Massive Multiple-input Multiple-output Non-orthogonal Multiple Access Research Articles

Enhanced Energy Transfer Efficiency for IoT-Enabled Cyber-Physical Systems in 6G Edge Networks with WPT-MIMO-NOMA

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks.

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Energy efficient MIMO–NOMA aided IoT network in B5G communications

To accelerate future intelligent wireless systems , we designed an energy-efficient Massive multiple-input-multiple-output (MIMO)- non-orthogonal multiple access (NOMA) aided internet of things (IoT) network in this paper to support the massive number of distributed users and IoT devices with seamless data transfer and maintain connectivity between them. Massive MIMO has been identified as a suitable technology to implement the energy efficient IoT network in beyond 5G (B5G) communications due to its distinct characteristics with large number of antennas. However, to provide fast data transfer and maintain hyper connectivity between the IoT devices in B5G communications will bring the challenge of energy deficiency. Hence, we considered a massive MIMO–NOMA aided IoT network considering imperfect channel state information and practical power consumption at the transmitter. The far users of the base stations are selected to investigate the power consumption and quality of service. Then, calculate the power consumption which is non-convex function and non-deterministic polynomial problem. To solve the above problem, fractional programming properties are applied which converted polynomial problem into the difference of convex function . And then we employed the successive convex approximation technique to represent the non-convex to convex function. Effective iterative based branch and the reduced bound process are utilized to solve the problem. Numerical results observe that our implemented approach surpasses previous standard algorithms on the basis of convergence, energy-efficiency and user fairness.

Beamspace NOMA Using User Clustering and Throughput Optimisation Algorithms for Massive MIMO

Massive Multiple Input Multiple Output (MIMO) using millimetre wave transmissions received significant attention due to its significance of high data rate. However, achieving energy and spectrum efficient millimetre wave communications is challenging due to the dedicated Radio Frequency (RF) chain. Non-Orthogonal Multiple Access (NOMA) is used in beamspace MIMO (BS MIMO) significantly overcome such challenges. This paper proposes the enhanced approach of beamspace MIMO NOMA using a simple yet effective clustering solution with a C-NOMA throughput optimisation algorithm. This proposal involves the lightweight user clustering, lens antenna, and clustering-based iterative power allocation algorithm to enhance each cluster's spectral and energy efficiency performance. After cluster formation, the throughput optimisation function applies. Iterative power optimisation method is proposed to allocate power to each user in each cluster dynamically. Therefore, compared to recent clustering and NOMA methods, the proposed BS MIMO C-NOMA improves Energy Efficiency (EE) and Spectral Efficiency (SE) with minimum computational overhead. Results demonstrate that high EE and SE, respectively, as compared with the percentage of improvement of 26% and 37% in the existing BS MIMO NOMA and improvement of 16.47 % and 27.72 % in User Clustering based on Channel Gain (UCCG) MIMO NOMA among 50 and 100 users.

Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks

With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed data rate and massive connectivity requirements. Nevertheless, the unreliability of THz transmissions and the extreme heterogeneity of device requirements pose critical challenges for practical applications. To address these challenges, we propose a novel smart reconfigurable THz MIMO-NOMA framework, which can realize customizable and intelligent communications by flexibly and coordinately reconfiguring hybrid beams through the cooperation between access points (APs) and reconfigurable intelligent surfaces (RISs). The optimization problem is formulated as a decentralized partially-observable Markov decision process (Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the diversified users’ performance, via a joint RIS element selection, coordinated discrete phase-shift control, and power allocation strategy. To solve the above non-convex, strongly coupled, and highly complex mixed integer nonlinear programming (MINLP) problem, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph-embedded value-decomposition actor-critic (GE-VDAC)</i> , that embeds the interaction information of agents, and learns a locally optimal solution through a distributed policy. Numerical results demonstrate that the proposed algorithm achieves highly customized communications and outperforms traditional MADRL algorithms.

A weighted-beam-superposition method for mmWave massive MIMO-NOMA systems

Millimeter wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) are recognized as key technologies in the forthcoming beyond the fifth-generation (B5G) and the sixth-generation (6G) wireless networks. In this paper, we propose a multi-beam MIMO NOMA scheme where each beam is augmented by weighted beam superposition to focus the beam in a manner targeted at each user. This method exploits the full array of antennas within each beam to maximize the gain of each beam. To guarantee fairness for the weak user (lower channel gain) in each group, we use a method of power allocation, which satisfies the weak user with a minimum demanded for the quality of service (QoS). To improve further the sum rate when the QoS of the weak users is satisfied, the coefficient of strong users are set to the largest value. In system simulations, we compare the performance of differnet multi beam schemes, together with a single beam scheme, and a Time Division Multiple Access (TDMA) scheme at different levels of the signal noise ratio (SNR). Based on the simulation results, the proposed method significantly increases system sum rate.

Clustering and power optimization in mmWave massive MIMO–NOMA systems

Integrating massive multiple input multiple output (MIMO) architecture and non-orthogonal multiple access (NOMA) technology is deemed as a promising solution to the gridlock of ever increasing demand in data rate via extending communication frequency to millimeter wave (mmWave) band and improving system spectrum efficiency. In this paper, we consider the downlink operation of MIMO–NOMA systems, where the base station (BS) is equipped with massive antenna array that uses hybrid precoding to reduce the number of required radio-frequency (RF) chains without performance loss with less hardware cost. A clustering strategy and a joint power allocation scheme of such systems are proposed to maximize the sum-rate. Specifically, we adopt the concept of chordal distance in the clustering strategy, i.e., the selection of cluster head users and assignment of other users in each beam. Finally, we consider a power allocation scheme for each user that takes care of inter-beam and intra-beam interference. Upon the formulation of the power allocation problem while considering all relevant constraints, it is transformed to a dual problem via the Lagrange duality theorem, and then the optimal power allocation is derived by the optimal dual value. Simulation results show that the proposed scheme can achieve better performance in terms of spectrum efficiency compared to the existing methods, and the mmWave-based massive MIMO–NOMA system outperforms the MIMO–OMA system.

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks With Wireless Power Transfer: A Distributed ADMM Approach

In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.

Low Complexity Optimization of the Asymptotic Spectral Efficiency in Massive MIMO NOMA

Massive multiple-input multiple-output (MIMO) technology facilitates huge increases in the capacity of wireless channels, while non-orthogonal multiple access (NOMA) addresses the problem of limited resources in traditional orthogonal multiple access (OMA) techniques, promising enhanced spectral efficiency. This work uses asymptotic capacity computation results to reduce the complexity of a power allocation algorithm for small-scale MIMO-NOMA, so that it may be applied for systems with massive MIMO arrays. The proposed method maximizes the sum-capacity of the considered system, subject to power and performance constraints, and demonstrates greater accuracy than alternative approaches despite remaining low-complexity for arbitrarily large antenna arrays.

Massive MIMO–NOMA Networks With Multi-Polarized Antennas

This paper aims to design and evaluate the performance of multi-cluster multi-user dual-polarized massive multiple-input multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode in which a single base station communicates with multiple users, with all terminals being equipped with multiple co-located dual-polarized antennas, two precoder designs are proposed: (i) the first one aims to maximize the number of user groups that are simultaneously served within a cluster; and (ii) the second approach aims to provide further improvements compared to the first one by exploring polarization diversity. Closed-form expressions for the outage probability are derived for both approaches, based on which the respective asymptotic studies are carried out and the diversity gains are determined. The ergodic sum-rates are also derived. Representative numerical examples are presented along with insightful discussions. For instance, our results show that the proposed dual-polarized MIMO-NOMA designs outperform conventional single-polarized systems, even for high cross-polar interference. Simulation results are plotted to corroborate the analytical framework and analysis.

User assisted cooperative relaying in beamspace massive MIMO NOMA based systems for millimeter wave communications

A novel scheme ‘user assisted cooperative relaying in beamspace massive multiple input multiple output (M-MIMO) non-orthogonal multiple access (NOMA) system’ has been proposed to improve coverage area, spectrum and energy efficiency for millimeter wave (mmWave) communications. A downlink system for M users, where base station (BS) is equipped with beamforming lens antenna structure having NRF radio frequency (RF) chains, has been considered. A dynamic cluster of users is formed within a beam and the intermediate users (in that cluster) between beam source and destination (user) act as relaying stations. By the use of successive interference cancellation (SIC) technique of NOMA within a cluster, the relaying stations relay the symbols with improved power to the destination. For maximizing achievable sum rate, transmit precoding and dynamic power allocation for both intra and inter beam power optimization are implemented. Simulations for performance evaluation are carried out to validate that the proposed system outperforms the conventional beamspace M-MIMO NOMA system for mmWave communications in terms of spectrum and energy efficiency.

A C-V-BLAST spread spectrum massive MIMO NOMA scheme for 5G systems with channel imperfections

In this paper, a spread spectrum non orthogonal multiple access (NOMA) scheme with massive multiple input multiple output (MIMO) deployment is investigated. The proposed scheme utilizes a low complexity Vertical Bell Laboratories Layered Space Time (C-V-BLAST) receiver in an uplink setting with distributed single-antenna users and a co-located base-station with massive antenna array system. We study the performance of the proposed scheme under a variety of channel imperfections including imperfect channel estimation, channel correlation, and mutual coupling among antenna elements. Analytical evaluation of the system performance under these conditions is presented in terms of the group error probability and validated through extensive simulations. It is shown that the proposed spread spectrum NOMA scheme provides significant improvements in performance compared to conventional V-BLAST system in case of a large MIMO deployment under channel imperfections.