Patent Document

RELATED APPLICATION 
   Related subject matter is disclosed in the following applications assigned to the same Assignee hereof: U.S. patent application entitled “Segmented Correlator Architecture For Signal Detection In Fading Channels”, Ser. No. 09/665,511, filed Sep. 9, 2000 now abandoned; and U.S. patent application entitled “Segmented Architecture For Multiple Sequence Detection And Identification In Fading Channels”, Ser. No. 09/664,646, filed Sep. 19, 2000 now U.S. Pat. No. 6,771,688. 

   BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   The present invention relates to communications; more specifically, wireless communications. 
   2. Description of the Related Art 
   Wireless communications involve creating a voice or data communication channel between a mobile communication station and a base station. Setting up the communication channel typically involves the mobile station transmitting a known sequence on an access channel that is monitored by the base station. The base station detects the known sequence and uses it for functions such as estimating a timing difference between the mobile station and base station. 
   The signal transmitted by the mobile station to the base station over an access channel typically includes a known sequence based on one of M possible signature sequences comprising S symbols. In one such system, M=16 different signature sequences are available, where each signature sequence comprises S=16 symbols. UMTS W-CDMA uses length 16 Walsh-Hadamard sequences as signature sequences. These sequences are well known in the art and are described on pages 15-16 of 3GPP TSG RAN “Spreading and Modulation (FDD),” TS25.213 V3.2.b. Once one of the 16 symbol signature sequences is selected, it is used to generate a sequence that is transmitted to the base station.  FIG. 1  illustrates how the transmit sequence is generated from a 16 symbol signature sequence. Sequence  10  represents a 16 symbol signature sequence with symbol periods  12 , where each symbol is +1 or −1. Each of the 16 symbol periods is divided into C chip or sample periods  14 , in this example C=256. As a result, the signature sequence comprises a total of K chip or sample periods, where K=4,096 (S=16 symbol periods×C=256 chip periods per symbol period). The signature sequence is used to generate interleaved sequence  18 . The interleaved sequence comprises 256 (K/C) repeating periods  20 , each with 16 (S) chip periods  22  for a total of 4,096 (K) chip periods. The interleaved sequence is created by using the symbol values in the first chip period of symbol periods  0  through  15  of signature sequence  10  to populate the first 16 chip periods of repeating period  0  of interleaved sequence  18 . The chip periods of repeating period  1  of interleaved sequence  18  are populated using the symbol values in the second chip periods of each of the 16 symbol periods of signature sequence  10 . Similarly, the chip periods of repeating period  2  of interleaved sequence  18  are populated using the symbol values in the third chip periods of symbol periods  0  through  15  of signature sequence  10 . This process continues until the 16 chip periods of the last repeating period (repeating period  255 ) are populated using the symbol values in the last chip period of each of the 16 symbol periods of signature sequence  10 . As a result, interleaved sequence  18  consists of 256 repeating periods each containing 16 chip periods. Each of the repeating periods contains 16 chip periods having values equal to the value of one chip period from each symbol period of signature sequence  10 . Therefore, a sample of symbol periods  0  through  15  of signature sequence  10  is contained in chips  0  through  15 , respectively, of each repeating period of interleaved sequence  18 . 
   The final step in generating a known sequence that is transmitted from the mobile station to the base station involves performing a chip period by chip period multiplication of interleaved sequence  18  with a 4,096 (K) chip period binary sequence  24 . Binary sequence  24  is known and assigned to the particular base station with which the mobile will communicate. The result of the chip period by chip period multiplication is transmit sequence  26  which is then transmitted by the mobile to the base station. 
   The set of possible transmit sequences  26  is known by the base station that will receive the mobile transmission. The available signature sequences, the binary sequence and the interleave pattern are known, and as a result, the set of possible transmit sequences  26  is also known for each of the available signature sequences. 
     FIG. 2  illustrates a multiple signal detector used by the base station to identify and detect known sequences transmitted by a mobile station and received at the base station. Shift register  30  receives samples of the received sequence. Shift register  30  has 4,096 (K) locations in order to provide for 4,096 samples which correspond to the 4,096 chip periods that compose the received sequence. In order to account for the interleaving that was used to create the received sequence, a deinterleaving process is carried out while providing samples from shift register  30  to correlators  32 ,  34  and  36 . It should be noted that the first chip period of each 16 chip long repeating period is provided to correlator  32 . Similarly, the second chip period of each 16 chip long repeating period is provided to correlator  34 . This process continues for a total of 16 correlators where the 16 th  correlator or correlator  36  receives the last chip of each 16 chip long repeating period. This deinterleaving process provides each correlator with 256 chip period samples of a symbol period. Each of the correlators is provided with coefficients representative of a sequence of values associated with the 256 chip period values that represent a symbol. It should be noted that the sequence of coefficients provided to the correlator take into account the chip period by chip period multiplication that occurred between interleaved sequence  18  and binary sequence  24 . The output provided by each correlator indicates how well the 256 chip period values from a symbol period match the sequence of chip period values that are expected for a +1 or −1 symbol. As a result, Fast Hadamard Transform (FHT)  40  receives an input from each of the 16 correlators where each input represents how well the 256 chip period values being examined by the correlator correspond to a symbol and whether that correspondence is to a +1 or −1 symbol. 
   FHT is well known in the art and are discussed in references such as “Fast transforms: algorithms, analysis, applications,” pages 301-329, by D. Elliot and K. Rao, Academic Press, Orlando, Fla., 1982. FHT  40  is provided with coefficients that are used to identify which of 16 possible signature sequences are being received based on the outputs provided by the correlators. The FHT provides 16 outputs each corresponding to one of the possible signature sequences, where the magnitude of the output indicates how well the samples in shift register  30  matches each sequence. FHT  40  outputs are each provided to absolute value generator  42  which takes the absolute value or the square of the absolute value of the output for each FHT output. Each of the outputs of absolute value generator  42  is provided to thresholder  44  which compares the value from absolute value generator  42  with a predetermined threshold. When the value exceeds the threshold, a detection is declared and the received sequence is identified as corresponding to a particular signature sequence by the FHT output that produced the threshold-exceeding signal. 
   It should be noted that the base station attempts to detect the sequence over a period of time referred to as a search window. A search window is typically N times the sampling period of the received sequence. Once shift register  30  is filled with an initial set of samples, it shifts in new samples and shifts out older samples N- 1  times. This results in N attempts to detect the expected sequence over a search window that is equal to N times the time period between samples provided to shift register  30 . A detected sequence&#39;s position in the search window is determined by the number of shifts made by shift register  30  when one of the FHT&#39;s outputs corresponding to a signature pattern to be detected exceeds a threshold. The detected sequence&#39;s position in the search window is a measure of the round trip delay between the mobile station and the base station. 
   When the mobile station is in a fast moving motor vehicle or train, the signal supplied to the shift register is subjected to fast fading and frequency offsets. As a result, the sequence received by the shift register is partially corrupted and produces a low FHT output. As a result, the FHT outputs that are compared with a threshold do not exceed the threshold and thereby result in a failure to detect or identify a received signature sequence. 
   SUMMARY OF THE INVENTION 
   The present invention provides detection and identification of known sequences such as sequences composed of Walsh-Hadamard sequence and scrambling sequence in a fast fading and frequency offset environment using a segmented correlator and FHT (Fast Hadamard Transform) architecture with frequency offset compensation. The incoming sequence of samples or data is segmented into blocks. Each block is individually detected using a correlator/FHT segment. Each sequence identifying output of each correlator/FHT segment is multiplied by a sinusoid for frequency offset compensation. The frequency offset compensated output from each correlator/FHT segment is summed with the corresponding frequency offset compensated output of other correlator/FHT segments. Each sum is compared with a threshold to determine whether a particular sequence has been detected and identified. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  illustrates how a signature sequence is used to generate a transmit sequence that is transmitted from a mobile station to a base station; 
       FIG. 2  illustrates a prior art signal detector and identifier; 
       FIG. 3  illustrates a functional block diagram of a segmented sequence detector and identifier with frequency offset compensation; 
       FIG. 4  illustrates a segment of a segmented correlator/FHT; 
       FIG. 5  illustrates an architecture using multiple segmented correlator/FHTs; 
       FIG. 6  illustrates accumulations performed using the outputs of frequency offset compensating multipliers; and 
       FIG. 7  illustrates threshold values for different probabilities of false alarms. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 3  illustrates a functional block diagram of a segmented sequence detector and identifier that compensates for frequency offsets. Input signal  50  is received by segmented correlator/FHT  52 . Segmented correlator/FHT  52  segments the collection of K input samples from input signal  50  into L segments. Each of the L segments are individually used as inputs to a correlator/FHT combination to detect and identify one of M possible expected sequences (m=0 to M- 1 ). As a result, segmented correlator/FHT  52  produces L sets of M outputs. Each set of M outputs correspond to a separate correlator/FHT pair within segmented correlator/FHT  52 . Each output in the set of M outputs is associated with a particular expected sequence and indicates how well the segment of input samples being examined by the particular correlator/FHT pair correspond to a particular expected sequence. 
     FIG. 4  illustrates a functional block diagram of correlator/FHT segment  54  of segmented correlator/FHT  52 . In this example, segmented correlator/FHT  52  comprises 16 (L=16) of the segments illustrated in FIG.  3 .  FIG. 5  illustrates the relationship between the L segments composing segmented correlator/FHT  52 . Each of the L segments receives 
       K   L         
samples of the input signal, where K is the number of chip periods or samples composing the sequence. As new samples are shifted into the first segment, the oldest samples are shifted out of the first segment and into the second segment. Similarly, each of the remaining segments receives inputs from the prior segment and shifts out its oldest sample to the next segment.
 
   The detection and identification process begins after an initial set of K samples of the input signal has been received (i.e., each segment has an initial set of 
       K   L       
 
samples). Returning to  FIG. 4 , shift register  60  receives the initial or 
       K   L       
 
(256) samples of the received sequence, where L is the number of segments composing the correlator/FHT segment and where K is the number of chips periods or samples composing the sequence received by a base station from a mobile station. (In this example, K=4096.) Other values of L may be used where larger values of L improve performance in a fast fading environment at the expense of increased hardware and/or processing. The received sequence in shift register  60  is deinterleaved and provided to correlators  62 ,  64  through  66 . The deinterleaving is used to reverse any interleaving that was performed when the sequence was produced by the mobile station. The deinterleaving may be skipped if the transmitted sequence was produced without interleaving. It should be noted that only three correlators are shown, but in this embodiment 16 correlators of length  16  are used. Sixteen correlators are used in this example because it is assumed that the signature sequence contains S=16 symbols. Generally, the number of correlators should match the number of symbols (S) in the sequence to be identified, and the correlators should have a length of 
         C   L     ,       
 
where C is the number of chip periods per symbol period in the signature sequence. In this example, C=256. Returning to the 16 symbol example, the first period chip value of each 16 chip long repeating period  70  is provided to correlator  62 ; the second chip period value of each 16 chip long repeating period  70  is provided to correlator  64 ; and in a similar fashion the remaining correlators are populated with input values until correlator  66  receives the last chip value of each  16  chip long repeating period  70 . The coefficients or representative symbols provided to correlators  62 ,  64  and  66  are a 
       C   L       
 
(16) chip value sequence that is expected when taking into account the chip period by chip period multiplication between the interleaved sequence and the base station associated binary sequence. Each correlator output indicates how well the 
       C   L       
 
chip values provided to the correlator correspond to the sequence of chip period values that are expected for a +1 or −1 symbol. The symbol correlation outputs of correlators  62 ,  64 , and  66  are provided to 16×16 (S×M) FHT  72 , where S is the number of symbols in a signature sequence and M is the number of different signature sequences that may be received. Based on the outputs from the correlators, FHT  72  provides an output value on each of its 16 (M) signal identity outputs indicating how well the signal represented by the symbol correlation inputs from the correlators correspond to each of 16 (M) possible signature sequences. For example, output  74  indicates how well the sequence in register  60  corresponds to a first signature sequence (m=0). Similarly, output  76  indicates how well the sequence in register  60  corresponds to a second signature sequence (m=1). Finally, output  78  indicates how well the sequence in register  60  corresponds to a sixteenth signature sequence (m=M- 1 ). It should be noted that if M possible signature sequences are to be identified, an M output FHT should be used. Additionally, it is desirable for the number of symbols S to equal M.
 
   The input signal is shifted through segments  54  via input shift registers  60  and examined to attempt detection/identification of the known or expected sequence until a search window of N input signal sample periods has been examined. This is accomplished by examining the initial K samples of the input signal and then examining each of the following N- 1  new sets of K samples. A new set of K samples is produced each time shift registers  60  shift in a new input signal sample and shift out the oldest sample. A detected/identified sequence&#39;s position in the search window is determined by the number of shifts made by shift registers  60  when a sequence is detected. 
   The outputs from segmented correlator/FHT  52  are provided to multiplier sets  90 ,  92  and  94 . Each set of multipliers receives a complete set of the outputs from segmented correlator/FHT  52 . Each set of multipliers is used to compensate for a different frequency offset affecting the input signal. The frequency offset is examined in terms of frequency bins. The frequency resolution or difference between the bins is frequency f Δ  which may, for example, be a value such as 200 Hz. The number of bins used to compensate for the frequency offset is equal to B+1 where, for example, B may equal a number such as 4. Each set of multipliers is associated with a different frequency bin or offset frequency and multiplies the signals received from segmented correlator/FHT  52  with a sinusoid represented by e −j2πbf     Δ     λT     s    where b×f Δ  defines the offset frequency, where b=−B/2, −B/2+1, . . . B/2 and where T s  the sampling period of the FHT output signal. With C=256, T s  corresponds to the symbol period of the signal, or a 256 chip period. For example, multipliers  96 ,  98  and  100  of multiplier set  90  will have a frequency offset correction factor of −B/2×f Δ . It should be noted that multiplier set  96  receives L groups of M inputs, that is, M outputs from each of the L segments within segmented correlator/FHT  52 . Each of the M outputs from each of the L segments is multiplied by a sinusoid that has a frequency that is indexed by the index value λ where the value of λ is based on the segment from which the outputs originate. It should be noted that multiplier  96  simply multiplies by the value 1 since λ=0 for segment  0 . Segment  1  (λ=1) is multiplied by the sinusoid that has its frequency indexed by the value 1. Similarly, segment L- 1  where λ=L- 1  is multiplied by a sinusoid whose frequency is indexed by the value L- 1 . This results in the separate segments produced by segmented correlator/FHT  52  being multiplied by a different multiple of the frequency offset where (−B/2×f Δ ) is the frequency offset or frequency bin associated with multiplier set  90 . This multiplication compensates for a frequency offset in the input signal equal to (−B/2×f Δ ). Since the frequency offset in the input signal is not known, multiplier sets  92  through  94  are each used to compensate for a different frequency offset in the input signal. For example, multiplier set  90  compensates for a frequency offset of −B/2f Δ , multiplier set  92  compensates for a frequency offset of (−B/2+1)f Δ , and multiplier set  94  compensates for a frequency offset of B/2f Δ . This results in the outputs of segmented correlator/FHT  52  receiving different frequency offset compensation for each of the multiplier sets. The multiplier set associated with the frequency offset closest to the actual frequency offset in the input signal will produce an output having larger magnitudes. 
   The outputs of each multiplier set  90 ,  92  through  94  are provided to accumulators  110 . Each of the accumulators receives L sets of M inputs from the multipliers. The accumulators perform the accumulation of L values for each of the M inputs. For example, for m=0, a sum of the L different m=0 values received from the multipliers is formed and stored. This type of summation and accumulation is carried out for each set of L values for m=0 to m=M- 1 . This is illustrated by accumulation positions  112  thorough  114 , where L values of m=0 are accumulated in position  112  and where L values of m=M- 1  are accumulated in position  114 . 
     FIG. 6  illustrates the accumulation process carried out by accumulator  110 . Outputs λ=0, λ=1 and λ=L- 1 , are received from segmented correlator/FHT  52 . The outputs of segmented correlator/FHT  52  are multiplied by multipliers  96 ,  98  and  100  and then passed to accumulator  110 . Accumulator  110  forms a separate accumulation for each of the M inputs received from the multipliers. For example, output m=0 for each of the segments λ=0 to λ=L- 1  is summed by summer  120  and stored in accumulation  122 . Similarly, output m=1 for each of the segments λ=0 to λ=L- 1  is summed by summer  124  and stored in accumulation  126 . This process continues for each of the M inputs where m=0 to M- 1  so that output m=M- 1  for each of the segments λ=0 to λ=L- 1  is summed by summer  128  and stored in accumulation  130 . After accumulations have been created for each of the M inputs, a new sample of the input signals is obtained by shifting a new input sample signal into segmented correlator/FHT  52 . This results in a one input signal sample period shift in the N sample period wide search window. As a result of the new input signal sample set, new outputs are produced by multipliers  86  through  100 . These new outputs are also accumulated by accumulator  110 . Once again, L values of each of the M inputs are accumulated in separate sums. In this case, since the search window has been shifted by one sample period, the separate accumulations are stored in accumulations  132 ,  134  and  136  for accumulations associated with inputs m=0, m=1, m=M- 1 , respectively. It should be noted that for each potion in the search window (n=0 to N- 1 ), a separate accumulation is provided for each set of M inputs received by accumulator  110  from the multipliers. 
   Energy calculators  140  determine an energy content associated with each of the accumulations received from accumulators  110 . As a result, each of energy calculators  140  perform M×N energy calculations. The energy calculations are executed by taking either the absolute value or the square of the absolute value of each accumulation received from accumulator  110 . Each energy calculation is stored so that it may be identified with a particular expected sequence m and a particular position n in the search window. 
   The output of energy calculation  140  is provided to threshold units  150 . Each of threshold unit  150  compare each energy calculation stored in energy calculator  140  with a predetermined threshold.  FIG. 7  illustrates threshold values for different probabilities of false alarms. The energy calculations that exceed the threshold are passed from threshold unit  150  to maximum detector  160 . Maximum detector  160  selects the largest threshold crossing energy calculation provided by threshold units  150 . The largest threshold crossing energy calculation is used to identify and detect the sequence represented by the input signals and to establish the frequency offset associated with the input signal. The expected sequence contained in the input signal is identified by the value of m that is associated with the output selected by maximum detector  160 . The time delay associated with the input signal is determined by the value of n or the position in the search window associated with the output selected by maximum detector  160 . Additionally, the frequency offset associated with the input signal is identified by the frequency bin (b×f Δ ) associated with the set of multipliers that were used to produce the output selected by maximum detector  160 . 
   It should be noted that it is also possible to select the maximum output from energy calculations  140  and then compare that maximum output to the threshold. The maximum output that exceeds the threshold identifies and detects the sequence represented by the input signal, and determines the frequency offset associated with the input signal. 
   Derivation of Threshold 
   Consider the sequence at the transmitter
 
 s ( k )= A   c   p ( k ) c ( k ) v ( k ) , k= 0, 1, . . . , 4095  (Eq. 1)
 
where A c  is the chip amplitude at the transmitter, p(k) is the scrambling code, c(k) is the signature sequence,
 
and v(k) is the modulation sequence.
 
The signal at the base station receiver is modeled as
 
 r ( k ) =s ( k ) h ( k ) +z ( k )  (Eq. 2)
 
where h(k) is the channel gain and z(k)=z_I(k)+j z_Q(k) is complex white Gaussian noise with variance σ 2 .
 
At the receiver, the following hypothesis testing is carried out for every time delay
 
 H   0   :r ( k ) =z ( k )
 
 H   1   :r ( k ) =s ( k ) h ( k ) +z ( k ).  (Eq. 3)
 
Define the following symbols
         N c : Coherent integration length in number of chips   N seg : Number of segments for noncoherent combining   N div : Number of diversity antennas   I: Number of frequency offset candidates   q: Threshold for detection   P FA : False alarm probability       

   For hypothesis H 0 , the decision statistic formed by energy computation (or, squared L 2  norm) for each frequency bin i has chi-squared distribution with N=2 N seg  N div  degrees of freedom. The underlying noise variance after coherent accumulation is σ 2 N c /2. The cumulative distribution function (cdf) of the decision statistic for each frequency bin for hypothesis H 0  is given by 
                   F   Y     (   y        ⁢     H   0     )     =     1   -     ⅇ         -   y     /     (       σ   2     ⁢     N   c       )       ⁢       ∑     k   =   0         N   /   2     -   1       ⁢           ⁢       1     k   !       ⁢         (     y       σ   2     ⁢     N   c         )     h     .                       (     Eq   .           ⁢   4     )             
 
The threshold for detection for I frequency bins is computed from the relation 
                     P   FA     =     Pr   ⁡     [       y   o     &gt;     q   ⁢           ⁢   or   ⁢           ⁢     y   1       &gt;     q   ⁢           ⁢   or   ⁢           ⁢   …   ⁢           ⁢   or   ⁢           ⁢     y     I   -   1         &gt;   q     ]                   =     1   -     Pr   ⁡     [       y   o     ≤     q   ⁢           ⁢   and   ⁢           ⁢     y   1       ≤     q   ⁢           ⁢   and   ⁢           ⁢   …   ⁢           ⁢   and   ⁢           ⁢     y     I   -   1         ≤   q     ]                     =     1   -         ∐     I   -   1         i   =   0       ⁢           ⁢       F     Y   i       ⁡     (   q   )                       =     1   -         F   Y   I     ⁡     (   q   )       .                     (     Eq   .           ⁢   5     )             
 
Using the relation (Eq. 4) and (Eq. 5), we can find the threshold by a recursive search. For a desired value of P FA , we do a sequential search of threshold value q in steps of Δq. The following pseudo-code summarizes the recursion.
         q=0;   F Y (q)=0;   Δq=Desired resolution for threshold search;   do while {
           ((1−P FA ) 1/I &gt;F Y (q))   q:=q+Δq;   Update F Y (q);   
           }       

     FIG. 7  illustrates the difference in threshold values for various detection algorithms for UMTS access preamble detection. The threshold is compared for three detection algorithms, i.e., fully-coherent detection, 4 segment noncoherent detection, and the detection algorithm with frequency offset compensation. Squared L 2  norm is used as the decision statistic. We assume that N div =2. The signals from two antennas are noncoherently combined. For fully-coherent detection, N seg =1, N c =4096 and for 4 segment noncoherent detection, N seg =4, N c =1024. For coherent detection with frequency offset compensation, we have N seg =1, N c =4096 and I=5. The noise is normalized to σ 2 =1.

Technology Category: 5