Large-scale Multi-input Multi-output Research Articles

Performance enhancement of large-scale linear dynamic MIMO systems using GWO-PID controller

The multi-input multi-output (MIMO) technique is becoming grown and integrated into wireless wideband communication. MIMO techniques suffer from a large-scale linear dynamic problem, it will be easy to adjust the proportional-integral-derivative (PID) of a continuous system, unlike the nonlinear model. This work displays the tuning of the PID controller for MIMO systems utilizing a statistical grey wolf optimization (GWO) and evaluated by objective function as integral time absolute error (ITAE). The instantaneous adjusting characteristic GWO approach is the criterion that distinguishes such a combination-proposed strategy from that existing in the traditional PID approach. The GWO algorithm searching-based methodology is used to determine the adequate gain factors of the PID controller. The suggested approach guarantees stability as the initial scheme for a steady state condition. A combination of ITAE combined with the GWO reduction method is adopted to reduce the steady-state transient time responses between the higher-order initial scheme and the unit amplitude response. Simulation outcomes are illustrated using MATLAB software to show the capability of adopting the GWO scheme for PID controlling.

Large Scale MIMO Analysis Using Enhanced LAMA

In this research work, we propose a enhanced large scale multi-input multi-output (MIMO) approximate message passing (LAMA) based optimal data detector for large scale MIMO systems. Existing LAMA and sub-optimal detection techniques suffer from iteration complexity and performance loss in finite dimensional systems due to large scale user fading. To overcome these, Gram matrix and message damping techniques are incorporated in the traditional LAMA. The effectiveness of the proposed enhanced LAMA and existing techniques are analyzed with 64, 32 and 16 user antennas, 256, 128, 64 and 16 base station elements with 64QAM, 16QAM, QPSK and BPSK. The simulation results show that the proposed enhanced LAMA gives better performance when compared to existing matrix inversion methods such as Gauss-Seidel and Neumann, box techniques such as optimal co-ordinate descent and alternating direction method of multipliers based on the infinity norm, minimum mean square error and LAMA.

Protograph LDPC Code Design For LS-MIMO 1-bit ADC Systems

Recently, two emerging research topics are protograph low-density parity-check (P-LDPC) and large-scale multi-input multi-output (LS-MIMO) with low-resolution analog-to-digital (ADC) converters (LS-MIMO-LOW-ADC). In these directions, many research works have proposed 1-bit ADC as a good candidate for LS-MIMO systems in order to save both transmission power and circuit energy dissipation. However, we observed that previously reported P-LDPC codes might not have good performance for LS-MIMO systems with 1-bit ADC. Hence, we perform a re-design of the P-LDPC codes for the above systems in this paper. The new codes demonstrate a good coding gain from 0:3 dB at rate 1/2 to 0:5 dB at rate 2/3 in different LS-MIMO configurations with 1-bit ADC.

On the spectral efficiency of cell-free large-scale MIMO non-orthogonal multiple access systems

The spectral efficiency (SE) of an uplink cell-free large-scale multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) system which relies on the superimposed pilot (SP) transmission strategy is investigated. Closed-form expressions of the achievable SE with the maximal ratio combining (MRC) receiver filters are derived and compared against the orthogonal multiple access (OMA) counterparts. Based on the tractable results, we quantitatively explore the impacts of the pilot training strategy, the OMA and NOMA schemes, the number of user equipments (UEs), the power balance factor between pilot and data signals, and the length of the coherent interval (CI) on SE enhancement. Several insightful conclusions are drawn by assuming that the SP interference in the data detection process can or cannot be perfectly eliminated from the data estimates. All the analytical results are verified by numerical simulation experiments.

Machine Learning for RF Slicing Using CSI Prediction in Software Defined Large-Scale MIMO Wireless Networks

In this paper, we investigate the machine learning approaches (sparse Bayesian linear regression (SBLR) and support vector machine (SVM)) for channel state information (CSI) prediction and dynamic radio frequency (RF) slicing for software defined virtual wireless networks in large-scale multi-input multi-output (MIMO) wireless networks. Specifically, a subset of the antennas of virtual wireless networks transmits pilot symbols for estimating the CSI and use the estimated CSI dataset to train and estimate the remaining channels and future CSI for virtual networks using machine learning algorithms. This helps not only to predict the CSI with least overhead and fulfills the service demands of users but also to reduce the power consumption and computation overhead in the network. Predicted CSI is leveraged for RF slicing for virtual wireless networks. Simulation results show that the proposed SBLR for predicting CSI results in lower BER and higher data rate for the wireless users. Furthermore, SBLR outperforms the other approaches when we have sparse CSI information and we need to generalize the prediction process.

On Design of Protograph LDPC Codes for Large-Scale MIMO Systems

Protograph low-density parity-check (LDPC) codes and large-scale multi-input multi-output (LS-MIMO) systems have achieved great interest with various practical applications. However, how to effectively evaluate and design protograph LDPC codes for LS-MIMO systems remains a challenging yet critical problem, especially for low-latency applications. To solve that design challenge, the protograph extrinsic information transfer chart (PEXIT) algorithm for LS-MIMO systems, so-called LS-MIMO-PEXIT algorithm, is first derived based on the mutual information functions of messages that are passed on the joint MIMO detection and LDPC decoding graph. The proposed LS-MIMO-PEXIT algorithm plays a vital role in the optimization process of designing new protograph LDPC codes, tailored for LS-MIMO communications systems. Experiment results demonstrate that the analytical results based on the LS-MIMO-PEXIT algorithm are in good agreement with the simulation results under various input constraints, including the coding rate, the number of decoding iterations, and the LS-MIMO configuration. On top of that, the new protograph LDPC codes designed using our LS-MIMO-PEXIT algorithm achieve a coding gain from 0.2 dB at a low coding rate to 0.4 dB at a high coding rate in comparison with the state-of-the-art protograph codes in the literature. Additionally, we incorporate the practical design experience and the theoretical analysis of mutual functions into a two-step procedure to search for protograph LDPC codes that do not have error-floor behavior at frame error rate (FER) or bit error rate (BER) as low as 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> or 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> , respectively. Together with the coding gain, the error-floor-free feature of the proposed protograph LDPC codes is vitally important for future wireless networks where the ultra-reliability is one of the critical requirements.

MIMO Cross-Layer Secure Communication Algorithm for Cyber Physical Systems Based on Interference Strategies

In this paper, a detailed analysis of multi-input multi-output (MIMO) cross-layer secure communication algorithms in information-physical systems is investigated employing an interference strategy. A three-stage data-assisted channel estimation method is proposed in this paper for the acquisition of channel state information for complex jamming channels in large-scale MIMO two-layer systems. To implement the data-assisted scheme, assuming that there are no errors and no delay in the system, if the data detection and decoding data sequences are completed at a small cell base station, they are sent to the macro base station via a wired backhaul. Due to the sparsity of the channel at the macro base station after user grouping, a channel estimation algorithm based on optimal block orthogonal matching tracking(s) is proposed in the case where the downlink channel at the macro base station utilizes the decoded uplink data and known training sequences. The simulation results show that the data-assisted method proposed in this paper is effective in improving channel estimation accuracy. A machine learning algorithm is directly used to classify the channel difference or channel matrix to obtain the authentication results. In this paper, the scheme is first simulated using channel data from dynamic communication scenarios, its feasibility is analyzed, and the parameters in the scheme are compared, and the optimal scheme is the bagging tree authentication scheme using a 128-dimensional channel matrix as input. To address the interference problem caused by the dense arrangement of SAPs in heterogeneous networks and the unbalanced network load, large-scale MIMO techniques are introduced to reduce the downlink interference caused by the microcell boundary expansion in heterogeneous networks.

Low Complexity Message Passing Detection Algorithm for Large-Scale MIMO Systems

The original message passing detection (MPD) algorithm, which exploits channel hardening theory, achieves very good performance in large-scale multi-input multi-output (MIMO) systems. However, the complexity of the MPD algorithm grows rapidly when the number of users or the order of modulation increases, presenting a great implementation challenge for practical massive MIMO systems. In this letter, based on a novel approximate probability updating (APU) scheme, a low complexity MPD (LCMPD) algorithm is proposed. The developed APU scheme considers only the most reliable constellation point during the message passing process. Combined with a serial message updating schedule, no exponential operation or division is required in the LCMPD algorithm. Compared to the existing MPD algorithm, the overall computational complexity is reduced by 11.2 to 29.4 times under various antenna configurations with almost no degradation in the bit error rate performance. Hence, the proposed algorithm is very suitable for efficient hardware implementation.

Performance analysis of large-scale MIMO system for wireless backhaul network

In this paper, we present a performance analysis of large‐scale multi‐input multi‐output (MIMO) systems for wireless...

Benefits of Beamforming Training Scheme in Distributed Large-Scale MIMO Systems

The downlink training yields rather modest performance gains due to the high degree of channel hardening effect in co-located large-scale multi-input multi-output (MIMO) systems. However, in distributed large-scale MIMO systems, the potential benefits of the downlink training become larger, since there is a high probability that each user is very close to only a part of remote antenna units and is effectively served by them which results in less channel hardening effect. In this paper, we study the benefits of the beamforming training (BT) scheme (users estimate the channel state information by using the received training symbols, which are processed by linear beamforming in advance) in distributed large-scale MIMO systems. We derive accurate and tractable closed-form analytical expressions for the spectral efficiency (SE) of the BT scheme in distributed large-scale MIMO systems with maximum ratio transmission and zero-forcing (ZF) beamforming. Based on these expressions, we analyze the benefits of the BT scheme and investigate how the number of transmit antennas and the length of coherence interval affect the SE of the BT scheme in the co-located and distributed large-scale MIMO systems. The analytical results show that the BT scheme is more suitable for distributed large-scale MIMO systems with ZF beamforming, and a less number of transmit antennas and longer coherence interval can improve the performance gain of BT scheme.

Achievable Rates for Full-Duplex Massive MIMO Systems Over Rician Fading Channels

We study the uplink and downlink achievable rate of full-duplex large-scale multi-input multi-output (MIMO) systems with a base station (BS) and users over Rician fading channels, based on maximum ratio combining/maximum ratio transmission and zero-forcing reception/zero-forcing transmission processing. Contrary to previous related works over Rayleigh fading channels, this paper assumes that the fast fading MIMO channel matrix follows the Rice distribution which will be more common in future 5G wireless communication systems. We derive approximate expressions of the uplink and downlink achievable rate for perfect channel state information when the number of BS antennas grows large. Based on the theoretical analysis, it is found that when the antennas of the BS are large enough and the power scaling law is applied properly, the impact of multi-user interference, loop interference, inter-user interference, and noise can be suppressed. In addition, the simulation results show that the uplink and downlink achievable rates increase with the number of BS antennas and they will converge to fixed values with the increasing Rician $K$ -factor.

A global rational Arnoldi method for model reduction

In this paper, we propose two new approaches for model order reduction of large-scale multi-input multi-output (MIMO) linear time invariant dynamical systems (LTI). These methods are based on a generalization of the global Arnoldi algorithm which is used to generate projection subspaces. An adaptive procedure for the selection of shift parameters is proposed and some simple Arnoldi relations are established in order to compute error bounds on the transfer function. Numerical experiments showing the effectiveness of these approaches are provided.

Scattered Pilots-Based Frequency Synchronization for Multiuser OFDM Systems With Large Number of Receive Antennas

Large-scale multi-input multi-output (MIMO) technique has drawn considerable research interest recently. However, multiuser transmissions in large-scale MIMO systems would face challenging estimation and compensation for multiple carrier frequency offsets (CFOs) that co-exist at the receiver. In this paper, we design a new frequency synchronization scheme in multiuser orthogonal frequency division multiplexing uplink with the aid of scattered pilot symbols. We specifically consider a base station with a large number of antennas and propose to assign a number of scattered pilot subcarriers to each user. With sufficient spatial dimensions offered by the large number of antennas, the designed scheme could estimate CFO for each user individually and eliminate the necessity of the multidimensional search. Moreover, after the CFO estimation, the receive beamforming matrix is further designed for inter-user interference cancelation, which yields an equivalent single user transmission model. The conventional single user channel estimation and data detection can then be performed. Finally, the numerical results are provided to verify the proposed studies.

Hybrid Precoding-Beamforming Design With Hadamard RF Codebook for mmWave Large-Scale MIMO Systems

This paper proposes a hybrid structure for multi-stream large-scale multi-input multi-output (MIMO) beamforming systems, in single-user scenario, using a Hadamard radio frequency (RF) codebook with low-bit resolution phase shifters. We show that Hadamard transform can be used in RF beamsteering/beamcombining to achieve better performance in terms of average achievable spectral efficiency and low hardware cost using 1- or 2-b resolution APSs. In contrast, the state-of-the-art RF codebook designs available in the literature requires more than 7-b resolution to achieve the same performance as the proposed scheme, for large antenna arrays with up to 256 elements. The performance gains of the proposed RF codebook design is thoroughly investigated using MATLAB simulations for typical mmWave MIMO system, and the simulation results are closely verified by the analytical expressions.

MIMO System Using Eigen Algorithm and Improved Pade Approximations

A mixed method by combining a Eigen algorithm and Improved pade approximations is proposed for reducing the order of the large-scale multi-input multi-output (MIMO) systems. The most dominant Eigen value of both original and reduced order systems remain same in this method. The proposed method guarantees stability of the reduced model if the original high-order system is stable and is comparable in quality with the other well known existing order reduction methods. The viability of the proposed method is shown through numerical examples taken from the literature and compared with few well-known order reduction methods.

On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes

Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed "input-output channels of communication" corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.