NOMA Networks Research Articles

Full-Duplex Cooperative NOMA Network With Multiple Eavesdroppers and Non-Ideal System Imperfections: Analysis of Physical Layer Security and Validation Using Deep Learning

Full-Duplex Cooperative NOMA Network With Multiple Eavesdroppers and Non-Ideal System Imperfections: Analysis of Physical Layer Security and Validation Using Deep Learning

Finite SNR Diversity-Multiplexing Trade-Off in Hybrid ABCom/RCom-Assisted NOMA Networks

Finite SNR Diversity-Multiplexing Trade-Off in Hybrid ABCom/RCom-Assisted NOMA Networks

A Distributed Learning Algorithm for Power Control in Energy Efficient IRS Assisted SISO NOMA Networks

A Distributed Learning Algorithm for Power Control in Energy Efficient IRS Assisted SISO NOMA Networks

Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks

Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks

Throughput Maximization for RF Powered Cognitive NOMA Networks With Backscatter Communication by Deep Reinforcement Learning

Throughput Maximization for RF Powered Cognitive NOMA Networks With Backscatter Communication by Deep Reinforcement Learning

Incremental Relaying with Partial Relay Selection for Enhancing the Performance of Underlay Cognitive NOMA Networks

Incremental Relaying with Partial Relay Selection for Enhancing the Performance of Underlay Cognitive NOMA Networks

Energy-Harvesting-Enabled UAV Relay-Assisted NOMA Network Under Finite Blocklength

Energy-Harvesting-Enabled UAV Relay-Assisted NOMA Network Under Finite Blocklength

Outage and average sum‐rate analysis of intelligent reflecting surface assisted nonorthogonal multiple access system over η−μ fading channel

SummaryIntelligent reflecting surface (IRS) is evolved as a one of key technology by enabling a reconfigurable, intelligent, and low power for the sixth‐generation (6G) wireless communication. In this research, an IRS‐assisted NOMA network is explored over fading channel, where IRS is placed on top of the base station (BS). IRS aids in fine‐tuning the phase of the incoming signal from BS in a meticulous way, which improves the performance of the system. The statistical channel modeling of downlink IRS‐NOMA system is proposed and validated with Monte Carlo (MC) simulation. Also, analytical expressions of OP and average sum‐rate are derived for user in IRS‐NOMA system over fading channel. Furthermore, the influence of performance factors such as number of reflecting elements (M), power allocation factor, and imperfect successive interference cancelation (I‐SIC) on OP are examined. Simulation results reveal that the IRS‐NOMA system experiences less outage compared to IRS‐OMA and conventional relaying techniques.

Performance evaluation of NOMA networks powered by harvested energy with antenna selection under $$\kappa -\mu $$ shadowed fading

Performance evaluation of NOMA networks powered by harvested energy with antenna selection under $$\kappa -\mu $$ shadowed fading

Exploiting Active RIS in NOMA Networks With Hardware Impairments

Exploiting Active RIS in NOMA Networks With Hardware Impairments

Performance analysis of covert communication in IRS-assisted NOMA networks

Performance analysis of covert communication in IRS-assisted NOMA networks

BER Analysis of Multi-User NOMA Networks in the Presence of Interference

BER Analysis of Multi-User NOMA Networks in the Presence of Interference

Multi-armed bandit approach for mean field game-based resource allocation in NOMA networks

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process.

Outage probability constrained resource allocation scheme in two-tier cooperative NOMA network with SWIPT

Outage probability constrained resource allocation scheme in two-tier cooperative NOMA network with SWIPT

Self‐attention convolutional neural network optimized with Remora Optimization Algorithm for energy aware resource management in Non‐orthogonal Multiple Access networks

SummaryNon‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively.

Mobile Power Allocation Intelligent Optimization Algorithm for Cooperative NOMA Network Based on CBAM-BiLSTM

Mobile Power Allocation Intelligent Optimization Algorithm for Cooperative NOMA Network Based on CBAM-BiLSTM

Secure Communication of Active RIS Assisted NOMA Networks

Secure Communication of Active RIS Assisted NOMA Networks

Secure MIMO NOMA transmission with energy harvesting-aided full-duplex jammer under erroneous channel information

NOMA allows for the simultaneous transmission of multiple user signals and full-duplex (FD) communication enables concurrent transmit-and-receive operations, both improving spectral efficiency. Additionally, NOMA networks utilize energy harvesting (EH) to efficiently harvest energy from radio frequency sources, contributing to improved energy efficiency. In addition, deploying multiple antennas at NOMA users facilitates energy transfer and harvesting, as well as reliable information transmission through antenna array processing. Moreover, jamming is presented as a useful method to secure wireless transmission. Therefore, multiple-input multiple-output (MIMO) NOMA transmission with EH-aided FD jammer (MMnOehFD) is envisioned to acquire high performance like energy efficiency, security, reliability, and throughput. The paper numerically assesses the reliability and security of MMnOehFD where evaluation metrics include energy efficiency, throughput, and outage probability. Further, practical factors such as erroneous channel information (ECI) and multi-antenna users are taken into account in the evaluation. Results indicate that the ECI significantly degrades system performance, and this degradation is influenced by multiple parameters. Also, the proposed MMnOehFD can avoid complete outage and attain optimal performance with appropriate system configuration. The performance of the MMnOehFD is notably improved with an increasing number of antennas. Further, the MMnOehFD outperforms two baseline schemes (MIMO OMA transmission with EH-aided FD jammer and MIMO NOMA transmission without jammer), highlighting the effectiveness of combining jammer and NOMA.

FPGA-based hardware accelerator for SIC in uplink NOMA networks

FPGA-based hardware accelerator for SIC in uplink NOMA networks

Optimal RIS Partitioning and Power Control for Bidirectional NOMA Networks

Optimal RIS Partitioning and Power Control for Bidirectional NOMA Networks