MBER Space-Time Decision Feedback Equalization Assisted Multiuser Detection for Multiple Antenna Aided SDMA Systems

This paper proposes a space-time decision feedback equalization (ST-DFE) assisted multiuser detection (MUD) scheme for multiple receiver antenna aided space division multiple access systems. A minimum bit error rate (MBER) design is invoked for the MUD, which is shown to be capable of improving the achievable bit error rate performance and enhancing the attainable system capacity over that of the standard minimum mean square error (MMSE) design. An adaptive implementation of the MBER ST-DFE assisted MUD is proposed using a stochastic gradient-based least bit error rate algorithm, which is demonstrated to consistently outperform the classical least mean square (LMS) algorithm, while achieving a lower computational complexity than the LMS algorithm for the binary signalling scheme. Our simulation results demonstrate that the MBER ST-DFE assisted MUD is more robust to channel estimation errors as well as to potential error propagation imposed by decision feedback errors, compared to the MMSE ST-DFE assisted MUD.     

Hybrid SNR-Adaptive Multiuser Detectors for SDMA-OFDM Systems

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.     

Multiuser Detection in SDMA–OFDM Wireless Communication System Using Complex Multilayer Perceptron Neural Network

The space division multiple access–orthogonal frequency division multiplexing (SDMA–OFDM) wireless system has become very popular owing high spectral efficiency and high load capability. The optimal maximum likelihood multiuser detection (MUD) technique suffers from high computational complexity. On the other hand the linear minimum mean square error (MMSE) MUD techniques yields poor performance and also fails to detect users in overload scenario, where the number of users are more than that of number of receiving antennas. By contrast, the differential evolution algorithm (DEA) aided minimum symbol error rate (MSER) MUD can sustain in overload scenario as it can directly minimizes probability of error rather than mean square error. However, all these classical techniques are still complex as these do channel estimation and multiuser detection sequentially. In this paper, complex multi layer perceptron (CMLP) neural network model is suggested for MUD in SDMA–OFDM system as it do both channel approximation and MUD simultaneously. Simulation results prove that the CMLP aided MUD performs better than the MMSE and MSER techniques in terms of enhanced bit error rate performance with low computational complexity.     

Multistage MMSE PIC Space–Time Receiver With Non-Mutually Exclusive Grouping

The arrival of new data services for wireless mobile communications requires an efficient use of the available bandwidth. Interference-limited cellular systems based on code-division multiple access (CDMA) can benefit from multiuser detection (MUD) and beamforming with antenna array to reduce multiple-access interference. Group-based techniques have been proposed to reduce the complexity of space-time MUD and have been shown to provide a performance-complexity tradeoff between matched filtering and full MUD. In this paper, the intergroup interference, which is a limiting factor in group-based systems, is reduced using multistage parallel interference cancellation after group-based minimum mean square error (MMSE) linear filtering. In addition, the extra resources that are available at the receiver are exploited by sharing users among groups. The proposed receiver is shown to converge, as the number of stages increases, to the full space-time MMSE linear MUD filter. The results show that the new approach provides bit error rate (BER) performance close to the full MUD receiver at a fraction of the complexity.     

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station's or radio port's coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems.     

Improved hybrid MMSE detection for turbo-trellis-coded modulation-assisted multi-user OFDM systems

A genetic algorithm assisted minimum mean-square error (MMSE) multi-user detector (MUD) is proposed for a turbo-trellis-coded modulation-based space division multiple access aided orthogonal frequency division multiplexing system. The simulation results show that the system employing the concatenated MMSE-GA MUD was capable of achieving a similar performance to that attained by its optimum counterpart employing the maximum likelihood MUD, while maintaining a significantly lower complexity, especially when supporting a high number of users.     

A Novel Multiuser Detection Algorithm for CDMA-Based MIMO-OFDM System

1Introduction Recently,MultipleInputMultipleOutput(MIMO)wirelesstechnologieshavecapturedalotofresearchin terest,giventhatthecapacityincreaseturnsouttobe achievable[1-2].OrthogonalFrequencyDivisionMulti plexing(OFDM)techniquehastheremarkablecharac teristic…     

Downlink capacity of interference-limited MIMO systems with joint detection

The capacity of downlink cellular multiple-input multiple-output (MIMO) systems, where co-channel interference is the dominant channel impairment, is investigated in this paper, mainly from a signal-processing perspective. Turbo space-time multiuser detection (ST MUD) is employed for intracell communications and is shown to closely approach the ultimate capacity limits in Gaussian ambient noise for an isolated cell. Then, it is combined with various multiuser detection methods for combating intercell interference. Among various multiuser detection techniques examined, linear minimum-mean-square-error (MMSE) MUD and successive interference cancellation are shown to be feasible and effective. Based on these two multiuser detection schemes, one of which may outperform the other for different settings, an adaptive detection scheme is developed, which together with a Turbo ST MUD structure offers substantial performance gain over the well-known V-BLAST techniques with coding in this interference-limited cellular environment. The obtained multiuser capacity is excellent in the high to medium signal-to-interference ratio scenario. Nonetheless, numerical results also indicate that a further increase in system complexity, using base-station cooperation, could lead to further significant increases of the system capacity. The asymptotic multicell MIMO capacity with linear MMSE MUD preprocessing is also derived, and this analysis agrees well with the simulation results.     

Multiuser Detection Based on Adaptive LMS and Modified Genetic Algorithm in DS-CDMA Communication Systems

In this paper, we present an efficient evolutionary algorithm for the multi-user detection (MUD) problem in direct sequence-code division multiple access (DS-CDMA) communication systems. The optimum detector for MUD is the maximum likelihood (ML) detector, but its complexity is very high and involves an exhaustive search to reach the best fitness of transmitted and received data. Thus, there has been considerable interest in suboptimal multiuser detectors with less complexity and reasonable performance. The proposed algorithm is a combination of adaptive LMS Algorithm and modified genetic algorithm (GA). Indeed the LMS algorithm provides a good initial response for GA, and GA will be applied for this response to reach the best answer. The proposed GA reduces the dimension of the search space and provides a suitable framework for future extension to other optimization algorithms. Our algorithm is compared to ML detector, Matched Filter (MF) detector, conventional detector with GA; and Adaptive LMS detector which have been used for MUD in DS-CDMA. Simulation results show that the performance of this algorithm is close to the optimal detector with very low complexity, and it works better in comparison to other algorithms.     

Performance of multiuser detectors based on EM-like method for ultra-wideband communications systems in multipath fading channel

An adaptive multiuser detector (MUD) is proposed for direct-sequence ultra-wideband (DS-UWB) multiple access communication systems to suppress both multiple access interference (MAI) and inter-symbol interference (ISI). In this contribution, considering the MUD from a combination viewpoint, we proposed a MUD based on electromagnetism-like (EM) method, which applied the concept of EM search to Hopfield neural network (EMHNN) for solving optimization problems. We analyze the performance of the EMHNN MUD in multipath fading channel, and compare it with the optimum detector and several suboptimum schemes such as conventional, decorrelator detector (DD), minimum-mean-squared error (MMSE) and HNN MUD. Simulation results will demonstrate that the proposed EMHNN MUD, which alleviates the detrimental effects of the MAI problem, can significantly improve the system performance.     

Improved ML Channel Estimation for Uplink MC-CDMA Systems in Closely Spaced Multipath Channels

In order to attain near-single user performance in uplink multicarrier code- division multiple-access (MC-CDMA) systems, multiuser detection (MUD) methods may be employed which rely on simultaneous estimation of the channel frequency responses of multiple users. Pilot symbol assisted (PSA) channel estimation is needed in fast fading channels and it can be performed either by applying maximum likelihood (ML) criterion or minimum mean-squared error (MMSE) criterion. The performance of ML estimation technique degrades significantly in the case of fractionally spaced (FS) multipath channels where dominant paths are closely spaced with respect to the time resolution of the system. In such situation, the number of effective paths (which contribute more towards signal power) becomes considerably less than the actual number of multipaths at low and moderate SNR values. We propose an improved ML estimation method which considers only effective paths during the estimation process. The proposed method performs nearly identical to the MMSE estimation method and it can also provide significant reduction in the computational complexity when a large number of users are accommodated in the system.     

分集接收的空时分组码MC-CDMA系统中的GA-多用户检测

提出了一种频率选择性信道下使用多天线分集接收的空时分组码多载波码分多址(MC-CDMA)系统上行链路中基于遗传算法(GA)的多用户检测(MUD)。考虑了两种个体选择机制对空时分组码MC-CDMA系统的GA-MUD性能的影响。第一种个体选择机制根据不同接收天线分支代价函数的线性合并选择个体;第二种个体选择机制针对不同天线分支代价函数根据Pareto优化准则选择个体,独立利用了不同天线分支信号携带的有用信息。仿真结果表明,在相同计算复杂度下,基于Pareto优化准则的个体选择机制的误码率(BER)性能要远远优于基于代价函数线性合并的个体选择机制;在相同BER性能下,基于Pareto优化准则的个体选择机制的计算复杂度要低于基于代价函数线性合并的个体选择机制。在子载波数为16的半载系统中,当BER性能逼近单用户界时,基于Pareto优化准则个体选择机制的GA-MUD的复杂度仅相当于最优化搜索MUD复杂度的0.01。     

Neural network-based multiuser detection for SDMA–OFDM system over IEEE 802.11n indoor wireless local area network channel models

Space division multiple access – orthogonal frequency division multiplexing-based wireless communication has the potential to offer high-spectral efficiency, system performance and capacity. This article proposes an efficient blind multiuser detection (MUD) scheme using artificial neural network models such as the radial basis function. The proposed MUD technique is consistently outperforming the existing minimum mean square error and minimum bit error rate (MBER) MUDs with the performance close to the optimal maximum likelihood (ML) detector. Besides that, the computational complexity of the proposed one is comparatively lower than both the MBER and ML detectors. Further, it can also outperform MBER MUD in the overload scenario, where the number of users is more than that of the number of receiving antennas simulation-based study showing BER performance and complexity are carried out to prove the efficiency of the proposed techniques. This analysis is carried through the IEEE 802.11n standard channel models, which are designed for indoor wireless local area network applications of bandwidth up to 100?MHz at frequencies 2 and 5?GHz.     

Multiple-antenna-aided OFDM employing genetic-algorithm-assisted minimum bit error rate multiuser detection

The family of minimum bit error rate (MBER) multiuser detectors (MUD) is capable of outperforming the classic minimum mean-squared error (MMSE) MUD in terms of the achievable bit-error rate (BER) owing to directly minimizing the BER cost function. In this paper, we will invoke genetic algorithms (GAs) for finding the optimum weight vectors of the MBER MUD in the context of multiple-antenna-aided multiuser orthogonal frequency division multiplexing (OFDM) . We will also show that the MBER MUD is capable of supporting more users than the number of receiver antennas available, while outperforming the MMSE MUD.     

FD-MC-CDMA: A Frequency-Based Multiple Access Architecture for High Performance Wireless Communication

Multi-carrier code division multiple access (MC-CDMA) demonstrates good probability of error performances in frequency selective fading channels, a direct result of its ability to resolve the available frequency diversity. However, MC-CDMA performances may be limited by degradation due to large multiple-access interference (MAI). Frequency division multi-carrier code division multiple access (FD-MC-CDMA), a novel multiple access architecture proposed in this paper, exploits the available frequency diversity benefits while reducing MAI. Specifically, instead of transmitting all users' information bits over all carriers, FD-MC-CDMA employs a subset of carriers to support a subset of users (while maintaining the same overall system capacity and throughput as in MC-CDMA). By careful selection of each subset of carriers, the available frequency diversity benefits are fully exploited, while the MAI experienced by each user is reduced. Furthermore, since the number of carriers employed by each user is significantly reduced, the complexity of the truly optimal multiuser detection (MUD) receiver for FD-MC-CDMA is low. An optimal MUD based on the maximum likelihood (ML) criteria is employed to optimize the system performance of FD-MC-CDMA. The proposed FD-MC-CDMA (with MUD) provides significantly better bit error rate (BER) performances than traditional MC-CDMA systems with little increase in system complexity.     

Minimum symbol error rate multiuser detection using an effective invasive weed optimization for MIMO/SDMA–OFDM system

Space division multiple access–orthogonal frequency division multiplexing system has become a potential wireless communication system by offering high spectral efficiency, performance and capacity. This article deals with minimum symbol error rate (MSER)‐based multiuser detection (MUD) technique for the space division multiple access–orthogonal frequency division multiplexing system using an efficient invasive weed optimization (IWO) algorithm. The IWO algorithm is used for finding optimal weights such that the probability of error is directly minimized rather than minimizing the mean square error. Because of this, the MSER MUD is able to detect users even in overload scenario, where the number of users is more than the number of receiving antennas, unlike several classical detection techniques. The IWO is inspired from the nature of invasive colonization of weeds and relatively simple compared with other optimization techniques. The bit error rate performance of the proposed IWO‐aided MSER MUD is found to be better than minimum means square error and differential evolution algorithm‐aided MSER MUDs. Simulation results show that the proposed IWO MSER achieves faster convergence and lower complexity compared with the differential evolution MSER MUD. Copyright © 2013 John Wiley & Sons, Ltd.     

Selective Multiuser Detection for Cellular CDMA Systems

Multiuser detection-oriented CDMA systems have been anticipated to significantly improve system capacity in third-generation W-CDMA-based systems. However, they are greatly limited by the computational complexity of multiuser receivers. In this work, we propose a new, computationally efficient approach to multiuser detection (MUD), consisting in MUD of the subset of preselected users, and conventional detection of the rest of users, called selective multiuser detection (SMD). It allows for full exploitation of available processing power at the receiver by use of MUD and provides remedy for computational complexity of MUD techniques when the number of active users increases beyond the processing capability. We propose and examine three different criteria for selection of users to be processed by the multiuser receiver and analyze the capacity for the single-cell and the multicell CDMA cellular system. The capacity improvement with respect to the conventional CDMA detector combines the gain from MUD and reduction of other-cell interference. We apply the analysis to two SMD schemes using decorrelator and successive interference canceller (SIC) as the multiuser receiver. The results indicate that the SMD is a promising alternative for MUD-oriented CDMA systems with large numbers of active users.     

A signal power adaptive regularized sphere decoding algorithm based multiuser detection technique for underdetermined OFDM/SDMA uplink system

This work considers a regularized Fincke‐Pohst Sphere Decoder (R‐FSD) to detect the multiuser data of orthogonal frequency division multiplexing/space division multiple access (OFDM/SDMA) uplink system with underdetermined and rank‐deficient channel. Rank‐deficiency of the detected channel coefficients makes error‐free multiuser detection (MUD) a difficult task. In literature, most of the papers deal with either a determined or over‐determined full‐rank system. The method proposed in this work transforms an original ill‐posed least squares (LS) problem to a well‐posed one at the receiver, by using the standard Tikhonov regularization method. This is an efficient, direct, and less complex approach where the channel is modified using a regularization parameter that adapts to the signal power at the receiver. The result obtained is compared with maximum likelihood (ML), zero forcing (ZF), minimum mean squared error (MMSE) and ordered successive interference cancellation (OSIC) based detection techniques.     

基于伪并行遗传算法的MC-CDMA多用户检测技术研究

介绍MC-CDMA的多用户检测技术。在分析频率选择衰落信道中单小区MC-CDMA系统上行多址干扰的基础上,引入基于＂联姻＂策略的伪并行遗传算法用于多用户检测技术。通过仿真表明,与解相关和MMSE检测器以及传统遗传算法应用于MUD中相比,其在误比特率性能上更加优秀,而运行时间却明显小于最佳多用户检测。     

编码的多载波CDMA系统中Turbo时空多用户检测

联合MAP多用户检测和信道译码的迭代多用户检测技术可显著提高多载波CDMA系统的容量和性能,本文给出了结合智能天线和迭代MAP多用户检测的Turbo时空多用户检测算法,该方法进一步提高了系统的性能.Turbo时空多用户检测算法不仅极大减小了传统最优MAP多用户检测算法的运算量,而且,此算法性能在AWGN和频率选择性衰落信道中都能逼近单用户编码多载波CDMA系统多天线接收的性能.