IEEE Transactions on Green Communications and Networking

Miao Zhang

Xuran Ding

Yanqun Tang

Shixun Wu

Kai Xu

STAR-RIS Assisted Secrecy Communication With Deep Reinforcement Learning

The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become significant concerns for the mobile operators. As the continuous decline of the renewable energy cost, equipping the power-hungry BSs with renewable energy generators could be a sustainable solution. In this work, we propose an energy storage aided renewable energy supply solution for the BS, which could supply clean energy to the BS and store surplus energy for backup usage. Specifically, to flexibly regulate the battery's discharging/charging, we propose a deep reinforcement learning based regulating policy, which can adapt to the dynamical renewable energy generations as well as the varying power demands. Our experiments using the real-world data on renewable energy generations and power demands demonstrate that, our power supply solution can achieve an cost saving ratio of 77.9%, compared to the case with traditional power grid supply.

Hao Yuan

Guoming Tang

Deke Guo

Kui Wu

Xun Shao

Keping Yu

Wei Wei

IEEE Trans. on Green Commun. Netw.

BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

With 5G technology evolving, Open Radio Access Network (O-RAN) solutions are becoming crucial, especially for handling the diverse Quality of Service (QoS) needs in vehicular networks. These networks are dynamic and have many different applications, calling for effective O-RAN strategies. This paper examines a three-tier hierarchical O-RAN slicing model, created to address the unique challenges of vehicular networks. The top-level follow 3GPP standards like ultra-reliable and low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). The middle level is organized by vehicle types, and the lowest level is designed for specific vehicle applications. This approach leads to better network resource management. Additionally, this study explores the advantages of a federated deep reinforcement learning (DRL) approach for efficient learning while maintaining privacy. It introduces a federated DRL approach incorporating federated averaging and deep deterministic policy gradient (DDPG) techniques, to enhance inter-slice operations and resource allocation in vehicular networks. Lastly, the effectiveness of this algorithm is demonstrated through a small simulation in a vehicular framework.

Bishmita Hazarika

Prajwalita Saikia

Keshav Singh

Chih-Peng Li

Enhancing Vehicular Networks With Hierarchical O-RAN Slicing and Federated DRL

利用分层O-RAN切片和联邦深度强化学习增强车载网络

Intelligent reflecting surface (IRS) has emerged as a promising technology for improving the spectrum and energy efficiency of next-generation wireless communications. Accurately acquiring channel state information (CSI) of the IRS-assisted wireless system is an essential task for reaping the passive beamforming gain of IRS. However, it is quite difficult to directly obtain the CSI of the IRS-assisted wireless system due to the inability of signal processing at the IRS. In this paper, we investigate the downlink channel estimation problem for IRS-assisted massive MIMO systems. We first present a new sparse recovery problem formulation for the cascaded downlink channel estimation, which exploits the sparsity of massive MIMO channels at the base station (BS) side and adopts the sub-surface idea to significantly reduce computational complexity and training overhead. In this case, the corresponding sparse signal recovery problem exhibits a row-sparse feature but is additionally affected by a coupling matrix. It is challenging to recover the row-sparse matrix with the traditional sparse representation methods, as the elements in the row-sparse matrix are highly coupled with each other. To meet the challenge, we employ a hybrid approximate message passing (AMP) framework for the cascaded downlink channel estimation, which consists of a part of expectation propagation approximation (EPA) and a part of two-level generalized approximate message passing (GAMP). We illustrate that combining EPA and GAMP into a hybrid framework can make up for their respective shortcomings. Numerical simulation results verify the superiority of the proposed method.

Lei Zhou

Jisheng Dai

Weichao Xu

Chunqi Chang

Sparse Channel Estimation for Intelligent Reflecting Surface Assisted Massive MIMO Systems

The working scope and working time of small size unmanned aerial vehicles (UAVs) are limited because of the limited battery capacity. In this work, inspired by aerial refueling for aircrafts, we propose a wireless power transmission (WPT) scheme by categorizing the UAVs into charging UAV (CUAV) and mission UAV (MUAV) for charging UAVs without interrupting the mission. In the proposed aerial refueling scheme, mission UAVs (MUAV) can be recharged by charging UAVs on the fly and operate in a perpetual manner. The feasibility of aerially wireless charging for small UAVs is firstly discussed and evaluated. Then we consider a practical application scenario of multiple MUAVs for collecting data from several points of interest, where the MUAVs are recharged by CUAVs. Accordingly, the issue of scheduling the flying path and charging process of each CUAV to minimize the mission time arises. Deep reinforcement learning (DRL) based algorithms for scheduling both single and multiple CUAVs are proposed and deployed. Extensive simulation evaluations demonstrate that, by applying the proposed aerial refueling scheme, CUAVs can explore and optimize the scheduling strategies, thereby improving the system performance in terms of mission completion and charging efficiency.

Kun Zhu

Jia Yang

Yang Zhang

Jiangtian Nie

Wei Yang Bryan Lim

Hongliang Zhang

Zehui Xiong

Aerial Refueling: Scheduling Wireless Energy Charging for UAV Enabled Data Collection

空中加油：为无人机数据采集调度无线能量充电

Yijie Mao

Bruno Clerckx

Derrick Wing Kwan Ng

Wolfgang Utschick

Ying Cui

Timothy N. Davidson

Guest Editorial Special Issue on Rate-Splitting Multiple Access for Future Green Communication Networks

特刊客座编辑：面向未来绿色通信网络的速率分割多址技术

Multi-access edge computing (MEC) and wireless power transfer (WPT) have emerged as promising paradigms to address the bottlenecks of computing power and battery capacity of mobile devices. In this paper, we investigate the integrated scheduling of WPT and task offloading in a rechargeable multi-access edge computing network (RMECN). Specifically, we focus on exploring the tradeoff between energy efficiency, buffer stability, and battery level stability in the RMECN to obtain reasonable scheduling. In addition, we adopt a dynamic Li-ion battery model to describe the charge/discharge characteristics. Given the stochastic nature of channel states and task arrivals, we formulate a stochastic optimization problem that minimizes system energy consumption while ensuring buffer and battery level stability. In this problem, we jointly consider offloading decisions, local central processing unit (CPU) frequency, transmission power, and current of charge/discharge as optimization variables. To solve this stochastic non-convex problem, we first transform it into an online optimization problem using the Lyapunov optimization theory. Then, we propose a distributed algorithm based on game theory to overcome the excessive computation and time consumption of traditional centralized optimization algorithms. The numerical results demonstrate that the proposed tradeoff scheme and corresponding algorithm can effectively reduce the system's energy consumption while ensuring the stability of buffer and battery level.

Chang Liu

Jun-Bo Wang

Cheng Zeng

Yijian Chen

Hongkang Yu

Yijin Pan

Joint Optimization of Transmission and Computation Resources for Rechargeable Multi-Access Edge Computing Networks

可充电多接入边缘计算网络中传输与计算资源的联合优化

Kapal Dev

Chih-Lin I

Raouf Boutaba

Sunder Ali Khowaja

Shao-Yu Lien

Yue Wang

Guest Editorial Special Issue on Green Open Radio Access Networks: Architecture, Challenges, Opportunities, and Use Cases

特刊客座编辑：绿色开放无线接入网络：架构、挑战、机遇与用例

Yihang Tao

Jun Wu

Qianqian Pan

Ali Kashif Bashir

Marwan Omar

O-RAN-Based Digital Twin Function Virtualization for Sustainable IoV Service Response: An Asynchronous Hierarchical Reinforcement Learning Approach

基于O-RAN的可持续IoV服务响应数字孪生功能虚拟化：一种异步分层强化学习方法

Engin Zeydan

Luis Blanco

Josep Mangues-Bafalluy

Suayb S. Arslan

Yekta Turk

Post-Quantum Blockchain-Based Decentralized Identity Management for Resource Sharing in Open Radio Access Networks

后量子区块链驱动的开放无线接入网络中资源共享的去中心化身份管理

短名	IEEE Trans. on Green Commun. Netw.
Journal Impact	5.31
国际分区	TELECOMMUNICATIONS(Q1)
期刊索引	SCI Q2中科院 2 区

ISSN	2473-2400
h-index	46
国内分区	计算机科学(2区)计算机科学电信学(3区)

涉及主题	计算机科学工程类电信计算机网络数学操作系统电气工程物理量子力学统计生物生态学无线人工智能经济热力学数学优化算法功率（物理）频道（广播）分布式计算能量（信号处理）计算机安全能源消耗万维网电子工程高效能源利用
出版信息	出版商: Institute of Electrical and Electronics Engineers Inc.，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2017，原创研究文献占比: 100.00%，自引率:3.80%， Gold OA占比: 8.45%

IEEE Transactions on Green Communications and Networking

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