Performance Of Maximum Likelihood Detector Research Articles

A Novel RIS-Aided Code Index Modulation Scheme With Low-Complexity Detection

In order to achieve high throughput and low-power consumption of the transmitter and improve the error performance of the receiver, a novel reconfigurable intelligent surface (RIS)-aided code index modulation (RIS-CIM) is proposed in wireless communications. RIS-CIM utilizes the index of a spreading code to convey additional information, which further improves the spectral efficiency (SE) and error performance of the receiver by adding and adjusting the reflected phase shift of low-cost reflective elements at the RIS-aided relay. To reduce the computational complexity incurred by the maximum likelihood (ML) detection, we propose two low-complexity detections, namely the match correlation (MC) detection and the optimal low complexity (OLC) detection. MC and OLC approaches separate the dispreading from the demodulation in the detection, which achieves a significant reduction of the computational complexity over ML detection demonstrated by the analysis of the computational complexity measurements. We present a thorough analysis for the upper bound on the average bit error probability (ABEP) of the ML detection and the bit error probability (BEP) of the OLC detection of RIS-CIM, which is verified by numerical simulations. Numerical simulations also demonstrate that the proposed RIS-CIM performs better in terms of the error performance of ML detection than several existing CIM and RIS-related schemes at a given SE setting. In addition, the proposed ML and OLC detections achieve an error performance close to the ML detection under various measures of signal-to-noise ratio (SNR) and the increasing SE in the simulations. Moreover, RIS-CIM can utilize a large number of reflective elements at the RIS-aided relay to offset the performance degradation caused by the improvement of SE.

Novel detection schemes for generalized quadrature spatial modulation

SummarySpatial Modulation (SM) is a technology that incorporates digital modulation and coding with multiple antennas to achieve high data rates along with improved energy efficiency. Quadrature spatial modulation (QSM), which extends the transmitted signals to in‐phase and quadrature domains, has recently been proposed to improve spectral efficiency (SE) for SM. In this paper, we have considered a combination of Generalized QSM (GQSM) and Quasi Orthogonal Space Time Block Codes (QOSTBC). We propose a new detection scheme for the GQSM and use three different prevailing detection schemes for QOSTBC. We present Bit Error Rate (BER) versus average SNR performance for the combined system using M‐QAM constellations with quasi‐static spatially uncorrelated Rayleigh fading channels and computational complexity in terms of numbers of addition/multiplication and search time. We have compared the considered system with prevailing GQSM system and shown that the prevailing system, as per active antenna permutation (AAP) in GQSM, offers number of transmitted bits ranging from 2 to 9 whereas the proposed system offers number of transmitted bits ranging from 6 to 16 in the same bandwidth. Thus, there is a visible gain in SE. Furthermore, we have compared the BER performance of the considered system with the performance of maximum likelihood detector (MLD) and spatial modulation (SM). In the considered system, the QOSTBC improves the diversity gain. Hence, we have shown that the considered Scheme 3 substantially outperforms the SM. Further, the considered system with Scheme 3 has nearly similar BER performance with the maximum likelihood detector (MLD), which has higher computational complexity.

Low Complexity Quasi-Optimal Detector for Generalized Spatial Modulation

Generalized Spatial Modulation (GSM) is a promising technique for high data-rates systems where the complexity of the optimal joint detector based on the Maximum Likelihood (ML) criterion increases dramatically in terms of the modulation order and the number of activated transmit antennas. In this letter, a novel detection scheme for GSM systems denoted by O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SIC-ML is proposed to achieve the optimal performance with a controllable complexity-performance trade-off. The proposed detector is accompanied with ordering techniques for the transmit antenna combination and symbols to further reduce the complexity and improve the performance, respectively. The result reveals that O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SIC-ML allows reaching the non-linear ML detector performance with a 99% complexity reduction.

Fixed-Complexity Tree Search Schemes for Detecting Generalized Spatially Modulated Signals: Algorithms and Hardware Architectures

In the generalized spatial modulation (GenSM) multiple input multiple output (MIMO) system, each block of data bits is mapped to a set of spatially multiplexed (SMX) symbols and an index of transmit antenna combination (TAC) of active antennas. The difficulty for the GenSM MIMO receiver is to detect the SMX symbols and TAC index simultaneously. Recently, we applied the conventional sphere decoding algorithm (SDA) successively to achieve the exact maximum likelihood detection (MLD) of GenSM MIMO signals. The SDA scheme suffers from variable computational complexity and leads to hardware detectors with variable throughput rate. Instead, fixed-complexity tree search algorithms, e.g., reduced fixed sphere decoding (rFSD), with nearly MLD performance are employed to facilitate hardware implementation. Here, we propose to apply the rFSD successively and design a hardware architecture for detecting GenSM MIMO signals under the scenario of 5 transmit antennas, 2 transmit radio frequency chains, 4 receive antennas, and 64-QAM SMX symbols. The VLSI implementation results under the TSMC 90nm CMOS technology reveal that our architecture requires 276.7K gates and provides detection throughput 1.613 Gbps, while operating at 322.6 MHz. Compared with other related architectures, our architecture provides higher detection throughput rate and is of better hardware efficiency.

Learning to Search for MIMO Detection

This paper proposes a novel learning to learn method, called learning to learn iterative search algorithm (LISA), for signal detection in a multi-input multi-output (MIMO) system. The idea is to regard the signal detection problem as a decision making problem over tree. The goal is to learn the optimal decision policy. In LISA, deep neural networks are used as parameterized policy function. Through training, optimal parameters of the neural networks are learned and thus optimal policy can be approximated. Different neural network based architectures are used for fixed and varying channel models, respectively. LISA provides soft decisions and does not require any information about the additive white Gaussian noise. Simulation results show that LISA 1) obtains near maximum likelihood detection performance in both fixed and varying channel models under QPSK modulation; 2) achieves significantly better bit error rate (BER) performance than classical detectors and recently proposed deep/machine learning based detectors at various modulations and signal to noise (SNR) ratios both under i.i.d and correlated Rayleigh fading channels in the simulation experiments; 3) is robust to MIMO detection problems with imperfect channel state information; and 4) generalizes very well against channel correlation and SNRs.

Deep Convolutional Neural Network-Based Detector for Index Modulation

In this letter, a convolutional neural network-based detection framework is proposed for the wireless communication systems using orthogonal frequency-division multiplexing with index modulation (OFDM-IM). In the proposed framework, the received symbols are transformed to polar coordinates to help the neural network detect the indices of the activated subcarriers. We parallel the amplitude and the phase of the received symbols to form 2-dimensional matrices and use 2-dimensional convolutional layers to fully exploit the inherent information in the OFDM-IM symbols. After offline training, the proposed detector can be employed to implement online detection of the OFDM-IM symbols. Simulation results demonstrate that the proposed detector is capable of achieving near maximum likelihood detection performance with much lower complexity.

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

A Low-Complexity Maximum Likelihood Detector for the Spatially Modulated Signals: Algorithm and Hardware Implementation

Aiming to increase the transmission data rate, the spatial modulation (SM) scheme maps each block of information bits to a digitally modulated symbol and additionally a transmit antenna index in a multiple transmit antenna system. In this brief, we focus on the maximum likelihood detection (MLD) scheme that detects SM signals with low computational complexity. By reformulating the MLD problem, we obtain a new mathematical expression as the MLD solution. To avoid the numerically unstable division operation, we apply the Givens rotation to realize this new expression and obtain a low-complexity MLD algorithm. We also propose a coordinate rotation digital computer-based hardware architecture to detect SM signals with 64-quaternary amplitude modulated (QAM) symbols over the correlated channel with eight transmit antennas and four receive antennas. The VLSI implementation results under the TSMC 90-nm CMOS technology reveal that our division-free architecture requires 70.5K gates and provides detection throughput 432.68 Mb/s, while operating at frequency 384.6 MHz. Our design is the first hardware architecture to achieve the exact MLD performance and is easily modified to detect SM signals with arbitrary ${M}$ -ary QAM symbols and arbitrary numbers of transmit and receive antennas.

Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters

In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly.

Suboptimal Maximum Likelihood Detection of Integer Carrier Frequency Offset for Digital Terrestrial Television Broadcasting System

The integrated services digital broadcasting-terrestrial (ISDB-T) system adopts band segmented transmission orthogonal frequency division multiplexing (BST-OFDM) to support different services using hierarchical transmission. In the ISDB-T system, in particular, a hierarchical configuration depends on modulation type used in OFDM segment, which has to be decoded at the receiver. Therefore, it is necessary to detect integer carrier frequency offset (IFO) before decoding the modulation type carried through transmission and multiplexing configuration control (TMCC) signal. To address this issue, an effective IFO estimation scheme is presented for the ISDB-T system, considering information-carrying TMCC signal as pilot symbol without any need of additional pilots. In order to verify the performance of the proposed estimation scheme, theoretical detection error rate is derived. It is shown from simulation results that the redundantly transmitted TMCC signal can be efficiently utilized for IFO detection, offering an approximate performance of maximum likelihood detection.

Achieving optimum performance with ZF detector by using a novel precoding approach in spatial multiplexing MIMO systems

Zero forcing (ZF) detector is unable to exploit all of the available diversity order of a spatial multiplexing multiple-input multiple-output (MIMO) systems. Thus, its performance is clearly inferior to the maximum likelihood (ML) detector. However, this is no longer true if the MIMO channel matrix is orthogonal. Motivated by this statement, in this paper, we propose a novel precoding approach for channel matrix orthogonalization for spatial multiplexing MIMO systems. In general, at the transmitter, we first find out the precoding matrix that transforms a MIMO channel matrix into an orthogonal matrix. After that, at the receiver, the ZF detector applied to the orthogonal channel matrix obtain the same optimum performance of ML detector. Simulation results show that the proposed approach achieves the optimum performance with reduced complexity at the expense of a small increase in the total average transmitted power.

Bayesian Compressive Sensing Assisted Space–Time Block Coded Quadrature Spatial Modulation

A novel multiple-input and multiple-output transmission scheme termed as space–time block coded quadrature spatial modulation (STBC-QSM) is proposed. It amalgamates the concept of quadrature spatial modulation (QSM) and space–time block coding (STBC) to exploit the diversity benefits of STBC relying on sparse radio frequency chains. In the proposed STBC-QSM scheme, the conventional constellation points of the STBC structure are replaced by the QSM symbols; hence, the information bits are conveyed both by the antenna indices as well as by conventional STBC blocks. Furthermore, an efficient Bayesian compressive sensing (BCS) algorithm is developed for our proposed STBC-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes. Moreover, the proposed BCS detector is capable of approaching the maximum likelihood detector's performance despite only imposing a complexity near similar to that of the minimum mean square error detector in the high signal-to-noise ratio regions.

Compressed-Sensing-Aided Space-Time Frequency Index Modulation

In space-time shift keying (STSK), the information is conveyed by both the spatial and time dimensions, which can be used to strike a tradeoff between the diversity and multiplexing gains. On the other hand, orthogonal frequency-division multiplexing (OFDM) relying on index modulation (IM) conveys information not only by the conventional signal constellations as in classical OFDM, but also by the indices of the subcarriers. In this paper, we combine the benefits of STSK and IM in order to strike a flexible tradeoff between the throughput and bit-error performance by transmitting extra information bits in each subcarrier block, while increasing the diversity order. In order to further improve this tradeoff, as well as to decrease the complexity of the detector, compressed sensing is combined with the proposed STSK-aided IM system. We first present the maximum likelihood (ML) detection, which forms the best-case bound on the proposed system's performance. Then, we propose a pair of reduced-complexity detection algorithms capable of approaching the ML detector's performance. Furthermore, in order to attain a near capacity performance, we propose a soft-input soft-output decoder that is capable of exchanging soft information with a channel decoder through iterative decoding.

Cooperative Ambient Backscatter Communications for Green Internet-of-Things

Ambient backscatter communication (AmBC) enables a passive backscatter device to transmit information to a reader using ambient RF signals, and has emerged as a promising solution to green Internet-of-Things (IoT). Conventional AmBC receivers are interested in recovering the information from the ambient backscatter device (A-BD) only. In this paper, we propose a cooperative AmBC (CABC) system in which the reader recovers information not only from the A-BD, but also from the RF source. We first establish the system model for the CABC system from spread spectrum and spectrum sharing perspectives. Then, for flat fading channels, we derive the optimal maximum-likelihood (ML) detector, suboptimal linear detectors as well as successive interference-cancellation (SIC) based detectors. For frequency-selective fading channels, the system model for the CABC system over ambient orthogonal frequency division multiplexing carriers is proposed, upon which a low-complexity optimal ML detector is derived. For both kinds of channels, the bit-error-rate expressions for the proposed detectors are derived in closed forms. Finally, extensive numerical results have shown that, when the A-BD signal and the RF-source signal have equal symbol period, the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios, and when the A-BD symbol period is longer than the RF-source symbol period, the existence of backscattered signal in the CABC system can enhance the ML detection performance of the RF-source signal, thanks to the beneficial effect of the backscatter link when the A-BD transmits at a lower rate than the RF source.

Joint interference cancellation and relay selection algorithms based on greedy techniques for cooperative DS-CDMA systems

In this work, we propose interference cancellation techniques and a multi-relay selection algorithm based on greedy methods for the uplink of cooperative direct-sequence code-division multiple access (DS-CDMA) systems. We first devise low-cost list-based successive interference cancellation (GL-SIC) and parallel interference cancellation (GL-PIC) algorithms with RAKE receivers as the front-end that can approach the maximum likelihood detector performance and be used at both the relays and the destination of cooperative systems. Unlike prior art, the proposed GL-SIC and GL-PIC algorithms exploit the Euclidean distance between users of interest and the potential nearest constellation point with a chosen threshold in order to build an effective list of detection candidates. A low complexity multi-relay selection algorithm based on greedy techniques that can approach the performance of an exhaustive search is also proposed. A cross-layer design strategy that brings together the proposed multi-user detection algorithms, and the greedy relay selection is then developed along with an analysis of the proposed techniques. Simulations show an excellent bit error rate performance of the proposed detection and relay selection algorithms as compared to existing techniques.

A low complexity soft-output detection algorithm for 2×2 multiple-input multiple-output multiband-ofdm systems using dual carrier modulation

This paper presents a low complexity soft-output detection algorithm for 2×2 multiple-input multiple-output (MIMO) multiband-orthogonal frequency division multiplexing (MB-OFDM) systems using dual carrier modulation (DCM) or modified DCM (MDCM). By detecting each symbol pair of DCM or MDCM separately, the proposed detection algorithm reduces the complexity of the partial Euclidean distance (PED) calculation and the preprocessing complexity, which is the complexity of QR decomposition and channel ordering. This paper also proposes an efficient candidate list extension method for soft-output MIMO detection by using the calculated PEDs. Because the proposed algorithm has a low complexity and can also be implemented in a fully-pipelined hardware, the proposed algorithm is suitable for MB-OFDM systems requiring high speed hardware. The simulation results of applying the proposed algorithm to 2×2 MIMO MB-OFDM systems using DCM or MDCM show that the performance of the proposed algorithm has near soft-output maximum likelihood detection performance. The hardware complexity of the proposed algorithm is approximately 40% of that of the existing algorithm with similar performance.

A Low Complexity Geometric Mean Decomposition Computing Scheme and Its High Throughput VLSI Implementation

Geometric Mean Decomposition (GMD) is considered an efficient precoding scheme in joint MIMO transceiver designs capable of facilitating asymptotically equivalent performance of maximum likelihood detector (MLD). In this paper, a low complexity and non-iterative GMD computing scheme featuring a divide-and-conquer approach is presented. It requires no iterative singular value decomposition (SVD) as pre-processing and is thus exempted from the convergence problem adverse to a constant throughput hardware implementation. The divide-and-conquer approach reduces the computing complexity and provides abundant computing parallelism. The basic operation of the proposed scheme is a real valued Givens rotation, which can be efficiently implemented using CORDIC algorithm. Computing complexity analyses indicate that the proposed scheme is at least 30% more computing efficient than other SVD based GMD computing schemes. Finally, a unified GMD/QRD design using a fully parallel and deeply pipelined architecture is presented. One GMD or QRD computation on a 4x4 complex-valued matrix can be accomplished every 4 clock cycles. Chip implementation in TSMC 90 nm CMOS technology shows that, with a maximum clock frequency up to 170 MHz, the design can perform 42.5 M GMD computations per second. The equivalent data rate is 1.02 Gbps for a 64 QAM modulation scheme.

スーパーハイビジョン映像の素材伝送に向けたブロックQR分解を用いた演算量削減型MLD

We have been developing a portable digital transmission system for next generation television called “Super Hi-Vision (SHV)” with hundreds of Mbps-class transmission and high link reliability. To fulfill these requirements, we have been researching a multiple-input multiple-output (MIMO) multiplexing technology using a millimeter-wave (42/55-GHz) band allocated for broadcasting organizations. In this paper, we propose a low-complexity MIMO detection scheme that uses block QR decomposition with M-algorithm to reduce the computational complexity of the maximum likelihood detection (MLD) method. In the proposed algorithm, a MIMO channel is converted into a block upper triangular matrix multiplied by a unitary matrix and divided into some small block matrices. Then, the transmit signals are selected by Manhattan metrics to reduce the complexity, and squared Euclidean metrics of the selected signal candidates are calculated in these block matrices. Computer simulations with a correlated 4 x 4 MIMO channel demonstrate that the proposed scheme shows almost the same BER performance as that of the MLD and better BER performance than that of the conventional QRM-MLD (QR decomposition with M-algorithm MLD) with less complexity.

The Effect of Macrodiversity on the Performance of MLD in Flat Rayleigh/Rician Fading

In this letter, we consider the effect of macrodiversity on the performance of maximum likelihood detection in flat Rayleigh/Rician fading. Macrodiversity MIMO, where both the transmit antennas and receive antennas are widely separated, is becoming increasingly important with the advent of network MIMO, edge excited cells, base station cooperation, etc. The analysis builds on an exact evaluation of the pairwise error probability which leads to a union bound for symbol error rate (SER). SER results are shown, as well as a diversity analysis and an investigation of the impact of macrodiversity.