Multiple-input Multiple-output Receiver Research Articles

SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection

In this paper, we propose a multiple-input multiple-output receiver with deep learning. To obtain the high performance of the maximum likelihood receiver while maintaining the low computational complexity of the linear receiver, we apply a signal-to-noise ratio-based receiver-type decision method. Specifically, the criterion and threshold, which are based on the ratio between the symbol error probabilities of both receivers, to decide the receiver type are presented. For this, we analyze the signal-to-noise ratio gain of the zero-forcing receiver and exploit it as an input feature of the DNN. The simulation results demonstrate that the proposed receiver achieves a symbol error rate performance nearly identical to that of the maximum likelihood receiver with low computational complexity.

Towards artificial intelligence-aided MIMO detection for 6G communication systems: A review of current trends, challenges and future directions

In recent times, artificial intelligence (AI) has gained considerable attention as a highly promising technology for enhancing the performance of multiple-input multiple-output(MIMO) detection in wireless communication networks. AI approaches such as deep learning and reinforcement learning have enabled MIMO receivers to learn and adapt to complex channel conditions and interference scenarios, significantly improving throughput and error rate performance. This study provides a detailed review of the existing status of AI-aided MIMO detection and discusses the potential of this technology for future wireless communication systems, such as 6G. The study also discusses the challenges and opportunities associated with integrating AI techniques into MIMO detection and emphasizes the necessity for additional research in this field.

Variational autoencoder-enhanced deep neural network-based detection for MIMO systems

Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) have empowered MIMO receivers to understand intricate channel dynamics and interference contexts, ultimately leading to noteworthy enhancements in throughput and the performance of error rates. However, the techniques in existing literature face challenges in effectively reconciling accuracy and efficiency across a spectrum of channel conditions. Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby enhancing the DNN’s ability to adapt to complex channel scenarios and achieve near-optimal performance. Simulations are carried out to contrast the bit error rate (BER) performance of the proposed scheme with traditional methods, where the proposed VAE-DNN-Det method attains a signal-to-noise ratio gain of nearly 1 dB compared to the traditional detection methods.

BER analysis of an optimum MIMO linear receiver in optical SDM systems with mode-dependent loss.

Multiple-input multiple-output (MIMO) receivers designed to optimize the minimum mean square error (MMSE) are a common choice in coherent optical communication systems based on spatial division multiplexing (SDM). This kind of receivers naturally integrate both MIMO equalization and matched filtering functions. However, when the optical channel exhibits significant mode-dependent loss (MDL) and/or mode-dependent gain (MDG), the impact of inter-symbol interference (ISI) and crosstalk that arise, even using an ideal MIMO MMSE linear receiver, is barely analyzed. Moreover, due to the random nature of the MDL/MDG model, the resulting ISI, crosstalk, and bit error rate (BER) also become random variables and, hence the system performance is more unpredictable. In this paper, we first evaluate the residual distortion (ISI and crosstalk) after the MIMO receiver and then we study the validity of assuming it as an additional Gaussian noise term independent of the channel noise. Next, the probability density distribution (PDF) of the BER is analyzed, from both an analytical perspective and numerical simulations. For the latter, we use a single-carrier 2-PAM (pulse amplitude modulation) system, with pulse shaping, and the MIMO MMSE receiver implementation by means of a MIMO fractionally-spaced equalizer (FSE). We carry out simulations of the system under different conditions of MDL/MDG level and signal to noise ratio (SNR), measured at the receiver input. Additionally, we address possible fits of the BER PDF to known closed-form distributions, among which the Generalized Extreme Value (GEV) family of distributions is selected, and polynomial functions are proposed that relate the system parameters with the GEV PDF parameters. Finally, we present contour maps of BER according to a giving target of system outage probability (OP) that depend on the MDL/MDG and SNR conditions.

Successive Cancellation Integer Forcing via Practical Binary Codes

A new multiple-input multiple-output (MIMO) receiver scheme for practical binary codes is proposed that provides consistent gains over conventional linear receivers. We first develop a practical successive integer forcing (IF) scheme based on practical binary codes rather than lattice codes. We then present the successive cancellation integer forcing (SC-IF) scheme, which combines and enhances successive IF and minimum mean squared error successive interference cancellation (MMSE-SIC). In this scheme, the receiver first decides whether individual decoding or IF sum decoding is appropriate for each data stream, and then conducts successive IF sum decoding only for selected streams while decoding the remaining streams using MMSE-SIC. The proposed SC-IF methodology mitigates the performance loss caused by mismatched IF filtering in fading channels, while attenuating the noise amplification caused by MMSE filtering. Extensive link-level simulations demonstrate that the proposed successive IF significantly improves the basic IF, and the SC-IF improves both the successive IF and MMSE-SIC, offering uniform improvements over conventional linear receivers for most channel correlation and variation parameters and modulation orders at comparable computational costs. These results illustrate the viability of SC-IF as a fundamental building block for high-performance MIMO receivers in 5G-Advanced and/or subsequent-generation communication systems.

Mode converter using bent and twisted coupled-multicore fibers for group delay spread reduction

A mode converter using bent and twisted coupled-multicore fiber for the reduction of the group delay spread is proposed. The bent and twisted fiber, which is one of the periodic structures, can work as a long-period grating mode converter by properly setting the twist rate. To investigate the mode conversion characteristics, a simple and effective eigenmode analysis for three-dimensional periodic structures is proposed. We found that to convert the specified modes, the core layout is also important as well as the twist rate. For example, the mode conversion behaviors of 4CFs with square lattice and triangular lattice layouts are greatly different although each 4CF enables the specified mode conversion. It is revealed that, by switching the higher and lower order modes, the group delay can be equalized, resulting in the suppression of the group delay spread. It leads to reducing the complexity of the multiple-input multiple-output receiver in the mode division multiplexing system.

Spatial multiplexing in near field MIMO channels with reconfigurable intelligent surfaces

AbstractWe consider a multiple‐input multiple‐output (MIMO) channel in the presence of a reconfigurable intelligent surface (RIS). Specifically, our focus is on analysing the spatial multiplexing gains in line‐of‐sight and low‐scattering MIMO channels in the near field. We prove that the channel capacity is achieved by diagonalising the end‐to‐end transmitter‐RIS‐receiver channel, and applying the water‐filling power allocation to the ordered product of the singular values of the transmitter‐RIS and RIS‐receiver channels. The obtained capacity‐achieving solution requires an RIS with a non‐diagonal matrix of reflection coefficients. Under the assumption of nearly‐passive RIS, that is, no power amplification is needed at the RIS, the water‐filling power allocation is necessary only at the transmitter. We refer to this design of RIS as a linear, nearly‐passive, reconfigurable electromagnetic object (EMO). In addition, we introduce a closed‐form and low‐complexity design for RIS, whose matrix of reflection coefficients is diagonal with unit‐modulus entries. The reflection coefficients are given by the product of two focusing functions: one steering the RIS‐aided signal towards the mid‐point of the MIMO transmitter and one steering the RIS‐aided signal towards the mid‐point of the MIMO receiver. We prove that this solution is exact in line‐of‐sight channels under the paraxial setup. With the aid of extensive numerical simulations in line‐of‐sight (free‐space) channels, we show that the proposed approach offers performance (rate and degrees of freedom) close to that obtained by numerically solving non‐convex optimization problems at a high computational complexity. Also, we show that it provides performance close to that achieved by the EMO (non‐diagonal RIS) in most of the considered case studies.

An Artificial Intelligence-Assisted Expectation Propagation Detection for MIMO Systems

Multiple-input multiple-output (MIMO) technology is one of the key technical approaches to improve the spectrum efficiency of wireless communication. Modern communication systems employ MIMO and high-order quadrature amplitude modulation (QAM) to maximize spectral efficiency. However, with the increase in the number of antennas and modulation orders, it is very challenging to design a low-complexity and high-efficiency MIMO receiver. In recent years, with the rapid development of new technologies such as artificial intelligence, more and more researchers have tried to apply machine learning techniques in the field of communication to break through the performance of traditional communication algorithms. In this paper, we propose a new low-complexity MIMO detection algorithm: an artificial intelligence-assisted expectation propagation (EP) detection algorithm. Neural network models are used to learn and map some of the time-consuming steps in the EP detection algorithm, converting the complex operation process into a few matrix multiplication operations in order to reduce the complexity of the detection algorithm. It is verified that the method proposed in this paper can approximate the performance of the original EP detection algorithm with reduced complexity and is applicable in different scenarios.

Capacity Bounds for One-Bit MIMO Gaussian Channels With Analog Combining

The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising approach to significantly reduce the power consumption and hardware cost of multiple-input multiple-output (MIMO) receivers. However, the nonlinear distortion due to 1-bit quantization fundamentally changes the optimal communication strategy and also imposes a capacity penalty to the system. In this paper, the capacity of a Gaussian MIMO channel in which the antenna outputs are processed by an analog linear combiner and then quantized by a set of zero threshold ADCs is studied. A new capacity upper bound for the zero threshold case is established that is tighter than the bounds available in the literature. In addition, we propose an achievability scheme which configures the analog combiner to create parallel Gaussian channels with phase quantization at the output. Under this class of analog combiners, an algorithm is presented that identifies the analog combiner and input distribution that maximize the achievable rate. Numerical results are provided showing that the rate of the achievability scheme is tight in the low signal-to-noise ratio (SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs analog temporal and spatial processing is proposed. The proposed receiver attains the capacity in the high SNR regime.

A Deep Learning-Based Intelligent Receiver for Improving the Reliability of the MIMO Wireless Communication System

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.

Mode-Group Division Multiplexing: Transmission, Node Architecture, and Provisioning

Few Mode Fibers (FMF) and Space DivisionMultiplexing (SDM) are an attractive solution to offer high capacity in optical networks. Although transmission along FMF presents several issues mainly due to the cross-talk among modes, the use of Multiple Input Multiple Output (MIMO) coherent receivers permits to limit the impact of such physical impairment. However, the complexity of MIMO is not negligible (especially with a large number of modes) and the spatial modes must cover the same path, thus limiting network flexibility, e.g., routing modes along different paths is not admitted. In this paper we exploit the concept of Mode-Group Division Multiplexing (MGDM) and we investigate the network architecture and provisioning supporting MGDM. Modes are divided in groups: the modes within a group are co-routed and received with a reduced-complexity MIMO receiver, while the different groups can be routed along different paths. Different node architectures supporting MGDM are presented taking in account state-of-the-art components and devices, even commercially available on the market. A quality of transmission (QoT) model is also presented accounting to the inter mode group crosstalk, that cannot be neglected in the reach evaluation. QoT is exploited by a proposed connection provisioning scheme for MGDM. Simulations are carried out in metro/rural network scenarios with different link span lengths. Simulations show high throughput increase while limiting the complexity of receivers.

Low Complexity Optimal and Suboptimal Detection at Spatial Modulation MIMO Receivers

This paper develops three low-complexity detection schemes for the spatial modulation (SM) Multiple Input Multiple Output (MIMO) systems. We first propose a reduced complexity maximum likelihood (RC-ML) detection scheme. For the practical values of the modulation index, the proposed RC-ML method is simpler than the existing hard-limiting-based ML schemes. The two other proposals are based on a suboptimal Maximum Ratio Combining (MRC) detection method. We present a new analysis of the MRC detector in the uncorrelated Rayleigh fading channel and identify the system operational scenarios for which the MRC performance is nearly identical to the ML performance. Our proposals — a hybrid MRC-ML method and an MRC scheme with rank reduction — are inspired by this analysis. The analytical and simulation results show that the proposed MRC-based schemes can attain a near-optimal performance while reducing the complexity of the ML-based SM receiver.

Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels.

The use of digital signal processors (DSP) to equalize coherent optical communication systems based on spatial division multiplexing (SDM) techniques is widespread in current optical receivers. However, most of DSP implementation approaches found in the literature assume a negligible mode-dependent loss (MDL). This paper is focused on the linear multiple-input multiple-output (MIMO) receiver designed to optimize the minimum mean square error (MMSE) for a coherent SDM optical communication system, without previous assumptions on receiver oversampling or analog front-end realizations. The influence of the roll-off factor of a generic pulse-amplitude modulation (PAM) transmitter on system performance is studied as well. As a main result of the proposed approach, the ability of a simple match filter (MF) based MIMO receiver to completely eliminate inter-symbol interference (ISI) and crosstalk for SDM systems under the assumption of negligible MDL is demonstrated. The performance of the linear MIMO fractionally-spaced equalizer (FSE) receiver for an SDM system with a MDL-impaired channel is then evaluated by numerical simulations using novel system performance indicators, in the form of signal to noise and distortion ratio (SNDR) loss, with respect to the case without MDL. System performance improvements by increasing the transmitter roll-off factor are also quantified.

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.

Mine MIMO Depth Receiver: An Intelligent Receiving Model Based on Densely Connected Convolutional Networks.

Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs multiple binary classifiers to recover the original information. Compared with conventional MIMO receivers, the model has no error accumulation caused by processes such as decoding and demodulation. The experimental results show that the model has better performance than conventional decoding methods under different modulation codes and variations in the number of transmitting terminals. Furthermore, we demonstrate that the model can still achieve effective decoding and recover the original information with some data loss at the receiver.

Interference Mitigation by Adaptive Analog Spatial Filtering for MIMO Receivers

Exposed to strong cochannel/adjacent-channel interference, digital multiple-input–multiple-output (MIMO) receivers require high-dynamic-range analog-to-digital converters (ADCs). Hybrid beamforming featuring spatial filtering before the ADCs can adaptively mitigate interference in both the analog and digital domains; hence relaxing the required dynamic ranges of the ADCs. This article demonstrates the effectiveness of hybrid beamforming in this mitigation utilizing adaptive minimum mean square error (MMSE) and error vector magnitude (EVM) as an optimization criterion. Extensive EVM measurements are carried out with a conductive setup using a four-element 22-nm FD-SOI CMOS prototype MIMO receiver chip to verify the performance of the adaptive MMSE algorithm. Over-the-air (OTA) measurements with a linear four-element dipole antenna array with half-wavelength spacing in the 2.4-GHz industrial scientific and medical (ISM) band quantify the improvement for real-world scenarios, e.g., having a multipath propagation channel and mutual coupling between the antenna array elements. OTA results show that a rejection of 22.5 and 24.5 dB can be achieved on average in an in-door laboratory environment utilizing vector modulator (VM) constellations with 16 and 64 points, respectively.

Analysis of an optical SDM ring network model for applying MIMO processing

Optical multiple-input multiple-output (MIMO) technology is introduced into optical space division multiplexing (SDM) communication as an effective solution for the crosstalk issue. Unfortunately, this application is limited because all the channel information over the transmission link must be transmitted to MIMO receivers for complete crosstalk compensation, which indicates difficulty in applying MIMO to the transmission path via optical switches. We newly propose that one of the solutions to this limitation is a ring network because all channel information can be maintained in the ring. In particular, a typical ring network model comprising optical add-drop multiplexers (OADMs) and multicore fibers (MCFs) is introduced, and the ability of MIMO to compensate for capacity degradation because of the inter-core crosstalk is investigated. Moreover, the protection function, which is essential for the ring network, is also evaluated under the fiber and fiber core failures. As per the simulation results, although the protection operation affects the transmission capacity, the capacity loss is suppressed in the event of a failure to < 5%. We believe that these results provide useful information for designing SDM-based optical networks.

MMSE Indicator Error Rate Performance on MIMO When Increasing Modulation Order

Research in estimation theory has given evidence that the rich scattering of wireless channels is capable of great theoretical capacity if the multipath is efficiently utilized. Spatial multiplexing using multiple-input multiple-output (MIMO) systems makes efficient use of a limited frequency spectrum and increases the transmission throughput in wireless communications systems. This paper reviews the performance of the minimum mean squared error estimator for MIMO. In this context, it shows that the minimum mean square error (MMSE) for MIMO receiver exhibits an intricate error behavior that depends on channel condition, the antenna configuration, and the degree of modulation order. To this purpose, an explicit step-by-step mathematical model is derived for the receiver estimator and the error covariance based on the MMSE MIMO. Analysis, simulation, and experiment based on open source and programmable hardware and software results show that the MMSE estimator receiver efficiency is also based on the modulation order.

Hybrid MIMO: A New Transmission Method For Simultaneously Achieving Spatial Multiplexing and Diversity Gains in MIMO Systems

Multiple input multiple output (MIMO) technology has evolved over the past few years into a technology with great potential to drive the direction of future wireless communications. MIMO technology has become a solid reality when massive MIMO systems (MIMO with large number of antennas and transceivers) were commercially deployed in several countries across the world in the recent past. Moreover, MIMO has been integrated into state-of-the-art paradigms such as fifth-generation (5G) networks as one of the main enabling technologies. MIMO possesses many attractive and highly desirable properties such as spatial multiplexing, diversity gains, and adaptive beamforming gains that leads to high data rates, enhanced reliability, and other enhancements. Nevertheless, beyond 5G technologies demand wireless communication systems with, among other properties, immensely higher data rates and better reliability simultaneously at the same time. In this work, a new, novel MIMO technique for simultaneously achieving multiplexing and diversity gains as well as completely eliminating any processing at the MIMO receiver, leading to advantages such as low complexity and low power consumption, is proposed. The proposed technique employs the design of interference-canceling matrices, which are calculated from the channels between the transceiver antennas, where the matrices are employed at the base station to help achieve multiplexing and diversity gains simultaneously. The novelty and efficiency of the introduced paradigm is demonstrated via mathematical models and validated by Monte Carlo simulations. Results indicate that the proposed system outperforms conventional MIMO models.

Precoder Design for Communication-Efficient Distributed MIMO Receivers With Controlled Peak-Average Power Ratio

We consider the problem of communicating over a relay-assisted multiple-input multiple-output (MIMO) channel with additive noise, in which physically separated relays forward quantized information to a central decoder where the transmitted message is to be decoded. We assume that channel state information is available in the transmitter and show that the design of a rational-forcing precoder - a precoder which is matched to the quantizers used in the relays - is beneficial for reducing the symbol error probability. It turns out that for such rational-forcing precoder based systems, there is natural tradeoff between the peak to average power ratio in the transmitter and the rate of communication between the relays and the central decoder. The precoder design problem is formulated mathematically, and several algorithms are developed for realizing this tradeoff. Optimality of the decoder communication rate is shown based on a result in distributed function computation. Numerical and simulation results show that a useful tradeoff can be obtained between the excess decoder communication rate and the peak-average power ratio in the transmitter.