Multi-user Massive MIMO Research Articles

AoD Based Joint User-Antenna Scheduling Schemes with Limited Feedback for Multi-User Massive MIMO FDD Systems

AoD Based Joint User-Antenna Scheduling Schemes with Limited Feedback for Multi-User Massive MIMO FDD Systems

Analysis of Reduced Quantized Feedback-Based User-Antenna Scheduling Scheme for Multi-user Massive MIMO FDD Systems

Analysis of Reduced Quantized Feedback-Based User-Antenna Scheduling Scheme for Multi-user Massive MIMO FDD Systems

Detecting malicious pilot contamination in multiuser massive MIMO using decision trees

Detecting malicious pilot contamination in multiuser massive MIMO using decision trees

Statistical spatial information based power optimization algorithm for multi-user massive MIMO systems

Power optimization has a significant role in multi-user massive multiple-input and multiple-output (mMIMO) wireless communication systems, whereas the acquisition of instantaneous channel state information (I-CSI) is a serious challenge in practical scenarios. The statistical spatial information (SPI), which contains angle-of-departures (AoDs) and corresponding large-scale fading factors, is slowly varying. The power optimization based on SPI to improve system performance needs to be explored for multi-user mMIMO systems. In this paper, two novel algorithms for SPI-based power optimization are proposed to maximize the ergodic sum-rate (ESR) under total power constraint for the system. Specifically, the minorization-maximization-based power optimization (MM-PO) algorithm and the closed-form power optimization (CF-PO) algorithm are proposed utilizing the minorization-maximization (MM) method and the fractional programming (FP) method, respectively. Moreover, the ESR is expressed based on the extracted SPI. The approximate expression of the ESR is derived. The complexity of the algorithms depends on the number of users, and the algorithms are scalable in the system. The proposed algorithms can effectively achieve the advantages of SPI while being robust to the uncertainty of I-CSI. Simulation results verify the ESR of our two proposed algorithms is 1.47 and 1.4 times higher than that of the equal power allocation algorithm at a high SNR when the number of antenna and user are equal to 64 and 20, respectively.

Deep Learning-Based CFO Estimation for Multi-User Massive MIMO With One-Bit ADCs

Deep Learning-Based CFO Estimation for Multi-User Massive MIMO With One-Bit ADCs

Operating Multi-User Massive MIMO Networks: Trade-Off Between Performance and Runtime

Operating Multi-User Massive MIMO Networks: Trade-Off Between Performance and Runtime

Spectral efficiency of hybrid precoding and combining design for mm-Wave multi-user massive MIMO systems

Spectral efficiency of hybrid precoding and combining design for mm-Wave multi-user massive MIMO systems

QRD-based precoding design for multiuser massive MIMO null-space expansion

QRD-based precoding design for multiuser massive MIMO null-space expansion

Location-driven beamforming for massive multi-user MIMO systems

Location-driven beamforming for massive multi-user MIMO systems

Design and analysis of quantized feedback based user-antenna joint scheduling scheme for ongoing 5G and beyond multi-user massive MIMO FDD communication systems

For the efficient user scheduling (US) and interference mitigation, channel state information (CSI) is mandatory at the base station (BS), particularly for the frequency division duplexing (FDD) systems where the users need to provide some CSI back to the BS for performing the scheduling process. As the feedback overhead increases drastically with increase of antennas as well as users, so, US with limited/reduced feedback is essential. Deploying massive multiple-input multiple-output frequency division duplexing (mMIMO FDD) systems remains an open area for research with major difficulties of associated uplink CSI feedback load as the downlink and uplink channels are not reciprocal. Therefore, smart balancing is required between feedback data and achievable throughput while scheduling the users. In order to handle the above mentioned issue, we employed a selective 4-bit quantized CSI feedback based user-antenna paired/joint scheduling scheme for the multi user (MU) FDD mMIMO systems. The key idea involves in this process is that, only a limited set of users qualifying a predefined selection threshold feed back the 4-bit quantized signal-to-interference-plus-noise ratio (SINR) values based on angle-of-departure (AoD) along with the antenna indices for the scheduling process. The BS schedules the users with respective best antenna. Furthermore, using this scheme the multi user diversity gain is also achieved. This dynamic adjustment of antenna/user selection and collision free antenna sharing between the users majorly helps in improving the throughput of the system with this selective 4-bit quantized limited feedback. The mathematical sum rate performance analysis has been demonstrated along with simulation results for different system parameters.

Interleaved Training Scheme for Multi-User Massive MIMO Downlink With User SINR Constraint

Interleaved Training Scheme for Multi-User Massive MIMO Downlink With User SINR Constraint

Energy Harvesting From Jamming Attacks in Multi-User Massive MIMO Networks

Energy Harvesting From Jamming Attacks in Multi-User Massive MIMO Networks

Receive Beamforming Designed for Massive Multi-User MIMO Detection via Gaussian Belief Propagation

Receive Beamforming Designed for Massive Multi-User MIMO Detection via Gaussian Belief Propagation

Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink

Full-dimensional (FD) multi-user massive multiple-input multiple output (m-MIMO) systems employ large two-dimensional (2D) rectangular antenna arrays to control both the azimuth and elevation angles of signal transmission. We introduce the sum of two outer products of the azimuth and elevation beamforming vectors having moderate dimensions as a new class of FD beamforming. We show that this low-complexity class is capable of outperforming 2D beamforming relying on the single outer product of the azimuth and elevation beamforming vectors. It is also capable of performing close to its FD counterpart of massive dimensions in terms of either the users' minimum rate or their geometric mean rate (GM-rate), or sum rate (SR). Furthermore, we also show that even FD beamforming may be outperformed by our outer product-based improper Gaussian signaling solution. Explicitly, our design is based on low-complexity algorithms relying on convex problems of moderate dimensions for max-min rate optimization or on closed-form expressions for GM-rate and SR maximization.

Underlay Spectrum Sharing in Massive MIMO Systems

We consider an underlay spectrum sharing scheme, where a multi-user massive MIMO network (referred to as the primary network (PN)) allows another multi-user massive MIMO network (called the secondary network (SN)) to use the spectrum, owned by the PN, to communicate with the SN’s users. For such a scheme, we devise joint power allocation and beamforming schemes at the SN for both conventional time-division duplexing (C-TDD) and reverse time-division duplexing (R-TDD) protocols. This is accomplished by maximizing the weighted uplink and downlink sum-rates of the SN, while protecting the PN from the SN’s interference. Computationally efficient solutions are presented for both protocols. Our simulation results show that our solution for the R-TDD protocol mostly outperforms the solution to the C-TDD mode in terms of the SN’s total sum-rate.

Hybrid Beamforming With Sub-Connected Structure for MmWave Massive Multi-User MIMO Relay Systems

In this paper, we investigate the hybrid beamforming design with sub-connected structure for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) amplify-and-forward (AF) relay systems. Considering the constant-modulus and block-diagonal constraints, an iterative algorithm is proposed to sequentially design the analog beamforming of the base station, relay, and users. Next, the relay baseband combiner is designed by transforming the highly complicated non-convex mutual information maximization problem into an easily tractable weighted minimum mean squared error one. To mitigate the inter-user interference, a successive serial interference cancellation based and a piecewise successive approximation based methods are developed for the uniform sparse distribution and dense distribution scenarios, respectively. Simulation results demonstrate that the two proposed hybrid beamforming schemes can achieve good performance in terms of sum-rate and energy efficiency, and outperform benchmarks significantly. In addition, the two proposed schemes are shown to be robust to imperfect channel state information (CSI), even though they are studied based on perfect CSI.

Performance Evaluation of Hybrid Precoder Design for Multi-User Massive MIMO Systems with Low-Resolution ADCs/DACs

This paper presents a comprehensive analysis and design of a hybrid precoding system tailored for mmWave multi-user massive MIMO systems in both downlink and uplink scenarios. The proposed system employs a two-stage precoding approach, incorporating UQ and NUQ techniques, along with low-resolution DACs in downlink and ADCs in uplink to address hardware limitations. The system considers Zero Forcing and Minimum Mean Square Error algorithms as digital precoding methods for the uplink scenario, while exploring the impact of different DAC resolutions on system performance. Extensive simulations reveal that the proposed system surpasses conventional analog beamforming methods, particularly in multi-user scenarios involving inter-user interference. In downlink, the system demonstrates a trade-off between SE and EE, achieving higher Energy Efficiency with NUQ. In uplink, NUQ and UQ converters exhibit similar performance trends regardless of the chosen combiner algorithm. The proposed system attains enhanced Spectral and Energy Efficiency while maintaining reduced complexity and overhead. The study significantly contributes to the advancement of efficient and effective mmWave multi-user massive MIMO systems by providing a thorough analysis of various quantization schemes and precoding techniques. The findings of this research are expected to aid in the optimization of 5G and beyond technologies, particularly in high-density deployment scenarios.

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.

Deep neural network design with SLNR and SINR criterions for downlink power allocation in multi-cell multi-user massive MIMO systems

In this paper, we propose a deep learning approach for solving power allocation problems in massive MIMO networks. We use signal-to-interference-plus-noise-ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design to define the max–min and max-prod power allocation challenges. The power allocation process to each user equipment in the base station coverage takes a long time and is inefficient, hence numerous base stations are deployed to serve multiple user equipments. As a result, we develop a deep neural network (DNN) framework in which the user’s equipment position is utilized to train the deep model, which is then used to forecast the ideal power distribution depending on the user’s location. Compared to the traditional optimization approach, the DNN design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning achieves excellent results in forecasting power allocation with an accuracy of 85% for the max–min strategy and 99% for the max-product approach.

Planning 5G Networks for Rural Fixed Wireless Access

We study the planning of a rural 5G multi-user massive MIMO fixed wireless access system to offer fixed broadband service to homes. Specifically, we aim to determine the user limit, i.e., the maximum number of homes that can simultaneously receive target minimum bit rates (MBRs) on the downlink (DL) and on the uplink (UL) given a set of network resources and a cell radius. To compute that limit, we must understand how resources should be shared between the DL and UL and how user and stream selection, precoding and combining, and power distribution should be performed. We use block diagonalization and propose a static grouping strategy that organizes homes into fixed groups (of possibly different sizes) in the DL and UL; then we develop a simple approach to compute the user limit that we validate numerically. We study the impact of group size and show that smaller groups yield larger user limits in a 3.5 GHz band. We show how the user limit at different cell radii is impacted by the system bandwidth, the number of antennas at the base station and homes, the transmit power, and the DL and UL MBRs. Lastly, we offer insights into how the network could be operated.