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

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.

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.

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.