Low-complexity MIMO Research Articles

Recursive Least Squares Based Low-Complexity Frequency-Domain MIMO Equalization for MDL-Tolerant Long-Haul Space Division Multiplexing Transmission

Recursive Least Squares Based Low-Complexity Frequency-Domain MIMO Equalization for MDL-Tolerant Long-Haul Space Division Multiplexing Transmission

Low-complexity list MIMO demodulator with quasi-optimal error rate performance

Low-complexity list MIMO demodulator with quasi-optimal error rate performance

A Low-Complexity Soft-Output Massive MIMO Detector With Near-Optimum Performance

A Low-Complexity Soft-Output Massive MIMO Detector With Near-Optimum Performance

Low-Complexity MIMO Detection Based on DRSD With ISMMSE-PIC

Low-Complexity MIMO Detection Based on DRSD With ISMMSE-PIC

Low complexity MIMO equalization for long-haul SDM systems

Space-division multiplexing (SDM) has been expected to support the continuous growth of transmission capacity. However, it suffers from high computation complexity that limits its physical implementations. In this paper, we propose and experimentally demonstrate a low-complexity MIMO equalization method to leverage the sparsity of weights and reduce the complexity by L1&L2-regularization in long-haul space-division multiplexing (SDM) systems. The L1-regularization finds the sparse solution of equalizer filters and substitutes it for optimal solution, reducing the complexity with performance degradation. On the other hand, the L2-regularization tends to produce a smoother estimation than L1 regularization and is therefore more robust to large variance. We conduct a 39.87-GBaud QPSK coherent optical transmission experiment based on a 4-core coupled-core fiber with the transmission distance from 1206-km to 7236-km. Comparisons on the equalization performance and computational complexity show that the sparse equalizer using L1&L2-regularization achieves a 30% reduction in complexity at the similar system performance, compared with the traditional time-domain MIMO.

Superiorized Adaptive Projected Subgradient Method With Application to MIMO Detection

In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient. To illustrate a potential application of this result, we propose a set-theoretic framework for MIMO detection, and we devise algorithms based on a superiorized APSM. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models (e.g., correlated channels) are considered. Compared to existing low-complexity iterative detectors such as individually optimal large-MIMO approximate message passing (IO-LAMA), the proposed algorithms can achieve considerably lower symbol error ratios over correlated channels. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to IO-LAMA.

WMMSE-Based Alternating Optimization for Low-Complexity Multi-IRS MIMO Communication

Recently, intelligent reflecting surface (IRS) has emerged as a promising cost-efficient technology to enhance communication performance. In this paper, we start with a single-user IRS-assisted multiple-input-multiple-output (MIMO) system, aiming to maximize spectral efficiency. To deal with the complicated non-convex problem, we exploit the equivalence between the weighted mean square error minimization (WMMSE) and the spectral efficiency maximization problem, and propose a low-complexity and low-latency WMMSE-based alternating optimization (WMMSE-AO). In addition, it can be executed in parallel for further speedup of the IRS computation due to its non-coupling characteristic. Moreover, to fully exploit spatial multiplexing, we extend our proposed WMMSE-AO to general multi-IRS systems with better performance and coverage. Simulation results show that the proposed WMMSE-AO can reduce 20 times multiplications with only 1% performance degradation compared with the state-of-the-art algorithm.

Low-Complexity Large MIMO Detection via Layered Belief Propagation in Beam Domain

In large multi-user multi-input multi-output systems, the computational cost and circuit scale of base stations (BSs) are effectively reduced using two-stage signal processing consisting of a slow varying outer beamformer (OBF) based on long-term channel statistics and group-specific multi-user detection for instantaneous channel variations. However, the dimensionality reduction of the group-specific beam-domain channel based on the OBF causes significant performance degradation in the subsequent spatial-filtering detection. To compensate for this drawback, this paper introduces a novel layered belief propagation (BP) detector with a concatenated structure of beam- and antenna-domain BP layers for post-stage OBF processing. The proposed detector is designed for improving the convergence of iterative detection by suppressing intra- and inter-group interference in stages. The layered structure provides the advantages of both beam and antenna domains while maintaining low signal-processing complexity. Numerical results show the validity of our proposed method in terms of the bit error rate performance in both the uncoded and coded cases and the computational complexity.

A Low-Complexity MIMO Radar STAP Strategy for Efficient Sea Clutter Suppression

When sparse reconstruction (SR) space-time adaptive processing (STAP) strategy is employed for sea clutter suppression, the computational burden imposed by the spectrum reconstruction process is still a challenge. Hence, a low-complexity MIMO radar SR STAP strategy is developed via reducing model dimension and optimizing the sparse reconstruction scheme. On one hand, STAP echo model is projected into space and time domains owing to the uncoupled relationship, thus the computation burden involved with large measurement matrix can be lessened by low-dimension structure. Meanwhile, a coprime scheme is exerted to improve the degree of freedom (DOF) in sub models and ensure lower sampling consumption. On the other hand, in order to realize efficient spectral reconstruction, an improved SR algorithm is formulated via developing a prior matrix factor and a tradeoff factor. Convergence speed of the algorithm is promoted by tuning the factors reasonably, clutter spectrum is quickly accessible with less iterations so that adaptive filter can be constructed efficiently. According to the experiment results, high-efficiency spectral reconstruction is realized and effective clutter suppression performance can be ensured.

Low-Complexity MIMO Detection Based on Reinforcement Learning With One-Bit ADCs

This paper proposes a low-complexity reinforcement learning detection (RLD) algorithm for multi-input multi-output systems with one-bit analog-to-digital converters. The proposed algorithm exploits pairs of quantized received signals and detected symbols as training examples to train the likelihood function (LF) of the system. A major challenge in optimizing the RLD algorithm is to determine the optimal policy that decides whether to exploit the training examples based on their reliabilities. Determining the optimal policy inherently involves huge complexities in reflecting all possible transitions among candidate symbols. Thus, we simplify the optimal policy by considering only the most probable candidates among all possible decisions to reduce this complexity. Another major challenge in applying the RLD algorithm is that it requires high computational complexity to produce soft information for detection. Thus, we define new branch and path metrics derived from the LF and then remove the candidate symbols whose path metrics are smaller than a pre-defined value to alleviate the complexity. Moreover, we analyze the complexity of the proposed algorithm by deriving the expected number of surviving candidates. Simulation results show that the proposed algorithm provides a better performance-complexity tradeoff than the conventional RLD algorithm.

An Effective Channel Estimation for Massive MIMO–OFDM System

The massive Multiple-Input and Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO–OFDM) system provides a high data transmission for the next generation mobile communication i.e. 4G, 5G, etc. In the practical MIMO–OFDM system, N points of IFFT/FFT is larger than M data subcarriers ( $$N>M$$ ) in each OFDM symbol to reject the aliasing after D/A converter. To demodulate information data, the channel responses for all MIMO channel links need to be estimated so as to employ in MIMO data detection for demodulation at the receiver. The discrete Fourier transform estimator (DFE) was proposed for the system which can estimate the MIMO channels accurately when $$N=M$$ . However, its accuracy will be hugely degraded when $$N>M$$ because of the oversampling of data transmission. To improve the estimation accuracy when $$N>M$$ , the maximum likelihood estimator (MLE) was proposed for the system which can achieve higher estimation accuracy than that of the DFE. However, its accuracy will be degraded a lot in the massive MIMO–OFDM system when $$N>M$$ , due to the estimation error increased in proportion to the increasing of $$N_T$$ transmit antennas. To solve these problems, this paper proposes a direct time-domain estimator (DTE) with preamble symbol with scattered-pilot (preamble-SCP) for the massive MIMO–OFDM system when $$N>M$$ . In the proposed method, it is presented with three salient features; achieving higher estimation accuracy with keeping almost the same computational complexity as the conventional estimators, improving $$Bit-Error-Rate$$ (BER) with low-complexity MIMO data detection, and providing higher transmission data rate compared with the MLE. Using the $$normalized\,MSE$$ , BER and throughput evaluated by computer simulations, it can be verified that the proposed DTE with preamble-SCP obviously provides higher estimation accuracy, better BER with low-complexity MIMO data detection, and much higher transmission data rate which is approximately 32.5 Mbps gain over the MLE at 5 MHz-BW respectively.

ISSOR Signal Detection for Energy Efficient and Low Complexity Large Scale MIMO System

Scalable version of multiuser MIMO called Large-scale MIMO is a one of the powerful technology in future wireless communication systems in which huge amount of BS (base station) antennas utilized to process multiple user equipment. Energy consumed is high with more antennas and also it leads to increase the signal detection complexity and overall circuit power consumption. Designing energy efficient and low complexity MIMO system is considered as a challenging issue. This paper presents the ISSOR signal detection for energy efficient and low complexity large scale MIMO system. VA-GSM (Variable Antenna Generalized spatial modulation) is used in which the number of active antenna transmissions are varied for every transmission in the large scale MIMO. In transmitter side, Eigen value based approach is used for antenna selection. Then, improved symmetric successive over relaxation (ISSOR) approach is proposed for low complexity signal detection in receiver side. The number of user equipment, transmit power, as well as the amount of antennas at the base station, are considered as the optimal system parameters which are chosen for enhancing the efficiency of utilized energy in the system. The proposed scheme implemented in MATLAB software. The proposed scheme attained the high energy efficiency compared to other approaches. Moreover, the BER is utilized to estimate the performance of an offered algorithm and also compared to the previously determined algorithm of existing literatures.

ISSOR Signal Detection for Energy Efficient and Low Complexity Large Scale MIMO System

Scalable version of multiuser MIMO called Large-scale MIMO is a one of the powerful technology in future wireless communication systems in which huge amount of BS (base station) antennas utilized to process multiple user equipment. Energy consumed is high with more antennas and also it leads to increase the signal detection complexity and overall circuit power consumption. Designing energy efficient and low complexity MIMO system is considered as a challenging issue. This paper presents the ISSOR signal detection for energy efficient and low complexity large scale MIMO system. VA-GSM (Variable Antenna Generalized spatial modulation) is used in which the number of active antenna transmissions are varied for every transmission in the large scale MIMO. In transmitter side, Eigen value based approach is used for antenna selection. Then, improved symmetric successive over relaxation (ISSOR) approach is proposed for low complexity signal detection in receiver side. The number of user equipment, transmit power, as well as the amount of antennas at the base station, are considered as the optimal system parameters which are chosen for enhancing the efficiency of utilized energy in the system. The proposed scheme implemented in MATLAB software. The proposed scheme attained the high energy efficiency compared to other approaches. Moreover, the BER is utilized to estimate the performance of an offered algorithm and also compared to the previously determined algorithm of existing literatures.

Low-Complexity Massive MIMO Detectors Under Spatial Correlation and Channel Error Estimates

The performance, complexity and effectiveness of various massive MIMO (M-MIMO) detectors are analyzed operating under highly spatial correlated uniform planar arrays (UPA) channels. In such context, M-MIMO systems present a considerable performance degradation and also, in some cases, an increased complexity. Considering this challenging, but realistic practical scenario, various sub-optimal M-MIMO detection structures are evaluated in terms of complexity and bit error rate (BER) performance trade-off. Specifically, the successive interference cancellation, lattice reduction (LR) and likelihood ascent search (LAS) schemes, as well as a hybrid version combining such structures with conventional linear MIMO detection techniques are comparatively investigated, aiming to improve performance. Hence, to provide a comprehensive analysis, we consider the number of antennas varying in a wide range (from conventional to massive MIMO condition), as well as low and high modulation orders, aiming to verify the potential of each MIMO detection technique according to its performance–complexity trade-off. We have also studied the correlation effect when both transmit and receiver sides are equipped with UPA antenna configurations. The BER performance is verified under different conditions, varying the array configurations, combining the detection techniques, increasing the number of antennas and/or the modulation order, especially aiming a near M-MIMO condition, i.e. up to $$64\times 64$$ and $$121\times 121$$ antennas has been considered. The aggregated LAS technique has demonstrated good performance in scenarios with high number of antennas, while LR and OSIC operates better in high correlated arrangements.

Low-Complexity MIMO Signal Detection and Evaluation of its Characteristics using Dual-Polarized Channel Measurement

Low-Complexity MIMO Signal Detection and Evaluation of its Characteristics using Dual-Polarized Channel Measurement

LDMC design for low complexity MIMO detection and efficient decoding

Low-density multiple-input multiple-output code (LDMC) can reduce the complexity of tree-search detection in MIMO systems. In this paper, we present a new modified progressive edge-growth (PEG) algorithm to construct large girth LDMCs, which are referred to as PEG-LDMCs. We analyze the complexity of the LDMC constrained sphere decoding (SD) and show that the LDMC constrained SD detection can be used for high reliability of the data transmission in MIMO systems. Furthermore, we propose two new efficient iterative decoding algorithms for LDMCs, which are high speed serial decoding and fast convergence shuffled decoding. Finally, we compare the bit error rate (BER) performance of PEG-LDMCs to that of the existing LDMCs. The simulation results show that the PEG-LDMCs can achieve better BER performance than that of the existing LDMCs.

Low-Complexity Massive MIMO Subspace Estimation and Tracking From Low-Dimensional Projections

Massive MIMO is a variant of multiuser MIMO, in which the number of antennas $M$ at the base-station is very large. It has been observed that in many realistic propagation scenarios, although the user channel vectors have a very high-dim $M$ , they lie on low-dim subspaces because of their limited angular spread (spatial correlation). This low-dim subspace structure remains stable across several coherence blocks and can be exploited to improve the system performance. In a recent work, we addressed this problem and proposed a very effective novel algorithm referred to as Approximate Maximum-Likelihood (AML), which was formulated as a semi-definite program (SDP). In this paper, we address two problems left open in our previous work, namely, computational complexity and tracking. We propose a new algorithm that is reminiscent of Multiple Measurement Vectors (MMV) problem in Compressed Sensing and prove that it is equivalent to the AML Algorithm for sufficiently dense angular grids. It has also a very low computational complexity and is able to track the sharp transitions in the channel statistics very quickly. We provide numerical simulations to assess the estimation/tracking performance of our proposed algorithm, with a particular emphasis on situations where a direct implementation of the SDP is infeasible in real-time. Our proposed algorithm is of independent interest in applications other than massive MIMO. We provide numerical simulations to compare the performance of our algorithm with that of other related subspace estimation algorithms in the literature.

Low-Complexity Massive MIMO Detectors Based on Richardson Method

Low-Complexity Massive MIMO Detectors Based on Richardson Method

Characterization of distributed mode crosstalk in few-mode fiber links with low MIMO complexity

We theoretically model and numerically analyze the linear behavior of distributed mode crosstalk in a step-index weakly-coupled 7-mode fiber. This fiber is assumed to be used for one-polarization uncoupled mode-division multiplexing (MDM) systems with: (1) sparse MIMO equalizers which are performed over only degenerate LP modes, or (2) one single differential mode delay-independent MIMO equalizer which is performed over all non-degenerate LP modes. For the above two low-complexity MIMO configuration schemes, the impacts of distributed mode coupling on the multi-path interference-dependent achievable distance and system quality are empirically evaluated, through the numerical simulations for uncoupled MDM transmissions of a single-channel 28 GBaud QPSK signal over the fiber.

Low-Complexity MIMO Detection: A Mixture of Basic Techniques for Near-Optimal Error Rate

The research of finding better ways for multiple-input multiple-output signal detection is important and still goes on. In this brief, a low-complexity detection method mainly with basic and hardware-friendly operations, i.e., reduced-complexity zero forcing, successive interference cancelation, and single-symbol maximum likelihood detection with $M$ - algorithm, is proposed. Essentially, this detection method has fixed complexity and can achieve near-optimal error-rate performance with affordable complexity in many cases. Results show that, in terms of giving a good tradeoff between the implementation complexity and error rate, the proposed low-complexity detection scheme can remarkably outperform some state-of-the-art counterparts, including the conventional ML detector employing QR-decomposition and $M$ - algorithm and fixed-complexity sphere decoder, and owns an error rate very close to that of the optimal one.