Abstract:
In a GSM/EDGE Single Antenna Interference Cancellation (SAIC) operation environment, a mobile station is required to operate in a wide range of interference levels. An SAIC linear equalizer that takes advantage of the GMSK signal structure performs better than a conventional Maximum Likelihood Sequence Estimation (MLSE) equalizer in high interference levels, while it performs worse in low interference levels. A dynamic selection between the SAIC linear equalizer and the MLSE equalizer for each received burst is achieved to provide the optimal performance across the entire required operation environments. The dynamic selection is based on the estimated noise plus interference energy relative to the total received signal energy. The soft information calculated by the two categories of equalizers is properly scaled to generate soft information with balanced magnitude.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is related to the following U.S. patent applications, and incorporated herein by reference in their entirety: 
     Ser. No. 11/205,450, entitled “Modulation Detection In A SAIC Operational Environment”, filed on Aug. 16, 2005. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates in general to digital communication systems and in particular to mobile radio systems. Still more particularly, the invention relates to a method of reception and a receiver in a mobile radio system operating in a single antenna interference cancellation environment. 
     2. Description of the Related Art 
     The most widespread standard in cellular wireless communications is currently the Global System for Mobile Communications (GSM). GSM employs a combination of Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) for the purpose of sharing the spectrum resource. GSM networks typically operate in the 900 MHz frequency range. Radio spectrum in the 890-915 MHz bands is for the uplink (mobile station to base station) and in the 935-960 MHz bands is for the downlink (base station to mobile station). The spectrum for both uplink and downlink is divided into 200 kHz-wide carrier frequencies using FDMA, and each base station is assigned one or more carrier frequencies. Each carrier frequency is divided into eight time slots using TDMA. Eight consecutive time slots form one TDMA frame, with a duration of 4.615 ms. A physical channel occupies one time slot within a TDMA frame. Each time slot within a frame is also referred to as a burst. TDMA frames of a particular carrier frequency are numbered, and formed in groups of 26 or 51 TDMA frames called multi-frames. 
     GSM systems typically employ one or more modulation schemes to communicate information such as voice, data, and/or control information. These modulation schemes may include GMSK (Gaussian Minimum Shift Keying), M-ary QAM (Quadrature Amplitude Modulation) or M-ary PSK (Phase Shift Keying), where M=2 n , with n being the number of bits encoded within a symbol period for a specified modulation scheme. GMSK is a constant envelope binary modulation scheme allowing raw transmission at a maximum rate of 270.83 kilobits per second (kbps). While GSM is sufficient for standard voice services, high-fidelity audio and data services demand higher data throughput rates. 
     General Packet Radio Service (GPRS) is a non-voice service that allows information to be sent and received across a mobile telephone network. It supplements Circuit Switched Data (CSD) and Short Message Service (SMS). GPRS employs the same modulation schemes as GSM, but higher data throughput rates are achievable with GPRS since it allows for all eight time slots to be used by a single mobile station at the same time. 
     The EDGE (Enhanced Data rates for GSM Evolution) and the associated packet service EGPRS (Enhanced General Packet Radio Service) have been defined as a transitional standard between the GSM/GPRS (Global System for Mobile Communications/General Packet Radio Service) and UMTS (Universal Mobile Telecommunications System) mobile radio standards. Both GMSK modulation and 8-PSK modulation are used in the EDGE standard, and the modulation type can be changed from burst to burst. GMSK is a non-linear, Gaussian-pulse-shaped frequency modulation, and 8-PSK modulation in EDGE is a linear, 8-level phase modulation with 3π/8 rotation. However, the specific GMSK modulation used in GSM can be approximated with a linear modulation (i.e., 2-level phase modulation with a π/2 rotation). The symbol pulse of the approximated GSMK and the symbol pulse of the 8-PSK are identical. 
     Wireless communication systems have an ever-increasing demand on capacity to transfer both voice and data services. In GSM communication systems, one way to increase system capacity is to increase the frequency reuse factor, whereby the communications system allocates the same frequency to multiple sites in closer proximity. However, stray signals or signals intentionally introduced by frequency reuse methods can interfere with the proper transmission and reception of voice and data signals and can lower capacity. As a result, a receiver must be capable of processing a signal with interference from other channels and extracting the desired information sent to a user. 
     It is well known that the major source of noise and interference experienced by GSM communication devices operating in typical cellular system layouts supporting a non-trivial number of users is due to co-channel or adjacent channel interference. Such noise sources arise from nearby devices transmitting on or near the same channel as the desired signal or from adjacent channel interference such as noise arising on the desired channel due to spectral leakage, for example. Additionally, even in the case where no other signal interference is present, the received signal may consist of multiple copies of the transmitted data sequence due to multi-path channel conditions, for example. This effect is sometimes referred to as self-interference. 
     Traditionally, interference cancellation techniques have had limited success focusing on adjacent channel suppression by using several filtering operations to suppress the frequencies of the received signal that are not also occupied by the desired signal. Correspondingly, co-channel interference techniques have been proposed, such as joint demodulation, which generally require joint channel estimation methods to provide a joint determination of the desired and co-channel interfering signal channel impulse responses. Given known training sequences, all the co-channel interference can be estimated jointly. However, this joint demodulation requires a large amount of processing power, which constrains the number of equalization parameters that can be used efficiently. 
     A recently proposed standard for advanced communications systems and receiver algorithms called Single Antenna Interference Cancellation (SAIC) is designed for the purpose of improving system capacity through increasing frequency reuse. SAIC performs in the presence of co-channel interference resulting from the increased frequency reuse by enhancing single-antenna receiver performance. Current SAIC receiver algorithms are generally optimized for GMSK modulated signals, since gains of SAIC tend to be smaller for 8-PSK modulated signals. In an SAIC operational environment, GMSK traffic on neighboring cells can reuse common frequencies, thereby significantly increasing network bandwidth, while still tolerating the significantly higher co-channel and multi-channel interference than can be accommodated by conventional GMSK/EDGE environments. 
     In Additive White Gaussian Noise (AWGN) dominated environments, such as a conventional GSM/EDGE environment, Maximum Likelihood Sequence Estimation (MLSE) is the optimal solution. However, in co-channel and adjacent channel dominated environments (low carrier/interference (C/I) environments), such as a SAIC operational environment, a linear equalizer technique that takes advantage of the GMSK signal structure provides the superior performance. In actual SAIC operational environments, however, co-channel and adjacent channel interference is not always strong, depending on the traffic and system allocation. For example, to provide different quality of services, the system may allocate different data rates and coding schemes or allocate higher priority channel access by controlling the levels of co-channel and adjacent channel interference from other users. In other words, a particular SAIC enabled mobile station will be expected to operate in a very wide range of co-channel and adjacent channel interference levels. What is needed is an equalization methodology that provides superior performance in both high and low interference levels for mobile stations capable of operating in SAIC operational environments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       This invention is described in a preferred embodiment in the following description with reference to the drawings, in which like numbers represent the same or similar elements, as follows: 
         FIG. 1  shows a block diagram of a wireless mobile communication device, in accordance with a preferred embodiment of the present invention. 
         FIG. 2  shows a logical flow diagram of a method for dynamically selecting an equalization method in a receiver of a wireless communication device based on detected signal interference, in accordance with a preferred embodiment of the present invention. 
         FIG. 3  shows a logical flow diagram of the operation of a linear equalizer soft information calculation, in accordance with a preferred embodiment of the present invention. 
         FIG. 4  shows a logical flow diagram of the operation of a MLSE soft-information calculation, in accordance with a preferred embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     In the following detailed description of exemplary embodiments of the invention, specific exemplary embodiments in which the invention may be practiced are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that logical, architectural, programmatic, mechanical, electrical and other changes may be made without departing from the spirit or scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims. In particular, although the preferred embodiment is described below with respect to a wireless mobile communication device, it will be appreciated that the present invention is not so limited and that it has application to other embodiments of electronic devices such as portable digital assistants, digital cameras, portable storage devices, audio players and portable gaming devices, for example. In the description below, any notation (.)*, (.) T , (.) H , (.) −1  represents the complex conjugate, transposition, conjugate transposition, and inversion of matrices, respectively. 
     With reference now to the Figures, and in particular in reference to  FIG. 1 , there is shown a block diagram of a wireless mobile communication device, in accordance with a preferred embodiment of the present invention. In this embodiment, the wireless mobile communication device  10  may be, for example, a cellular handset, a wireless-enabled laptop computer, a one or two-way pager, or some other wireless communication device. 
     Wireless mobile communication device  10  generally comprises antenna  18  coupled to a filter  30 , a power amplifier (PA)  54 , and a radio frequency (RF) detector  34 . Filter  30  is coupled to receiver (Rx) front-end block  36 , which contains standard receiver components such as gain stages, mixers, oscillators, etc., as generally depicted, and mixes the received RF transmission down to base band. Rx front-end block  36  has an output coupled to a synthesizer and transmitter/receiver (Tx/Rx) back-end block  38 , which in turn is coupled to a digital signal processor/central processing unit (DSP/CPU)  40  over transmit (Tx) and receive (Rx) connections, or alternatively a communications bus (not shown). The synthesizer and Tx/Rx block  38  is also coupled through loop filter  51  to a phase lock loop (PLL)  52  that generates RF transmission signals for amplification by power amplifier (PA) module  54  and transmission over antenna  18 . The receiver front-end block  36  and synthesizer and transmitter/receiver back-end block  38  are preferably integrated circuits (ICs), although other embodiments may be implemented. 
     DSP/CPU  40  has memories  48  associated therewith, for example read-only memory (ROM) and read/write random access memory (RAM). Various input and output devices are coupled to the CPU, including a display and keypad referenced with a common identifier  42 , a microphone  44  and speaker  46 . The exemplary embodiment also includes a power management module  50 . 
     Tx/Rx back-end block  38  is shown containing the block components of a message recovery path coupling Rx front-end block  36  to a receive (Rx) input of DSP/CPU  40 . As will be appreciated by those skilled in the art, Tx/Rx back-end block  38  is comprised of additional components that are not shown in  FIG. 1  to simplify the following description of a preferred embodiment. Analog-to-digital (A/D) converter  56  is coupled to Rx front-end block  36  to digitally convert the received transmission signals into data packets. Modulation detector  58  is coupled to A/D converter  56  to receive the digital transmission data from A/D converter  56 , and detect the modulation type of a received packet. 
     In a preferred embodiment, wireless communication device  10  is configured for EDGE operation in either a GMSK or 8-PSK modulation mode. If modulation detector  58  detects GMSK modulation in the received signal, the EDGE burst is output on GMSK signal  60  to be received by a SAIC-GMSK message recovery module shown at block  62 , where SAIC algorithms, such as Minimum Mean Square Error Block Linear Equalizer (“MMSE-BLE”) for example, perform message recovery of the GMSK modulated signals. Similarly, if 8-PSK modulation is detected by modulation detector  58 , the EDGE burst is output on connection  64  to be received by 8-PSK message recovery module shown at block  66 , where message recovery is performed on the 8-PSK modulated signals in a manner known in the art. Each of the message recovery modules shown at blocks  62 ,  66  rotates the received packet by the phase rotation factor for the particular modulation being detected in the data path, in this case, each of the two modulation types, GMSK and PSK. In the embodiment shown in  FIG. 1 , SAIC-GMSK message recovery block  62  performs a rotation of π/2 on the received symbols. Similarly, 8-PSK message recovery block  66  performs a rotation of 3π/8 on the received symbols. Accordingly, each of the message recovery blocks  62 ,  66  generate a recovered message at their outputs, respectively. 
     In accordance with a preferred embodiment of the present invention, SAIC-GMSK message recovery block  62  dynamically selects between the SAIC linear equalization method and the MLSE method to perform the message recovery as a function of the detected the level of carrier-to-interference ratio in the current operating environment. MLSE equalization has preferred performance in low interference operational environments and GMSK linear equalization has a preferred performance in high interference operational environments. The preferred embodiment performs MLSE equalization methodology when the total energy value is greater than the threshold value and performs GMSK linear equalization when the total energy value is less than the threshold value. The operating environment, as defined here, pertains to (a) the actual physical environment in which the system must operate, which in this case is well encapsulated in the mobile/wireless channel model (plus operating band), and (b) the application and service under consideration as particular attributes. 
     GMSK received signal  60  is coupled to each of SAIC Soft Information Calculation  74 , Switch Logic  72  and MLSE Soft Information Calculation  76  within SAIC-GMSK message recovery block  62 . Switch Logic  72  performs computations to generate a decision to enable, through select signal  78 , the SAIC Soft Information Calculation  74  or, through select signal  80 , the MLSE Soft Information Calculation  76  to recover the received burst. If the SAIC equalizer is to be used to recover the burst, the GMSK training sequence is also provided to the SAIC Soft Information Calculation  74 . If the MLSE Equalizer is to be used to recover the burst, the Switch Logic  72  also provides the estimated channel  116  to the MLSE Soft Information Calculation  76 . The Switch Logic  72  analyzes the received GMSK signal of a burst. If the analysis reveals that the received signal experienced small interference in the channel, the Switch Logic  72  enables the SAIC Soft Information Calculation  74  through select signal  78 . Otherwise, the received signal experienced large interference and the MLSE Equalizer block is enabled through select signal  80 . The SAIC Equalizer block uses the GMSK training sequence and the π/2-rotated GMSK signal to recover the message bits by taking advantage of the GMSK signal structure. The MLSE Equalizer uses the received GMSK signal and the estimated channel to recover the message bits by using Maximum Likelihood Sequence Estimation technique. As will be appreciated, the equalization methodology provided by SAIC-GMSK message recovery block  62  provides superior interference cancellation performance in both high and low interference environments by dynamically switching between the SAIC Linear Equalizer approach if the received signal has experienced high interference environments, such as that seen in SAIC (low carrier/interference (C/I)), and a MLSE methodology if the received signal experienced low interference environments (high C/I) to achieve full performance of the receiver implementing SAIC. 
     Channel decoder  68  is coupled to SAIC-GMSK and 8-PSK message recovery blocks  62 ,  66  to receive recovered message packets and perform channel decoding thereon. Channel decoder  68  is coupled to receive data interface  70 , which buffers and transfers decoded packets to DSP-CPU  40  for application processing. As will be appreciated, the functions performed by blocks  56 ,  58 ,  62 ,  66 ,  68  and  70  may be implemented in either hardware or software, or a combination thereof. 
     With reference now to  FIG. 2 , there is shown a logical flow diagram of a method for dynamically selecting an equalization method for a GMSK burst in switch logic  72  in a receiver of a wireless communication device  10 , in accordance with a preferred embodiment of the present invention. The mid-amble training sequence of the received GMSK burst  60  is sampled at one sample per symbol by GMSK channel estimation module  110 , resulting in training symbol signals (x n ). 
     The training sequence  114  is an original training sequence held in the receiver and known to be the training sequence used by the transmitter for a given transmission. Training sequence  114  is denoted by s n , n=1, 2 . . . N, where N denotes the length of the training sequence. For example, N=26 in a standard EDGE embodiment. Although preferred, the present invention is not restricted to using the training sequence, which is normally included in a data burst, in the described manner. In principle, it is also possible to use any other sequence of information data that is transmitted during the communication process to determine a preferred equalization type, in accordance with the invention. 
     The GMSK rotated training sequence is generated by rotation module  120  through a rotation of π/2 to each symbol of training sequence  114  as follows:
 
 s   n   gmsk   =s   n    e   j1/2π(n−6)  
 
     Accordingly, rotation module  120  generates a set of rotated training samples for the associated modulation type at its output, as rotated sequence  121 . 
     Channel estimation blocks  110  performs a correlation between the received training signals, (x n ), and the rotated training sequence  121 . Therefore, a channel estimation for the GMSK training sequences may then be estimated by: ĥ n =x n   gmsk ⊕s* −n   gmsk , where ⊕ represents a convolution operation and * represents a complex conjugate operation. 
     Accordingly, GMSK channel estimation module  110  produces a correlation vector, R (output as signal  111 ), of 13 symbol periods. R is a correlation between the received training sample signals (x n ) and the complex conjugate of the GMSK rotated training samples (S n   gmsk *) as follows: 
     
       
         
           
             
               R 
               l 
               gmsk 
             
             = 
             
               
                 1 
                 16 
               
               ⁢ 
               
                 
                   ∑ 
                   
                     n 
                     = 
                     6 
                   
                   21 
                 
                 ⁢ 
                 
                   
                     s 
                     n 
                     
                       gmsk 
                       * 
                     
                   
                   ⁢ 
                   
                     x 
                     
                       n 
                       + 
                       l 
                     
                   
                 
               
             
           
         
       
     
     Channel estimation module  110  then calculates a temporal position function using a five-point moving average of the magnitude squared of the correlation vector for GMSK as follows: 
     
       
         
           
             
               
                 
                   E 
                   h 
                   gmsk 
                 
                 ⁡ 
                 
                   ( 
                   l 
                   ) 
                 
               
               = 
               
                 
                   ∑ 
                   
                     n 
                     = 
                     1 
                   
                   5 
                 
                 ⁢ 
                 
                   
                      
                     
                       R 
                       
                         n 
                         + 
                         l 
                         - 
                         1 
                       
                       gmsk 
                     
                      
                   
                   2 
                 
               
             
             , 
             
               
 
             
             ⁢ 
             
               l 
               = 
               1 
             
             , 
             2 
             , 
             ⋯ 
             ⁢ 
             
                 
             
             , 
             9 
           
         
       
     
     These moving averages of the correlations allows the detection of the arrival time of the training sequence in the received burst. Channel estimation module  110  selects the index of the maximum of the 5-point moving average for the GMSK modulation to be output as signal  113 , as follows: 
     
       
         
           
             a 
             ⁢ 
             
                 
             
             ⁢ 
             r 
             ⁢ 
             
                 
             
             ⁢ 
             g 
             ⁢ 
             
                 
             
             ⁢ 
             m 
             ⁢ 
             
                 
             
             ⁢ 
             a 
             ⁢ 
             
                 
             
             ⁢ 
             x 
             ⁢ 
             
                 
             
             ⁢ 
             
               { 
               
                 
                   ∑ 
                   
                     l 
                     = 
                     1 
                   
                   5 
                 
                 ⁢ 
                 
                   
                      
                     
                       R 
                       
                         l 
                         + 
                         k 
                       
                       gmsk 
                     
                      
                   
                   2 
                 
               
               } 
             
           
         
       
     
     Signal  113  represents a timing of the training sequence in the received signal, which can be used to derive the starting point of the training sequence in the received signal. Then, module  120  identifies the 5 points of the correlation  111  corresponding to the maximum index, based on timing signal  113 , as the estimated channel (h k   gmsk )  116 . Similarly, module  136  identifies the 21 points of the received training sequence of the GMSK signal  60  with the correct timing based on timing signal  113 . This results in the correctly-timed, received GMSK training sequence on its output, as GMSK training signals  140 . 
     An estimation ({circumflex over (x)} n   gmsk ) of the received GMSK training signals (x n   gmsk ) can be synthesized from the estimated channel impulse response h k  and the training sequence (s n   gmsk ). As seen in  FIG. 2 , the GMSK estimated channel (h k   gmsk )  116  is passed to synthesis module  124  to produce the estimated training signal ({circumflex over (x)} n   gmsk ). Synthesis module  124  convolves the rotated training sequence  121  with the received channel estimation  116  to create the training signal  128 , which has been rotated for GMSK modulation and simulated with the channel characteristics. In the case that the estimated channel has a length of 5 symbol periods, the estimation of the received GMSK training signal can be represented by 
     
       
         
           
             
               
                 x 
                 ^ 
               
               n 
               gmsk 
             
             = 
             
               
                 
                   ∑ 
                   
                     k 
                     = 
                     1 
                   
                   5 
                 
                 ⁢ 
                 
                   
                     s 
                     
                       n 
                       - 
                       k 
                     
                     gmsk 
                   
                   ⁢ 
                   
                     h 
                     k 
                   
                 
               
               = 
               
                 
                   s 
                   n 
                   gmsk 
                 
                 * 
                 
                   h 
                   n 
                 
               
             
           
         
       
     
     Where h k   gmsk  is the composite channel estimation (impulse response)  116 , and k=1, 2, 3, 4, 5. This composite response includes the transmitter filter, over-the-air channel effects, receiver filter, Analog/Digital conversion, etc. 
     The received GMSK training signals  140 , timed by signal  113 , is subtracted from the synthesized signals  128  by combiners  132  to generate error signals  144 . Error signals  144  indicate the difference between the received training signal in signal  60  and the estimated training signal  128 . 
     A sum-squared calculation is applied to the GMSK error signals  144  by module  148  to calculate a total energy of the error signals as follows: 
     
       
         
           
             
               ∑ 
               
                 n 
                 = 
                 1 
               
               21 
             
             ⁢ 
             
               
                  
                 
                   e 
                   n 
                   gmsk 
                 
                  
               
               2 
             
           
         
       
     
     This generates an error energy (E NI   gmsk )  150  calculated as a function of the total noise plus co-channel interference detected in the GMSK channel. As will be appreciated, this error energy  150  will be lower for cases where the GMSK signal is transmitted in a high C/I environment. 
     Module  152  calculates the total energy (E T   gmsk )  153  of the portion of the received signal  60  corresponding to the training sequence using the GMSK time-corrected training signal  140 . In accordance with the preferred embodiment, module  152  calculates the total energy  153  by a sum-squared calculation, across 21 symbol periods, and in one embodiment, timed to the GMSK timing signal  113 , as: 
     
       
         
           
             
               ∑ 
               
                 n 
                 = 
                 1 
               
               21 
             
             ⁢ 
             
               
                  
                 
                   x 
                   
                     
                       k 
                       max 
                       gmsk 
                     
                     + 
                     n 
                     - 
                     1 
                   
                 
                  
               
               2 
             
           
         
       
     
     In an alternative embodiment, the total energy is calculated by a sum-squared calculation, across 21 symbol periods, using the nominal timing directly the GMSK received signal  60 . 
     The resulting error signal energy and total energy values, E NI   gmsk  and E T   gmsk , are generated from modules  148 ,  152  and then compared at decision module  154  to detect the preferred equalization type for the received transmission burst  60  in accordance with the invention. Module  154  compares the GMSK total energy (E T   gmsk ) to a threshold value set by the GMSK error energy (E NI   gmsk ) multiplied by a constant (Δ). The constant Δ identifies the threshold for the C/I level when choosing the preferred equalization type. Analysis has shown an optimized equalization determination under conditions where module  154  uses Δ=16. If the total energy level in the GMSK channel (E T   gmsk ) is found to be higher than the scaled GMSK error energy level (Δ E NI   gmsk ), it is determined that the SAIC operational environment has a high C/I and, accordingly, select signal  80  is set to enable MLSE equalization of the burst signal  60 . Alternatively, if the total energy level in the GMSK channel (E T   gmsk ) is found to be lower than the scaled GMSK error energy level (Δ E N+I   gmsk ), it is determined that the SAIC operational environment has a low C/I and, accordingly, select signal  78  is set to enable SAIC linear equalization of the burst signal  60 . Module  154  performs a single comparison to determine the optimal equalization type, in one embodiment, although more comparisons can be made to increase the reliability of the determination methodology in other embodiments. 
       FIGS. 3 and 4  show the soft information calculations used with the SAIC linear equalizer and the MLSE equalizer to produce the soft bits with balanced scaling between the two equalizations. With reference now to  FIG. 3 , there is shown a logical flow diagram of a soft-information calculation with the SAIC linear equalizer, in accordance with a preferred embodiment of the present invention. The GMSK received signal  60  is received at linear equalizer  302 , which performs a SAIC linear equalization, taking advantage of the GMSK signal structure and operating on the complex values of the signal. Linear equalizer  302  also receives the GMSK training sequence  121 , which has been rotated by π/2 per symbol. The SAIC linear equalized received signal  304  output from linear equalizer  302  is received by summer  306  at a positive input. Summer  306  also receives the rotated training sequence  121  at a negative input. Summer  306  calculates the difference between the equalized output and the desired output (the training sequence  121 ). The error signal  310  thus represents the difference between the rotated training sequence  121  and the equalized received signal  304 . Error signal  310  is received at block  312  to generate its magnitude squared, and subsequently received at block  314  to be summed over the 26 symbol periods corresponding to the training sequence. The resulting output is the total error energy  316  (E N+I ) related to the noise plus interference of the GMSK received signal  60 . This total error energy  316  is then inserted into a scaling factor  318  to balance the magnitude of soft information output from the SAIC linear equalizer with the soft information of the MLSE Equalization performed within different time slots where MLSE has been selected. Scaling factor  318  is comprised of the number “26” in the numerator and the error energy value  316  times the number “142” in the denominator 
               (       i   .   e   .     ,     26     142   ⁢     E     N   +   1             )     ,         
as a result of SAIC linear equalization using the average of 26 symbol periods and MLSE using the average of 142 symbol periods per burst. Therefore, the equalized received signal  304  is multiplied by the scaling factor  318  by multiplier  308  to generate soft bits  320  on the output.
 
     With reference now to  FIG. 4 , there is shown a logical flow diagram of a MLSE soft-information calculation, in accordance with a preferred embodiment of the present invention. GMSK received signal  60  is received by MLSE block  402  and at a positive input to summer  404 . GMSK received signal  60  can be represented by the data set {Hard Symbols1, Training Symbols, Hard Symbols2}, where the “Hard Symbols1” represents the first 58 symbols, the “Training Symbols” represent the next 26 symbols, and the “Hard Symbols2” represents the last 58 symbols of the 142 symbol packet. MLSE block  402  also receives the auto-correlation {r −4 , . . . , r −1 , r 0 , r +1  . . . , r +4 }  401 , of the estimated channel h n    116 . MLSE block  402  implements the Maximum Likelihood Sequence Estimation, which is usually implemented using the Viterbi algorithm to compensate for the effects of the inter-symbol interference resulting from multi-path channel interference and other channel filtering effects. The Hard Symbols  406  of MLSE block  402  is the estimated hard symbols (Hard symbols1 and Hard symbols2) transmitted. 
     Convolution (CONV) block  408  performs a convolution of the Hard Symbols  407  {Hard Symbols1, Training Symbols, Hard Symbols2} with {r −4 , . . . , r −1 , r 0 , r +1  . . . , r +4 }  403  to generate an interference plus noise estimate  410 . The output of the convolution performed by block  408  is received at a negative input of summer  404  and subtracted from the received GMSK signal  60  to generate soft bits  412  of the transmitted symbols. Soft bits  412  are received at positive inputs to summer  414  and multiplier  416 . Multiplier  418  multiplies Hard Symbols  407  {Hard Symbols1, Training Symbols, Hard Symbols2} with r 0  to generate a scaled hard symbols  420 , thereafter received at a negative input of summer  414 . The output of summer  414  has its magnitude squared by block  422 , and summed across 142 symbols by block  424  to generate error energy (E N+I )  426 , representing the total energy of the noise plus interference. As seen in  FIG. 4 , this energy value  426  is used as a divisor of the estimated channel energy (r 0 ), thereafter resulting in scaling factor  428  at the input of multiplier  416 . Multiplier  416  outputs the product of soft bits  412  and error energy  426  as balanced soft bits  428 . 
     While a preferred embodiment has been described as utilizing linear and MLSE equalization, the present invention is not restricted to using only the linear or MLSE equalization methods in the described manner. In principle, it is also possible to implement the invention by selecting between two or more equalization methods depending upon the interference level of the environment, in accordance with the invention. Also, as will be appreciated, the processes in preferred embodiments of the present invention may be implemented using any combination of computer programming software, firmware or hardware. As a preparatory step to practicing the invention in software, the computer programming code (whether software or firmware) according to a preferred embodiment will typically be stored in one or more machine readable storage mediums such as fixed (hard) drives, diskettes, optical disks, magnetic tape, semiconductor memories such as ROMs, PROMs, etc., thereby making an article of manufacture in accordance with the invention. The article of manufacture containing the computer programming code is used by either executing the code directly from the storage device, by copying the code from the storage device into another storage device such as a hard disk, RAM, etc., or by transmitting the code for remote execution. The method form of the invention may be practiced by combining one or more machine-readable storage devices containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing the invention could be one or more computers and storage systems containing or having network access to computer program(s) coded in accordance with the invention. 
     While the invention has been particularly shown and described with reference to preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention. Any variations, modifications, additions, and improvements to the embodiments described are possible and may fall within the scope of the invention as detailed within the following claims.