Multiple-input Multiple-output Wireless Research Articles

Joint Reflecting and Precoding Designs for SER Minimization in Reconfigurable Intelligent Surfaces Assisted MIMO Systems

This paper investigates the use of a reconfigurable intelligent surface (RIS) to aid point-to-point multi-data-stream multiple-input multiple-output (MIMO) wireless communications. With practical finite alphabet input, the reflecting elements at the RIS and the precoder at the transmitter are alternatively optimized to minimize the symbol error rate (MSER). In the reflecting optimization with a fixed precoder, two reflecting design methods are developed, referred as eMSER-Reflecting and vMSER-Reflecting. In the optimization of the precoding matrix with a fixed reflecting pattern, the matrix optimization is transformed to be a vector optimization problem and two methods are proposed to solve it, which are referred as MSER-Precoding and MMED-Precoding. The superiority of the proposed designs is investigated by simulations. Simulation results demonstrate that the proposed reflecting and precoding designs can offer a lower SER than existing designs with the assumption of complex Gaussian input. Moreover, we compare RIS with a full-duplex Amplify-and-Forward (AF) relay system in terms of SER to show the advantage of RIS.

Performance analysis of MIMO and MISO time division duplexing wireless links with SWIPT and antenna selection

It is expected that many small battery-operated devices will connect to next generation of cellular networks. In this work, a two-way wireless link is considered, where due to slow variations of the channel, forward link (FL) and reverse link channels are reciprocal, and the remote device’s battery is charged via wireless energy harvesting. We analyze the performance of a two-way link setup, with very low complexity signal processing at the node that relies on battery, and consists of antenna selection and maximum ratio combining-based multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) techniques, wherein FL simultaneous wireless information and power transmission is assumed to be utilized. By mathematical tractability, a closed-form analytical expression for probability of outage, ergodic capacity and bit error rate are derived and compared with simulation results. It is shown that the assumed low complexity setup achieves full diversity in both cases of MISO and MIMO.

An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting

Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) wireless transceivers to achieve high capacity and energy efficiency in frequency division duplex (FDD) mode. CSI reporting for massive MIMO systems could consume large bandwidth and degrade spectrum efficiency. Deep learning (DL)-based CSI reporting integrated with channel characteristics has demonstrated success in improving CSI compression and recovery. To further improve the encoding efficiency of CSI feedback, we develop an efficient DL-based compression framework CQNet to jointly tackle CSI compression, codeword quantization, and recovery under the bandwidth constraint. CQNet is directly compatible with other DL-based CSI feedback works for further enhancement. We propose a more efficient quantization scheme in the radial coordinate by introducing a novel magnitude-adaptive phase quantization framework. Compared with traditional CSI reporting, CQNet demonstrates superior CSI feedback efficiency and better CSI reconstruction accuracy.

Approximate Gram-Matrix Interpolation for Wideband Massive MU-MIMO Systems

Linear and non-linear data-detection and precoding algorithms for wideband massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems that rely on orthogonal frequency-division multiplexing (OFDM) or single-carrier frequency-division multiple access (SC-FDMA) often require computation of the Gram matrix for each active subcarrier. Computing the Gram matrix for each active subcarrier, however, results in excessively high computational complexity. In this paper, we propose novel, approximate algorithms that significantly reduce the complexity of Gram-matrix computation by simultaneously exploiting channel hardening and correlation across subcarriers. We show analytically that a small fraction of Gram-matrix computations in combination with approximate interpolation schemes are sufficient to achieve near-optimal error-rate performance at low computational complexity in massive MU-MIMO systems. We furthermore demonstrate that our approximate interpolation algorithms are more robust against channel-estimation errors than exact Gram-matrix interpolation algorithms that require high computational complexity.

Measurement-Based Feasibility Exploration on Detecting and Localizing Multiple Humans Using MIMO Radio Channel Properties

This paper explores the feasibility of using the multiple-input multiple-output (MIMO) radio channel properties to passively detect and localize multiple humans in indoor environments. We propose to utilize the unique reverberation characteristics of indoor channels for the purpose of detecting, and the power angular delay profile (PADP) for localizing humans. On the one hand, the reverberation time corresponds with the decay rate of multipath in a closed or partially closed cavity, and varies with the change of the number of humans or the moving of humans relative to the antennas at link ends. On the other hand, the PADP is proposed to be calculated by the Multiple Signal Classification (MUSIC) super resolution algorithm with frequency smoothing preprocessing. The proposed approach is evaluated based on real-world MIMO radio channel measurements obtained from a meeting room. Measurements with and without the presence of humans have been conducted, where the maximum number of humans considered is four. Humans facing different directions, either in parallel or orthogonal to the direct line between the transmit and the receive antennas have been taken into account. In term of the detection feasibility, it is found that the change of the number of humans as well as the change of their facing/moving directions inside the partial reverberant region can be reflected on the change of the reverberation time estimated from the power delay profile of channel. In term of the localization feasibility, it is found that single human location can be well associated to the peak of the variation of the PADP during his/her movement, while multiple humans' movements result in obvious power variation in the very vicinity of some of them, and also in the vicinity of some background objects that is far from target humans.

Efficient and Self-Recursive Delay Vandermonde Algorithm for Multi-Beam Antenna Arrays

This paper presents a self-contained factorization for the delay Vandermonde matrix (DVM), which is the super class of the discrete Fourier transform, using sparse and companion matrices. An efficient DVM algorithm is proposed to reduce the complexity of radio-frequency (RF) $N$-beam analog beamforming systems. There exist applications for wideband multi-beam beamformers in wireless communication networks such as 5G/6G systems, system capacity can be improved by exploiting the improvement of the signal to noise ratio (SNR) using coherent summation of propagating waves based on their directions of propagation. The presence of a multitude of RF beams allows multiple independent wireless links to be established at high SNR, or used in conjunction with multiple-input multiple-output (MIMO) wireless systems, with the overall goal of improving system SNR and therefore capacity. To realize such multi-beam beamformers at acceptable analog circuit complexities, we use sparse factorization of the DVM in order to derive a low arithmetic complexity DVM algorithm. The paper also establishes an error bound and stability analysis of the proposed DVM algorithm. The proposed efficient DVM algorithm is aimed at implementation using analog realizations. For purposes of evaluation, the algorithm can be realized using both digital hardware as well as software defined radio platforms.

Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification

In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool to support advanced single- and multi-user MIMO beamforming/precoding. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas, leading to quantization complexity, as well as, feedback overhead issues for larger MIMO systems. We have recently proposed a multi-stage recursive Grassmannian quantizer that enables a significant complexity reduction of CSI quantization. In this paper, we show that this recursive quantizer can effectively be combined with deep learning classification to further reduce the complexity, and that it can exploit temporal channel correlations to reduce the CSI feedback overhead.

Non-Asymptotic Linear Growth of Energy Efficiency in Distributed Autonomous D2D MIMO Wireless Communications

Device-to-device (D2D) communications is a cell-free enabler of 5G and rising wireless communications demand between low-orbit satellite constellation, self-driving vehicles and unmanned aerial systems. Distributed computation (where channel state information is not available) schemes facilitate direct exchanges between mobile user equipment (UE) and if necessary a UE can serve as a relay. Thus, it is essential to develop robust D2D communications frameworks agnostic to the channel distribution which can deliver scalable data rates to self-driving vehicles while the spectral efficiency(SE) is non-asymptotic and energy consumption is increasing to yield an energy efficiency (EE) that is a unimodal function. In this paper, we evaluate the optimal EE by using the computation of the SE in distributed multiple input- multiple output (MIMO) wireless communications. A generalized water-filling framework over arbitrary channel distribution is used to evaluate the SE and then the EE to compare with single-input single-output optimized approximate schemes based on Taylor expansion. The computation yields the optimal power allocation at the transmitters which is derived to illustrate the performance gain of the generalized water-filling distributed MIMO over the approximate scheme. Our simulations results show novel achievable EE linear growth as a function of SE and robust trade-offs in performance gains determined by the total power consumption optimal profile when switching MIMO dimensions. Our trade-off prevents power consumption while EE is decreasing. Thus by switching the number of MIMO operating antenna elements at both the receiver and transmitter sides, EE is no more a unimodal function as illustrated in the literature.

Design of Cooperative MIMO Wireless Sensor Networks With Partial Channel State Information

Wireless sensor networks (WSNs) play a key role in automation and consumer electronics applications. This paper deals with joint design of the source precoder, relaying matrices, and destination equalizer in a multiple-relay amplify-and-forward (AF) cooperative multiple-input multiple-output (MIMO) WSN, when partial channel-state information (CSI) is available in the network. In particular, the considered approach assumes knowledge of instantaneous CSI of the first-hop channels and statistical CSI of the second-hop channels. In such a scenario, compared to the case when instantaneous CSI of both the first- and second-hop channels is exploited, existing network designs exhibit a significant performance degradation. Relying on a relaxed minimum-mean-square-error (MMSE) criterion, we show that strategies based on potential activation of all antennas belonging to all relays lead to mathematically intractable optimization problems. Therefore, we develop a new joint relay-and-antenna selection procedure, which determines the best subset of the available antennas possibly belonging to different relays. Monte Carlo simulations show that, compared to conventional relay selection strategies, the proposed design offers a significant performance gain, outperforming also other recently proposed relay/antenna selection schemes.

Design and Chip Implementation of a SMI/MVDR Dual-Mode Beamformer for Wireless MIMO Communication Systems

This paper presents a low complexity chip design supporting dual-mode beamforming, i.e. sampling matrix inversion (SMI) and the minimum variance distortionless response (MVDR), for wireless Multiple-Input Multiple-Output (MIMO) communication systems. The auto-correlation matrix inversion is the critical computing kernel shared by the two beamforming schemes. To alleviate the computing complexity, the auto-correlation matrix is approximated by a Toeplitz counterpart, which can be decomposed efficiently by applying the Cholesky decomposition and the Schur algorithm. This leads to an O (N 3 ) to O (N 2 ) complexity reduction, where N is the matrix size, while preserving computing parallelism for the hardware design. In addition, a diagonal loading technique is employed to mitigate the stability problem when the matrix is ill-conditioned. Simulation results indicate that no performance loss is observed due to the algorithm simplification measures. A systolic array based mapping procedure converts the two beamforming algorithms to a unified hardware accelerator design with 80% shared circuitry. Complex-valued divisions are achieved by adopting a hardware efficient coordinate rotation digital computer (CORDIC) scheme. In chip implementation, a TSMC 90nm UTM process technology is used and the design specs largely follow the requirements of IEEE 802.11ac standard. The core size of the chip design is 0.68mm2. The measurement results show that the chip can operate up to 200MHz with a power consumption of 49.03mW. It can complete the computation of a new beamforming vector (of size 8) every 0.64us and exhibits the highest throughput among the 6 compared designs.

An Angle Rotate-QAM aided Differential Spatial Modulation for 5G Ubiquitous Mobile Networks

As a novel multiple-input multiple-output (MIMO) wireless transmission technique, differential spatial modulation (DSM) can reduce the computational complexity, and henceforth provides a feasible communications option in terms of the intelligence computing for fifth generation (5G) ubiquitous mobile networks. In this paper, a novel high-rate design scheme relying on angle rotate quadrature amplitude modulation (ARQAM) is proposed for DSM schemes. Through layering QAM symbols, the final transmit matrix can be expressed as the superposition of the different layered matrices. Numercial results indicate that the proposed scheme outperforms the identical-throughput multi-ring APSK-aided and PSK-aided DSM schemes. Additionally, we investigate the impact of two-dimensional (2D) and three-dimensional (3D) regular-shaped geometry-based stochastic model (RS-GBSM) for non-isotropic scattering narrowband MIMO vehicle-to-vehicle (V2V) Ricean fading channel for the proposed ARQAM-aided DSM. Its performance is limited by the V2V channel required for differential detection. Moreover, the influences adopted with 3D MIMO channel model are more significant than 2D MIMO channel model.

Converged Radio Over Fiber and OFDM-PON based on Single-Sideband Frequency Translation Technique

Future passive optical networks will require simultaneous provision of wired and wireless services to provide high-capacity and high-speed information access network to overcome the capacity demand. In this paper, a converged fiber-wireless (FiWi) network architecture including an orthogonal frequency division multiplexing passive optical network (OFDM-PON) and a radio over fiber (RoF) system is proposed. Two multiple-input multiple-output radio over fiber channels are inserted into the left and right side of OFDM-PON spectrum, using a single-sideband frequency translation (SSB-FT) technique. The significant merit of the proposed architecture is its high spectral efficiency as the two multiple-input multiple-output radio over fiber channels and OFDM-PON transmit at the same frequency, which reduces the complexity of transceiver design by applying a novel method for the implementation of local oscillators in both transmitter and receiver. A proof-of-concept downstream link over 20 km standard-PON was conducted by simulation to demonstrate the performance of the proposed converged fiber-wireless network architecture and the link performance was assessed using error vector magnitude and bit error rate.

Digital Predistortion of 5G Massive MIMO Wireless Transmitters Based on Indirect Identification of Power Amplifier Behavior With OTA Tests

In this article, we present a novel digital predistortion (DPD) architecture for multiple-input–multiple-output (MIMO) transmitters using a real-time single-channel over-the-air (OTA) data acquisition loop. The proposed feedback data acquisition strategy captures OTA signals from a fixed location and indirectly identifies the nonlinear behavior of all power amplifiers (PAs) in the array, as well as their combined signals in the far-field direction. The DPD can, therefore, be effectively constructed without direct measurement at PA output or at user end. The proposed linearization solution can run in real-time and, thus, does not interfere with data transmission in the MIMO transmitters. It can also achieve robust performance when mutual coupling occurs between antenna elements. Simulation and experimental results demonstrate that the proposed scheme can accurately estimate both PA outputs and far-field main beam data. Excellent linearization performance can be achieved with low complexity hardware implementation and reduced computational complexity.

Behavioral Modeling and Digital Predistortion of 2x2 MIMO Wireless Transmitters with Enhanced Multivariable Polynomials

In this paper we propose a novel concept named Enhanced Multivariable Memory Polynomials (EMVP) for the behavioral modeling and linearization of Multiple Input Multiple Output (MIMO) transmitters in the presence of linear (LC) and nonlinear (NLC) coupling effects. The proposed model improves the linearization performance of the conventional Multivariable Memory Polynomials (MVP) model. Its performance is experimentally compared with the ones of three popular polynomial models. EMVP model has succeeded in offering better linearization of MIMO transmitters, with fewer coefficients, compared to the conventional models. Experimental results showed improvement in Adjacent Channel Power Ratio (ACPR

Integration of MIMO and Cognitive Radio for Sub-6 GHz 5G Applications

A multiple-input–multiple-output (MIMO) antenna system has been designed on a single substrate representing both types of cognitive radio (CR), i.e., interweave and underlay. The design consists of a four-port MIMO antenna for three selectable modes of the operation (modes). These modes are wideband antenna, frequency-tunable narrowband antenna, and wideband antenna with frequency-tunable band notch. The first, second, and third modes correspond to the sensing antenna, interweave CR communicating antenna, and underlay CR communicating antenna, respectively. These modes are achieved by incorporating multipurpose filters into the microstrip feedline part of the four antenna ports. The MIMO antenna system is designed for 5G applications in sub-6 GHz band (2.50–4.20 GHz). In wideband mode, the four-port MIMO antenna has a sensing frequency range of 2.00–5.70 GHz. While in the second mode of operation, it exhibits narrowband frequency tunability from 3.21 to 4.00 GHz. Finally, in the third mode of operation, the band-notch frequency of the antenna is tunable from 3.37 to 4.00 GHz. The complete structure was fabricated on Rogers RO4350 substrate, and the simulation results were verified by measurement.

A 354 Mb/s 0.37 mm2 151 mW 32-User 256-QAM Near-MAP Soft-Input Soft-Output Massive MU-MIMO Data Detector in 28nm CMOS

This letter presents a novel data detector application-specific integrated circuit (ASIC) for massive multiuser multiple-input multiple-output (MU-MIMO) wireless systems. The ASIC implements a modified version of the large-MIMO approximate message passing algorithm (LAMA), which achieves near-optimal error-rate performance (i) under realistic channel conditions and (ii) for systems with as many users as base-station (BS) antennas. The hardware architecture supports 32 users transmitting up to 256-QAM simultaneously and in the same frequency band, and provides soft-input soft-output capabilities for iterative detection and decoding. The fabricated 28nm CMOS ASIC occupies 0.37 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , achieves a throughput of 354 Mb/s, consumes 151 mW, and improves the SNR by more than 11 dB compared to existing data detectors in systems with 32 BS antennas and 32 users for realistic wireless channels. In addition, the ASIC achieves $4\boldsymbol \times $ higher throughput per area than a recently proposed message-passing detector.

Asynchronous Designs for Multiuser MIMO Wireless Powered Communication Networks

This paper considers multiuser multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). In this system, a multiantenna power beacon (PB) transfers wireless energy to several multiantenna users, and then the users transmit data to a multiantenna base station. Time durations for the energy harvesting (EH) operations at each user can be differently set based on the users’ channel conditions, which results in an asynchronous protocol on the EH and the information transmission. We maximize the sum rate performance of the asynchronous WPCN by jointly optimizing the energy precoding matrices at the PB, the information precoding matrices, and the EH time durations at the users. By using convex optimization techniques, the optimal algorithm for the sum rate maximization problem is provided with analytical expressions of the optimal precoding matrices. Simulation results verify that the proposed optimal algorithm outperforms conventional schemes.

Approximate Message Passing Detector Based Upon Probability Sorting for Large-Scale GSM Systems

Large-scale generalized spatial modulation (GSM) technology has great potential in constructing low-complexity massive multiple-input multiple-output wireless systems, with a simplified structure which employs only a few radio frequency (RF) chains. However, detection is a very complex problem for large-scale GSM systems. In this correspondence paper, we have proposed an innovative probability sorting based approximate message passing (PS-AMP) detector. Specifically, in the proposed scheme, an approximate message passing method is used to obtain the probability for activation of each transmit antenna, and a method based upon probability sorting is developed to ascertain the signal search space. Simulation results demonstrate that the proposed PS-AMP detector exhibits a better bit error rate performance with a reduced complexity compared to its conventional counterparts.

Distributed Detection in Massive MIMO Wireless Sensor Networks Under Perfect and Imperfect CSI

This paper considers the problem of distributed detection for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). Neyman-Pearson criterion based fusion rules are developed at the fusion center (FC) that also incorporate the local probabilities of detection and false alarm of the constituent sensor nodes. Closed-form expressions are obtained for the probabilities of detection and false alarm at the FC for various signaling schemes employed by the sensors. The fusion rules and analysis are extended to the scenario with imperfect channel state information (CSI). Furthermore, signaling matrices are determined for the massive MIMO WSN to enhance detection performance. The asymptotic detection performance of the WSN is analyzed for the large antenna regime, which yields pertinent power scaling laws with respect to the number of antennas at the FC. Simulation results demonstrate the improved performance of the proposed schemes and also validate the theoretical findings.

A 2.4-mm2 130-mW MMSE-Nonbinary LDPC Iterative Detector Decoder for 4 $\times$ 4 256-QAM MIMO in 65-nm CMOS

Iterative detection and decoding (IDD) employs a soft-in soft-out (SISO) detector and an SISO forward error correction (FEC) decoder in an iterative loop to improve the receiver performance in multiple-input multiple-output (MIMO) wireless communications. This paper describes a 256-QAM 4 × 4 prototype IDD design made up of a minimum mean square error (MMSE) detector and a nonbinary low-density parity-check (NBLDPC) decoder with the symbol size of the NBLDPC code matched to the modulation to enhance performance. By directly translating between nonbinary symbols and constellation points, the detector-decoder interface is simplified. We present a Gb/s MMSE detector using a shortened tandem scheduling, a low-latency dual-lookup reciprocal unit, an optimized interleaved microarchitecture, and a Gb/s NBLDPC decoder with efficient internal skipping paths and memory allocation. The designs were demonstrated in a 0.7-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 1.38-Gb/s MMSE detector and a 1.7-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 1.02-Gb/s-NBLDPC decoder that are integrated in a 65-nm CMOS test chip. The chip is measured to achieve 19.2 pJ/b in detection and 20.1 pJ/b/iteration in decoding.