Efficient LDPC‐Based,Threaded Layered Space‐Time‐Frequency System with Iterative Receiver

We present a low‐density parity‐check (LDPC)‐based, threaded layered space‐time‐frequency system with emphasis on the iterative receiver design. First, the unbiased minimum mean‐squared‐error iterative‐tree‐search (U‐MMSE‐ITS) detector, which is known to be one of the most efficient multi‐input multi‐output (MIMO) detectors available, is improved by augmentation of the partial‐length paths and by the addition of one‐bit complement sequences. Compared with the U‐MMSE‐ITS detector, the improved detector provides better detection performance with lower complexity. Furthermore, the improved detector is robust to arbitrary MIMO channels and to any antenna configurations. Second, based on the structure of the iterative receiver, we present a low‐complexity belief‐propagation (BP) decoding algorithm for LDPC‐codes. This BP decoder not only has low computing complexity but also converges very fast (5 iterations is sufficient). With the efficient receiver employing the improved detector and the low‐complexity BP decoder, the proposed system is a promising solution to high‐data‐rate transmission over selective‐fading channels.     

Improved VBLAST MAP: A Novel Point-to-Point Symbol Detection Algorithm for MIMO Wireless Communication Systems

Anovel point-to-point symbol detection algorithm in multiple input multiple output (MIMO) system is proposed. This algorithm is an augmentation of two popular algorithms, namely, vertical Bell laboratories layered space-time (VBLAST) and maximum a posteriori probability (MAP). Here, layers are distinguished or ordered based on the a posteriori probabilities of output symbols and not on signal-to-noise ratio (SNR). For each layer, a set of a posteriori probabilities is computed for all output symbols using all possible signal constellations. The layer corresponding to the output symbol having minimum a posteriori probability is selected first from the set of a posteriori probabilities for detection by doing the comprehensive search over all the possible signal constellations. Then, the remaining layers are detected by the conventional VBLAST MAP technique. The relationship of MIMO symbol error rate (MIMO SER) versus MIMO symbol SNR is presented using simulations for 16×16 MIMO systems and 16-QAM constellation. The results show that the proposed algorithm outperforms conventional VBLAST MAP and improved VBLAST algorithms.     

Efficient detection in uniform linear and planar arrays MIMO systems under spatial correlated channels

In this paper, the efficiency of various multiple‐input multiple‐output (MIMO) detectors was analyzed from the perspective of highly correlated channels, where MIMO systems have a lack of performance, besides in some cases, an increasing complexity. Considering this hard but a useful scenario, various MIMO detection schemes were accurately evaluated concerning complexity and bit error rate performance. Specifically, successive interference cancellation, lattice reduction, and the combination of them were associated with conventional linear MIMO detection techniques. To demonstrate effectiveness, a wide range of the number of antennas and modulation formats have been considered aiming to verify the potential of such MIMO detection techniques according to their performance‐complexity trade‐off. We have also studied the correlation effect when both transmit and receiver sides are equipped with uniform linear array and uniform planar array antenna configurations. The performance of different detectors is carefully compared when both antenna array configurations are deployed considering a different number of antennas and modulation order, especially under near‐massive MIMO condition. We have also discussed the relationship between the array factor and the bit error rate performance of both antenna array structures.     

Layered MAP algorithm for MIMO ISI channels

The layered maximum a posteriori （L-MAP） algorithm has been proposed to detect signals under frequency selective fading multiple input multiple output （MIMO） channels. Compared to the optimum MAP detector, the L-MAP algorithm can efficiently identify signal bits, and the complexity grows linearly with the number of input antennas. The basic idea of L-MAP is to operate on each input sub-stream with an optimum MAP sequential detector separately by assuming the other streams are Gaussian noise. The soft output can also be forwarded to outer channel decoder for iterative decoding. Simulation results show that the proposed method can converge with a small number of iterations under different channel conditions and outperforms other sub-optimum detectors for rank-deficient channels.     

GPU Acceleration of a Configurable N-Way MIMO Detector for Wireless Systems

Multiple-input multiple-output (MIMO) wireless is an enabling technology for high spectral efficiency and has been adopted in many modern wireless communication standards, such as 3GPP-LTE and IEEE 802.11n. However, (optimal) maximum a-posteriori (MAP) detection suffers from excessively high computational complexity, which prevents its deployment in practical systems. Hence, many algorithms have been proposed in the literature that trade-off performance versus detection complexity. In this paper, we propose a flexible N-Way MIMO detector that achieves excellent error-rate performance and high throughput on graphics processing units (GPUs). The proposed detector includes the required QR decomposition step and a tree-search detector, which exploits the massive parallelism available in GPUs. The proposed algorithm performs multiple tree searches in parallel, which leads to excellent error-rate performance at low computational complexity on different GPU architectures, such as Nvidia Fermi and Kepler. We highlight the flexibility of the proposed detector and demonstrate that it achieves higher throughput than existing GPU-based MIMO detectors while achieving the same or better error-rate performance.     

Soft-Output MMSE V-BLAST Detector under ML Channel Estimation and Channel Correlation

Channel estimation errors have not been taken into account by existing soft-output minimum mean square error (MMSE) vertical Bell Lab Space Time (V-BLAST) detectors. As a result, the system performance will be degraded under practical channel estimation. In this letter, we propose a novel soft-output MMSE V-BLAST detector, which takes the estimation error of maximum likelihood (ML) multiple-input multiple-output (MIMO) channel estimation and receiver spatially correlation into account in the computation of the MMSE filter and loglikelihood ratio (LLR) of each coded bit. When compared with existing MMSE V-BLAST detectors, simulation results show that the proposed novel detector can obtain sizable performance gain.     

Low-complexity iterative decoding with decision-aided equalizationfor magnetic recording channels

Turbo codes are applied to magnetic recoding channels by treating the channel as a rate-one convolutional code that requires a soft a posteriori probability (APP) detector for channel inputs. The complexity of conventional APP detectors, such as the BCJR algorithm or the soft-output Viterbi algorithm (SOVA), grows exponentially with the channel memory length. This paper derives a new APP module for binary intersymbol interference (ISI) channels based on minimum mean squared error (MMSE) decision-aided equalization (DAE), whose complexity grows linearly with the channel memory length, and it shows that the MMSE DAE is also optimal by the maximum a posteriori probability (MAP) criterion. The performance of the DAE is analyzed, and an implementable turbo-DAE structure is proposed. The reduction of channel APP detection complexity reaches 95% for a five-tap ISI channel when the DAE is applied. Simulations performed on partial response channels show close to optimum performance for this turbo-DAE structure. Error propagation of the DAE is also studied, and two fixed-delay solutions are proposed based on combining the DAE with the BCJR algorithm     

A PDF estimation-based iterative MIMO signal detection with unknown interference

The equivalent diversity order of iterative minimum mean squared error (MMSE) multiuser detectors for multiple input multiple output (MIMO) channels is decreased if they aim at suppressing unknown cochannel interference (UCCI) while detecting multiple users' signals. In this letter, we propose a new MIMO signal detection scheme with the aim to preserve the detector's diversity order by estimating the probability density function of the UCCI plus background noise. It is shown that the proposed detector significantly outperforms the conventional detector based on UCCI's covariance matrix estimation.     

Lattice Reduction Aided Detector for MIMO Communication Via Ant Colony Optimisation

In this work heuristic ant colony optimisation (ACO) procedure is deployed in conjunction with lattice reduction (LR) technique aiming to improve the performance-complexity tradeoff of detection schemes in MIMO communication. A hybrid LR-ACO MIMO detector using the linear minimum mean squared error (MMSE) criterion as initial guess is proposed and compared with two other traditional (non)linear MIMO detectors, as well as with heuristic MIMO detection approaches from the literature, in terms of both performance and complexity metrics. Numerical results show that the proposed LR-ACO outperforms the traditional ACO-based MIMO detectors and the ACO detector with the MMSE solution as initial guess, with a significant complexity reduction while is able to reach full diversity degree in all scenarios considered, including different channel correlation levels, modulation orders, and antennas configuration.     

Belief Propagation over SISO/MIMO Frequency Selective Fading Channels

In this letter, we propose an iterative belief propagation (BP) channel detector (equalizer) over single-input single- output (SISO) and multiple-input multiple-output (MIMO) frequency selective fading channels as an alternative to the typically used maximum a-posteriori (MAP) or maximum likelihood (ML) detectors. The proposed detector has a parallel structure, resulting in fast hardware implementations. Moreover, BP detector is less complex than the MAP detector and it has a short decoding delay. We analyze the bit error rate and the mutual information and show that, over frequency selective fading channels, the proposed BP detector achieves a near-optimal performance, even in the presence of the length 4 cycles in the corresponding channel factor graph.     

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.     

LR-Aided MIMO Detectors under Correlated and Imperfectly Estimated Channels

In this contribution, lattice reduction (LR) technique is applied to improve the multiple-input multiple-output (MIMO) detector performance under correlated channels and imperfect channel estimation constrains. Zero-forcing, minimum mean squared error, ordered successive interference cancellation and sphere decoding (SD) detectors are analysed taking into consideration (a) different correlated fading channel indexes, (b) increasing spectral efficiency, by combining number of transmit antennas and modulation formats, and (c) channel coefficient error estimations. Analysis of correlated channel effects over the MIMO system performance equipped with different LR-aided detectors are carried out, indicating the robustness of those detectors and the SD–MIMO detector deficiency to deal with such channel condition. Besides, computational complexities are compared aiming to determine the best LR–MIMO detection scheme under the perspective of performance-complexity tradeoff.     

An asynchronous multiuser CDMA detector based on the Kalman filter

We introduce a multiuser receiver based on the Kalman filter, which can be used for joint symbol detection and channel estimation. The proposed algorithm has the advantage of working even when the spreading codes used have a period larger than one symbol interval (“long codes”), unlike adaptive equalizer-type detectors. Simulation results which demonstrate the performance advantage of the proposed receiver over the conventional detector, the minimum mean squared error (MMSE) detector and a recursive least squares (RLS) multiuser detector are presented. A thorough comparison of the MMSE detector and the proposed detector is attempted because the Kalman filter also solves the MMSE parameter estimation problem, and it is concluded that, because the state space model assumed by the Kalman filter fits the code division multiple access (CDMA) system exactly, a multiuser detector based on the Kalman filter must necessarily perform better than a nonrecursive, finite-length MMSE detector. The computational complexity of the detector and its use in channel estimation are also studied     

Receiver design for multicarrier CDMA using frequency-domain oversampling

Based on the frequency-domain oversampling and minimum mean-square error (MMSE) principles, we propose three linear single-user detectors for downlink multicarrier codedivision multiple-access (MC-CDMA) systems. We begin with an optimal linear MMSE detector, which is computationally demanding. To reduce the complexity, a two-stage MMSE detector and a diagonal one-stage MMSE detector are developed subsequently. Simulation results show that the proposed detectors can efficiently suppress the multiple access interference (MAI) caused by frequency-selective fading, near-far effect, frequency offset, and nonlinear power amplification.     

Adaptive joint maximum‐likelihood detection and minimum‐mean‐square error with successive interference canceler over spatially correlated multiple‐input multiple‐output channels

We develop an efficient hard detector for multiple‐input multiple‐output (MIMO) channels, which adaptively combines maximum‐likelihood detection (MLD) and minimum‐mean‐square error with a successive interference canceler together. Unlike the conventional joint combination scheme, which may suffer from considerable degradation in bit‐error‐rate (BER) performance over correlated channels and where only one data stream is detected by MLD, our proposed scheme adaptively controls the number of data streams to be detected by MLD based on an analytical characterization of reliability for the detection. Simulation results illustrate that near‐optimal BER performance can be obtained at much lower computational complexity by the proposed method as compared with existing techniques, regardless of the spatial correlation of the MIMO channels. Copyright © 2012 John Wiley & Sons, Ltd.     

Soft‐output MMSE V‐BLAST receiver with MMSE channel estimation under correlated Rician fading MIMO channels

For wireless multiple‐input multiple‐output (MIMO) communications systems, both channel estimation error and spatial channel correlation should be considered when designing an effective signal detection system. In this paper, we propose a new soft‐output MMSE based Vertical Bell Laboratories Layered Space‐Time (V‐BLAST) receiver for spatially‐correlated Rician fading MIMO channels. In this novel receiver, not only the channel estimation errors and channel correlation but also the residual interference cancellation errors are taken into consideration in the computation of the MMSE filter and the log‐likelihood ratio (LLR) of each coded bit. More importantly, our proposed receiver generalizes all existing soft‐output MMSE V‐BLAST receivers, in the sense that, previously proposed soft‐output MMSE V‐BLAST receivers can be derived as the reduced forms of our receiver when the above three considered factors are partially or fully simplified. Simulation results show that the proposed soft‐output MMSE V‐BLAST receiver outperforms the existing receivers with a considerable gain in terms of bit‐error‐rate (BER) performance. Copyright © 2011 John Wiley & Sons, Ltd.     

Space-time turbo equalization in frequency-selective MIMO channels

A computationally efficient space-time turbo equalization algorithm is derived for frequency-selective multiple-input-multiple-output (MIMO) channels. The algorithm is an extension of the iterative equalization algorithm by Reynolds and Wang (see Signal Processing, vol.81, no.5, p.989-995, 2001) for frequency-selective fading channels and of iterative multiuser detection for code-division multiple-access (CDMA) systems by Wang and Poor (see IEEE Trans. Commun., vol.47, p.1046-1061, 1999). The proposed algorithm is implemented as a MIMO detector consisting of a soft-input-soft-output (SISO) linear MMSE detector followed by SISO channel decoders for the multiple users. The detector first forms a soft replica of each composite interfering signal using the log likelihood ratio (LLR), fed back from the SISO channel decoders, of the transmitted coded symbols and subtracts it from the received signal vector. Linear adaptive filtering then takes place to suppress the interference residuals: filter taps are adjusted based on the minimum mean square error (MMSE) criterion. The LLR is then calculated for adaptive filter output. This process is repeated in an iterative fashion to enhance signal-detection performance. This paper also discusses the performance sensitivity of the proposed algorithm to channel-estimation error. A channel-estimation scheme is introduced that works with the iterative MIMO equalization process to reduce estimation errors.     

Closed-Form Exact BER and Optimization of Generalized Orthogonal STBCs

The closed-form exact bit error rate (BER) expressions of generalized orthogonal space-time block codes (OSTBCs) are first derived for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations in spatially white Rayleigh multiple input multiple output (MIMO) channels. Then this analysis is generalized to arbitrarily spatially correlated MIMO channels. Based on the BER analysis, the transmission power of the generalized OSTBCs is optimized such that the average BER is minimized. Furthermore, the optimum transmit power which maximizes the upper-bound of the capacity is derived in closed-form.     

基于V-BLAST的特征波束形成技术在大规模MIMO中的运用

为了获得较高的频谱效率及较低的误码率,提出了大规模MIMO系统中基于V-BLAST的特征波束形成技术(the Eigen-Beamforming combined with V-BLAST,V-BLAST&E-BF),利用大规模天线形成多个特征波束,在这些特征波束上传输多个码流,既可以获得阵列增益又可以获得复用增益.仿真结果表明:提出的方案较MF预编码和传统特征波束形成技术具有较好的性能,并且在接收端无须进行传统V-BLAST的检测算法(如MMSE检测、ZF检测)亦可分离出信号.     

Reduced-dimension MAP turbo-BLAST detection

The Bell Labs layered space-time (BLAST) architecture is a simple and efficient multiantenna coding structure that can achieve high spectral efficiency. Many BLAST detectors require more receiver antennas than transmitter antennas. We propose two novel turbo-processing BLAST detectors that can operate in systems with fewer receiver antennas than transmitter antennas. Both detectors are based on the group-detection strategy. The first proposed detector, the reduced-dimension maximum a posteriori (RDMAP) detector uses a dynamically formed group for each bit decision, while the second proposed detector, the group maximum a posteriori (GMAP) uses a static grouping. For both detectors, a maximum a posteriori (MAP) decision is made using a group of transmitted symbols, and the remaining signal contribution is treated as interference. The interference is characterized as nonzero mean colored-noise source that is whitened before a decision is made. Both proposed detectors are generalizations of the MAP detector and the turbo-processing minimum mean-squared error (MMSE) detector in Sellathurai and Haykin, and Abe and Matsumoto. An uncoded bit-error rate analysis for an independent Rayleigh fading environment is also presented. Simulated results are presented which show that both the RDMAP and GMAP detectors have a performance improvement over the MMSE detector, especially in systems having an excess number of transmitter antennas.