Symbol detection in spatial multiplexing system using particle swarm optimization meta-heuristics

Symbol detection in multi-input multi-output (MIMO) communication systems using different particle swarm optimization (PSO) algorithms is presented. This approach is particularly attractive as particle swarm intelligence is well suited for real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exhaustive search method is prohibitively complex, PSO-assisted MIMO detection algorithms give near-optimal bit error rate (BER) performance with a significant reduction in ML complexity. The simulation results show that the proposed detectors give an acceptable BER performance and computational complexity trade-off in comparison with ML detection. These detection techniques show promising results for MIMO systems using high-order modulation schemes and more transmitting antennas where conventional ML detector becomes computationally non-practical to use. Hence, the proposed detectors are best suited for high-speed multi-antenna wireless communication systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Multiuser square-law detection with applications to fiber optic communications

Optimal and suboptimal multiuser noncoherent detection for square-law receivers is studied in this paper. Great potential is found for the multiuser square-law detector when compared with the conventional single-user square-law detector. We study the optimal detector and two detectors with simpler structures: the asymptotically optimal detector and the pairwise linear detector. The saddle-point approximation method is used to study the error performance and its asymptotic behavior as noise reduces. Due to difficulty in optimizing the detectors at an arbitrary noise level, we propose to use the asymptotic efficiency for detector optimization. For a low-error-rate system like a fiber optic communication system, the asymptotic efficiency is found to be an efficient way to design detectors. Numerical results show that the asymptotically optimized detectors perform as well as the optimal detector, even for nonzero noise levels of practical interest. Despite their exponential complexity, these detectors are applicable to wavelength-division multiplexed systems in which only a few neighboring channels produce strong interference.     

Toward favorable detection performance and low complexity by incorporating MIMO detection with ARQ

Although multiple-input-multiple-output (MIMO) detection has received much research attention in the past years,to the author’s knowledge,few detection methods demonstrate optimal/near-optimal performance with low complexity.This paper proposes to incorporate automatic retransmission request (ARQ) with sub-optimal MIMO detectors so as to achieve both favorable performance and low complexity.In the study,retransmission delay induced by ARQ is exploited as a source of improving the detection performance of low complexity algorithms.In particular,the detection performance of sub-optimal algorithms improved by introducing ARQ is analyzed theoretically.A sufficient condition for such scheme to achieve full-diversity performance is also derived which relates detection performance with number of transmission times.Moreover,throughput cost by retransmission is deduced as well as its lower bound.The zero-forcing (ZF) equalizer cooperating with ARQ,as a case study,is shown to have evident performance improvement through theoretical analysis.And numerical results are presented to verify the effectiveness of the proposed scheme which boosts the performance of sub-optimal detector and possesses lower implementation complexity for practical reality simultaneously.     

Optimal and mismatched detection of QAM signals in fast fading channels with imperfect channel estimation

In this paper, we derive an optimal detector for pilot-assisted transmission in Rayleigh fading channels with imperfect channel estimation. The classical approach is based on obtaining channel estimates and treating them as perfect in a minimum distance detector (this is called mismatched detector). The optimal detector jointly processes the received pilot and data symbols to recover the data. The optimal detector is specified for fast frequency-flat fading channels.We consider spline approximation of the channel gain time variations and compare the detection performance of different mismatched detectors with the optimal one. Further, we investigate the detection performance of an iterative receiver in a system transmitting turbo-encoded data, where a channel estimator provides either maximum likelihood estimates, minimum mean square error (MMSE) estimates or statistics for the optimal detector. Simulation results show that the optimal detector outperforms the mismatched detectors. However, the improvement in the detection performance compared to the mismatched detector with the MMSE channel estimates is modest.     

Noise Enhanced Nonparametric Detection

This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.      

A Low Complexity Near Optimal Signal Detection for Large Scale MIMO SC-FDMA Uplink System

This article proposes an improved Newton algorithm as a low complexity signal detection scheme for linear receiver in large scale multiple- input multiple- output (LS-MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system, where a large number of antennas are set up at the base station and active users are with a single antenna system. Data detection for uplink SC-FDMA system is one of the specific challenges due to the significant rise in the dimension of antennas and number of subcarriers. Especially for symbol detection process, LS-MIMO SC-FDMA system with linear detector requires to perform a large matrix inverse computation. Even though linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance, they still introduce high computational complexity and obliviously involve in the computation of matrix inversion. Therefore, a design of complexity reduction algorithm based near-optimal detector for LS-MIMO SC-FDMA system attains research interest. The improved Newton algorithm is employed to obtain linear detection solution which iteratively performs matrix free-inversion operation. The new algorithm performs matrix–matrix multiplication into matrix–vector multiplication, which substantially reduces receiver detection complexity. The efficacy of the proposed method is investigated at 16-QAM. Both ZF and MMSE criteria are proposed and compared through simulations. Simulation results illustrate that the proposed scheme outperforms the conventional detection schemes and exhibits near-optimal performance with a small number of iterations. Further, bit-error-rate performance is closer to classical linear detector with affordable computational complexity.

     

Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch     

Soft-output detection of CPM signals in frequency flat, Rayleighfading channels

Symbol-by-symbol detection algorithms are useful in systems in which soft-decision metrics are important, e.g., systems with interleaved coded modulation. A soft-output algorithm for the detection of continuous phase modulated (CPM) signals transmitted over frequency flat, Rayleigh fading channels is developed. Since the optimum detector is computationally too complex for any practical implementation, some suboptimal detectors which give near optimal performance are proposed. Some theoretical approximations for the performance of the interleaved coded system are given. The performance of the soft-output algorithms is also extensively characterized by means of Monte-Carlo simulations     

A near-optimal multiuser detector for DS-CDMA systems using semidefinite programming relaxation

A multiuser detector for direct-sequence code-division multiple-access systems based on semidefinite programming (SDP) is proposed. It is shown that maximum likelihood (ML) detection can be carried out by "relaxing" the associated integer programming problem to a dual SDP problem, which leads to a detector of polynomial complexity. Computer simulations that demonstrate that the proposed detector offers near-optimal performance with considerably reduced computational complexity compared with that of existing primal-SDP-relaxation based detectors are presented.     

Reduced-Complexity Approach to Iterative Detection of Coded SOQPSK

We develop a reduced-complexity approach for the detection of coded shaped-offset quadrature phase-shift keying (SOQPSK), a highly bandwidth-efficient and popular constant-envelope modulation. The complexity savings result from viewing the signal as a continuous-phase modulation (CPM). We give a simple and convenient closed-form expression for a recursive binary-to-ternary precoder for SOQPSK. The recursive nature of this formulation is necessary in serially concatenated systems where SOQPSK serves as the inner code. We show that the proposed detectors are optimal in the full-response case, and are near-optimal in the partial-response case due to some additional complexity reducing approximations. In all cases, the proposed detectors achieve large coding gains for serially concatenated coded SOQPSK. These gains are similar to those reported recently by Li and Simon, which were obtained using a more complicated cross-correlated trellis-coded quadrature modulation (XTCQM) interpretation.     

基于谱范数的矩阵CFAR检测器

基于信息几何的矩阵恒虚警率（Constant False Alarm Rate,CFAR）检测器为雷达目标检测问题提供了新的解决思路,其主要组成部分是均值矩阵的估计和检测统计量的计算,且检测性能与矩阵流形上的几何测度有紧密关系.现有的信息几何测度都是从Frobenius范数考虑,本文则基于谱范数考虑了矩阵流形上的几何测度和均值矩阵估计问题.将均值矩阵估计问题转化为矩阵流形上的优化问题,根据目标函数的性质得到了计算简便的近似均值矩阵.利用不同方法得到的均值矩阵,提出了几种新的基于谱范数的矩阵CFAR检测器.通过检测势分析和仿真实验验证了其检测性能优于现有的其他矩阵CFAR检测器,复杂度分析也表明了其计算复杂度低于现有的其他矩阵CFAR检测器,为海杂波背景下的雷达目标检测提供了新的有效技术手段.     

Graph-based detection algorithms for layered space-time architectures

We consider a unified framework to develop various graph-based detection algorithms for layered space-time architectures. We start with a factor graph representation for the communication channel, apply a belief propagation (BP) based algorithm for channel detection, and show that the detector achieves a near optimal performance even when number of receive antennas is smaller than number of transmit antennas. Based on this baseline algorithm, we further develop three different extensions of the BP detector that provide a good complexity/performance trade-off, which are especially useful for systems with a large number of antennas or when we encounter a frequency-selective fading channel with a long ISI span. Moreover, all the proposed detectors are soft-input soft-output in nature and they can be directly applied for use in turbo processing without any additional modifications. We study the performance of the new detectors via both simulations and convergence analysis using the measure of average mutual information.     

Symbol detection based on Voronoi surfaces with emphasis on superposition modulation

A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability (APP) detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two-dimensional digital modulation schemes which cannot be split-up into real and imaginary parts (like phase shift keying and phase-shifted superposition modulation (PSM)) is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection.     

Noncoherent multiuser detection for CDMA systems with nonlinearmodulation: a non-Bayesian approach

This paper considers the problem of multiuser detection for a system in which each user employs nonlinear modulation, with an emphasis on noncoherent detection techniques which do not require knowledge of the users' channel parameters at the receiver. Our goals are to gain fundamental insight into the capabilities of multiuser detection in such a setting, and to provide practical algorithms that perform better than conventional matched-filter reception. We begin by providing fundamental performance benchmarks by considering coherent maximum-likelihood (ML) detection, which requires knowledge of the users' channel parameters, as well as noncoherent detection, formulated in a non-Bayesian generalized likelihood ratio test (GLRT) framework. The asymptotic performance of each detector, as the noise level vanishes, is characterized, yielding simple geometric criteria for near-far resistance. In general, both the ML and GLRT detectors have complexity which is exponential in the number of users. We, therefore, propose the more practical sequential decision projection (SDP) detector which has complexity which is quadratic in the number of users. It is shown that the SDP detector has nonzero asymptotic efficiency if the users' powers are suitably disparate     

Asymptotically optimum finite-memory detectors in φ-mixingdependent processes

The design of a finite-memory partition system for the detection of a constant signal in φ-mixing noise is investigated. It is found that the new detector converges to the locally optimal finite-memory practically intractable detector characterized by a multidimensional Fredholm integral equation of the second kind. The new detector encompasses many classes of known detectors. Numerical calculations demonstrate that the finite-memory detector compares favorably, using asymptotic relative efficiency as a fidelity criterion, to other classes of detectors even if extremes of dependent noise distributions are considered. The same calculations also suggest that a dependent process may be treated as an M-dependent process in finite-memory detectors without causing significant detrimental effects, provided M is sufficiently large. To reduce excessive computational complexity, a priori knowledge regarding properties of system parameters (such as matrix symmetry) as well as noise distributions (especially Gaussian and its independently nonlinear transformations) are exploited. Generalizations and extensions of the proposed detectors are also discussed. The operation of the detector may be easily extended to include adaptability and/or sequential operation     

Efficient detection in hyperspectral imagery

Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data. Such sensors provide fully registered high resolution spatial and spectral images that are invaluable in discriminating between man-made objects and natural clutter backgrounds. The price paid for this high resolution data is extremely large data sets, several hundred of Mbytes for a single scene, that make storage and transmission difficult, thus requiring fast onboard processing techniques to reduce the data being transmitted. Attempts to apply traditional maximum likelihood detection techniques for in-flight processing of these massive amounts of hyperspectral data suffer from two limitations: first, they neglect the spatial correlation of the clutter by treating it as spatially white noise; second, their computational cost renders them prohibitive without significant data reduction like by grouping the spectral bands into clusters, with a consequent loss of spectral resolution. This paper presents a maximum likelihood detector that successfully confronts both problems: rather than ignoring the spatial and spectral correlations, our detector exploits them to its advantage; and it is computationally expedient, its complexity increasing only linearly with the number of spectral bands available. Our approach is based on a Gauss-Markov random field (GMRF) modeling of the clutter, which has the advantage of providing a direct parameterization of the inverse of the clutter covariance, the quantity of interest in the test statistic. We discuss in detail two alternative GMRF detectors: one based on a binary hypothesis approach, and the other on a "single" hypothesis formulation. We analyze extensively with real hyperspectral imagery data (HYDICE and SEBASS) the performance of the detectors, comparing them to a benchmark detector, the RX-algorithm. Our results show that the GMRF "single" hypothesis detector outperforms significantly in computational cost the RX-algorithm, while delivering noticeable detection performance improvement.     

Optimal multiband joint detection in cognitive radio networks with the Taguchi method

This paper studies multiband joint detection in cognitive radio networks with the Taguchi method. At the fusion center, linear fusion rule is adopted to collect the observations from distributed secondary users. We aim to achieve maximum aggregate throughput with limited aggregate interference. The problem is challenging due to its nonconvexity and high computational complexity to find the global optimum. Existing works attempt to convert the problem into convex optimization problem. In this paper, the Taguchi method is employed to estimate the gradient of the aggregate throughput, determine the optimal thresholds of the energy detectors and combination weights of the linear fusion rule regardless of convexity. To optimize thresholds and linear weights simultaneously, we employ newly defined variables to represent the changing ranges of detector thresholds when we search for optimal linear weights. In addition, the sensing duration is another factor to be optimized in the Taguchi method. The simulation results show that the proposed method is efficient and applicable for all classes of cognitive radio without considering convexity. The optimization performance is considerably improved. Moreover, the Taguchi method is insensitive to parameter initialization, which provides a relatively robust output. Copyright © 2013 John Wiley & Sons, Ltd.     

A Systematic Approach to Multistage Detectors in Multipath Fading Channels

We consider linear multistage detectors with universal (large system) weighting for synchronous code-division multiple access (CDMA) in multipath fading channels with many users. A convenient choice of the basis of the projection subspace allows a joint projection of all users. Taking advantage of this property, the complexity per bit of multistage detectors with universal weights scales linearly with the number of users on the uplink CDMA channel, while other known multistage detectors with universal weights and different bases of the projection subspace keep the same quadratic complexity order per bit as the linear minimum mean-square error (LMMSE) detector. We focus on the design of two kinds of detectors with linear complexity. The detector of Type I is obtained as an asymptotic approximation of the polynomial expansion detector proposed by Moshavi The detector of Type II has the same performance as the multistage Wiener filter (MSWF) in large systems. Additionally, general performance expressions for large systems, applicable to any multistage detector with the same basis of the projection subspace (e.g., linear parallel interference canceling detectors), are derived. As a by-product, the performance analysis disproves the widespread belief that the MSWF and the polynomial expansion detector are equivalent. We show that, in general, the MSWF outperforms the latter one and they are equivalent only asymptotically in the case of equal received powers.     

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.     

Optimal Noise Benefits in Neyman–Pearson and Inequality-Constrained Statistical Signal Detection

We present theorems and an algorithm to find optimal or near-optimal ldquostochastic resonancerdquo (SR) noise benefits for Neyman-Pearson hypothesis testing and for more general inequality-constrained signal detection problems. The optimal SR noise distribution is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence of such optimal SR noise in inequality-constrained signal detectors. There exists a sequence of noise variables whose detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman-Pearson and other signal detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find near-optimal noise. The appendix presents the proofs of the main results.