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Found 27 papers, 1 papers with code
Precoder Design and Statistical Power Allocation for MIMO-NOMA via User-Assisted Simultaneous Diagonalization
In this paper, we investigate the downlink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA).
Information Theory Information Theory
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks
In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated.
NOMA for Energy-Efficient LiFi-Enabled Bidirectional IoT Communication
In order to improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of the system.
Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems
In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good tradeoff between the two performance metrics.
Energy-Efficient Design of IRS-NOMA Networks
Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency.
Joint Optimization of Beamforming, Phase-Shifting and Power Allocation in a Multi-cluster IRS-NOMA Network
To address this non-convex problem, we propose an alternating optimization based algorithm.
Energy-Efficient Resource Allocation for NOMA enabled MEC Networks with Imperfect CSI
In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions.
Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks
We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts.
What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?
Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks.
Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems
Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs).
Information Theory Signal Processing Information Theory
On the Application of BAC-NOMA to 6G umMTC
This letter studies the application of backscatter communications (BackCom) assisted non-orthogonal multiple access (BAC-NOMA) to the envisioned sixth-generation (6G) ultra-massive machine type communications (umMTC).
Information Theory Information Theory
IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization
By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain.
I/Q Imbalance Aware Nonlinear Wireless-Powered Relaying of B5G Networks: Security and Reliability Analysis
Physical layer security is known as a promising paradigm to ensure security for the beyond 5G (B5G) networks in the presence of eavesdroppers.
IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity
In this paper, the appealing features of a dual-polarized intelligent reflecting surface (IRS) are exploited to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC).
Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks
This paper investigates the application of deep deterministic policy gradient (DDPG) to intelligent reflecting surface (IRS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks.
A Reinforcement Learning Approach for an IRS-assisted NOMA Network
This letter investigates a sum rate maximizationproblem in an intelligent reflective surface (IRS) assisted non-orthogonal multiple access (NOMA) downlink network.
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications
Due to the non-convexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL).
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning
Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of the wireless network, where users located at the different sides of the surfaces can be simultaneously served by the transmitting and reflecting signals.
An Interdisciplinary Approach to Optimal Communication and Flight Operation of High-Altitude Long-Endurance Platforms
We design optimal power allocation for downlink (DL) NOMA users along with the ideal position and speed of flight with the aim to maximize sum data rate during the day and minimize power expenditure during the night, while ensuring quality of service.
Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications
A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design.
Joint Robust Beamforming Design for WPT-assisted D2D Communications in MISO-NOMA: Fractional Programming and Deep Reinforcement Learning
The proposed PFP algorithm and the DDPG-based algorithm are compared in the presence of different channel estimation errors.
Secrecy Sum Rate Maximization for a MIMO-NOMA Uplink Transmission in 6G networks
Non-orthogonal multiple access (NOMA), as a well-qualified candidate for sixth-generation (6G) mobile networks, has been attracting remarkable research interests due to high spectral efficiency and massive connectivity.
Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach
The problem of joint control of the RIS's amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS.
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning
Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated.
GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks
This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station.
Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks
The numerical results are presented and show that: 1) ASTARS-NOMA with pSIC outperforms ASTARS assisted-orthogonal multiple access (ASTARS-OMA) in terms of outage probability and ergodic data rate; 2) The outage probability of ASTARS-NOMA can be further reduced within a certain range by increasing the power amplification factors; 3) The system throughputs of ASTARS-NOMA are superior to that of ASTARS-OMA in both delay-limited and delay-tolerant transmission modes.
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks
This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels.
