WO1997021292A2 - Detecteur multiutilisateur pour systemes a acces multiple par difference de code - Google Patents

Detecteur multiutilisateur pour systemes a acces multiple par difference de code Download PDF

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Publication number
WO1997021292A2
WO1997021292A2 PCT/US1996/018824 US9618824W WO9721292A2 WO 1997021292 A2 WO1997021292 A2 WO 1997021292A2 US 9618824 W US9618824 W US 9618824W WO 9721292 A2 WO9721292 A2 WO 9721292A2
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interference
signal
processing
signals
correlation
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PCT/US1996/018824
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Lin-Lang Yang
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Yang Lin Lang
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/69—Spread spectrum techniques
    • H04B1/707—Spread spectrum techniques using direct sequence modulation

Definitions

  • the present invention relates to multiple access communication systems, and more specifically, to a detection system for code division multiple access systems.
  • CDMA Code Division Multiple Access
  • a digital signal from each user in a CDMA system is modulated with a pseudo-noise (PN) binary sequence that is unique to that particular user.
  • PN pseudo-noise
  • This modulation causes the spreading over the wide bandwidth.
  • Each PN sequence appears random to a naive observer but can be reproduced in a deterministic manner by an intended receiver.
  • Any two PN sequences are made substantially orthogonal to each other but some degree of cross correlation still exists.
  • the mutual interference in the same frequency range of multiple users is greatly reduced in a CDMA system. This orthogonality allows multiple access within the same frequency spectrum and makes CDMA systems less vulnerable to intentional or unintentional interference.
  • the cross correlation becomes limiting the system capacity as the number of users increases.
  • Detection of a CDMA system usually involves cross correlation with a locally-generated version of the PN sequence.
  • a desired user signal is generally detected by cross correlating with the exact same PN sequence that is uniquely assigned to that particular user by the system.
  • Signals from other users contribute a small amount of wideband noise due to non-zero cross correlation value produced by the cross correlation operation.
  • the ratio of the transmission bandwidth over the information bandwidth indicates the degree of the spectrum spreading of a CDMA system. This ratio is often referred as the processing gain. Many beneficial properties of CDMA relate to the processing gain.
  • FIG. 1 shows a simplified CDMA system 100.
  • User messages are modulated with a carrier frequency and decoded by PN sequence waveforms prior to transmission.
  • a receiver system 130 process the received signals by filtering and correlation operation to recover the
  • a CDMA detector 137 is an
  • CDMA systems have many unique properties. For example, multiple access communication by a large number of users in a common frequency range in the same and neighboring geographical areas are possible. This has particular significance in satellite and cellular
  • the number of simultaneous users is usually proportional to the processing gain of the CDMA system.
  • the efficiency of bandwidth usage of CDMA increases as the number of simultaneous users grows.
  • the resistance to interference increases with the processing gain by suppressing the power in a narrow band.
  • Adverse effects of multipath signals can be significantly reduced due to the spectrum spreading since only a small portion of signal will undergo fading at any given time.
  • position location and velocity estimation can be measured with high accuracy in a CDMA system.
  • CDMA Code Division Multiple Access
  • the application of CDMA in communication has been demonstrated in a number of systems in use including the IS-95 based CDMA cellular system disclosed in "On the Capacity of A Cellular CDMA System", by Gilhousen et al., IEEE Trans. Veh. Technol., Vol. VT-40(2), pp303-312, May, 1991, a PCS system based on IS-95, and the Globalstar LEO satellite system.
  • CDMA communication systems one of the most promising candidates for multiuser communications.
  • the multiuser detection system is an important aspect of the CDMA technology.
  • a portion of a simplified CDMA detection system is indicated by the signal mixers 133 and 135 and the detector 137 in FIG. 1.
  • the CDMA detection system plays a substantially role in terms of the system capacity, processing speed, interference resistance, and noise reduction.
  • Verd ⁇ 's algorithm for binary phase shift keying (BPSK) waveforms makes the total complexity of Verd ⁇ ' s algorithm for binary phase shift keying (BPSK) waveforms to be 2 k plus the computation of the weight of 2 ⁇ 2 k branches in the trellis. Such complexity renders Verd ⁇ 's system even less feasible. Verd ⁇ 's system also requires information about the received power from each user in order to detect the received signals. In BPSK) waveforms to be 2 k plus the computation of the weight of 2 ⁇ 2 k branches in the trellis. Such complexity renders Verd ⁇ 's system even less feasible. Verd ⁇ 's system also requires information about the received power from each user in order to detect the received signals. In
  • Verd ⁇ 's detector aims at reducing the complexity of Verd ⁇ 's system.
  • Xie et al. described a detector using sequential decoding in
  • the coded symbol signals are processed one at a time in each channel.
  • a new coded symbol signal is accepted for decoding usually after the decoding of the previous symbol in the same channel is completed.
  • the present invention describes an optimal multiuser detector with a dynamical system control at a high processing speed.
  • a double-sampling front- end processor is implemented in the preferred embodiment in accordance with the present invention.
  • the detector further includes a dynamic control module for generating a system control ⁇ -parameter based on the characteristics of the received signals, a correlation processor for
  • approximation processors for recursively suppressing the cross correlation noise below a predetermined noise tolerance level based on the optimized ⁇ -parameter, and a decision processor for generating the decoded data.
  • the approximation processors form a plurality of substantially identical processing layers connected to each other in series.
  • the correlation processor feeds information on both auto correlation and cross correlation of the input signals of multiple users to the first approximation processor in the series.
  • received signals are processed layer by layer until the cross correlation noise is reduced below the predetermined level.
  • One aspect of the present invention is the linear dependence of the processing complexity with the number of the simultaneous users present in the system. This feature in combination with other unique properties of the preferred embodiment allows greater system capacity at faster processing speed in comparison with the prior-art systems.
  • Each processing channel of the preferred embodiment is pipelined to increase the
  • a layer proceeds to accept and process a new signal upon completion of processing the previous data while the correlation processor feeds a new control ⁇ -parameter adjusted and optimized for the new signal.
  • Still another aspect is the use of cross correlation information to substantially cancel the interference contributed by the cross correlation signals. This feature presents significant advantage over the prior-art system wherein the cross correlation signals is neglected as white noise. The minimized interference allows an increased system capacity of processing simultaneous users at the same and adjacent spatial locations. Still another aspect is the dynamically adjusted system control for a high-speed convergence in the multi- layer processing.
  • the detector of the preferred embodiment is the detector of the preferred embodiment.
  • embodiment controls the decoding processing in each processing layer by feeding the layer with a dynamic control parameter ⁇ .
  • ⁇ An optimized ⁇ -parameter is
  • ⁇ -parameter is a simple digital operation at a high speed in the present invention.
  • aspects of the present invention include additional system enhancement by optimally combining the two output signals from the double-sampling front-end processor, digital generation of the PN sequence, and a mechanism for handling decoding process at very small user delay times relative to the signal bit duration.
  • FIG. 1 shows a block diagram of a simplified CDMA system.
  • FIG. 2 shows the preferred embodiment of the multiuser detector of the present invention with a circuitry for the front-end processor.
  • FIG. 3 illustrates the delay of each user signal and the corresponding transmitted data symbols during
  • FIG. 4 shows the ⁇ -adjusted Mth order detection module in accordance with the present invention.
  • FIG. 5 shows the correlation processor
  • FIG. 6 shows the approximation processor in
  • FIG. 7 shows the decision processor in accordance with the present invention.
  • FIG. 8 illustrates the kth user's spectral-spreading signal, a k (t), during a observation time from iT to
  • FIG. 10 illustrates computation of c mn (i) for Y k >Y l .
  • FIG. 11 illustrates computation of c mn (i) for Y k ⁇ Y l .
  • FIG. 12 shows the process and the corresponding circuitry for obtaining cos( ⁇ k - ⁇ l ).
  • FIG. 13 shows the magnitude of V (2k-1)(2l-1) , V (2k-1)(2l) , V (2k)(2l-1) , V (2k)(2l) when Y k ⁇ Y l .
  • FIG. 15 shows a comparison of the exact and
  • FIG. 16 shows the ⁇ k as a function of K/N and ⁇ for .
  • FIG. 17 shows the ⁇ k as a function of K/N and ⁇ for .
  • FIG. 18 shows the ⁇ k as a function of K/N and ⁇ for .
  • FIG. 19 shows the ⁇ k as a function of K/N and ⁇ for .
  • FIG. 21 shows the contour of ⁇ opt as a function of K/N at different .
  • FIG. 22 shows the contour of exact and approximated values of ⁇ opt as a function of K/N at different .
  • FIG. 23 shows as a function of K/N for g.
  • FIG. 24 shows a comparison of ⁇ low and ⁇ opt .
  • FIG. 26 shows the results of the simulation of the symbol error rate probability for each user for the preferred embodiment of the multiuser detector.
  • FIG. 27 shows the simulated average symbol error probability of all users versus 3K/N in units of dB.
  • FIG. 2 shows the preferred embodiment 200 of the multiuser detector of the present invention with a circuitry for the front-end processor 202.
  • the detector 200 is a portion of the receiving system in an
  • a received analog signal 201, r (t) at the input of the receiver is a sum of K spread-spectrum signals s k (t- ⁇ k ,b), wherein l ⁇ k ⁇ K, and an AWGN random process, n(t), with two-sided power spectrum density N o /2:
  • T denotes the symbol length which is the same for each of the K users
  • s k (t) is the waveform transmitted by the kth user during a T-second interval
  • a k (t) is the kth user's spectral-spreading signal which is normalized to have unit power
  • ⁇ k is the time delay of the kth user
  • ⁇ k is the carrier phase of the kth user
  • ⁇ c is the common carrier frequency
  • w k is the power of the kth user which is assumed constant for each user during the observation
  • b k (i) is the ith transmitted data symbol of the kth user with b k (i)e ⁇ -1,1 ⁇ for all k and 1.
  • (L+1)T is the message length for each user.
  • FIG. 3 shows an example of the delay of each user and the corresponding transmitted data symbols during
  • a preprocessor (not shown in FIG. 2) including a plurality of matched filters processes the composite signal 201 and decomposes it into corresponding K
  • Each component is sampled in the respect sampling channel in the front-end processor 202 and converted into digital signal by analog-to-digital converters 210.
  • the output digital signals from the processor 202 are then subsequently decoded by the
  • multiuser detection module 220 to recover the data symbols b k (i) from all K users embedded in the received signal 201.
  • the front-end processor 202 has K channels, one per user. Each channel includes a first signal mixer 204, a second signal mixer 206, and two parallel integrate-and- dump detectors 208 and 209.
  • the signal mixer 204 is used to demodulate the received signal at a common carrier frequency ⁇ c generated by a local oscillator.
  • the second mixer 206 mixes the PN sequence generated the a local PN sequence generator with the received signal in each channel for further demodulation.
  • the received signal r(t) is despread and
  • the K demodulated outputs are then integrated, sampled, and converted to digital signals for post-end signal processing.
  • each channel are preferably sampled twice by two integration processes with different integration times.
  • the first integrate-and-dump detector 208 for user k integrates for a duration of ⁇ k from time iT to time iT+ ⁇ k and outputs value y 2k-1 (i).
  • the second integrate-and-dump detector 209 for the same user k integrates for a duration of (T- ⁇ k ) from time iT+ ⁇ k to time (i+1)T and outputs value y 2k (i). Since each channel generates two output signals, a total of 2K signals are generated for K users by the double-sampling front-end processor 202.
  • One advantage of this double sampling is to facilitate the implementation the novel multi-order detection module 220 for minimizing the cross correlation interference and pipelined processing.
  • the digital output signals y 2k (i) and y 2k-1 (i) can be used to form a vector y(i) of 2K dimension:
  • the received data symbols b k (t) embedded in the received signal r(t) can also be defined in form of a vector z(i):
  • vector a k (i, t) represent the waveform of the received PN sequence corresponding to y(i) :
  • a matrix C(i) can be formed by c kl (i) to represent the correlation of the received PN sequence waveform: where a'(t,i) is the transpose matrix of a(t,i).
  • n(t) the additive white Gaussian noise term
  • the double frequency terms can also be filtered by the integrate-and-dump detectors 208 and 209. Thus, neglect of the AWGN and elimination of the double frequency terms lead to the following:
  • r ll (i) is independent of the data symbol index i, that is, r ll (i) is time invariant.
  • Equation (12) indicates that the received data vector z(i) can be decoded from the sampled digital output vector y(i) by the following operation:
  • R(i) -1 is the inverse of R(i).
  • Gaussian elimination is generally used to perform the operation of inversing a matrix. This is described, for example, by Strang in "Linear Algebra and Its Application", 3rd edition, Harcout Brace Jovanovich, 1988.
  • the matrix R(i) is a 2K ⁇ 2K matrix.
  • the Gaussian elimination is known to require a number of [(2K) 3 -2K]/3 "multiply-subtract" operations in forward elimination, and a number of (2K) 2 /2 "multiply-subtract" operations in back-substitution. Therefore, a total number of operations is required. However, this number is usually too large for real time processing even when 2K is
  • Gaussian elimination requires at least 2K serial operations in forward elimination, and at least 2K serial operation in back-substitution. This implies that the computation time will be lower bounded because of the number of serial operations required.
  • Equation (14) The inventor recognized the limitations of the conventional Gaussian elimination method and thereby devised a new approach to perform the matrix inverse operation shown in Equation (14).
  • This new approach utilizes the small off-diagonal components inherent in the matrix R (i) to reduce the number of operations required by the Gaussian elimination. In particular, the reduced number of operations in this new approach can be
  • the multi-order detection module 220 of FIG. 2 is designed to perform the operation shown in equation (14) to recover the data symbols represented by vector z (i) from the sampled digital output vector y(i) .
  • the new approach of obtaining R (i) -1 is implemented.
  • This can be expressed as the following:
  • a positive system control parameter ⁇ > 0 is introduced along with three matrices D ⁇ , O ⁇ , and F ⁇ :
  • the parameter ⁇ is dynamically adjusted based on the operation state of the CDMA system.
  • the matrix R can be further decomposed in terms of D ⁇ and F ⁇ :
  • the received data b k (i) can also be
  • the estimated b k (M) (i) can be determined by either z 2K (M) (i) or z 2K-1 (M) (i + 1) .
  • a combining ratio ⁇ is therefore optimally obtained in implementing the approximation processing of Z (i) .
  • This combining ratio significantly affects the performance of the multiuser detector 200 in accordance with the present invention.
  • the optimized value, ⁇ also called maximal signal combining ratio, is achieved through a zeroth order approximation but will be used for all of orders in the preferred embodiment of the present invention.
  • the estimation noise n bk (M) is the interference contribution from the cross correlation of the signals from all other ( K-1 ) users except the kth user.
  • Many prior-art CDMA systems simply neglect this estimation noise n bK (M) as white noise in the background and consequently the resistance to the interference is compromised. This further adversely affect the maximal number of simultaneous users that the CDMA system can handle.
  • the multiuser detector 200 of the preferred embodiment is the multiuser detector 200 of the preferred embodiment
  • One aspect of the present invention is to use the dynamically adjusted and optimized ⁇ parameter to ensure the convergence of the estimation noise n bk (M) , i.e., where E ⁇ ) denotes the operation of expected value.
  • the ⁇ parameter is chosen to produce a fast convergence. Determination of the optimal ⁇ value will be described hereafter.
  • M contributes less variance to b k (M) (i) as more orders of the approximation processing are performed.
  • the exact number of orders, M, for a particular CDMA system is determined by the maximal interference tolerance thereof.
  • the estimated b k (M) (i) can be expressed as
  • Equation (29) A recursion of x (M) and s (M) can be readily established from Equation (29):
  • the estimated z (M) , wt (i) in Mth order can be extracted from the (M-1)th order of s (M-1) according to Equation (29) and Equation (31).
  • FIG. 4 shows a preferred implementation of the multi- order detector module 220 in the detector 200.
  • the detector module 220 includes multi-order processing layers to implement the recursion processes in the above- described approximation method.
  • the detector module 220 includes four sections, a correlation processor 410, a ⁇ -generator 440, an approximation processor block 420 having a plurality of approximation processors 422, and a
  • the ⁇ -generator 440 monitors the operation status of the CDMA system and produces an optimized ⁇ for each pair of the input signals 412 and 414 to the zeroth order approximation processor (in fact, they are identical to each other).
  • the optimized ⁇ to the correlation processor 410 automatically changes with operation status of the CDMA system. This dynamic mechanism is to keep the processing efficiency and speed at optimal.
  • the correlation processor 410 processes cross correlation information based on the PN sequence a (t , i) 402, relative phase difference between users 404, i.e., cos ( ⁇ k - ⁇ l ), and the dynamically adjusted ⁇ value 442.
  • FIG. 5 further illustrates the main tasks performed by the correlation processor 410 including computing the matrix R (i) , and computing the matrix D ⁇ .
  • the information of the autocorrelation is included in the output matrix D ⁇ and the information of the cross correlation of different users is included in the output matrix O ⁇ .
  • Both output signals D ⁇ and O ⁇ are ⁇ -dependent and thereby are
  • the output signals from the correlation processor 410 are fed to the approximation processor block 420 to generate an output S (M) 424 with a minimized interference due to cross correlation of different users.
  • a plurality of substantially identical approximation processors 422 are connected in series relative to each other to form multiple processing stages. As described previously, the ⁇ is so chosen to ensure fast convergence of the multi-order processing so that the estimation noise n bk (M)
  • FIG. 6 further illustrates the operation of the approximation processor 422.
  • One unique feature of the approximation processor block 420 is the capability of continuous processing in a pipeline fashion.
  • Each processor 422 at jth stage accepts and starts processing a new set of data from (j-1)th stage upon completion of feed previous set of data from (j-1)th stage to the next stage (j+1). Few prior-art systems implement such pipeline operation.
  • the pipeline operation allows significant enhancement in processing efficiency and speed, thereby making the real-time implementation of multiuser detector feasible.
  • the approximation processor block 420 feeds the resulting signal s (M) to the decision processor 430 if the noise level from the cross correlation is reduced below a pre-set tolerance level.
  • the operation of the decision processor 430 is illustrated in FIG. 7.
  • processors and the operations thereof can be implemented with the existing electronic circuitry and VLSI technology.
  • the preferred embodiment uses a new and unobvious approach to reduce the complexity of the such correlation processing by processing the auto correlation and cross correlation in a unique way. This is partially shown in the
  • correlation matrix R (i) that organizes the correlation information for further processing.
  • the correlation matrix R (i) is defined by Equations (12) and (13). Information about the matrix C (i) and cos ( ⁇ k - ⁇ 1 ) are needed in order to obtain the matrix R (i) at the receiver.
  • a symbol bit is spectrally spread by the spectral-spreading signal waveform of a square wave p (t) .
  • a single pulse of the PN waveform p (t) is called a chip.
  • the chip duration T c determines the symbol
  • the spectral spreading signal for the kth user (normalized to have unit power), a k (t) , over a T-second interval can be expressed as the following:
  • the waveform a k (t) is illustrated in FIG. 8.
  • the computation of the correlation matrix C (i) is preferably obtained digitally in accordance with the present invention. This is due to the special structure of the above spectral-spreading signal a k (t) .
  • a digital circuitry suitable for generating such function is well-known in the art. Digital generation provides higher stability than the analog counterparts at lower cost.
  • sh k (i) of length N+1 for the kth user between time iT and (i+1)T be defined by
  • sh k RT (i) denote the right-shifted version of sh k (i), that is,
  • ->j denotes right-shift by j chips. If the chip delays Y k and Y l are both the same as shown in FIG. 9, then it is straightforward that c (2k-1)(2l-1) (i), c (2k-1) (2l) (i), c (2k)(2l-1) (i), and c (2k)(2l) (i) are functions of sh k (i) ⁇ sh l (i), ⁇ k and ⁇ l , where ⁇ denotes the exclusive or operator.
  • the calculation of sh k (i) ⁇ sh l (i) can be implemented in VLSI technology. This is well known to the art.
  • the preferred embodiment first operates to move a k (t) leftwards in time domain by an amount of ( Y k -Y l ) to achieve chip alignment. This is to calculate the correlation values digitally.
  • the correlation thus obtained is represented by
  • c mn LT (i) is a function of sh k (l) ⁇ sh l (i).
  • the preferred embodiment first operates to shift a k (t) rightwards in time by an amount of T c - (Y k -Y l ) and the corresponding correlation thus obtained is represented by in this case, c mn RT (i) is a function of sh k RT (i) ⁇ sh l (i) . It is observed that the exact correlation value is given by
  • FIG. 10 illustrates the derivation of c mn (i) for Y k >Y l .
  • the cornerstone of the proposed multiuser detector is that the derivation of c mn LT (i) and c mn RT (i) mostly are the sum of random +1 and -1. This particular feature makes the characteristics of the matrix R (i) readily obtainable digitally. Furthermore, a method of obtaining an approximation to the inverse of R (i) can be determined based on the characteristics of R (i) .
  • FIG. 12 illustrates the circuitry and method for generation of cos ( ⁇ k - ⁇ l ) that are implemented in the preferred embodiment.
  • system time index will be omitted mostly thereafter. Moreover, only time invariant symbol delays are considered.
  • Vmn as the time overlap between a n (t,i) and a m (t,i) in unit of chips.
  • FIG. 13 illustrates examples of
  • Equation (13) and Equation (43) indicate
  • the random variable ⁇ k and ⁇ l are independent and identically distributed with uniform distribution between - ⁇ and ⁇ , and they are independent of c (2k-1l(21-1) ;
  • the random variable ⁇ kl is uniformly distributed between 0 and 1, and is independent of c LT (2k- 1)(2l-1) and
  • Matrix F ⁇ represents information of the off-diagonal elements of the correlation matrix R(i).
  • F ⁇ is ⁇ -dependent and allows optimal control of the correlation processing in minimizing the interference.
  • the first and second moment of f kk (1) , f kl (1) , f kk (2) , and f kl (2) can be computed as follows:
  • the maximal signal combining algorithm dictates the performance of the proposed system. Due to the complexity that is involved in deriving the combining ratio, the maximal signal combining ratio is calculated only for the zeroth order multiuser detector. The same combining ratio will be used for other orders of multiuser detector.
  • the zeroth order multiuser detector will have estimation signal-to-noise ratio, SNR (O), k (i), given by
  • the multiuser detector 200 relies on the adjustment ⁇ to reduce the impact from n bk (M) (i) instead of the combining ratio ⁇ .
  • ⁇ (M),k is defined as the estimation noise variance for user k that is generated by the Mth order multiuser detector: where ⁇ (M),k denotes the estimation noise variance
  • Equation (62) The order of the expectation and the summation in Equation (62) is exchangeable, because the received data of different users are independent relative to each other. Also, it is assumed that the received data are equally likely to be +1 or -1, that the random variable f kl (M) (i) is independent to the received data b j (i), and that the random matrix F ⁇ (M) (i) is independent of the random matrix F ⁇ (M) (l) when i ⁇ 1
  • Equation (53) Since the first and second moments of F ⁇ and F ⁇ 2 are available in Equation (53), ⁇ j (O),k and ⁇ j (l),k can be derived as the following:
  • ⁇ for order M is the one that achieves the minimum estimation noise variance from all users, i.e.,
  • Equation (64) indicates that the complexity of the estimation noise variance as a function of ⁇ increases as the order increases. Therefore, it is very difficult to find an optimal value of ⁇ for all orders.
  • the multiuser detector 200 of preferred embodiment instead searches for a ⁇ which assures the fast
  • the optimal value of ⁇ should not be order-dependent or symbol-delay-dependent in order to reduce the
  • Equation (67) When M is a moderately large number, the first term in Equation (67) will be much smaller than the second term when the estimation noise variance is a decreasing function of ⁇ , i.e.,
  • the ⁇ -generator 440 shown in FIG. 4 is thus configured to obtain the optimal ⁇ opt by the following criterion instead,
  • the ratio of the estimation noise variance of the first order to that of the zeroth order for the kth user can be found as
  • ⁇ k is less than 1 for all k in order to assure convergence of the preferred multiuser detector 200 in FIG. 2.
  • ⁇ shall satisfy:
  • FIG. 14 shows the exact value of ⁇ low for , 12, 16, and 20. The exact value and the approximated value of ⁇ low are compared in FIG. 15 for and 20.
  • FIGs 16-19 shows ⁇ k (in dB) against K/N and ⁇ for , 12, 16, and 20, respectively. It can be seen that the area above 0 dB increases with , especially for small ⁇ and large K/N.
  • V ll the information carried by V ll ⁇ ⁇ N where ⁇ 1.
  • the lth row and the 1th column of matrices O ⁇ and D ⁇ - 1 are set to zeros, and the 1th component of vector y(i) is set to zero.
  • the combining ratio, ⁇ , in Equation (55) will be zero if 1 is an even number and 1 if 1 is an odd number.
  • the output signals, y 2k-1 (i), from the integrate-and-dump detector 209 will be set to zero when the delay ⁇ k of the kth user is smaller than a threshold value, ⁇ min , since the filtering effect of the integration sampling process is substantially eliminated when ⁇ k ⁇ min .
  • a threshold value ⁇ min
  • T is the symbol bit duration and N is the processing gam.
  • the optimal ⁇ value produces the minimum average value of ⁇ k for all K users:
  • FIG. 21 shows ⁇ opt vs. K/N for different values of .
  • ⁇ opt can be approximated by the following formula:
  • the preferred embodiment of the present invention uses the approximated value of ⁇ opt of Equation (82) as a criterion for choosing an optimal the approximated value of ⁇ opt .
  • Parameter in Equation (82) usually does not change significantly.
  • the optimal ⁇ opt is
  • FIG. 23 shows the average in dB as a function of
  • FIG. 24 compares ⁇ opt and ⁇ low in terms of K/N for different values of . It is shown that ⁇ opt satisfies the convergence requirement for g k ⁇ 16.
  • multiuser detector 200 were stimulated to demonstrate the some aspects of the present invention.
  • each users' PN sequence is generated by using the following generator function
  • the symbol delay, ⁇ k , the received carrier phase, ⁇ k , and the transmitted data, b k (i) , for all users are randomly generated.
  • the input signal y(i) to the multi-order detector module 220 of FIG. 2 is obtained by using
  • a symbol data rate is subsequently generated by comparing the estimated data, b k (M) (i) with the transmitted data, b k (i).
  • the parameters, except K and ⁇ opt are set to the same values as in the first simulation shown in FIG. 26. Again, each point is generated with 10 6 simulated symbols.
  • FIG. 27 indicates that the system capacity increases as M increases.
  • the above described multiuser detector can also work for a system with inclusion of the background noise such as additive white Gaussian noise.
  • the present invention can also used to improve incoherent system.
  • Many terrestrial wireless applications use non-coherent detection because each user has independent random phase due to Rayleigh fading channel.
  • the coherent detection can easily be used in forward link (base station to mobile station) detection in an IS-95 based CDMA cellular system because the existence of the pilot signal.
  • the multiuser detector of the present invention can be used to cancel interference caused by signal from adjacent cells, especially when the mobile station is in a hand-off region.

Abstract

L'invention concerne un détecteur multiutilisateur destiné à un système AMDC (à accès multiple par différence de code) pour le traitement en temps réel. Les données de brouillage issues de la corrélation croisée à partir des signaux produits par différents utilisateurs, sont traitées de sorte que le rapport signal/densité de bruit soit augmenté par la suppression du bruit de brouillage dans un traitement à plusieurs étapes. Un processeur d'échantillonnage est prévu pour la production d'au moins deux ensembles de données pour chaque utilisateur avec différents temps d'intégration. Le fonctionnement du détecteur est ajusté dynamiquement quasiment en temps réel par rapport à l'état de fonctionnement du système AMDC.
PCT/US1996/018824 1995-11-22 1996-11-22 Detecteur multiutilisateur pour systemes a acces multiple par difference de code WO1997021292A2 (fr)

Applications Claiming Priority (4)

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US747595P 1995-11-22 1995-11-22
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7099374B2 (en) 2001-03-14 2006-08-29 Mercury Computer Systems, Inc. Wireless communication systems and methods for long-code communications for regenerative multiple user detection involving matched-filter outputs

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7099374B2 (en) 2001-03-14 2006-08-29 Mercury Computer Systems, Inc. Wireless communication systems and methods for long-code communications for regenerative multiple user detection involving matched-filter outputs
US7110431B2 (en) 2001-03-14 2006-09-19 Mercury Computer Systems, Inc. Hardware and software for performing computations in a short-code spread-spectrum communications system
US7218668B2 (en) 2001-03-14 2007-05-15 Mercury Computer Systems, Inc. Wireless communications systems and methods for virtual user based multiple user detection utilizing vector processor generated mapped cross-correlation matrices

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