Improved Multiuser Detectors Employing Genetic Algorithms in a Space-Time Block Coded System

A combined minimum mean square error (MMSE) and maximum likelihood (ML) multiuser detector with reduced-complexity is proposed for uplink space-time block coded (STBC) orthogonal frequency division multiplexing (OFDM) systems. In this combined detector, received signals from co-channels are first detected by an MMSE multiuser detector. Then an ML-based post-detection processor (PDP) is designed to detect estimated outputs from the MMSE detector with the highest probability of errors and correct them to reduce the system bit error rate (BER). An efficient algorithm is proposed for the PDP which requires only a small additional complexity but can improve the system BER performance significantly compared with the conventional MMSE multiuser detector.

We first develop a reduced-rank minimum mean square error (MMSE) detector for direct-sequence (DS) code division multiple access (CDMA) by forcing the linear MMSE detector to lie in a signal subspace of a reduced dimension. While a reduced-rank MMSE detector has lower complexity, it cannot outperform the full-rank MMSE detector. We then concentrate on the blind reduced-rank MMSE detector which is obtained from an estimated covariance matrix. Our analysis and simulation results show that when the desired user's signal is in a low-dimensional subspace, there exists an optimal subspace so that the blind reduced-rank MMSE detector lying in this subspace has the best performance. By properly choosing a subspace, we guarantee that the optimal blind reduced-rank MMSE detector is obtained. An adaptive blind reduced-rank MMSE detector, based on a subspace tracking algorithm, is developed. The adaptive blind reduced-rank MMSE detector exhibits superior steady-state performance and fast convergence speed.

As the promising novel multiple access scheme for the 5th generation (5G) wireless communication system, pattern division multiple access (PDMA) technology can improve the spectral efficiency and support the massive connection based on the reasonable unequal diversity between users. Compared with traditional multiple access technologies, PDMA brings more serious multiple address interference to hinder the multiuser detection. The universal detection algorithm with low complexity and high performance is one of the main challenges of PDMA technology. In this paper, we proposed a new iterative detection and decoding algorithm, which is a successive interference cancellation (SIC) iterative processing based on the minimum mean square error (MMSE) detection and channel decoding algorithm. It is essentially a SIC iterative processing based on some users' information has been correctly decoded by the MMSE detection and the channel decoder. The proposed multiuser detection algorithm can prevent the error propagation by the cyclic redundancy check (CRC). The simulation results show that the proposed algorithm can get significant performance gains over traditional multiuser detection algorithms.

The receiver in a wireless communication system has the task of computing good estimates for the data symbols that have been transmitted. The best (optimum) detector is the Maximum Likelihood (ML) detector. However, it requires a high computational complexity. This work aims to efficiently detect the transmitted symbols with a reduced complexity. In order to produce a near optimum receiver, two methods are presented. These methods are obtained by convex optimization relaxations which yield global optimum solutions. The relaxations are combined with the idea of using the structure of the channel matrix to reduce the computational complexity. The channel matrix exhibits a banded Toeplitz structure. In each case, the dual problem of the convex optimization relaxation is solved to estimate the noise power. Gradient descent algorithm is used to solve the dual problem in the first relaxation while the bisection method is applied for the second relaxation. In both cases, the result is a Generalized Minimum Mean Squared Error (GMMSE) detector which has a form similar to the Minimum Mean Square Error (MMSE) detector and a performance almost the same as the MMSE detector, but it is not require the knowledge of the noise power. The GMMSE detectors can be used in scenarios where adapted or blind adaptive detection is not suitable, for instance when the channel is rapidly changing. Using a circular approximation of banded Toeplitz matrix the Fast Fourier Transform (FFT) can be applied to reduce the computational complexity of the detectors. Finally, the local search method is applied to enhance the performance of the proposed GMMSE detector. The proposed detector is a near optimum detector with low computational complexity.

An analytical study is presented on the performance of the variable spreading factor orthogonal frequency and code division multiplexing (VSF-OFCDM) system with hybrid multi-code interference (MCI) cancellation and minimum mean square error (MMSE) detection. The weights of MMSE are derived, and should be updated stage by stage of MCI cancellation. Various values of time and frequency domain spreading factors are dealt with in a QPSK-modulated VSF-OFCDM system. It is shown that the hybrid detection scheme performs much better than the conventional MMSE. For the hybrid detection, a larger value of the frequency domain spreading factor is expected.

The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) and Rayleigh fading conditions. However, unlike the MMSE detector, the SVM detector does not require the knowledge of spreading codes of other users in the system or the estimate of the channel noise variance. The optimization of this algorithm is considered in this paper and its complexity is compared with the MMSE detector. This detector is much more suitable to work in the forward link than MMSE. In addition, original theoretical bit-error rate expressions for the SVM detector under both AWGN and Rayleigh fading are derived to verify the simulation results.

Minimum mean square error (MMSE) linear detector is able to achieve the near-optimal bit error rate (BER) performance for uplink multi-user massive Multiple-Input Multiple-Output (MIMO) systems due to the increase in the number of base station (BS) antennas. However, this detector involves matrix inversion with high complexity, especially when the number of users is large. In order to reduce the complexity, in this paper, a low-complexity MMSE detector algorithm based on the Damped Jacobi (DJ) method is proposed. Further, a simple approach to determine the optimum and quasi-optimum damped parameter by exploiting the massive MIMO channel property of asymptotic orthogonality is developed. The analysis shows that the proposed algorithm can reduce the complexity of the classical MMSE detector by about one order of magnitude without performance loss. Finally, it is verified through simulation results that the proposed algorithm outperforms the recently proposed linear detector based on the conventional Jacobi method, and achieves the near-optimal performance of the classical MMSE detector with a small number of iterations in Rayleigh fading channels.

This paper proposes a Group Detection (GD) algorithm with Max-Log-MAP Sphere Decoder (MLM-SD) in order to reduce the complexity of signal detection in a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. The proposed algorithm divides spatial streams into multiple partial spatial streams by using Minimum Mean Square Error (MMSE) detector, followed by multiple MLM-SDs with reduced number of spatial streams. Although the spatial diversity gain in the MLM-SD degrades because of the lack of the degrees of freedom exploited by the MMSE detector, its diversity gain is recovered by combining the metrics obtained by the multiple MLM-SDs. In a MIMO wireless LAN multipath fading environment, the complexity of the proposed algorithm is 10% of that of the original MLM-SD and the performance degradation in terms of SNR is slightly less than that of the original MLM-SD in 4-by-4 MIMO architecture with 64QAM achieving 216 Mbps. It is also found that the proposed algorithm is robust against the limitation of the number of searches in sphere decoder.

This paper considers a low-complexity Gaussian Message Passing Iterative Detection (GMPID) algorithm for Multiple-Input Multiple-Output systems with Non-Orthogonal Multiple Access (MIMO-NOMA), in which a base station with $N_r$ antennas serves $N_u$ sources simultaneously. Both $N_u$ and $N_r$ are very large numbers and we consider the cases that $N_u>N_r$. The GMPID is based on a fully connected loopy graph, which is well understood to be not convergent in some cases. The large-scale property of the MIMO-NOMA is used to simplify the convergence analysis. Firstly, we prove that the variances of the GMPID definitely converge to that of Minimum Mean Square Error (MMSE) detection. Secondly, two sufficient conditions that the means of the GMPID converge to a higher MSE than that of the MMSE detection are proposed. However, the means of the GMPID may still not converge when $ N_u/N_r< (\sqrt{2}-1)^{-2}$. Therefore, a new convergent SA- GMPID is proposed, which converges to the MMSE detection for any $N_u> N_r$ with a faster convergence speed. Finally, numerical results are provided to verify the validity of the proposed theoretical results.

Extreme learning machine detector for millimeter-wave massive MIMO systems

Massive multiple‐input multiple‐output (M‐MIMO) is one of the cutting edge technologies that provides significant improvement in throughput, coverage and spectral efficiency. The challenge with M‐MIMO systems is to extract individual signals from the composite signal, thus making optimal detectors prohibitively complex. Recently, approximate message passing (AMP) and its variant detectors have gained substantial importance due to their decreased complexity and improved performance. However, AMP algorithm does not always converge. Non‐linear detectors like vertical Bell‐Labs layered space‐iime improve the bit error rate (BER) but with high complexity, whereas, linear minimum mean square error (MMSE) detector offers low complexity while compromising on BER performance. Neumann series based MMSE detectors further reduce MMSE computational complexity, however, the BER performance remains the same. In this work, the authors propose a hybrid Neumann series based MMSE detector which decomposes the detected signal into its constituent components and apply a neighbourhood selection algorithm on the obtained components thus improving the overall performance. Another contribution of this work is derivation of an off‐set value for optimised neighbourhood set selection that enables more accurate detection while further reducing computational complexity. Simulation results confirm that the proposed scheme outperforms aforementioned algorithms in terms of BER performance and computational complexity in a continuously changing Rayleigh channel.

The combination of space-time block codes (STBC) and spatial multiplexing (SM) schemes has the advantages of diversity gain and high data rate. For such STBC/SM hybrid systems, we propose a new detector based on the block structure of STBC and the sphere decoder (SD). By employing the SD, optimal detection is performed with computational complexity much less than the brute-force maximum likelihood (ML) search. Simulation results show that the new decoder significantly outperforms the previously developed zero-forcing (ZF) group/STBC detector, the minimum mean square error (MMSE) group/STBC detector, and the MMSE and ML detectors for equivalent SM systems.

Feasibility of applying genetic algorithms in space-time block coded multiuser detection systems

General-purpose processor (GPP), which is suitable for software radio processing because of its powerful processing capability and flexibility, has aroused industry-wide attention and exploration. By taking advantage of streaming single instruction multiple data extensions (SSE), this paper presents the GPP-based design of multi-input multi-output (MIMO) detection which is of high complexity in LTE downlink receiver. The performance simulation results illustrate that with dramatically reduced complexity soft minimum mean square error (MMSE) detection is able to approximate ML performance in low signal-to-noise-ratio (SNR), and about 4 dB lose in high SNR. The complexity comparison results show that the implementation of soft MMSE detection in parallel can reduce the processing time by about 86% compared with not in parallel.

This paper investigates low complexity detection for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with co-channel interference (CCI). We firstly derive minimum mean square error (MMSE) detection based on sorted QR decomposition (SQRD) and modified MMSE SQRD detection for MIMO-OFDM systems with CCI. Although better performance can be achieved by the new detectors than liner MMSE (LMMSE) detection, the computational complexity is still high due to the required matrix inversion operation. In order to overcome such an issue and design low complexity detection method for MIMO-OFDM systems with CCI, then, we propose an improved MMSE SQRD detector by utilizing a regularization method. The simplification of the regularization parameter calculation in the proposed method is then discussed. Simulation results show that the error performance of the proposed method approaches modified MMSE SQRD detection in presence of CCI, while keeping a much lower complexity.