mMIMO Systems Research Articles

Joint Spatial Division and Multiplexing for FDD in Intelligent Reflecting Surface-Assisted Massive MIMO Systems

Channel reciprocity, assumed in fifth-generation (5G) systems such as massive multiple-input-multiple-output (mMIMO), appears to be questioned in practice by recent studies on intelligent reflecting surface (IRS). Hence, contrary to previous works, we consider frequency division duplexing (FDD) to study the performance of an IRS-assisted mMIMO system. However, FDD is not suitable for large number of antennas architectures. For this reason we employ the joint spatial division and multiplexing (JSDM) approach exploiting the structure of the correlation of the channel vectors to reduce the channel state information (CSI) uplink feedback, and thus, allowing the use even of a large number of antennas at the base station. Specifically, we derive the sum spectral efficiency (SE) based on statistical CSI in terms of large-scale statistics by using the deterministic equivalent (DE) analysis while accounting for correlated Rayleigh fading. Subsequently, we formulate the optimization problem concerning the sum SE with respect to the reflecting beamforming matrix (RBM)and the total transmit power, which can be performed at every several coherence intervals by taking advantage of the slow time variation of the large-scale statistics. This notable property contributes further to the decrease of the feedback overhead. Numerical results, verified by Monte-Carlo (MC) simulations, enable interesting observations by elucidating how fundamental system parameters such as the rank of the covariance matrix and the number of groups of UEs affect the performance. For example, the selection of a high rank improves the channel conditioning but increases the feedback overhead.

Electromagnetic Based Communication Model for Dynamic Metasurface Antennas

Dynamic metasurface antennas (DMAs) arise as a promising technology in the field of massive multiple-input multiple-output (mMIMO) systems, offering the possibility of integrating a large number of antennas in a limited —and potentially large— aperture while keeping the required number of radio-frequency (RF) chains under control. Although envisioned as practical realizations of mMIMO systems, DMAs represent a new paradigm in the design of signal processing techniques (such as beamforming) due to the constraints inherent to their physical implementation, for which no complete models are available yet. In this work, we propose a complete and electromagnetic-compliant narrowband communication model for a generic DMA based system. Specifically, the model accounts for: i) the wave propagation and reflections throughout the waveguides that feed the antenna elements, ii) the mutual coupling both through the air and the waveguides, and iii) the insertion losses. Also, we integrate the electromagnetic model in the conventional digital communication model, providing a complete and useful framework to design and characterize the performance of these systems. Finally, the accuracy of the model is verified through full-wave simulations.

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.

Coverage Probability of STAR-RIS-Assisted Massive MIMO Systems With Correlation and Phase Errors

In this letter, we investigate a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisting a massive multiple-input multiple-output (mMIMO) system. In particular, we derive a closed-form expression for the coverage probability of a STAR-RIS assisted mMIMO system while accounting for correlated fading and phase-shift errors. Notably, the phase configuration takes place at every several coherence intervals by optimizing the coverage probability since the latter depends on statistical channel state information (CSI) in terms of large-scale statistics. As a result, we achieve a reduced complexity and overhead for the optimization of passive beamforming, which are increased in the case of STAR-RIS networks with instantaneous CSI. Numerical results corroborate our analysis, shed light on interesting properties such as the impact of the number of RIS elements and the effect of phase errors, along with affirming the superiority of STAR-RIS against reflective-only RIS.

Renew

Massive multiple-input multiple-output (mMIMO) technology uses a very large number of antennas at base stations to significantly increase efficient use of the wireless spectrum. Thus, mMIMO is considered an essential part of 5G and beyond. However, developing a scalable and reliable mMIMO system is an extremely challenging task, significantly hampering the ability of the research community to research nextgeneration networks. This "research bottleneck" motivated us to develop a deployable experimental mMIMO platform to enable research across many areas. We also envision that this platform could unleash novel collaborations between communications, computing, and machine learning researchers to completely rethink next-generation networks.

Guaranteeing QoS for NOMA-Enabled URLLC Based on κ-μ Shadowed Fading Model.

Sixth-generation (6G) wireless communication scenarios are complex and diverse. Small-scale fading is a key part of wireless channels and its impact on performance in scenarios with time sensitivity and 6G ultrareliable and low latency communications (URLLC) quality-of-service requirements cannot be ignored. Therefore, it is necessary to accurately characterize small-scale fading when designing wireless communication systems. In this paper, we derive approximate closed form expressions for the probability density function, cumulative distribution function and moment-generating function of the postprocessing signal-to-noise ratio following the zero-forcing detector in a cell-free massive multiple-input multiple-output (CF mMIMO) system. CF mMIMO system is a nonorthogonal multiple access (NOMA) system that enables users to share all channel uses and can ensure the fairness of the communication quality experienced by different users. Our key contributions include the extension of the – shadowed fading model to a CF mMIMO system and the proposal of theoretical tools (the derived closed-form expression) to improve its mathematical tractability. By exploiting the statistical characterizations of the arrival and service processes, another important contribution is the exploitation of the upper bound of the queuing delay violation probability (UB-QDVP) over the Mellin transforms of the arrival and service processes in the proposed CF mMIMO system under the – shadowed fading model. Corroborated by extensive simulations, our analyses validate that the CF mMIMO system outperforms the orthogonal multiple access and power-domain NOMA systems and reveal the relationships among different small-scale fading types, energy efficiency, delay and the UB-QDVP, as well as the accuracy and effectiveness of the proposed theoretical tools based on the – shadowed fading model.

Hybrid Precoding Algorithm for Millimeter-Wave Massive MIMO-NOMA Systems

In this paper, the performance of the millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) systems is investigated under multiple user scenarios. The performance of the system has been analyzed in terms of spectral efficiency (SE), energy efficiency (EE), and computational complexity. In the case of the mMIMO system, the linear precoder with matrix inversion becomes less efficient due to its high computational complexity. Therefore, the design of a low-complex hybrid precoder (HP) is the main aim of this paper. Here, the authors have proposed a symmetric successive over-relaxation (SSOR) complex regularized zero-forcing (CRZF) linear precoder. Through simulation, this paper demonstrates that the proposed SSOR-CRZF-HP performs better than the conventional linear precoder with reduced complexity.

Can Dynamic TDD Enabled Half-Duplex Cell-Free Massive MIMO Outperform Full-Duplex Cellular Massive MIMO?

We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs), with the goal of maximizing the sum UL-DL spectral efficiency (SE). We develop a new, low complexity, greedy algorithm for the combinatorial AP scheduling problem, with an optimality guarantee theoretically established via showing that a lower bound of the sum UL-DL SE is sub-modular. We also consider pilot sequence reuse among the UEs to limit the channel estimation overhead. In CF systems, all the APs estimate the channel from every UE, making pilot allocation problem different from the cellular case. We develop a novel algorithm that iteratively minimizes the maximum pilot contamination across the UEs. We compare the performance of our solutions, both theoretically and via simulations, against a full duplex (FD) multi-cell mMIMO system. Our results show that, due to the joint processing of the signals at the central processing unit, CF-mMIMO with dynamic HD AP-scheduling significantly outperforms cellular FD-mMIMO in terms of the sum SE and 90% likely SE. Thus, DTDD enabled HD CF-mMIMO is a promising alternative to cellular FD-mMIMO, without the cost of hardware for self-interference suppression.

Mathematical Analysis and Performance Evaluation of the Cell-Free mMIMO Networks Based on Cognitive Relays

The cell-Free massive multiple input multiple output “mMIMO” networks can provide a satisfied performance for the fifth generation “5G” systems and beyond even there are shadowed users or indoor ones. The performance of the cell-Free mMIMO systems was mathematically analyzed and simulated in a lot of previous works. However, this work aims to provide a complete mathematical model and simulation for a cell-Free mMIMO system that applies relays which operate with the cognitive radio theory. The proposed work can provide novel contributions on two axes. Firstly, the relays can improve the coverage performance of a cell-Free mMIMO network especially at shadowed users, indoor users, and the cell-edge ones. Secondly, the cognitive radio can mitigate the interference in a cell-Free mMIMO network. The interference mitigation results in performance enhancement. Therefore, the cell-Free mMIMO network, based on cognitive relays, is studied. The proposed system is mathematically analyzed and simulated. The spectral efficiency “SE” and signal to interference plus noise ratio “SINR” is deduced. They are given in a closed form formula. It can be concluded that the cognitive relays can improve; SINR, SE, and Energy Efficiency “EE” of a cell-Free mMIMO system.

A Computationally Efficient Gradient Algorithm for Downlink Training Sequence Optimization in FDD Massive MIMO Systems

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date very challenging. Although advanced iterative algorithms have been developed to address this challenge, they exhibit slow convergence speed and thus deliver high latency and computational complexity. To overcome this challenge, we propose a computationally efficient conjugate gradient-descent (CGD) algorithm based on the Riemannian manifold in order to optimize the DL training sequence at base station (BS), while improving the convergence rate to provide a fast CSI estimation for an FDD m-MIMO system. To this end, the sum rate and the computational complexity performances of the proposed training solution are compared with the state-of-the-art iterative algorithms. The results show that the proposed training solution maximizes the achievable sum rate performance, while delivering a lower overall computational complexity owing to a faster convergence rate in comparison to the state-of-the-art iterative algorithms.

Joint polar with physical layer network coding and massive MIMO: performance analysis

Large spectral efficiency, reliability, coverage, and energy efficiency are all major requirements to meet the targets of fifth-generation (5G) and beyond communication networks. The transmitted signal is usually susceptible to errors that reduced reliability and throughput of the system. Physical layer network coding (PLNC) is a promising technology to achieve better throughput, low latency, and high transmission rate. This paper considers the combination of PLNC with polar coding using massive multi-input multi-output (MIMO) system to enhance the transmission reliability by reducing the bit error rate (BER) and improve system reliability. This arrangement is investigated in two-way wireless relay transmission over millimetre wave (mmWave) band channel model. The results of the extensive simulation tests demonstrated improvements in throughput and BER achieved using polar code with PLNC and a sufficient number of antenna elements in the mMIMO system. The BER performance of polar-coded PLNC arrangement outperformed PLNC without coding for 128 and 256 receiving antenna elements in the relay node regardless the number of antenna elements in the user equipment side. The tests showed that the combination of the polar code with PLNC and MIMO system is not encouraging at low Signal-to-noise ratio (SNR).

Deep Unfolded Sparse Refinement Network Based Detection in Uplink Massive MIMO

Massive multiple-input multiple-output (mMIMO) is a promising technique to realize the ever-increasing demand for high-speed data, quality of service (QoS), and energy efficiency for 5G and beyond wireless systems. However, the increased number of users in mMIMO systems significantly affects performance of the existing approximate matrix inversion based and matrix inversion less iterative symbol detection techniques. Conventional detection algorithms cannot learn the inter-relations of input-output parameters based on available data without having specific mathematical models of communication scenarios. Moreover, existing deep learning (DL) based symbol detection models lack in-network compression, resulting in large training time and high computational load while expected to be deployed in a low latency communication system. In this article, a sparse refinement architecture is proposed for symbol detection in uplink mMIMO. The proposed DL architecture requires less trainable parameters as compared to a conventional fully connected detection network and refines the estimated symbol vector in each layer. Convergence of the proposed symbol detection technique is analytically justified. An expression for the approximate upper bound on the BER is derived which is supported by simulations. The obtained results prove viability of the proposed symbol detection model as compared to the several existing state-of-art uplink mMIMO detection techniques, in terms of superior the error performance and low computational complexity.

Spatially-Correlated IRS-Aided Multiuser FD mMIMO Systems: Analysis and Optimization

We consider a two-way full-duplex (FD) system where a massive multi-input-multi-output FD base station (BS) communicates with multiple FD users via an intelligent reflecting surface (IRS). We derive a closed-form network spectral efficiency lower bound by considering spatial correlation at the BS and IRS. We demonstrate the importance of modelling spatial correlation by simplifying this lower bound for uncorrelated channels to show that the IRS capability to modify the wireless medium now is significantly impeded. This lower bound, which is a function of only the long-term channel statistics, is also used to maximize the non-concave global energy efficiency metric by optimally allocating the transmit powers, and by designing the IRS phases. We derive closed-form updates for optimizing the transmit powers using Lagrangian dual, Quadratic, and Dinkelbach’s transforms. We then optimize the IRS phases by using the projected gradient ascent algorithm. We numerically show that increasing the number of IRS elements can help a FD mMIMO BS outperform its half-duplex counterpart, which otherwise under-performs due to its limited ability to cancel various FD interferences.

Cylindrical Massive MIMO System with Low-Complexity Angle-Based User Selection for High-Altitude Platform Stations

High-altitude platform stations (HAPSs) are recognized as a promising technology for coverage extension in the sixth generation (6G) mobile communications and beyond. The purpose of this study is to develop a HAPS system with a coverage radius of 100km and high capacity by focusing on the following two aspects: array antenna structure and user selection. HAPS systems must jointly use massive multiple-input multiple-output (mMIMO) and multiuser MIMO techniques to increase their capacity. However, the coverage achieved by a conventional planar array antenna is limited to a circular area with a radius of only tens of kilometers. A conventional semi-orthogonal user selection (SUS) scheme based on the orthogonality of channel vectors achieves high capacity, but it has high complexity. First, this paper proposes a cylindrical mMIMO system to achieve an ultra-wide coverage radius of 100km and high capacity. Second, this paper presents a novel angle-based user selection (AUS) scheme, where a user selection problem is formulated as a maximization of the minimum angular difference between users over all user groups. Finally, a low-complexity suboptimal algorithm (SA) for AUS is also proposed. Assuming an area with a 100km radius, simulation results demonstrate that the proposed cylindrical mMIMO system improves the signal-to-interference-plus-noise ratio by approx. 12dB at the boundary of the area, and it achieves approx. 1.5 times higher capacity than the conventional mMIMO which uses a planar array antenna. In addition, the results show that the proposed AUS scheme improves the lower percentiles in the system capacity distribution compared with SUS and basic random user selection. Furthermore, the computational complexity of the proposed SA is in the order of only 1/4000 that of SUS.

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.

Exploiting RIS for Limiting Information Leakage to Active Eavesdropper in Cell-Free Massive MIMO

This letter analyzes the potential of reconfigurable intelligent surfaces (RIS)s in boosting the secrecy capacities of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under pilot spoofing attacks. A novel RIS-based downlink (DL) transmission scheme is proposed in which the DL power coefficients at access points as well as the RIS phase shifts are jointly optimized to minimize the information leakage to eavesdropper while maintaining certain quality-of-service for legitimate users. Simulation results show the great potential of RISs in boosting the robustness of CF mMIMO systems against pilot spoofing attacks. Besides, various insights are drawn towards high-secure RIS-based CF mMIMO systems.

Downlink Processing of Massive MIMO-NOMA Networks Using Cell Sectored Approach for 5G Communication

This article shows the heterogeneous network of Massive Multiple-Input Multiple-Output (mMIMO) system and Non-Orthogonal Multiple Access (NOMA) scheme in Downlink (DL) scenario. The performance of mMIMO systems using Maximal Ratio Transmission (MRT) and Zero-Forcing (ZF) precoding techniques has been investigated and compared with mMIMO-NOMA systems. The problem of Pilot Contamination (PC) arises when the channel is estimated at the Base Station (BS) due to the reuse of the same pilot matrix in co-channel cells. To reduce the co-channel interference, it has been shown that the Cell Sectoring (CS) of 120 degree and 60 degree can be employed. Sum-Rate (SR) capacities have been derived for the mMIMO and the mMIMO-NOMA systems for un-sectored and sectored cells considering both Perfect Channel State Information (PCSI), and Imperfect Channel State Information (ICSI). In a multi-cell scenario, it has been shown that the mMIMO-NOMA system exploits the precoding advantage along with successive interference cancellation and outperforms the standalone mMIMO system. Further, the ZF precoded mMIMO-NOMA system with 60 degree CS has been observed to be the most appropriate candidate amongst all three systems viz. 120 degree CS, 60 degree CS, and un-sectored system in terms of reduced interference.

Uplink Performance of MmWave-Fronthaul Cell-Free Massive MIMO Systems

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising candidate to support the requirements of the fifth-generation (5 G) and beyond networks. However, the capacity of the fronthaul network dramatically influences its performance. While wired fronthaul links can be seen as the optimal choice, they may not be practically feasible. Exploiting the enormous bandwidth available in the millimeter-Wave (mmWave) band to support the fronthaul links paves the way to achieve the full potential of CF mMIMO systems. In this paper, we investigate the uplink (UL) performance of CF mMIMO systems supported by mmWave-fronthaul networks. Using tools from stochastic geometry, we derive analytical expressions for both the distribution of the provided fronthaul capacity and the average UL data rates. We show that although increasing the density of blockages degrades the average UL data rates, increasing the density of CPUs can limit such effect. Moreover, the obtained results reveal that the network deployment should be adjusted according to the available fronthaul bandwidth and the density of blockages. In particular, for a given fronthaul bandwidth, increasing the density of APs beyond a certain limit would not achieve further improvement in the UL data rates. Besides, increasing the number of antennas per AP may even cause a degradation in the system performance.

Adaptive Random Spatial based Channel Estimation (ARSCE) for Millimeter Wave MIMO System

Millimeter-wave and mMIMO communications are the most essential success systems for next-generation wireless sensor networks to have enormous amounts of accessible throughput and spectrum. Through installing huge antenna arrays at the base station and performing coherent transceiver processing, mMIMO is a potential technology for enhancing the bandwidth efficiency of wireless sensor networks. The use of mmWave frequencies for mMIMO systems solves the problem of high path-loss through offering greater antenna gains. In this work, we provide a design with a random spatial sample structure that incorporates a totally random step before the analogue is received. It contains a totally random step before the analogue received signals are sent into the digital component of the HBF receiver. Adaptive random spatial based channel estimation (ARSCE) is proposed for channel session measurement collection, and an analogue combiner with valves has been used to estimate the signals at each receiving antenna. The proposed optimization problem formulation attempts to discover the orientations and gains of wideband channel routes. In addition, our proposed model has compared to various state-of-art techniques while considering error minimization.

Low-Complexity and Robust Framework of Precoding for Multi-Panel Massive MIMO

Massive multiple-input multiple-output (m-MIMO) is used to improve the robustness of data transmission and high sum spectral efficiency in 5G systems. For 5G New Radio (NR) systems need to be designed to reduce cost and complexity, most notably hybrid analog-digital beamforming with large-scale antenna array which has become a major research target. In the downlink transmission, we propose a robust and low-complexity framework of precoding design to implement hybrid beamforming (BF) of large antenna arrays without limitation on the size of the antenna. Furthermore, a large number of antenna elements can be coupled into multiple panels m-MIMO for the purpose of reducing costs and saving power. This study proposes to use both azimuth and elevation angles parameters to provide low complexity adaptive beam control in panels m-MIMO system. In contrast to existing works, that aim to eliminate inter-user interference, this work is distinguished by adopting polarization function and space-time block code to the m-MIMO antenna arrays. We also propose a new multi-panel codebook design correspondingly with control parameters. The performance of the proposed framework is analysed with state-of-the-art research comparisons. To this end, the important factor of beamforming which is the Error Vector Magnitude (EVM) will be analyzed and simulated.