CDMA Multiuser Detection Research Articles

Research on Optimal CDMA Multiuser Detection Based on Stochastic Hopfield Neural Network

Background: Most of the common multi-user detection techniques have the shortcomings of large computation and slow operation. For Hopfield neural networks, there are some problems such as high-speed searching ability and parallel processing, but there are local convergence problems. Objective: The stochastic Hopfield neural network avoids local convergence by introducing noise into the state variables and then achieves the optimal detection. Methods: Based on the study of CDMA communication model, this paper presents and models the problem of multi-user detection. Then a new stochastic Hopfield neural network is obtained by introducing a stochastic disturbance into the traditional Hopfield neural network. Finally, the problem of CDMA multi-user detection is simulated. Conclusion: The results show that the introduction of stochastic disturbance into Hopfield neural network can help the neural network to jump out of the local minimum, thus achieving the minimum and improving the performance of the neural network.

Fixed-Complexity Quantum-Assisted Multi-User Detection for CDMA and SDMA

In a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multi-level K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses M^K CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-H\o yer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems.

Performance Analysis of Blind Multiuser Detectors for Cdma

This paper deals with the performance of blind multiuser detectors for CDMA is analyzed. The blind multi- user detectors are Direct Matrix Inversion (DMI) blind detector and subspace blind detector. The performance analysis is performed by means of the Signal to Interference Noise Ration (SINR) and Bit Error Rate (BER). The numerical results are plotted as variation of SINR Vs SNR and ρ , SINR with respect to correlation coefficient ( ρ ) and BER Vs signal samples (M) using MAT LAB software. It is observed that the SINR Vs SNR and ρ for both blind detectors. The performance of subspace blind detector deteriorates in the high cross correlation and low SNR region whereas the performance of the DMI blind detector is less sensitive to cross correlation and SNR in this region. The performance gain offered by the subspace detector is significant for smaller value of K, and the gain diminishes as K increases. For large number of signal samples (M), both detectors converge to the true linear MMSE detector, with the subspace blind detector converging much faster than the DMI blind detector and the performance gain offered by the subspace detector is quite significant for small values of M. The BER performance of subspace approach is better than the DMI approach with increasing SNR.

Adaptive IA-TCNN Multiuser Detector Based on Simulated Annealing of Optimization

In this paper, the action of annealing function in optimal process was researched. The sensibility of self-feedback connection weights was analyzed. The multiuser adaptive detector IA-TCNN based on simulated annealing of optimization is proposed. A new simulated annealing transiently chaotic network was applied to the CDMA multiuser detection technique. The IA-TCNN has powerful capability to escape from into the local minima. The simulation results show that the adaptive IA-TCNN multiuser detector based on simulated annealing is more effective than the existing neural network multiuser detectors.

CDMA Multiuser Detection Based on Improved Particle Swarm Optimization Algorithm

CDMA multiuser detection (MUD) is a crucial technique to mobile communication. We adopt improved particle swarm optimization (PSO) algorithm in MUD which incorporates factor and utilizes function to discrete PSO. Comparison of BER and near-far effect has verified its effectiveness on multi-access interference (MAI). The algorithm accelerates the convergent speed meanwhile it also displays feasibility and superiority in case simulation.

A new optimization algorithm for multi-user transmission using chaotic carriers.

In this paper, a new approach based on an optimization is proposed to realize the chaotic CDMA multi-user detection. In a multi-user transmission using chaotic carriers, it is shown that synchronizing N chaotic carriers emitting simultaneously on the same frequency band is feasible according to unicity and orthogonality of chaotic sequences. The carriers are generated from Chua's circuits and are modulated in amplitude by the information signal. Demodulation is ensured by the minimization of a cost function. Numerical simulations show that this synchronization method is robust in presence of noise and allows the integration of a high number of users.

Recursive CDMA multiuser detection method based on least p-norm state space criterion

Alpha stable distribution is better for modeling impulsive noises than Gaussian distribution in wireless communication system. This class of process has no close form of probability density function and finite second order moments. In general, Wiener filter theory is not meaningful in SαSG environments because the expectations may be unbounded. We proposed a new adaptive recursive least p-norm State space multiuser detection algorithm based on least p-norm of innovation process with infinite variances. The simulation experiments show that the proposed new algorithm is more robust than the conventional state space multi-user detection algorithm.

Transmitter Layering for Multiuser MIMO Systems

A novel structure for multiple antenna transmissions utilizing space-time dispersion is proposed, where the original data stream is divided into substreams which are modulated onto all available transmit antennas using stream-specific transmit signature sequences. In order to achieve this, the transmit antennas are partitioned into groups of antennas, called partitions. The signals from the data streams are independently interleaved by partition over the entire transmission frame. The interleaved partitions are then added over all substreams prior to transmission over the MIMO channel. At the receiver, a low-complexity iterative detector adapted from recent CDMA multiuser detection research is used. It is shown that with careful substream power assignments this transmission methodology can efficiently utilize the capacity of rank-deficient channels as it can approach the capacity limits of the multiple antenna channel closely over the entire range of available signal-to-noise ratios and system sizes. This transmission methodology and receiver structure are then applied to multiuser MIMO systems where several multiple antenna terminals communicate concurrently to a joint receiver. It is shown that different received power levels from the different MIMO terminals can be beneficial and that higher spectral efficiencies can be achieved than in the single-terminal case.

Vector Channel Estimation and Multiuser Detection for Multicarrier DS CDMA in Time and Frequency Selective Fading Channels

In a multicarrier direct-sequence code-division multiple access (MC DS CDMA) system, different fading channels for different users and/or different carriers are correlated in general; thus a vector channel model is more appropriate than disjoint scalar channel models. For multiuser MC DS CDMA systems, we propose (1) a generalized vector autoregressive model which accounts for correlation between different user/carrier fading channels, (2) the use of a two-phased algorithm to obtain the proposed model's parameters, and (3) a receiver structure that consists of a generalized decorrelator followed by maximal-ratio combining (MRC) of uncorrelated carrier channel outputs of each user. The estimated fading coefficients provide the necessary quantities to MRC. The computer simulation results show that the proposed scheme has performance close to the case in which the channel is perfectly known, and outperforms separate scalar channel estimation case.

Cordic based algorithms for software defined radio (SDR) baseband processing

Abstract. This paper presents two Cordic based algorithms which may be used for digital baseband processing in OFDM and/or CDMA based communication systems. The first one is a linear least squares based multiuser detector for CDMA incorporating descrambling and despreading. The second algorithm is a pure Cordic based FFT implementation. Both algorithms can be implemented using solely Cordic based architectures (e.g. coprocessors or ASIPs). The algorithms exactly fit the needs of a multistandard terminal as they both are freely parameterizable. This regards to the accuracy of the results as well as to the parameters of the performed function (e.g. size of the FFT).

A robust approach to channel estimation and detection for multi-carrier CDMA

We present a robust approach to channel estimation and multiuser detection (MUD) for multi-carrier CDMA by explicitly taking into account prior channel estimation errors. Specifically, robust channel estimates and MUD receivers are obtained by optimizing the worst-case performance over a properly selected bounded uncertainty set. Although the prior channel estimation error is not bounded, it is beneficial to refine the estimate over a properly chosen bounded uncertainty set. Numerical results show that linear MUD detectors resulting from our robust approach yield improved performance over those that ignore the prior estimation errors

The Application of Successive Quadratic Programming Algorithm to Multiuser Detection in CDMA

In this paper, based on the semidefinite programming relaxation of the CDMA maximum likelihood (ML) multiuser detection problem, a detection strategy by the successive quadratic programming algorithm is presented. Coupled with the randomized cut generation scheme, we obtain the suboptimal solution of multiuser detection problem. Comparing with the reported interior point methods based on semidefinite programming, simulations demonstrate that the successive quadratic programming algorithm often yields the similar BER performances of the multiuser detection problem. But the average CPU time of this approach is significantly reduced.

Part 3: Brain science, information science and associative memory model

I review recent progress on the associative memory model, which is a kind of neural network model. First, I introduce this model and a mathematical theory called statistical neurodynamics describing its properties. Next, I discuss an associative memory model with hierarchically correlated memory patterns. Initially, in this model, the state approaches a mixed state that is a superposition of memory patterns. After that, it diverges from the mixed state, and finally converges to a memory pattern. I show that this retrieval dynamics can qualitatively replicate the temporal dynamics of face-responsive neurons in the inferior temporal cortex, which is considered to be the final stage of visual perception in the brain. Finally, I show an unexpected link between associative memory and mobile phones (CDMA). The mathematical structure of the CDMA multi-user detection problem resembles that of the associative memory model. It enables us to apply a theoretical framework of the associative memory model to CDMA.

An Iterative Multiuser Detector for Turbo-Coded DS-CDMA Systems

We propose an iterative multiuser detector for turbo-coded synchronous and asynchronous direct-sequence CDMA (DS-CDMA) systems. The receiver is derived from the maximum a posteriori (MAP) estimation of the single user's transmitted data, conditioned on information about the estimate of the multiple-access interference (MAI) and the received signal from the channel. This multiple-access interference is reconstructed by making hard decisions on the users' detected bits at the preceding iteration. The complexity of the proposed receiver increases linearly with the number of users. The proposed detection scheme is compared with a previously developed one. The multiuser detector proposed in this paper has a better performance when the transmitted powers of all active users are equal in the additive white Gaussian noise (AWGN) channel. Also, the detector is found to be resilient against the near-far effect.

Multiuser Detection in CDMA—A Comparison of Relaxations, Exact, and Heuristic Search Methods

In this paper, we compare several optimization methods for solving the optimal multiuser detection problem exactly or approximately. The purpose of using these algorithms is to provide complexity constraint alternatives to solving this nondeterministic polynomial-time (NP)-hard problem. An approximate solution is found either by relaxation or by heuristic search methods, while the branch and bound algorithm is used to provide an exact solution. Simulations show that these approaches can have bit-error rate (BER) performance which is indistinguishable from the maximum likelihood performance. A tabu search method is shown to be an effective (in terms of BER performance) and efficient (in terms of computational complexity) heuristic when compared to other heuristics like local search and iterative local search algorithms. When the number of users increases, the tabu search method is more effective and efficient than the semidefinite relaxation approach.

Speed and Accuracy Comparison of Techniques for Multiuser Detection in Synchronous CDMA

In this letter, we compare the complexity and efficiency of several methods used for multiuser detection in a synchronous code-division multiple-access system. Various methods are discussed, including decision-feedback (DF) detection, group decision-feedback (GDF) detection, coordinate descent, quadratic programming with constraints, space-alternating generalized EM (SAGE) detection, Tabu search, a Boltzmann machine detector, semidefinite relaxation, probabilistic data association (PDA), branch and bound (BBD), and the sphere decoding (SD) method. The efficiencies of the algorithms, defined as the probability of group detection error divided by the number of floating point computations, are compared under various situations. Of particular interest is the appearance of an of algorithms, primarily composed of DF detector, GDF detector, PDA detector, the BBD optimal algorithm, and the SD method. The efficient frontier is the convex hull of algorithms as plotted on probability of error versus computational demands axes: algorithms not on this efficient frontier can be considered dominated by those that are.

Fast Optimal and Suboptimal Any-Time Algorithms for CDMA Multiuser Detection Based on Branch and Bound

A fast optimal algorithm based on the branch-and-bound (BBD) method is proposed for the joint detection of binary symbols of K users in a synchronous code-division multiple-access channel with Gaussian noise. Relationships between the proposed algorithms (depth-first BBD and fast BBD) and both the decorrelating decision-feedback (DF) detector and sphere-decoding algorithm are clearly drawn. It turns out that decorrelating DF detector corresponds to a "one-pass" depth-first BBD; sphere decoding is, in fact, a type of depth-first BBD, but one that can be improved considerably via tight upper bounds and user ordering, as in the fast BBD. A fast "any-time" suboptimal algorithm is also available by simply picking the "current-best" solution in the BBD method. Theoretical results are given on the computational complexity and the performance of the "current-best" suboptimal solution.

Fast Optimal and Suboptimal Any-Time Algorithms for CDMA Multiuser Detection Based on Branch and Bound

Fast Optimal and Suboptimal Any-Time Algorithms for CDMA Multiuser Detection Based on Branch and Bound

The Application of Nonlinear Programming for Multiuser Detection in CDMA

In this paper, a heuristic algorithm based on a nonlinear nonconvex programming relaxation of the CDMA maximum likelihood (ML) problem is presented. Simulation results have shown that the BER performances of a detection strategy based on the heuristic algorithm are similar to that of the detection strategy based on the semidefinite relaxation. Furthermore, average CPU time of the heuristic algorithm is significantly lower than that of the randomized rounding algorithm based on a semidefinite relaxation. This approach provides good approximations to the ML performance.

Fast blind channel and carrier offset estimation in multiuser CDMA

The problem of CDMA multiuser detection in the presence of a small residual carrier offset is considered, with subspace-based approaches being employed for channel estimation and signal demodulation. The channel response and the carrier offset are estimated separately to avoid the complicated computation in the joint estimation of two parameters. The simulation results show that the separate estimation approach results in lower BER than does a previous subspace-based joint estimation algorithm for a large range of carrier offsets that are generally considered in practical applications. Adaptive channel estimation in a time-variant channel is also considered. A novel adaptive algorithm for subspace estimation, namely the NOOja algorithm, is used to track the channel variations. The NOOja algorithm offers a set of advantages as compared to similar algorithms in the literature; in particular, it can be used for both signal and noise subspace estimation and has a very low computational complexity as compared to the batch eigendecomposition techniques.