Search Results for author: Zhiguo Ding

Found 29 papers, 1 papers with code

Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity

no code implementations24 May 2024 Kaidi Wang, Zhiguo Ding, Daniel K. C. So, Zhi Ding

To further improve learning performance by increasing device participation under the maximum time consumption constraint, we formulate an energy consumption minimization problem by including resource allocation and sub-channel assignment.

Federated Learning

On the Reliability and Security of Ambient Backscatter Uplink NOMA Networks

no code implementations11 May 2024 Athanasios P. Chrysologou, Nestor D. Chatzidiamantis, Alexandros-Apostolos A. Boulogeorgos, Zhiguo Ding

In addition, assuming that one NOMA-user transmits an artificial noise in order to enhance system's security, the physical layer security (PLS) of the system is investigated by extracting analytical expressions for NOMA-users' and BD's intercept probabilities (IPs).

Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks

no code implementations5 Mar 2024 Yushen Lin, Kaidi Wang, Zhiguo Ding

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.

Federated Learning

Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks

no code implementations25 Jan 2024 Xinwei Yue, Jin Xie, Chongjun Ouyang, Yuanwei Liu, Xia Shen, Zhiguo Ding

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.

GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks

no code implementations7 Nov 2023 Yuhang Li, Yang Lu, Bo Ai, Octavia A. Dobre, Zhiguo Ding, Dusit Niyato

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.

Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning

no code implementations3 Nov 2023 Bibo Wu, Fang Fang, Xianbin Wang, Donghong Cai, Shu Fu, Zhiguo Ding

Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated.

Federated Learning Problem Decomposition +1

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

no code implementations11 Apr 2023 Zhaoyuan Shi, Huabing Lu, Xianzhong Xie, Helin Yang, Chongwen Huang, Jun Cai, Zhiguo Ding

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.

reinforcement-learning

Joint Robust Beamforming Design for WPT-assisted D2D Communications in MISO-NOMA: Fractional Programming and Deep Reinforcement Learning

no code implementations25 Sep 2022 Shiyu Jiao, Fang Fang, Zhiguo Ding

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

no code implementations Physical Communication 2022 Yunus Dursun, Kaidi Wang, Zhiguo Ding

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.

Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications

no code implementations28 Apr 2022 Xiaoxia Xu, Yuanwei Liu, Qimei Chen, Xidong Mu, Zhiguo Ding

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.

Graph Neural Network Scheduling

An Interdisciplinary Approach to Optimal Communication and Flight Operation of High-Altitude Long-Endurance Platforms

no code implementations1 Mar 2022 Sidrah Javed, Mohamed-Slim Alouini, Zhiguo Ding

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.

Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning

no code implementations30 Nov 2021 Yi Guo, Fang Fang, Donghong Cai, Zhiguo Ding

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.

Reinforcement Learning (RL)

Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications

no code implementations17 Sep 2021 Zhaoyuan Shi, Xianzhong Xie, Huabing Lu, Helin Yang, Jun Cai, Zhiguo Ding

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).

Management reinforcement-learning +1

A Reinforcement Learning Approach for an IRS-assisted NOMA Network

no code implementations17 Jun 2021 Ximing Xie, Shiyu Jiao, Zhiguo Ding

This letter investigates a sum rate maximizationproblem in an intelligent reflective surface (IRS) assisted non-orthogonal multiple access (NOMA) downlink network.

reinforcement-learning Reinforcement Learning (RL)

Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks

no code implementations17 Jun 2021 Shiyu Jiao, Ximing Xie, Zhiguo Ding, Fellow, IEEE

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.

Position reinforcement-learning +1

IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity

no code implementations27 May 2021 Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, F. Rafael M. Lima, Liang Yang, Petar Popovski, Zhiguo Ding, Constantinos B. Papadias

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).

On the Application of BAC-NOMA to 6G umMTC

no code implementations12 Feb 2021 Zhiguo Ding, H. Vincent Poor

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

Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems

no code implementations2 Feb 2021 Weiheng Jiang, Yu Zhang, Jun Zhao, Zehui Xiong, Zhiguo Ding

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

IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization

no code implementations7 Dec 2020 Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, F. Rafael M. Lima, Liang Yang, Petar Popovski, Zhiguo Ding, Constantinos B. Papadias

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.

What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?

no code implementations20 Sep 2020 Arthur S. de Sena, Dick Carrillo, Fang Fang, Pedro H. J. Nardelli, Daniel B. da Costa, Ugo S. Dias, Zhiguo Ding, Constantinos B. Papadias, Walid Saad

Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks.

Fairness

Energy-Efficient Resource Allocation for NOMA enabled MEC Networks with Imperfect CSI

no code implementations14 Sep 2020 Fang Fang, Kaidi Wang, Zhiguo Ding, Victor C. M. Leung

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.

Edge-computing

Joint Optimization of Beamforming, Phase-Shifting and Power Allocation in a Multi-cluster IRS-NOMA Network

no code implementations14 Sep 2020 Ximing Xie, Fang Fang, Zhiguo Ding

To address this non-convex problem, we propose an alternating optimization based algorithm.

Energy-Efficient Design of IRS-NOMA Networks

no code implementations11 Sep 2020 Fang Fang, Yanqing Xu, Quoc-Viet Pham, Zhiguo Ding

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency.

Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks

no code implementations11 Sep 2020 Fang Fang, Yanqing Xu, Zhiguo Ding, Chao Shen, Mugen Peng, George K. Karagiannidis

We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts.

Edge-computing

Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems

no code implementations19 Jun 2020 Haitham Al-Obiedollah, Kanapathippillai Cumanan, Jeyarajan Thiyagalingam, Jie Tang, Alister G. Burr, Zhiguo Ding, Octavia A. Dobre

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.

I/Q Imbalance Aware Nonlinear Wireless-Powered Relaying of B5G Networks: Security and Reliability Analysis

no code implementations6 Jun 2020 Xingwang Li, Mengyan Huang, Yuanwei Liu, Varun G Menon, Anand Paul, Zhiguo Ding

Physical layer security is known as a promising paradigm to ensure security for the beyond 5G (B5G) networks in the presence of eavesdroppers.

NOMA for Energy-Efficient LiFi-Enabled Bidirectional IoT Communication

no code implementations20 May 2020 Chen Chen, Shu Fu, Xin Jian, Min Liu, Xiong Deng, Zhiguo Ding

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.

Precoder Design and Statistical Power Allocation for MIMO-NOMA via User-Assisted Simultaneous Diagonalization

1 code implementation5 May 2020 Aravindh Krishnamoorthy, Zhiguo Ding, Robert Schober

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

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