Multi-user Massive MIMO Research Articles

A POMDP-Based Approach to Joint Antenna Selection and User Scheduling for Multi-User Massive MIMO Communication

We devise a partially observable Markov decision process (POMDP) based joint antenna selection and user scheduling (JASUS) policy for a massive MIMO base station, equipped with only a small number of RF chains, that serves a large number of users. The users are served at different time slots within a frame. Relying on partial CSI obtained from training between the selected antennas and the users, at the beginning of each frame, the BS assigns each user to a time slot in the frame and selects a subset of antennas to serve the users scheduled in each time slot. Assuming that the channels evolve according to a Markov process and relying on zero-forcing beamforming, we formulate our JASUS problem using a POMDP framework to devise a real-time decision-making policy that maximizes the expected long-term sum-rate. We rigorously prove that for positively correlated two-state channel models, the myopic policy provides the optimal solution to our POMDP-based JASUS problem for any number of RF chains and for any number of users. Based on this, we model the Rayleigh fading channels as first-order Gauss-Markov processes and devise a low-complexity myopic policy-based JASUS algorithm for massive MU-MIMO systems that only relies on partial CSI.

Linear precoder optimization of spectral efficiency of time division duplex hyper MIMO system with pilot contamination

Our work is developed in context of studing Massive MIMO in a 5G context. The aim is to optimize spectral efficiency of several users hyper MIMO system during Uplink communication in a multi-cell contaminated pilot environment, using a new type of precoders called single cell-minimum mean square eroor (S-MMSE) and multicell-minimum mean square eroor (MMMSE). Indeed, we address two key and well-known issues of massive multiuser MIMO (MU-MIMO) environments in a test-driven development (TDD) operation scheme, namely acquisition of uplink channel state information (UL) and optimisation of the bit stream per unit frequency, the spectral efficiency (SE). From a practical point of view, these two notions are inclusively linked. Indeed, a very good channel estimation leads to a better spectral efficiency. In our approcah, we derive from the minimum mean square error estimator (MMSE) to two new types of precoders that can operate in a multicell environment with a contaminated pilot sequence, namely the SMMSE and the M-MMSE. A comparative study performance of these classical precoders such as regulated zero forcing (RZF), ZF (Zero Forcing) and MR (Minimum Ratio) encountered in multi-antenna processing shows an improvement of nearly 51% in terms of system gain and spectral efficiency.

Dual iterative algorithm for hybrid beamforming in mmWave downlink massive multi-user MIMO systems

Dual iterative algorithm for hybrid beamforming in mmWave downlink massive multi-user MIMO systems

Deep Reinforcement Learning for Multi-User Massive MIMO With Channel Aging

Deep Reinforcement Learning for Multi-User Massive MIMO With Channel Aging

Design of LDPC Coded Multi-User Massive MIMO Systems With MMSE-Based Iterative Joint Detection and Decoding

Design of LDPC Coded Multi-User Massive MIMO Systems With MMSE-Based Iterative Joint Detection and Decoding

Improved spectral efficiency in massive MIMO ultra-dense networks through optimal pilot-based vector perturbation precoding

In multi-user massive MIMO (mMIMO) Ultra Dense Networks (UDN), the acquisition of Channel State Information (CSI) is crucial at the transmitter to maximize the estimation, but it suffers from increased overhead and complexity. The processing of CSI from imperfect channels further increases the complexity of precoders. Because of the complexity, it is hard to find the optimal perturbing vector, which increases the transmit power with reduction in spectral efficiency. This research improves the spectral efficiency of mMIMO UDN using optimal pilot-based vector perturbation (OPVP) precoding. The optimal pilot design senses the CSI intelligently for feedback into the transmitter, and the perturbing signal, for transmission and efficient reception, is selected optimally using Evolutionary Chaotic Behavior (ECB). This integrated OPVP precoding uses compressive sensing to select the low-dimensional CSI, based on which the EEA allows the generation of perturbing signals within its constellation bounds to get efficiently detected with reduced transmit power, computational complexity, and overhead in comparison with the conventional CSI estimation schemes. The simulation is carried out in Matlab to evaluate if the OPVP scheme makes the transmission in MU-MIMO UDN a spectrally efficient one. Compared to state-of-the-art precoding schemes, the integrated model reduces the feedback overhead and avoids high-dimensional CSI recovery.

Performance Analysis and Optimization for RIS-Assisted Multi-User Massive MIMO Systems With Imperfect Hardware

The paper studies a reconfigurable intelligent surface (RIS)-assisted multi-user uplink massive multiple-input multiple-output (MIMO) system with imperfect hardware. At the RIS, the paper considers phase noise, while at the base station, the paper takes into consideration the radio frequency impairments and low-resolution analog-to-digital converters. The paper derives approximate expressions for the ergodic achievable rate in closed forms under Rician fading channels. For the cases of infinite numbers of antennas and infinite numbers of reflecting elements, asymptotic data rates are derived to provide new design insights. The derived power scaling laws indicate that while guaranteeing a required system performance, the transmit power of the users can be scaled down at most by the factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{M}$</tex-math></inline-formula> when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> goes infinite, or by the factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{MN}$</tex-math></inline-formula> when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> go infinite, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> is the number of antennas and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of the reflecting units. Furthermore, an optimization algorithm is proposed based on the genetic algorithm to solve the phase shift optimization problem with the aim of maximizing the sum rate of the system. Additionally, the optimization problem with discrete phase shifts is considered. Finally, numerical results are provided to validate the correctness of the analytical results.

Multi-User Massive MIMO System with Adaptive Antenna Grouping for Beyond 5G Communication Network

Error-correcting codes with limited errors and higher spectral efficiency are the main concern for wireless communications. In the current situation, research is increasing daily to satisfy the growing demand for users with improved QoS. Adaptive Antenna Grouping (AAG) with a multilevel space–time trellis coding scheme in the Multi-User Massive MIMO system is the better option to provide flexible data transfer speeds, encoding gains, and gain in diversity with improved spectral efficiency and low decoding complexity, including the power optimization by reduced SNR at the same Symbol Error Rate/Frame Error Rate (SER/FER). The prior aim of maintaining spectral efficiency is achieved by using Massive MIMO. This paper presents the AAG according to the channel state information in the Massive MIMO scenario. The impact of the proposed model on standard ITU-R M.2135 scenarios is also demonstrated in this paper.

Hybrid Beamforming in 5G NR Networks Using Multi User Massive MIMO at FR2 Frequency Bands

AbstractThe current Sub 6 GHz band is highly occupied and unable to meet the demands of future wireless data traffic. As the millimeter wave technology is a promising way of enhancing the performance of next-generation wireless networks, it is essential to explore FR2 frequency bands. Massive Multiple-Input Multiple Output (mMIMO) technology has been introduced to improve the capacity and coverage of 5G wireless networks. Hybrid beamforming (HB) is an efficient technique to improve the channel gain and minimize the interference at reasonable complexity. The aim of HB in mMIMO systems is to maximize the sum rate that can approach the fully-digital beamforming system performance. Combination of mMIMO systems, mmWave, and HB techniques achieve higher data rates and cell coverage in 5G wireless networks. It mitigates the hardware complexity, energy consumption, and cost as number of required radio-frequency chains at base station gets minimized. In this paper, we have investigated the performance of a multi user-mMIMO HB system at 28, 39 GHz of FR2 frequency bands as well as at 66 GHz by analyzing the effect of changing variables that include the number of users, transmitting and receiving antennas, and the type of modulation. Simulation results include RMS values of error vector magnitude (EVM) that gives quantitative analysis and receive constellation of the equalized symbols that shows qualitative analysis. EVM is computed for different scenarios and it is used as an indicator of performance to assess the suitable design configuration of mMIMO system.

Hybrid Precoder Design Using Alternating Minimization Algorithm in MultiCell Multi-User Millimeter-Wave Communications for Massive MIMO

In the field of upcoming generation in wireless communication technology there is an extreme demand for higher bandwidth which is expected to be fulfilled by the frequency spectrum ranging from 30GHz to 300GHz which in other words can be defined as millimeter wave (mmWave). But it is a fact that, the mmWave technology lacks the simplicity in the design of precoder. There is higher power loss and higher cost in the baseband precoding in the case of fully digital baseband precoder. As the supported number of analog-to-digital (ADC) converters is very few, the mmWave technology requires ‘Hybrid Precoder’ which may reduce the loss of extensive power and lessen cost. The cost-effective hybrid precoder transceiver formulation has not been standardized yet. In this paper, we proposed the design of an optimal hybrid precoder in fully connected structure which has a higher complexity but lower cost and lower power consumption in the case of multi-cell multi-user massive MIMO. We have used Alternating Minimization Algorithm for getting the optimal Reimannian manifold solution. Here the comparison between the Digital Baseband precoder performance and the Hybrid Precoder performance has been done using simulation tools.

Centralized and Decentralized Channel Estimation in FDD Multi-User Massive MIMO Systems

We design a centralized and a decentralized variational Bayesian learning (C- and D-VBL) algorithms for the base station (BS) of a frequency division duplex massive multiple input multiple output (mMIMO) cellular system, wherein users send compressed information for it to estimate their downlink channels. The BS in the decentralized algorithm consists of multiple processing units (PUs), and each PU separately estimates the channels of a group of users, by employing the proposed D-VBL algorithm. To reduce channel estimation error, the PUs exploit the structured sparsity inherent in multi-user mMIMO channels by exchanging information among themselves. We investigate the proposed C-VBL and low-complexity D-VBL algorithms and show that i) they substantially outperform the state-of-the-art centralized and decentralized algorithms in terms of the normalized mean squared error and the bit error rate. This is because they beneficially exploit the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inherent</i> channel sparsity, while the existing state-of-the-art solutions fail to do so. The proposed D-VBL is also robust to PU failures, and provides a similar performance as its centralized counterpart (C-VBL), but with a much reduced complexity.

Adaptive Pilot Allocation in Multi-Antenna Multi-Cell Multi-User Massive MIMO Systems for 5G Wireless Communication Network

Adaptive Pilot Allocation in Multi-Antenna Multi-Cell Multi-User Massive MIMO Systems for 5G Wireless Communication Network

Energy efficient data detection with low complexity for an uplink multi-user massive MIMO system

The current development of the Internet of Things (IoT) network in sixth-generation (6G) communication opens up various opportunities. When IoT devices with edge platforms connect to the telecommunication network, mobility, interferences, and intelligent device capacity issues might occur. The challenge with uplink MU-MIMO systems is data detection owing to noise, frequency allocations, and dense deployment of intelligent devices. The exponential complexity of extensive wireless networks makes optimal maximum likelihood detection impossible. Using MATLAB simulation-based analysis, we suggested an iterative data detection strategy based on a coordinate descent method (CDM) to reduce computing complexity while preserving an acceptable bit error rate (BER). Linear systems demand low co-channel interference (CCI) MU-MIMO uplink communications (CCI). A superior mean square error or BER performance is achieved using Maximum ratio combining with CDM. The proposed system outperforms the Richardson Method (RM), approximation message passing (AMP), and linear minimum mean square error (LMMSE) algorithms in terms of BER and complexity.

Performance and design of uplink multiuser massive MIMO with low-resolution ADCs

Performance and design of uplink multiuser massive MIMO with low-resolution ADCs

Joint User Grouping and Beam Selection for Beamspace mmWave Multi-User MIMO System

Lens antenna arrays based beamspace millimeter-wave multi-user massive MIMO (B-MIMO) can significantly improve energy efficiency without significant loss of spectral efficiency (SE), which is considered as a highly expected technology for future wireless communication networks. Motivated by the requirement of reducing SE loss by efficient user grouping (UG) and beam selection (BS), a joint user grouping and beam selection algorithm is studied. Specifically, a local search based BS algorithm is proposed by proving the convexity of the BS problem after relaxing the constraints. Moreover, a deep learning based UG algorithm is proposed by equivalently transforming the UG problem into a classification problem. The results of theoretical analysis and numerical simulation show that the proposed algorithms can be more closely with the performance of the exhaustive search algorithm and the complexity is lower than that of the traditional algorithms.

Detection of Spatially Modulated Signals via RLS: Theoretical Bounds and Applications

This paper characterizes the performance of massive multiuser spatial modulation MIMO systems, when a regularized form of the least-squares method is used for detection. For a generic distortion function and right unitarily invariant channel matrices, the per-antenna transmit rate and the asymptotic distortion achieved by this class of detectors are derived. Invoking an asymptotic characterization, we address two particular applications. Namely, we derive the error rate achieved by the computationally-intractable optimal Bayesian detector, and we propose an efficient approach to tune LASSO-type detectors. We further validate our derivations through various numerical experiments.

Uplink millimeter-wave multi-cell multi-user massive multi-input multi-output systems

In this paper, we delve into the maximized spectral efficiency (SE) of millimeter-wave (mmWave) multicell multiuser massive MIMO Systems for uplink transmission with low-resolution phase shifters (LRPSs). Millimeter-wave massive multiple-input multiple-output (mMIMO) is an important technology for upcoming cellular networks which will provide higher bandwidth and throughput than current wireless systems and networks. LRPSs are commonly used to minimize power consumption, maximize spectral efficiency and diminish the complexity of hybrid precoder and combiner. In this paper, we consider a hybrid analog-digital precoder and combiner design with LRPSs for mmWave multi-cell multiuser mMIMO systems for uplink transmission to spectral efficiency in terms of iterations. The proposed technique outperforms when compared to traditional optimization approaches concerning spectral efficiency and bit error rate (BER). We show through simulation results that our designs with LRPSs outperform standard iteration procedures.

Low-Complexity Antenna Selection and Discrete Phase-Shifts Design in IRS-Assisted Multiuser Massive MIMO Networks

We propose two novel antenna selection (AS) and discrete phase-shifts design (PSD) schemes for use in intelligent reflecting surface (IRS) assisted multiuser massive multiple-input multiple-output (mMIMO) networks. The first AS and PSD method aims at maximizing the gain of the channels; while the second method is an iterative sum-rate maximization (ISM) scheme that aims at maximizing the total achievable rate. For the AS part, we demonstrate that the ISM method achieves near optimal performance with much lower complexity compared to benchmark AS schemes, and can be utilized with any precoder at the mMIMO base station. For the PSD, our proposed successive-refinement optimization methods are not only efficient, but their complexities scale linearly with the number of elements at the IRS, making them highly attractive when dealing with large surfaces. A thorough complexity analysis for the proposed methods is carried out in terms of the number of floating point operations required for their implementations. Finally, extensive numerical results are provided and some key points are highlighted on the performance of the proposed schemes with both conjugate beamforming and zero-forcing precoders.

Intelligent Non-Orthogonal Beamforming With Large Self-Interference Cancellation Capability for Full-Duplex Multiuser Massive MIMO Systems

This work introduces a novel full-duplex hybrid beamforming (FD-HBF) technique for the millimeter-wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) systems, where a full-duplex (FD) base station (BS) simultaneously serves half-duplex (HD) downlink and uplink user equipments over the same frequency band. Our main goal is jointly enhancing the downlink/uplink sum-rate capacity via the successful cancellation of the strong self-interference (SI) power. Furthermore, FD-HBF remarkably reduces the hardware cost/complexity in the mMIMO systems by interconnecting the radio frequency (RF) and baseband (BB) stages via a low number of RF chains. First, the RF-stage is constructed via the slow time-varying angular information, where two schemes are proposed for both maximizing the intended signal power and canceling the SI power. Particularly, orthogonal RF beamformer (OBF) scheme only aims canceling the far-field component of SI, while non-orthogonal RF beamformer (NOBF) scheme applies perturbations to the orthogonal beams for also suppressing the near-field component of SI channel. Considering the high computational complexity during the search for optimal perturbations, we apply swarm intelligence to find the optimal perturbations. Second, the BB-stage is designed based on only the reduced-size effective intended channel matrices, where the BB precoder/combiner solutions are obtained via regularized zero-forcing (RZF) and minimum mean square error (MMSE). Hence, the proposed FD-HBF technique does not require the instantaneous SI channel knowledge. It is shown that FD-HBF with NOBF+MMSE achieves 78.1 dB SI cancellation (SIC) on its own. Additionally, FD-HBF with the practical antenna isolation can accomplish more than 130 dB SIC and reduce the SI power below the noise floor. The numerical results present that FD-HBF greatly improves the sum-rate capacity by approximately doubling it compared to its HD counterpart.

A Novel Low Complexity Scheme for Multiuser Massive MIMO Systems

A Novel Low Complexity Scheme for Multiuser Massive MIMO Systems