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This paper presents the performance evaluation of an amplify‐and‐forward (AF) relay‐assisted multiuser multiple input–multiple output (MU–MIMO) downlink transmission system for correlated fading channels. The overall system performance was improved by incorporating a double‐quadrature spatial modulation (DQSM) scheme. The bit error rate (BER) performance and detection complexity of the AF–MU–MIMO–DQSM system were analyzed and compared with those of a conventional AF–MU–MIMO system under the same conditions and parameters. The results showed that the correlated fading channel severely affected the performance of systems with higher spectral efficiency (SE). Considering an SE of 12 bpcu/user, the AF–MU–MIMO–DQSM system yielded a gain of up to 3 dB in BER performance compared with that of its conventional counterpart for the analyzed cases. In terms of detection complexity, the AF–MU–MIMO–DQSM system showed a reduction of up to 56 % compared with that of the conventional system for the optimal maximum likelihood detection criterion. [ABSTRACT FROM AUTHOR]

Abstract For wireless communications, the multiple‐input multiple‐output (MIMO) system efficiently makes use of the spectrum and enhances the transmission throughput. In this work, the maximum‐likelihood (ML) detection for the MIMO system is studied, and two ML detection algorithms are first considered for the MIMO system, including the sphere decoding (SD) algorithm and an algorithm based on differential metrics (DMs). Each of the two algorithms has its advantages and disadvantages. The two algorithms are first modified such that they are based on the same signal model. Then, a new ML detection algorithm is proposed for the MIMO system based on the hybrid operation of the two modified algorithms on the tree search process, in which both the branch‐and‐bound principle and indicative functions are applied to remove unnecessary searches. The proposed algorithm can attain the ML detection with lower average complexity over low and high ranges of signal‐to‐noise ratio (SNR), as the authors verify by simulations. The proposed ML detection can also generate soft output, and anti‐phase sequences are exploited to further reduce the complexity.

Exploiting generalized spatial modulation in massive MIMO system, we develop an efficient uplink multiuser detection scheme. Different from the maximum likelihood detection algorithm, the spatial and temporal data in the proposed detection scheme are decoded in a sequential manner: At the front end of base station receiver, a bank of spatial filters is first employed to mitigate the multiuser interference using the benefits of large-scale antenna array. There then, a reduced rank generalized Eigen-value decomposition-based algorithm is proposed to identify the antennas activated at each user terminal. In the second stage, a least-squares-based multiuser detector is employed to decode the multiplexed temporal symbols. Both theoretical analysis and computer simulations are conducted to evaluate system performance in terms of correct identification probability and overall symbol error rate. Simulation results demonstrate that the proposed algorithm outperforms most of the existing algorithms yet the complexity is extensively reduced. [ABSTRACT FROM AUTHOR]

In this research, we study the interference cancellation capabilities of receivers and transmitters in multiple-input-multiple-output (MIMO) systems using theoretical calculations and numerical simulations in Quadriga. We study so-called Reduced Channel Zero-Forcing (RCZF) class of precoding as well as Minimum MSE Interference Rejection Combiner (MMSE-IRC) and QR Maximum Likelihood Detection (QR-MLD) receivers. The precodings from the RCZF class are widely used in practice and include for example Zero-Forcing (ZF) and Eigen Zero-Forcing (EZF) precodings. Our theoretical and experimental results confirm that MMSE-IRC and QR-MLD receivers in combination with the RCZF precoding provide complete interference suppression asymptotically. The major findings and the calculation results far as the RCZF class of precoding as well as the MMSE-IRC and QR-MLD receivers' performance concerned, which is a subject of the paper. [ABSTRACT FROM AUTHOR]

For wireless communications, the multiple‐input multiple‐output (MIMO) system efficiently makes use of the spectrum and enhances the transmission throughput. In this work, the maximum‐likelihood (ML) detection for the MIMO system is studied, and two ML detection algorithms are first considered for the MIMO system, including the sphere decoding (SD) algorithm and an algorithm based on differential metrics (DMs). Each of the two algorithms has its advantages and disadvantages. The two algorithms are first modified such that they are based on the same signal model. Then, a new ML detection algorithm is proposed for the MIMO system based on the hybrid operation of the two modified algorithms on the tree search process, in which both the branch‐and‐bound principle and indicative functions are applied to remove unnecessary searches. The proposed algorithm can attain the ML detection with lower average complexity over low and high ranges of signal‐to‐noise ratio (SNR), as the authors verify by simulations. The proposed ML detection can also generate soft output, and anti‐phase sequences are exploited to further reduce the complexity. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a promising technology to meet the needs of next-generation wireless communication techniques that enhance the quality of service by effectively adjusting the phases of the signals through a reflective surface. On the other side, space shift keying (SSK), as one of the typical index modulation (IM) techniques, provides data transmission by using only the information of the active transmitting antenna index instead of applying traditional modulation methods. In this study, two of the RIS-assisted SSK schemes are studied to take potential benefits of both RIS and SSK techniques, and the average bit error rate (ABER) performance is investigated over Ricean fading channels. The activated transmitter antenna for each transmission instant is detected by utilizing the maximum likelihood (ML) detection algorithm at the receiver side. The ABER performance of the proposed RIS-based systems in proportion to the increasing number of reflecting elements and Ricean-K factor is presented by both theoretical derivations and computer simulations. The obtained results show that the Ricean-K parameter and the number of reflective elements on the RIS have an undeniable effect on the system performance. [ABSTRACT FROM AUTHOR]

This study explores the performance of multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs) over Rayleigh fading channels. We particularly aim to characterize the pairwise error probability (PEP) achieved by maximum likelihood detection (MLD), which provides the optimal detection performance of the considered systems. First, the upper bound of the PEP is derived by leveraging Jensen’s inequality and the Chernoff bound. The asymptotic bounds of the PEP are then characterized to clarify the impact of the one-bit ADCs on the diversity order of the MIMO systems. Notably, the diversity order of the MIMO systems with one-bit ADCs is the same as the number of receive antennas only when transmitting two symbol vectors with the opposite sign. Except for this special case, the asymptotic bound of the PEP is saturated at a high signal-to-noise ratio due to the use of the one-bit ADCs. Using simulations, we verify that the analytical results coincide with the asymptotic behavior of the PEP of the MLD for MIMO systems with one-bit ADCs over Rayleigh fading channels. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats. [ABSTRACT FROM AUTHOR]

Generalized Spatial Modulation is a recently developed technique that is designed to enhance the efficiency of transmissions in MIMO Systems. However, the procedure for correctly retrieving the sent signal at the receiving end is quite demanding. Specifically, the computation of the maximum likelihood solution is computationally very expensive. In this paper, we propose a parallel method for the computation of the maximum likelihood solution using the parallel computing library OpenMP. The proposed parallel algorithm computes the maximum likelihood solution faster than the sequential version, and substantially reduces the worst-case computing times. [ABSTRACT FROM AUTHOR]

Low-complexity improved-rate generalised spatial modulation (LCIR-GSM) is proposed to mitigate the high complexity of the mapping book design and demodulation of variable- ${N_a}$ GSM. Specifically, first of all, we propose two efficient schemes for mapping the information bits to the transmit antenna activation patterns, which can be readily scaled to massive MIMO setups. Secondly, we derive a pair of low-complexity near-optimal detectors, one of which has a reduced search scope, while the other benefits from a decoupled single-stream based signal detection algorithm. Finally, the performance of the proposed LCIR-GSM system is characterised by the improved error probability upper bound. Our simulation results confirm the improved error performance of our proposed scheme, despite its reduced signal detection complexity. [ABSTRACT FROM AUTHOR]

Recently, multiple‐input multiple‐output (MIMO) is studied actively such as a key technology for wireless communications. Space division (SD) multiplexing and spatial modulation (SM) have been proposed as the transmission technique of MIMO systems. In the full maximum likelihood detection (MLD), the QR decomposition with M‐algorithm MLD (QRM‐MLD), and the channel ranking QRM‐MLD (CR‐QRM‐MLD) for the SD‐MIMO, there are the trade‐off between the system performance and the computational complexity. Also, in the SM‐MIMO, the transmission rate is deteriorated due to a small number of transmitted signals. To solve these problems, in this article, we propose the CR‐QRM‐MLD for SD‐SM Hybrid MIMO (SD‐SM‐MIMO) systems. From the computer simulation results, the proposed method shows the reduction of the computational complexity and the good bit error rate performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a signal-level maximum likelihood detection for multiple-input multiple-output filter-bank multicarrier-quadrature amplitude modulation (MIMO FBMC-QAM) system with two prototype filters. Under high-frequency selective channels, the conventional symbol-level maximum likelihood detection suffers from degraded bit error rate performances because it cannot utilize the oversampling characteristics of the signal-level. However, due to intrinsic interference and high computational complexity, a maximum likelihood detection cannot be applied directly to the signal-level. To solve this problem, we propose a signal-level maximum likelihood detection based on partial candidates. Utilizing the diagonal dominant characteristics of effective channels, the proposed signal-level maximum likelihood detection can reduce the effect of intrinsic interference with practical computational complexity. Moreover, a region ordering algorithm based on Euclidean distance can improve the efficiency of the proposed signal-level maximum likelihood detection. Simulation results show that the proposed signal-level maximum likelihood detection can outperform the bit error rate performance of the conventional symbol-level maximum likelihood detection. [ABSTRACT FROM AUTHOR]

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An optical wireless intensity-modulation direct-detection multiple-input multiple-output communication system is considered. The performance of $M$ -PAM rate-1 direct current offset space-time block codes is studied in terms of average worst-case pairwise error probability (WC-PEP) in quasi-static channels. It is shown that within this code class, the average WC-PEP is minimized by repetition coding (RC) under both electrical and optical individual power constraints, irrespective of channel statistics. This agrees with previously published results related to ON–OFF keying RC. This is further extended to sum power constraints, where it is shown that spatial beamforming minimizes the average WC-PEP within this code class, which simplifies to RC if the channel matrix has independent and indentically distributed columns and a sum electrical power constraint. Under a sum optical power constraint, this also holds true at high signal-to-noise ratio (SNR), but not at low SNR. Generally, the time dimension of this code class is redundant from an average WC-PEP perspective. Numerical results are provided to support the theoretical findings and to show that the average WC-PEP leads to a good approximation of the actual error probability at high SNR. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient pruning algorithm for MIMO detector, which not only achieves the same performance of the maximum likelihood (ML) decoder, but also massively reduces the number of visiting nodes. The proposed algorithm applies correct sorting order so that it achieves the same performance of the ML decoder. Thanks to the use of the correct ordering, both child and sibling nodes can be pruned efficiently. As a result, the number of visiting nodes is significantly reduced. To acquire properly sorted order, we propose fast sorting algorithms that generate the exact sorted sequence. Furthermore, the proposed schemes can be applied in SISO MIMO detector. The advantages of the proposed sphere decoder become more pronounced in high-order modulation schemes. [ABSTRACT FROM AUTHOR]

This correspondence paper proposes a low-complexity adaptive spatial modulation (ASM) scheme, which is based on sphere search (SS). To find the optimal ASM mode, the existing ASM schemes examine each error vector in the search space. By contrast, in the proposed SS-aided strategy, the search space is reduced to the error vectors inside a sphere, which results in significant complexity reduction. By exploiting a similar structure between the error vectors in ASM and symbol vectors in generalized spatial modulation (GSM), we modify the sphere decoder for GSM to develop an efficient two-step SS scheme for ASM. In addition, an early termination scheme is employed to further reduce the computational load of the SS process for each ASM mode. Simulation results show that SS-aided ASM provides a significant reduction in computational complexity compared to the existing ASM schemes. [ABSTRACT FROM AUTHOR]

In order to improve on existing radio communications for railways, cognitive radio (CR) is a promising concept, allowing the combination of artificial intelligence and software-defined radios. To ensure the best environmental awareness, as required by CR receivers, automatic modulation identification is a key feature for promoting efficient and secure communications in the CR context. This paper deals with blind modulation identification in the railway transmission environment and specifically considers its two major constraints: the high-speed and the impulsive nature of the noise. To achieve this goal, we propose a feature-based process of blind identification, which is made up of three subsystems: impulsive noise mitigation (which is the main contribution of this paper), feature extraction, and classification. For the purpose of mitigating the impulsive noise, we introduce a blind filtering myriad-based approach, which, in turn, requires the estimation of the noise parameters. Simulation results prove that the developed filtering approach provides a good filtering performance, and consequently, high identification performances of the overall identification system. [ABSTRACT FROM AUTHOR]

We consider the multiple-input multiple-output (MIMO) communication channel impaired by phase noises at both the transmitter and receiver. We focus on the maximum likelihood (ML) detection problem for uncoded single-carrier transmission. We derive an approximation of the likelihood function, based on which we propose an efficient detection algorithm. The proposed algorithm, named self-interference whitening (SIW), consists in: 1) estimating the self-interference caused by the phase noise perturbation; 2) whitening the said interference; and 3) detecting the transmitted vector. While the exact ML solution is computationally intractable, we construct a simulation-based lower bound on the error probability of ML detection. Leveraging this lower bound, we perform extensive numerical experiments demonstrating that SIW is, in most cases of interest, very close to optimal with moderate phase noise. More importantly and perhaps surprisingly, such near-ML performance can be achieved by applying only twice the nearest neighbor detection algorithm. In this sense, our results reveal a striking fact: near-ML detection of phase noise corrupted MIMO channels can be done as efficiently as for conventional MIMO channels without phase noise. [ABSTRACT FROM PUBLISHER]

Multiple-input multiple-output (MIMO) system has achieved significant benefits by applying optimum detection techniques. Maximum likelihood (ML) detection technique improves the error rate performance of MIMO system at optimum level but with enhanced complexity. Therefore, we have considered minimum mean square error-ordered successive interference cancellation with candidate detection to approach the performance of ML with reduced complexity. The proposed system performance is analyzed in composite Weibull-gamma fading and additive white generalized Gaussian noise (AWGGN) scenario. Some special cases of WG fading and AWGGN yield the simplified results. [ABSTRACT FROM AUTHOR]

This article presents a field programmable gate array design and implementation of particle swarm optimisation (PSO) algorithm for multiple input multiple output (MIMO) detection systems. Although, the simulations results prove that the maximum likelihood (ML) detector presents better performances than the other detectors like zero forcing (ZF) and minimum mean square error (MMSE), but with MIMO systems this algorithm has a high computational complexity. To minimise this effect, we apply a new type of cost function and give an efficient calculation algorithm. We propose a PSO approach based on signal transmitted vectorfrom,and. The simulation result shows the effectiveness of the proposed algorithm in terms of achieving better bit error rate performance compared to ZF and MMSE with low complexity. This algorithm has been developed to reduce the ML detector complexity. [ABSTRACT FROM PUBLISHER]

Due to the interchannel interference and the varying active antennas in generalized spatial modulation (GSM), it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a novel algorithm based on the K-best sphere decoding for the soft detection of the GSM signal, which performs the breadth-first tree search in two stages sequentially. By exploiting the null space of the GSM channel, the inactive antenna searching is carried out antennawise in the first stage, in which a recursive algorithm for updating the QL decomposition (a decomposition of a matrix into a product of an orthogonal matrix and a lower triangular matrix) is proposed to calculate the branch metrics and an effective examination scheme is developed to prune those illegal or repetitive child nodes. Based on the available QL decomposition structure that has been updated recursively during the first stage, the second stage of the proposed detector is then readily concatenated to search for the modulated symbols carried on the active antennas in a one-by-one manner. Moreover, owing to the soft-input soft-output nature, the proposed detector can be applied in a turbo decoder for a coded GSM system. Both complexity analysis and simulation results corroborate the superiority of the proposed soft detector for the GSM system. [ABSTRACT FROM AUTHOR]

The computation of the achievable sum rate for integer-forcing (IF) linear receivers requires the search for the shortest set of linearly independent lattice vectors (SIVP). Since SIVP is considered an NP-hard problem, we propose two approximations to the IF achievable sum rate. The first approximation is suitable for systems with two antennas at both transmitter and receiver ( $2\times 2$ arrays) and is based on the analysis of the Gauss-Lagrange algorithm. The second approximation considers $n\times n$ array systems and is based on Minkowski’s second theorem. Assuming a block-fading channel with Rayleigh distribution, we have derived the exact probability density function of both approximations. We have used these results to obtain the mean sum-rate, outage probability, and outage rate for uncorrelated fading channels. For the second approximation, the effect of channel correlation on these metrics was also analyzed. An exhaustive series of simulations have been conducted to assess the tightness and robustness of both solutions. [ABSTRACT FROM AUTHOR]

We show that certain signal constellations invoked for classic differential encoding result in a phenomenon we term as the unbounded differential constellation size (UDCS). Various existing differential transmission schemes that suffer from this issue are identified. Then, we propose an enhanced algebraic field extension-based differential spatial modulation (AFE-DSM) scheme and its enhanced counterpart that strikes a diversity-rate tradeoff (AFE-DSM-DR), both of which overcome the UDCS issue without compromising its full transmit diversity advantage. Furthermore, the proposed schemes are extended to incorporate amplitude and phase shift keying (APSK) in order to exploit all the available degrees of freedom. Additionally, we propose a pair of detection schemes specially designed for APSK-aided differential transmission schemes. Explicitly, we conceive the buffered minimum mean squared error (B-MMSE) detector and buffered maximum likelihood (B-ML) detector, which exploit the knowledge of previously detected symbols in order to further improve the detection performance. Our simulation results have shown that the proposed detectors are capable of bridging the performance gap between the conventional differential detector (CDD) and the coherent detector that has full channel state information. Specifically, when employing the proposed APSK-aided AFE-DSM scheme operating at a rate of 2 b per channel use, the B-MMSE and B-ML detectors are observed to give about 3- and 3.5-dB signal-to-noise ratio gain with respect to their CDD counterpart at a bit error ratio of 10^-5 . [ABSTRACT FROM AUTHOR]

In this article, we propose an adaptive multiple stage K-best successive interference cancellation (AMS-KSIC) algorithm for symbol vector detection in multiple-input multiple-output systems. The proposed algorithm employs multiple successive interference cancellation (SIC) stages in parallel, where the number of stages depends on the number of positions at which the minimum mean squared error (MMSE) estimate of the received vector and the SIC solution differ, and each stage is initialized with the partial MMSE estimate of the received vector. In every stage, K-best solutions are generated by using the minimum Euclidean distance criteria. Furthermore, to reduce error propagation, we use two different ordering strategies namely, signal to noise ratio and log-likelihood ratio based orderings. The best solution among all the generated solutions is selected by using maximum likelihood (ML) cost metric. Multiple stages along with K-best solutions in every stage achieves a higher detection diversity, and hence, yield a better performance in terms of bit error rate (BER). From simulations, we observe that the proposed AMS-KSIC algorithm performs better than the MMSE and the SIC based detection schemes, and achieves a near ML performance. Further, the BER performance of the proposed algorithm improves with increase in the number of antennas and shifts towards single-input single-output additive white Gaussian noise performance. In addition, we also check and validate robustness of the proposed algorithm by simulating the BER performance under channel estimation errors. [ABSTRACT FROM AUTHOR]

The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm has variable complexity, and its complexity increases rapidly with decreasing signal-to-noise ratio (SNR). In this paper, we propose a novel ML detection algorithm for the MIMO system based on differential metrics. We define the differential metrics and derive the associated recursive calculation. We then give the indicative functions, which can be used to possibly find some ML-detected bits of the initial sequence. The indicative functions are further applied to implement an efficient tree search for ML detection. The proposed algorithm does not need QR decomposition and matrix inversion. The tree search process needs only the additive operation, while the number of multiplications before the tree search is constant. Our algorithm can achieve the exact ML detection as the SD algorithm. Unlike the SD algorithm, the complexity of our algorithm reduces with decreasing SNR, whereas at high SNR, the complexity is nearly constant. We also give the convergence analysis for the SD and proposed algorithms, and the simulation verifies our analysis. For the proposed algorithm, the number of necessary memory is constant during the tree search, and the implementation by parallel processing is possible. The soft output of ML-detected bits can also be generated in our algorithm. [ABSTRACT FROM PUBLISHER]

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM), which provides a flexible trade-off between spectral efficiency and error performance, is recently proposed as a promising transmission technique for energy-efficient 5G wireless communications systems. However, due to the dependence of subcarrier symbols within each subblock and the strong interchannel interference, it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose two types of low-complexity detectors based on the sequential Monte Carlo (SMC) theory for the detection of MIMO-OFDM-IM signals. The first detector draws samples independently at the subblock level, while the second detector draws samples at the subcarrier level with further reduced complexity. To meet the constraint of the subcarrier combinations within each subblock, the second detector is further coupled with a carefully designed legality examination method. Attributed to the effectiveness of legality examination and deterministic SMC sampling, both proposed detectors achieve near-optimal error performance for the MIMO-OFDM-IM system. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel generalized space shift keying (GSSK) detection algorithm by exploiting the inherent sparse property of GSSK signal. In particular, we formulate the GSSK detection into a sparse convex optimization problem. The key contribution of the proposed algorithm lies in the strategical adoption and transformation of the sparse reconstruction (SR) from image processing to GSSK detection. The proposed SR detector achieves better performance than all existing suboptimal solutions with comparable computational complexity. In order to further improve the performance of the SR algorithm, we develop another approach, i.e., the iterative SR algorithm, which utilizes the result of the SR algorithm as the start point and enhances the detection performance gradually in each iteration. The simulation results confirm the efficiency of both of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Generalized spatial modulation (GSM) is a spectral and energy efficient multiple-input multiple-output (MIMO) transmission scheme. It will lead to imperfect detection performance with relatively high computational complexity by directly applying the original QR-decomposition with M algorithm (QRD-M) to the GSM scheme. In this paper an improved QRD-M algorithm is proposed for GSM signal detection, which achieves near-optimal performance but with relatively low complexity. Based on the QRD, the improved algorithm firstly transforms the maximum likelihood (ML) detection of the GSM signals into searching an inverted tree structure. Then, in the searching process of the M branches, the branches corresponding to the illegitimate transmit antenna combinations (TACs) and related to invalid number of active antennas are cut in order to improve the validity of the resultant branches at each level by taking advantage of characteristics of GSM signals. Simulation results show that the improved QRD-M detection algorithm provides similar performance to maximum likelihood (ML) with the reduced computational complexity compared to the original QRD-M algorithm, and the optimal value of parameter M of the improved QRD-M algorithm for detection of the GSM scheme is equal to modulation order plus one. [ABSTRACT FROM AUTHOR]

Space shift keying (SSK) modulation is an emerging transmission technique for multiple-input multiple-output (MIMO) wireless channels, which exploits the spatial domain to convey information. In this paper, we present a layered space shift keying (LSSK) modulation scheme to fully exploit the spatial domain to transmit information bits, where a layered architecture is developed to achieve spatial multiplexing transmission in an SSK system. Specifically, LSSK leverages the rotated signals predetermined at the transceiver to identify different layers and improve the bit-error-rate (BER) performance. The layered signals are directly generated by the proposed LSSK modulation method with a low computational overhead. Furthermore, we propose a layered-and-joint (LJ) near-optimal detection algorithm based on the layered architecture of LSSK to reduce the detection complexity. In LJ detection, layered detection is performed to find a set of detection candidates for each layer, and then, joint detection is performed with these candidates. We show that the performance of LJ detection is quite close to that of optimal maximum-likelihood detection with significantly reduced detection complexity for high-spectrum-efficiency scenarios. Results demonstrate that the proposed LSSK scheme substantially improves the spectrum efficiency of an SSK system and outperforms other existing MIMO schemes. [ABSTRACT FROM PUBLISHER]

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) detection algorithms have received considerable research interest in recent years, as a result of the increasing need for high data-rate communications. Detection techniques range from the low-complexity linear detectors to the maximum likelihood detector, which scales exponentially with the number of transmit antennas. In between these two extremes are the tree search (TS) algorithms, such as the popular sphere decoder, which have emerged as attractive choices for implementing MIMO detection, due to their excellent performance-complexity trade-offs. In this paper, we survey some of the state-of-the-art VLSI implementations of TS algorithms and compare their results using various metrics such as the throughput and power consumption. We also present notable contributions that have been made in the last three decades in implementing TS algorithms for MIMO detection, especially with respect to achieving low-complexity, high-throughput designs. Finally, a number of design considerations and trade-offs for implementing MIMO detectors in hardware are presented. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) is a novel multicarrier transmission technique which has been proposed recently as an alternative to classical MIMO-OFDM. In this scheme, OFDM with index modulation (OFDM-IM) concept is combined with MIMO transmission to take advantage of the benefits of these two techniques. In this paper, we shed light on the implementation and error performance analysis of the MIMO-OFDM-IM scheme for next generation 5G wireless networks. Maximum likelihood (ML), near-ML, simple minimum mean square error (MMSE) and ordered successive interference cancellation (OSIC) based MMSE detectors of MIMO-OFDM-IM are proposed, and their theoretical performance is investigated. It has been shown via extensive computer simulations that MIMO-OFDM-IM scheme provides an interesting tradeoff between error performance and spectral efficiency as well as it achieves considerably better error performance than classical MIMO-OFDM using different type detectors and under realistic conditions. [ABSTRACT FROM PUBLISHER]

Operation of full-duplex systems requires efficient mitigation of the self-interference signal caused by the simultaneous transmission/reception. In this paper, we propose a maximum-likelihood (ML) approach to jointly estimate the self-interference and intended channels by exploiting its own known transmitted symbols and both the known pilot and unknown data symbols from the other intended transceiver. The ML solution is obtained by maximizing the ML function under the assumption of Gaussian received symbols. A closed-form solution is first derived, and subsequently, an iterative procedure is developed to further improve the estimation performance at moderate-to-high signal-to-noise ratios (SNRs). We establish the initial condition to guarantee the convergence of the iterative algorithm to the ML solution. In the presence of considerable phase noise from the oscillators, a phase noise estimation method is proposed and combined with the ML channel estimator to mitigate the effects of the phase noise. Illustrative results show that the proposed methods offer good cancelation performance close to the Cramer–Rao bound (CRB). [ABSTRACT FROM AUTHOR]

In practical applications, an imperfect channel estimate over a noisy channel is inevitable. This paper investigates an interference-aware receiver in which colored-estimation-noise-based maximum-likelihood (CEN-ML) detection is employed to mitigate the impact of imperfect channel estimates. It is shown that the CEN-ML detection, which treats the channel estimation errors as colored noise, has superior performance and is robust to imperfect channel estimation. However, a CEN-ML interference-aware detector usually requires computational efforts that are too complex for multiple-input–multiple-output (MIMO) interference-limited systems. To reduce the complexity, this paper proposes new methods for both the preprocessing and tree-search stages of an interference-aware sphere detector (SD). First, a channel extended matrix is constructed using the information from channel estimation errors in a preprocessing stage. The channel extension model can reduce the complexity without performance loss. Second, two tree pruning (TP) criteria are developed in a tree-search stage to make the implementation of the CEN-ML SD feasible. The first criterion is based on a lower bound of the CEN-ML metric and can achieve the same result as the optimal brute-force method. The second criterion is derived from an approximated metric and can further reduce the complexity with small performance loss. Simulation results for typical Third-Generation Partnership Project Long-Term Evolution (LTE)/LTE-Advanced scenarios show that the new methods provide the ability to deal with the channel estimation errors and to achieve a better tradeoff between complexity and performance (e.g., in terms of throughput or error rate) in interference-limited scenarios. The proposed low-complexity CEN-ML SD can have a gain of more than 10 dB than the other investigated detection for 16-ary quadrature amplitude modulation (QAM) or 64-QAM at a target coded block error rate (BLER) of \mbox10^-2. [ABSTRACT FROM AUTHOR]

In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a pair of high-resolution analog-to-digital converters (ADCs) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system. [ABSTRACT FROM PUBLISHER]

This paper presents a low-complexity channel-adaptive reconfigurable 4 x 4 QR-decomposition and M-algorithm-based maximum likelihood detection (QRM-MLD) multiple-input and multiple-output (MIMO) detector. Two novel design approaches for low-power QRM-MLD hardware are proposed in this work. First, an approximate survivor metric (ASM) generation technique is presented to achieve considerable computational complexity reduction with minor BER degradation. A reconfigurable QRM-MLD MIMO detector (where the M-value represents the number of survival branches in a stage) for dynamically adapting to time-varying channels is also proposed in this work. The proposed reconfigurable QRM-MLD MIMO detector is implemented using a Samsung 65 nm CMOS process. The experimental results show that our ASM-based QRM-MLD MIMO detector shows a maximum throughput of 288 Mbps with a normalized power efficiency of 10.18 Mbps/mW in the case of 4 x 4 MIMO with 64-QAM. Under time-varying channel conditions, the proposed reconfigurable MIMO detector also achieves average power savings of up to 35% while maintaining a required BER performance. [ABSTRACT FROM AUTHOR]

The problem of maximum likelihood (ML) detection in training-assisted single-input multiple-output (SIMO) systems with phase noise impairments is studied for two different scenarios, i.e., the case when the channel is deterministic and known (constant channel) and the case when the channel is stochastic and unknown (fading channel). Furthermore, two different operations with respect to the phase noise sources are considered, namely, the case of identical phase noise sources and the case of independent phase noise sources over the antennas. In all scenarios, the optimal detector is derived for a very general parameterization of the phase noise distribution. Furthermore, a high signal-to-noise-ratio (SNR) analysis is performed to show that symbol-error-rate (SER) floors appear in all cases. The SER floor in the case of identical phase noise sources (for both constant and fading channels) is independent of the number of antenna elements. In contrast, the SER floor in the case of independent phase noise sources is reduced when increasing the number of antenna elements (for both constant and fading channels). Finally, the system model is extended to multiple data channel uses and it is shown that the conclusion is valid for these setups, as well. [ABSTRACT FROM AUTHOR]

Using a spatial multiplexing transmission scheme can improve the data rate in multiple-input–multiple-output (MIMO) relaying systems, while making signal detection more difficult at receivers. Aiming at lowering the computational complexity of the receiver, this paper proposes a maximum likelihood combining (MLC) algorithm for spatial multiplexing MIMO amplify-and-forward (AF) relaying systems in a Rayleigh flat-fading environment, which is implemented before maximum likelihood (ML) detection. The combining signal and equivalent channel are opportunely designed based on the ML rule in the MLC algorithm. We also formulate the diversity gain of the systems that employ the MLC algorithm mathematically, induced by the Chernoff bound of pairwise error probability (PEP). An upper bound on the symbol error probability (SEP) for the MLC algorithm with multiple modulations is also given, based on the derived bound of PEP. Moreover, the complexities of ML receivers adopting the MLC algorithm and the conventional vector combining (VC) algorithm are analyzed. Numerical simulations indicate that systems with the MLC algorithm achieve the same performance while consuming lower computational complexity compared to that with the VC algorithm. [ABSTRACT FROM PUBLISHER]

To achieve more high-speed communication systems, multiple-input multiple-output orthogonal frequency division multiplexing (MIMO/OFDM) is adopted in several standardisations. On the other hand, the conventional MIMO/OFDM requires many pilot signals to estimate the channel state information (CSI). To achieve an accurate CSI with the small number of pilot signals, high time resolution carrier interferometry (HTRCI) for MIMO/OFDM has been proposed. Moreover, QR decomposition with M-algorithm maximum likelihood detection (QRM-MLD) and channel ranking has been proposed to achieve both the reducing complexity and the improving system performance. However, the conventional HTRCI-MIMO/OFDM with the QRM-MLD and channel ranking did not consider the problem of the fast fading channel. In the fast fading environment, many errors are caused in the last part of the packet. To overcome this problem, the decision-direct method has been proposed. However, it requires large complexity because of the iterative processing. Therefore, in this paper, we propose the non-linear prediction method for an HTRCI-MIMO/OFDM with the QRM-MLD and channel ranking. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we investigate adaptive zero-forcing uplink transmission for very large multi-user multi-antenna systems in Rayleigh fading environments. We assume that the number of antennas at the base station (denoted as $$M$$ ) is not less than the number of users (denoted as $$K$$ ) with each having single antenna, and power control can be done at the transmitter(s) as channel condition changes. Under constraints of individual rates and maximum transmit powers, we adopt the optimal transmit strategy of minimizing the total average transmit power (ATP). We derive and give individual ATP expressions for each link with short- and long-term rate constraints, respectively. Numerical results show that the individual ATP for each link with short term rate constraint is quite close to its long term counterpart when $$M-K$$ is large, and its corresponding outage probability can be designed to be nearly zero at the same time. Finally, we present two simple adaptive transmission schemes with constant transmit power satisfying short- and long-term rate constraints, respectively. Both of them are easy to implement, and asymptotically optimal when $$M-K$$ grows without bound. [ABSTRACT FROM AUTHOR]

A low-complexity MIMO detection scheme is presented that decomposes a MIMO channel into multiple decoupled subsets of streams that can be detected separately. The scheme employs QL decomposition followed by elementary matrix operations to transform the channel matrix into a generalized elementary structure matching the subsets of streams to be detected. The proposed scheme avoids matrix inversion operations, and allows subsets to overlap thus achieving better diversity gain. Simulations demonstrate that this approach performs to within a few tenths of a dB from the optimum detection algorithm. [ABSTRACT FROM AUTHOR]

A novel adaptive beamforming (BF) receiver is proposed for a multiple-input-multiple-output communication system over multipath fading channels. By dividing a large number of receive antennas into many adaptive beamformers (ABFs), the proposed system reduces the array dimension in order to simplify the signal detection process and enjoys the benefit of BF techniques to improve the symbol-error-rate (SER) performance. The effectiveness of the proposed system depends on the number of ABFs and the number of antennas per ABF. The statistical properties of complex Wishart matrices are applied to analyse the SER performance of the proposed system with both maximum-likelihood (ML) detection and zero-forcing detection. Furthermore, the authors have derived an upper bound for SER of the ML detection based on the smallest eigenvalue distribution. Through the authors analysis, it has been shown that the proposed system using ML detection provides comparable SER performance, but with a lower complexity as compared with that of a conventional ML receiver because of reduced array dimensions. [ABSTRACT FROM AUTHOR]

In this study, a new space time block code (STBC) is proposed for long term evolution-advanced (LTE-A) system that is equipped with three time slots and two antennas. The introduced STBC has the following characteristics: i) it achieves rate one and full diversity, ii) its maximum likelihood (ML) decoding requires a joint detection of three real symbols, iii) the minimum determinant value (MDV) does not vanish by increasing signal constellation size, and iv) bit error rate (BER) results show that the proposed STBC outperforms previously presented schemes for LTE-A at high SNRs. [ABSTRACT FROM AUTHOR]

We introduce a low-complexity detector with near-optimal performance for transmission over multi-antenna systems. By using lattice basis reduction for generating almost orthogonal channel submatrices, we enhance the conditional optimization technique to implement a fast yet efficient detector. The lattice-reduction-aided (LRA) conditional method is presented as a general detection technique over fading channels to yield significant saving in computational complexity while achieving close to Maximum Likelihood (ML) error performance. By employing the orthogonality defect factor as a universal measure to select a near-orthogonal channel submatrix for conditional detection, we implement efficient detectors for MIMO systems. In particular, an almost optimal decoder with linear complexity for the Golden code is presented over quasi-static channels. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an actually novel and simple method for detection of transmitted symbols in MIMO channels. This method is based on the energy level of the received signals. At the receiver, we assume the knowledge of channel state information which can be estimated by different methods, e.g. by sending pilots. So, we can determine all possible levels of energy. This computation of energy levels is done only once for the quasi-static channels. Energy of the received signals is a criterion by which we can estimate the transmitted symbols. Detection of transmitted signal is made based on the nearest energy level and the points which lie on it. In other words, we have restricted our search space to a new smaller space with different levels of energy. Simulation results confirm approximately the same performance between the maximum-likelihood detector and the proposed approach especially in high signal-to-noise ratios with a remarkable reduction in the computational complexity. [ABSTRACT FROM AUTHOR]

Certain optimization problems in communication systems, such as limited-feedback constant-envelope beamforming or noncoherent M-ary phase-shift keying (MPSK) sequence detection, result in the maximization of a fixed-rank positive semidefinite quadratic form over the MPSK alphabet. This form is a special case of the Rayleigh quotient of a matrix and, in general, its maximization by an MPSK sequence is \mathcalNP-hard. However, if the rank of the matrix is not a function of its size, then the optimal solution can be computed with polynomial complexity in the matrix size. In this work, we develop a new technique to efficiently solve this problem by utilizing auxiliary continuous-valued angles and partitioning the resulting continuous space of solutions into a polynomial-size set of regions, each of which corresponds to a distinct MPSK sequence. The sequence that maximizes the Rayleigh quotient is shown to belong to this polynomial-size set of sequences, thus efficiently reducing the size of the feasible set from exponential to polynomial. Based on this analysis, we also develop an algorithm that constructs this set in polynomial time and show that it is fully parallelizable, memory efficient, and rank scalable. The proposed algorithm compares favorably with other solvers for this problem that have appeared recently in the literature. [ABSTRACT FROM PUBLISHER]

In this letter, we propose an exact maximum likelihood (ML) criterion based on semidefinite relaxation (SDR) in multiple-input multiple-output systems. Although a conventional SDR criterion for determining whether a symbol is the ML solution exists, its results cannot be guaranteed when noise is present. In place of the conventional criterion's positive semidefinite (PSD) discriminant, we propose a new, exact ML criterion based on the condition that all diagonal values are positive (PDV), a simple characteristic and necessary condition of PSD. The proposed criterion has a lower calculation complexity for testing than does a PSD and can ensure that the ML solution is always satisfactory. [ABSTRACT FROM AUTHOR]

• Uplink MU-MIMO systems face data detection issues due to noise and frequency allocations. • The existing low-complexity AMP algorithm will converge incorrectly when the MIMO channels are spatially correlated. • We propose an iterative data detection strategy based on a coordinate descent method. • Proposed approach is substantially energy efficient and less complicated. • Proposed approach expendable to a multi-cellular in a dense environment for future communications. The current development of the Internet of Things (IoT) network in sixth-generation (6G) communication opens up various opportunities. When IoT devices with edge platforms connect to the telecommunication network, mobility, interferences, and intelligent device capacity issues might occur. The challenge with uplink MU-MIMO systems is data detection owing to noise, frequency allocations, and dense deployment of intelligent devices. The exponential complexity of extensive wireless networks makes optimal maximum likelihood detection impossible. Using MATLAB simulation-based analysis, we suggested an iterative data detection strategy based on a coordinate descent method (CDM) to reduce computing complexity while preserving an acceptable bit error rate (BER). Linear systems demand low co-channel interference (CCI) MU-MIMO uplink communications (CCI). A superior mean square error or BER performance is achieved using Maximum ratio combining with CDM. The proposed system outperforms the Richardson Method (RM), approximation message passing (AMP), and linear minimum mean square error (LMMSE) algorithms in terms of BER and complexity. [Display omitted]. [ABSTRACT FROM AUTHOR]