilot burst at the j th  rake finger of the i th  antenna after Walsh decover and c ij (m) is estimated by a fading coefficient estimation module  401 ′ using formula (6) provided above. The vector n(m) is generated by repeating this procedure for each rake finger of each antenna  112 ,  114 . 
     Alternatively, R nn  may be estimated by the following equation:                R   nn     =       1     2      M              ∑     m   =   1     M            n        (   m   )              n   H          (   m   )                     (   11   )                         
     where each component of vector n(m) is determined by the R nn (m) estimation module  407 ′ using the formula: 
     
       
           n   ij ( m )= y   pij ( m )− y   pij ( m+ 1)  (12) 
       
     
     where M is the number of samples used to perform the estimation (i.e., the number of symbols in the pilot burst, less one). 
     According to equation (12), one pilot symbol of a rake finger of the first antenna  112  is subtracted from a next pilot symbol of the same rake finger of the same antenna  112 . M is the number of samples used to perform the estimation, i.e. (the number of symbols in the pilot burst−1). 
     It should be noted that an alternative method for calculating R nn  is: 
     
       
           R   nn   =R   yy   −cc   H   (13) 
       
     
     where cc H  is the product of the fading coefficient vector with the transpose conjugate of the fading coefficient vector. 
     In one presently disclosed method and apparatus, the second interpolation module  411  interpolates the values of R nn  in the pilot to determine the value of R nn  in the data chips. Alternatively, the average module  409  averages a plurality of R nn  values determined from the pilot (i.e., two or three pilot values of R nn ). The average is then coupled to the second interpolation module  411  which interpolates the averages to determine the value of R nn  in the data chips, as shown and described above with reference to FIG.  5 . It should be noted that with the exception of the processing that is done by the estimation modules  407 ,  407 ′, and the processing done by the combining coefficient evaluation module  415 ,  415 ′, the functions of the combiner coefficient processors  224 ,  224 ′ are essentially identical. 
     In the presently disclosed method and apparatus shown in FIG. 4B, once the value of R nn  is determined in the data chips, the matrix R nn  is coupled to the inversion module  413 . The inverted R nn  output from the inversion module  413  is coupled to the combining coefficient evaluation module  415 ′. Equation (4) is then applied by the combining coefficient evaluation module  415 ′ to determine the optimum combining coefficients w′. 
     POST EVALUATION INTERPOLATION 
     FIG. 6A is a functional block diagram of a combiner coefficient processor  600  of an alternative method and apparatus. As shown in FIG. 6A, the combiner coefficient processor  600  includes essentially the same modules as the combiner coefficient processor  224  shown in FIG.  4 A. However, in the processor  600  of FIG. 6A, the interpolation is performed after the evaluation of the optimum combining coefficients. Accordingly, the fading coefficient estimation module  401  is coupled directly to the evaluation module  415 . Likewise, the R yy  estimation module  407  is coupled directly to the martix inversion module  413 . The evaluation module  415  is then coupled to an interpolation module  411  which performs a linear interpolation between the values of w output from the evaluation module  415  in order to determine the values of w during the data portions of each forward link slot. 
     Likewise, FIG. 6B is a functional block diagram of a combiner coefficient processor  600 ′ in yet another alternative method and apparatus. As shown in FIG. 6B, the combiner coefficient processor  600 ′ includes essentially the same modules as the combiner coefficient processor  224 ′ shown in FIG.  4 B. However, in the processor  600 ′ of FIG. 6B, the interpolation is performed after the evaluation of the optimum combining coefficients. Accordingly, the fading coefficient estimation module  401 ′ is coupled directly to the evaluation module  415 ′. Likewise, the R yy  estimation module  407 ′ is coupled directly to the martix inversion module  413 . The evaluation module  415  is then coupled to an interpolation module  411  performs a linear interpolation between the values of w′ output from the evaluation module  415 ′ in order to determine the values of w′ during the data portions of each forward link slot. 
     ESTIMATION OF THE SIGNAL-TO-INTERFERENCE-PLUS-NOISE (SINR) RATIO 
     In one of the presently disclosed methods and apparatus, the values of c ij (m) and w ij (m) from the combiner coefficient processor  224  are coupled to an SINR evaluation module  228  (shown in FIG.  2 B). In one of the presently disclosed methods and apparatus, a SINR is calculated as:              SINR   =         w   H        c       1   -       w   H        c                 (   14   )                         
     where w has been determined from equation (3) and c from equation (6). 
     In an alternative method and apparatus in which combiner coefficient processor  224 ′ is used to determine w′, the SINR is calculated as: 
     
       
           SINR=w′   H c  (15) 
       
     
     where c has been determined from (6). 
     In one of the presently disclosed methods and apparatus, SINR is used to determine the rate at which data can be transmitted from the base station  102  to the receiving station  101 . As shown in FIGS. 2A and 2B, the combiner coefficient processor  224 ,  224 ′ is coupled to a SINR evaluation module  228  by signal lines  230 ,  231 . Signal line  230  provides the SINR evaluation module  228  with the value of either w or w′, as calculated by the combiner coefficient processor  224 ,  224 ′. Signal line  231  provides the SINR evaluation module  228  with the value for c ij (m). The SINR evaluation module  228  determines the SINR in accordance with either equation (14) or (15). The SINR evaluation module  228  is coupled to a DRC module  232 . The DRC module  232  determines the rate at which data can be received from the base station  102 , taking into account the SINR of the signal being received from the base station  102 . This rate is then communicated to the base station. 
     CALCULATION OF LLR 
     Most communication systems require the evaluation of the log likelihood ratios (LLR) of the coded bits in order to perform decoding at the receiver (for example iterative or “turbo” decoding, conventional Viterbi decoding, etc.). One advantage of the presently disclosed method and apparatus is that values of LLR can be easily computed from the soft decision values represented by either w or w′. 
     For example, assume that quadrature phase-shift keying (QPSK) or 4-quadrature amplitude modulation (4-QAM) is used at the transmitter. Further, assume that d 0  and d 1  denote, respectively, the first and the second coded bit associated with the modulated symbol y d,ij , mapping coded bit  0  at the input of the modulator to the modulation value +1 and coded bit  1  at the input of the modulator to the modulation value −1. Then, the values of the LLR are computed using either equations (16) and (17) or equations (18) and (19): 
     
       
           LLR ( d   0   |y   d ( m ))=4 ·Re ( w′ ( m ) H   ·y   d ( m ))  (16) 
       
     
     
       
           LLR ( d   1   |y   d ( m ))=4 ·Im ( w′ ( m ) H   ·y   d ( m ))  (17) 
       
     
     
       
           LLR ( d   0   |y   d ( m ))=4·(1+ h )· Re ( w ( m ) H   ·y   d ( m ))  (18) 
       
     
     
       
           LLR ( d   0   |y   d ( m ))=4·(1 +h )· Im ( w ( m ) H   ·y   d ( m ))  (19) 
       
     
     where H denotes the transpose conjugate; Re(•) and Im(•) denotes real part and imaginary part of a complex number, respectively; y d (m)=[y d,11 (m), y d,12 (m), . . . y d,ij (m), . . . ] T  is the vector containing the sampled received data signal at each rake finger  213  associated with each antenna  112 ,  114  at time mT after Walsh decover; y d,ij  is the received data signal in the j th  rake finger  213  coupled to the i th  antenna at time mT after Walsh decover; w(m)=[w 11 (m), w 12 (m), . . . w ij (m) . . . ] T  is the vector containing the optimum combining coefficients at time mT evaluated using equation (3), w′(m)=[w′ 11 (m), w′ 12 (m), . . . w′ ij (m) . . . ] T  is the vector containing the optimum combining coefficients at time mT evaluated using equation (4), and (1+h) has been defined in equation (5). 
     For BPSK signaling, only equations (16) or (18) are needed because the imaginary part is zero. 
     Accordingly, in one of the presently disclosed methods and apparatus, the error correcting decoder  226  performs the calculations shown in equations (16) and (17), or equations (18) and (19). 
     SELECTION OF COMBINING METHOD 
     In accordance with one embodiment, a selection mechanism is employed to determine whether to use the optimal combining algorithm, a max-ratio combining algorithm, or a combination of the max-ratio combining algorithm and the optimal combining algorithm. The max-ratio combining algorithm differs from the optimal combining algorithm described hereinabove in the computation of the combining coefficients. The max-ratio combining coefficients are advantageously obtained by setting to zero the off-diagonal elements of the autocorrelation matrix of the received signal, which, as described hereinabove, is used to compute the optimal combining coefficients. The selection mechanism takes advantage of the fact that in the presence of omnidirectional interference such as noise, the max-ratio combining algorithm becomes optimal. 
     The method steps shown in the flow chart of FIG. 7 may be used to implement the selection mechanism. The steps may advantageously be performed with a processor that is integral to the combiner coefficient processor  600  described hereinabove. Alternatively, the steps may be performed with a DSP, an ASIC, discrete gate or transistor logic, discrete hardware components such as, e.g., registers, FIFO, a comparator, or a processor executing a set of firmware instructions or any conventional programmable software module. The processor may advantageously be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The software module could reside in RAM memory, flash memory, registers, or any other form of writable storage medium known in the art. 
     In step  700  the autocorrelation matrix of the received signal is formed by the processor to accomplish optimal combining, as described hereinabove. The processor then proceeds to step  702 . In step  702  the processor compares the magnitudes of the off-diagonal elements of the autocorrelation matrix with the magnitudes of the diagonal elements of the autocorrelation matrix. If the magnitudes of the off-diagonal elements are significantly less than the magnitudes of the diagonal elements, the processor proceeds to step  704 . If, on the other hand, the magnitudes of the off-diagonal elements are not significantly less than the magnitudes of the diagonal elements, the processor proceeds to step  706 . In step  704  some or all of the off-diagonal elements are set to zero and the max-ratio combining coefficients or a combination of max-ratio combining coefficients and optimal combining coefficients are computed with the modified autocorrelation matrix, e.g., in accordance with equation (3) hereinabove. In step  706  the optimal combining coefficients are computed with the unmodified autocorrelation matrix, e.g., in accordance with equation (3) hereinabove. 
     The comparison of step  702  is performed for each off-diagonal element of the autocorrelation matrix. The off-diagonal element c ij  (the off-diagonal element in the ith row and the jth column) is squared and then compared with the product of the associated diagonal elements c ii  and c jj  (the diagonal element in the ith row and the ith column and the diagonal element in the jth row and the jth column, respectively). For example, a given off-diagonal element c 23  is squared and then compared with the product of diagonal elements c 22  and c 33 . 
     In a particular embodiment, in step  704  all of the magnitudes of the off-diagonal elements are set to zero if and only if for every off-diagonal element in the autocorrelation matrix the square of the off-diagonal element is significantly less than the product of the associated diagonal elements. In an alternate embodiment, in step  704  the magnitude of a given off-diagonal element is set to zero only if its square is significantly less than the product of the associated diagonal elements. 
     In a particular embodiment, an autocorrelation matrix comprising multiple diagonal 2×2 submatrices is employed, as described hereinabove, and the determination made in step  702  of whether the magnitudes of the two off-diagonal elements of each 2×2 submatrix are significantly less than the magnitudes of the two diagonal elements of the same submatrix is performed by squaring the magnitudes of each of the two off-diagonal elements and obtaining the product of the magnitudes of the two diagonal elements. The squared magnitude of each of the two off-diagonal elements is compared to a proportion α of the product of the magnitudes of the two diagonal elements. If the squared magnitude of either of the two off-diagonal elements is less than α multiplied by the product of the magnitudes of the two diagonal elements, the two off-diagonal elements are set to zero in step  704  and the max-ratio combining coefficients are computed with the modified autocorrelation matrix. Otherwise (i.e., if neither of the squared off-diagonal element magnitudes is less than α multiplied by the product of the two diagonal element magnitudes), in step  706 , the optimal combining coefficients are computed without modifying the autocorrelation matrix. In one embodiment the proportion α is significantly less than one. In another embodiment the proportion  60   is equal to one-fiftieth ({fraction (1/50)}). 
     INDUSTRIAL APPLICATION 
     The disclosed method and apparatus is capable of exploitation in industry, and can be made and used, whenever is it desired to do wireless data transfer. The individual components of the apparatus and method shown herein, taken separate and apart from one another, may be entirely conventional, it being their combination that is claimed as the invention. 
     While various modes of apparatus and method have been described, the true spirit and scope of the invention is not limited thereto, but is limited only by the following claims and their equivalents, which are claimed as the invention.