MIMO Detection Research Articles

Likelihood ascent search‐aided low complexity improved performance massive MIMO detection in perfect and imperfect channel state information

SummaryMassive multiple‐input multiple‐output (MIMO) systems improve spectral efficiency and link reliability. Linear minimum mean‐squared error (MMSE) detectors can achieve optimal performance in massive MIMO detection but require large dimension matrix inversion, which is computationally intensive. Therefore, low complexity iterative detection schemes are proposed in the literature as an alternative to the exact MMSE method. However, the performance of these schemes is greatly influenced by the choice of the initial solution. Therefore, to improve the detection performance in this paper, we proposed three hybrid detection schemes, which are Newton–Schultz–Richardson (NS‐RI), Newton–Schultz–Chebyshev (NS‐Cheby), and Newton–Schultz–Gauss–Seidel (NS‐GS). The proposed hybrid schemes show significant performance improvement and a higher convergence rate compared to their original counterpart. The performance of the proposed detectors is further improved by the likelihood ascent search (LAS) stage, which corrects the detected symbols obtained from iterative MMSE methods through a neighborhood search. However, the complexity of the LAS algorithm primarily depends on the initialization step. In this work, we introduce an efficient Gram matrix computation in the real domain. Additionally, we have applied a band approximation of the Gram matrix for the LAS initialization, which reduces the order of computational complexity of the Gram matrix from to O(ωNTNR) where ω < <2NT.

Incredible VLSI Design for MIMO System Using SEC-QPSK Detection

Multiple Input Multiple Output (MIMO) is an advanced communication technology that is often used for secure data transfer for military and other applications while transmitting data with high error and noise. To address this issue, a step-by-step hybrid Quadrature Phase Shift Keying (QPSK) modulation scheme in the MIMO system for a complex Very Large-Scale Integration (VLSI) format is recommended. When compared to Binary Phase Shift Keying (BPSK), this approach provides twice the data rate while using half the bandwidth. The complexity is lowered through multiplication and addition, as well as error and noise reduction in data transport, and MIMO detection is accomplished through the combination of two techniques, Inverse Fast Fourier Transform (IFFT) and Stepwise Exponential Companding (SEC). IFFT converts frequency to the time domain, reducing complexity by 23.56 percent in multiplication and an extra 34.05 percent. Likewise, hierarchical high-speed mixing decreases difficult limits by 13% and the addition problem by 25%. This technological outcome results in a decreased Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER).

Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal detection where the models have fixed bias and weights. For performance improvement, the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver. The simulation results show that the proposed scheme has improved symbol error rate (SER) performance without additional receive antennas.

Applications of a Quantum Linear System Algorithm to Linear MIMO Detections

A linear system can be solved more efficiently by quantum computing. However, previously known quantum algorithms provide only a quantum state as the solution; consequently, we cannot obtain the value of each component of the solution. We propose a method to extract the component values of the solution, and we present an application to linear multiple-input multiple-output (MIMO) detections. In the proposed algorithm, we demonstrate a concrete method that applies a quantum linear system algorithm (QLSA) when the components of a solution have binary variables, quaternary variables, or roots of a complex number. Whereas the conventional method requires an additional process to read out the values of the components, the proposed algorithm does not need any post-procedure. Instead, our method uses a QLSA iteratively, and the number of uses is logarithmic in the size of the linear system. Thus, our method maintains the runtime with the quantum advantage, but the conventional approach increases the runtime significantly. Furthermore, the application of the proposed method shows that quantum computing can collaborate with communication systems for large-scale MIMO systems.

Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective

There has been a growing interest in developing data-driven, and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less computational efforts, less resources for acquiring channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment. This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. Specifically, we consider an ``episodically dynamic" setting where the environment statistics change in ``episodes", and in each episode the environment is stationary. We propose to build the notion of continual learning (CL) into wireless system design, so that the learning model can incrementally adapt to the new episodes, {\it without forgetting} knowledge learned from the previous episodes. Our design is based on a novel bilevel optimization formulation which ensures certain ``fairness" across different data samples. We demonstrate the effectiveness of the CL approach by integrating it with two popular DNN based models for power control and beamforming, respectively, and testing using both synthetic and ray-tracing based data sets. These numerical results show that the proposed CL approach is not only able to adapt to the new scenarios quickly and seamlessly, but importantly, it also maintains high performance over the previously encountered scenarios as well.

24 GHz FMCW MIMO Radar for Marine Target Localization: A Feasibility Study

Radar detection and tracking of targets in the marine environment are common tasks performed to ensure the safe navigation of ships or monitor traffic in harbor areas. More recently, radar technology has been proposed to support the collision avoidance system of autonomous surface vehicles, which are characterized by severe constraints in terms of payload and space. The paper investigates the performance of a small and lightweight 24 GHz Frequency Modulated Continuous Wave (FMCW) Multiple-Input Multiple-Output (MIMO) radar, originally developed for automotive applications, to localize marine targets at short range. A complete signal processing strategy is presented combining MIMO radar imaging, detection, and tracking algorithms. The validation of the proposed signal processing chain is firstly performed thanks to numerical tests based on synthetic data. After, results of experimental trials carried out in the marine environment are reported. These results demonstrate that the considered radar together with the adopted signal processing strategy allows the localization of static targets and the tracking of moving targets with satisfactory performance, thus encouraging its use in marine environments.

A Design of Fixed-Complexity Sphere Decoder Combined With Interference Mitigation Algorithm for Downlink MU-MIMO Systems

In this paper, a design and implementation of fixed-complexity sphere decoder (FSD) combined with interference mitigation algorithm for downlink (DL) multiuser (MU) multiple-input multiple-output (MIMO) system is presented. To overcome performance degradation from inter-user interference in DL MU-MIMO systems, a hardware friendly optimized interference mitigation algorithm is proposed. The proposed algorithm achieves 0.5 dB near-optimal performance, which is 4 dB better than existing interference whitening filter for the soft-output decision. In this paper, both hard and soft-output FSD detectors are implemented with the proposed interference mitigation algorithm to function 4 × 4 antenna MIMO detection and interference mitigation for 4 additional streams. A fully pipelined architecture is employed to support 3.6 Gbps at 150 MHz for signals modulated by 64-QAM. From the synthesis result, the designed symbol detector has a gate count of 887 K for the FSD without pre-processor and a gate count of 1220 K for interference mitigation. By comparing previously implemented MIMO detectors, the proposed design represents a promising architecture for DL MU-MIMO systems in terms of significant performance improvement relative to inter-user interference and the compatible hardware implementation maintaining high-throughput within acceptable gate count.

Low Complexity Linear Detectors for Massive MIMO with Several Antenna Array Configurations: A Comparative Study

In Multiple-Input Multiple-Output (MIMO), there is multi-user interference because of the limited number of antennas at the Base Station (BS). Deploying more antenna elements at the BS to decrease the multi-user interference is a costly solution. Therefore, changing the configuration of antenna arrays at the BS is implemented as an alternative. In this paper, the impact of BS antenna array configurations on the performance of massive MIMO detection techniques is investigated where four different antenna array configurations are implemented at the BS: Uniform Linear Array (ULA), Uniform Rectangular Planar Array (URPA), Uniform Circular Array (UCA) and Uniform Circular Planar Array (UCPA). The performance of the massive MIMO system is based on the millimeter wave (mmWave) channel that depends on the array response vector of the BS antenna array configuration. To the best of our knowledge, this is the first paper to investigate the impact of antenna array configurations at the BS on the performance and the computational complexity of massive MIMO detection techniques. Numerical results show that the implementation of URPA at the BS with MMSE detection algorithm can achieve the best performance of BER=10−4 at SNR = 9dB. The deployment of the UCA at the BS with the successive over-relaxation (SOR), the Gauss-Seidel (GS), and the conjugate-gradient (CG) detection methods provides a performance of BER=10−4 at SNR = 12dB when the number of iterations equals eight. In the case of using the UCA at BS with the Richardson (RI) detection method, the number of iterations is required to be increased to achieve the same performance. To achieve BER=10−4, the detector based on the CG method has the lowest computational complexity while the RI method has the highest complexity.

Randomized Iterative Methods for Low-Complexity Large-Scale MIMO Detection

In this paper, we introduce a randomized iterative method for signal detection in uplink large-scale multiple-input multiple-output (MIMO) systems, which not only achieves a low computational complexity but also enjoys a global and exponentially fast convergence. First of all, by adopting the random sampling into the iterations, the randomized iterative detection algorithm (RIDA) is proposed for large-scale MIMO systems. We show that RIDA converges exponentially fast in terms of mean squared error (MSE). Furthermore, this global convergence <i>always</i> holds, and does not depend on the standard requirements such as <inline-formula><tex-math notation="LaTeX"><?TeX $N\gg K$?></tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX"><?TeX $N$?></tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX"><?TeX $K$?></tex-math></inline-formula> denote the numbers of antennas at the sides of base station and users. This broadly extends the applications of low-complexity detection in uplink large-scale MIMO systems. Then, based on a new conditional sampling, optimization and enhancements are given to further improve both the convergence and efficiency of RIDA, resulting in the modified randomized iterative detection algorithm (MRIDA). Meanwhile, with respect to MRIDA, further complexity reduction by exploiting the matrix structure is given while its implementation by deep neural networks (DNN) is also presented for a better detection performance.

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.

Channel estimation in 5G MIMO-OFDM systems based on multibeam lens antennas

MIMO systems based on lens antennas are very promising for 5G networks. Some scientific papers have already been devoted to the problem of channel estimation in MIMO systems based on hemispherical lens antennas. However, Luneburg multipath lens antennas are also a promising type of lens antennas for 5G networks. This paper presents the results of channel estimation features research in 5G MIMO-OFDM systems with Luneburg lens antennas. The research results of six different interpolation methods effectiveness for estimating the channel characteristics based on pilot signals and MIMO detectors (Neumann, conjugate gradients, MMSE) in a 5G cellular communication system of the millimeter range are presented.

Linear detectors and precoding methods for massive MIMO

Detection in multiple-input-multiple-output (MIMO) wireless communication systems is a crucial procedure in receivers since the multiple access transmission schemes generate interference due to the simultaneous transmission along with the several antennas, unlike single-input-single-output (SISO) transmission schemes. Precoding is a technique in MIMO systems used to mitigate the effects of the channel over the received signal. Hence, it is possible to adjust continuously the transmitted information to reverse the effect of the wireless channel at the receiver side. In this work, linear sub-optimal detectors and precoders for massive MIMO (M-MIMO) systems are implemented, analyzed, and compared in terms of performance-complexity trade-off. It is also being considered numerical results in both channel scenarios: a) receiver and transmitter have perfect channel state information (CSI); b) complex channel coefficients are estimated with different levels of inaccuracy. Monte-Carlo simulations (MCS) reveal that linear zero-forcing (ZF) and minimum mean squared error (MMSE) massive MIMO detectors result in a certain robustness against multi-user interference when operating under low and medium system loading, L = K/M, thanks to the favourable propagation phenomenon arising in massive MIMO systems.

A New Approach to Problems of MIMO Detection and Multiuser Demodulation

A New Approach to Problems of MIMO Detection and Multiuser Demodulation

Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO

Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.

Algorithm Parameters Selection Method With Deep Learning for EP MIMO Detector

Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector achieves exceptional performance in high-dimensional systems with high-order modulations and flexible antenna configurations. However, based on our studies, the EP MIMO detector cannot achieve superior performance due to the empirical parameter selection, including initial variance and damping factors. According to the influence of the moment matching and parameter selection on the performance of the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). To obtain the initial variance and damping factors which lead to better performance, we adopt a deep learning scheme, the iterative process of MEPD is unfolded to establish MEPNet for parameters training. The simulation results show that MEPD with off-line trained parameters outperforms the original one in various MIMO scenarios. Besides, the proposed MEPD with deep learning parameters selection is more robust than EPD in practical scenarios.

MIMO Detectors: A Comprehensive Performance Analysis

Multiple-input multiple-output (MIMO) systems are increasingly becoming popular due to their ability to multiply data rates without any expansion in the bandwidth. This is critical in this era of high-data rate applications but limited bandwidth. MIMO detectors play an important role in ensuring effective communication in such systems and as such the performance of the following are compared in this paper with respect to symbol error rate (SER) versus signal-to-noise ratio (SNR): maximum likelihood (ML), zero forcing (ZF), minimum mean square error (MMSE) and vertical Bell laboratories layered space time (VBLAST). Results showed that the ML has the best performance as it has the least Symbol Error Rate (SER) for all values of Signal to Noise Ratio (SNR) as it was 91.9% better than MMSE, 99.6% better than VBLAST and 99.8% better than ZF at 20db for a 2x2 antenna configuration., it can also be deduced that the performance increased with increase in number of antenna for all detectors except the V-BLAST detector.

Distributed MIMO sonar for detection of moving targets in shallow sea environments

A joint processing framework is proposed for distributed multiple-input-multiple-output (MIMO) sonar systems to exploit spatial diversity and array gain for detection of moving targets. An extended moving target consisting of multiple scatterers is illuminated by widely-spaced transmitters from different angles to achieve spatial diversity. Target bearing is first estimated with high resolution by the deconvolved conventional beamforming technique. Then a replica correlation integration processor is utilized to estimate the target’s range and speed in a multipath propagation environment. A MIMO detector is designed with the estimated target position parameters and Doppler factor under the generalized likelihood ratio test framework. The effectiveness of the distributed MIMO sonar system has been validated by the localization of two moving targets in sea experiments.

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.

Review on various detectors in massive MIMO technology: a performance analysis

PurposeMultiple-input multiple-output (MIMO) combined with multi-user massive MIMO has been a well-known approach for high spectral efficiency in wideband systems, and it was targeted to detect the MIMO signals. The increasing data rates with multiple antennas and multiple users that share the communication channel simultaneously lead to higher capacity requirements and increased complexity. Thus, different detection algorithms were developed for the Massive MIMO.Design/methodology/approachThis paper focuses on the various literature analyzes on various detection algorithms and techniques for MIMO detectors. Here, it reviews several research papers and exhibits the significance of each detection method.FindingsThis paper provides the details of the performance analysis of the MIMO detectors and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be useful for future researchers to be accomplished in MIMO massive detectorsOriginality/valueThis paper has presented a detailed review of the detection of massive MIMO on different algorithms and techniques. The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa. The performance measures and the best performance of each of the detectors are described.

Extreme learning machine detector for millimeter-wave massive MIMO systems

In this paper, we present an extreme learning machine (ELM) neural network designed to perform multiple-input multiple-output (MIMO) detection for millimeter-wave (mm-wave) communications operating in the 28 GHz frequency band. The ELM strategy can perform online MIMO combining processing. This method does not require offline training like with deep neural networks. The proposed technique was compared in terms of the achievable bit error rate (BER) and spectral efficiency (SE) to the maximum ratio (MR) and minimum mean squared error (MMSE) MIMO detectors, considering an orthogonal frequency-division multiplexing (OFDM) uplink scheme based on the fifth generation (5G) New Radio standard. Numerical results show that the ELM strategy outperforms the MR and MMSE detectors since this method reduces the inter-user interference effects, specifically for low equivalent isotropic radiated power at the receiver during the uplink communication. Furthermore, the ELM method requires only 16 % of the floating-point operations required by the MMSE detector.