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Energy-Efficient Wireless Solutions for 6G/B6G
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Energy-Efficient Wireless Solutions for 6G/B6G

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 9927

Special Issue Editors

Dr. Pei Peng

E-Mail Website
Guest Editor
School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: edge computing; distributed systems; resource allocation; coded computing
Dr. Tianheng Xu

E-Mail Website
Guest Editor
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 200120, China
Interests: integrated sensing and communication; multiple access technologies; artificial intelligence
Dr. Shuyi Chen

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: reconfigurable intelligence surface and space-air-ground integrated network

Special Issue Information

Dear Colleagues,

Future 6G/B6G is designed to operate at multi-terabit-per-second data rates along with ultra-low latency, which can support large amounts of data transmissions. With the deployment of massive Internet of Things (IoTs) devices, the generated data will result in high energy demand; thus, energy efficiency becomes one of the important requirements of 6G/B6G. To achieve this goal, novel energy-efficient wireless solutions are required. For example, smart energy resource management is a mechanism that could be employed by future networks to dynamically optimize the balance between energy demand and energy availability. Edge computing allows some latency-sensitive computation tasks to be offloaded to the edge servers instead of being transferred to the cloud servers to shorten the communication distance. This Special Issue aims to bring together experts, scholars, and scientific researchers in relevant fields to share their latest research contributions and expert insights. Topics of interest include, but not limited to, the following:

  • Energy-efficient resource allocation;
  • Energy-efficient architecture for future networks;
  • Smart energy resource management;
  • Energy efficiency in edge computing;
  • Energy-efficient radio technologies;
  • Energy-efficient offloading for 6G;
  • AI-based energy-efficient multiple access technologies;
  • Integrated sensing and communication technologies.

Dr. Pei Peng
Dr. Tianheng Xu
Dr. Shuyi Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • energy-efficient resource allocation
  • energy-efficient architecture for future networks
  • smart energy resource management
  • energy efficiency in edge computing
  • energy-efficient radio technologies
  • energy-efficient offloading for 6G
  • AI-based energy-efficient multiple access technologies
  • integrated sensing and communication technologies

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Published Papers (5 papers)

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Research

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17 pages, 517 KiB  
Article
Joint Base Station Selection and Power Allocation Design for Reconfigurable Intelligent Surface-Aided Cell-Free Networks
by Qingyu Bie, Yuhan Zhang, Yufeng He and Yilin Lin
Electronics 2024, 13(9), 1688; https://doi.org/10.3390/electronics13091688 - 26 Apr 2024
Viewed by 779
Abstract
Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers [...] Read more.
Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers an RIS-aided CF system where each user is supported by a devoted RIS and can establish connections with multiple BSs for coherent transmission. Specifically, each RIS can enhance signal transmission between users and their selected BSs through passive beam-forming, but also randomly scattered signals from other non-selected BSs to users, causing additional signals and interference in the network. To gain insights into the system performance, we first derive the average signal-to-interference-plus-noise ratio (SINR) received by each user in a closed-form expression. Subsequently, we formulate an optimization problem aimed at maximizing the weighted sum-SINR of all users in the RIS-CF network. This optimization considers both BS transmit power allocation and BS selections as variables to be jointly optimized. To tackle the complexity of this nonconvex optimization problem, we develop an innovative two-layer iterative approach that offers both efficiency and efficacy. This algorithm iteratively updates the transmit power allocation and BS selections to converge to a locally optimal solution. Numerical results demonstrate significant performance improvement for the RIS-CF network using our proposed scheme. These results also highlight the effectiveness of jointly optimizing BS transmit power allocation and BS selections in the RIS-CF network. Full article
(This article belongs to the Special Issue Energy-Efficient Wireless Solutions for 6G/B6G)
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19 pages, 2318 KiB  
Article
Dynamic Multi-Sleeping Control with Diverse Quality-of-Service Requirements in Sixth-Generation Networks Using Federated Learning
by Tianzhu Pan, Xuanli Wu and Xuesong Li
Electronics 2024, 13(3), 549; https://doi.org/10.3390/electronics13030549 - 30 Jan 2024
Cited by 1 | Viewed by 991
Abstract
The intensive deployment of sixth-generation (6G) base stations is expected to greatly enhance network service capabilities, offering significantly higher throughput and lower latency compared to previous generations. However, this advancement is accompanied by a notable increase in the number of network elements, leading [...] Read more.
The intensive deployment of sixth-generation (6G) base stations is expected to greatly enhance network service capabilities, offering significantly higher throughput and lower latency compared to previous generations. However, this advancement is accompanied by a notable increase in the number of network elements, leading to increased power consumption. This not only worsens carbon emissions but also significantly raises operational costs for network operators. To address the challenges arising from this surge in network energy consumption, there is a growing focus on innovative energy-saving technologies designed for 6G networks. These technologies involve strategies for dynamically adjusting the operational status of base stations, such as activating sleep modes during periods of low demand, to optimize energy use while maintaining network performance and efficiency. Furthermore, integrating artificial intelligence into the network’s operational framework is being explored to establish a more energy-efficient, sustainable, and cost-effective 6G network. In this paper, we propose a small base station sleeping control scheme in heterogeneous dense small cell networks based on federated reinforcement learning, which enables the small base stations to dynamically enter appropriate sleep modes, to reduce power consumption while ensuring users’ quality-of-service (QoS) requirements. In our scheme, double deep Q-learning is used to solve the complex non-convex base station sleeping control problem. To tackle the dynamic changes in QoS requirements caused by user mobility, small base stations share local models with the macro base station, which acts as the central control unit, via the X2 interface. The macro base station aggregates local models into a global model and then distributes the global model to each base station for the next round of training. By alternately performing model training, aggregation, and updating, each base station in the network can dynamically adapt to changes in QoS requirements brought about by user mobility. Simulations show that compared with methods based on distributed deep Q-learning, our proposed scheme effectively reduces the performance fluctuations caused by user handover and achieves lower network energy consumption while guaranteeing users’ QoS requirements. Full article
(This article belongs to the Special Issue Energy-Efficient Wireless Solutions for 6G/B6G)
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15 pages, 707 KiB  
Article
Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
by Qixing Wang, Ting Zhou, Hanzhong Zhang, Honglin Hu, Edison Pignaton de Freitas and Songlin Feng
Electronics 2024, 13(2), 255; https://doi.org/10.3390/electronics13020255 - 5 Jan 2024
Cited by 4 | Viewed by 1877
Abstract
Recently, non-orthogonal multiple access (NOMA) has become prevalent in 5G communication. However, the traditional successive interference cancellation (SIC) receivers for NOMA still encounter challenges. The near-far effect between the users and the base stations (BS) results in a higher bit error rate (BER) [...] Read more.
Recently, non-orthogonal multiple access (NOMA) has become prevalent in 5G communication. However, the traditional successive interference cancellation (SIC) receivers for NOMA still encounter challenges. The near-far effect between the users and the base stations (BS) results in a higher bit error rate (BER) for the SIC receiver. Additionally, the linear detection algorithm used in each SIC stage fails to eliminate the interference and is susceptible to error propagation. Consequently, designing a high-performance NOMA system receiver is a crucial challenge in NOMA research and particularly in signal detection. Focusing on the signal detection of the receiver in the NOMA system, the main work is as follows. (1) This thesis leverages the strengths of deep neural networks (DNNs) for nonlinear detection and incorporates the low computational complexity of the successive interference cancellation (SIC) structure. The proposed solution introduces a feedback deep neural network (FDNN) receiver to replace the SIC in signal detection. By employing a deep neural network for nonlinear detection at each stage, the receiver mitigates error propagation, lowers the BER in NOMA systems, and enhances resistance against inter-user interference (IUI). (2) We describe its algorithm flow and provide simulation results comparing FDNN and SIC receivers under MIMO-NOMA scenarios. The simulations clearly demonstrate that FDNN receivers outperform SIC receivers in terms of BER for MIMO-NOMA systems. Full article
(This article belongs to the Special Issue Energy-Efficient Wireless Solutions for 6G/B6G)
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Review

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31 pages, 830 KiB  
Review
ISAC towards 6G Satellite–Terrestrial Communications: Principles, Status, and Prospects
by Yang Gu, Tianheng Xu, Kai Feng, Yuling Ouyang, Wen Du, Xin Tian and Ting Lei
Electronics 2024, 13(7), 1369; https://doi.org/10.3390/electronics13071369 - 4 Apr 2024
Viewed by 2881
Abstract
With the evolution of fifth-generation (5G) to sixth-generation (6G) communication systems, the utilization of spectrum resources faces incremental challenges. Integrated sensing and communication (ISAC) technology, as a crucial element in 6G technology, is expected to enhance energy efficiency and spectrum utilization efficiency by [...] Read more.
With the evolution of fifth-generation (5G) to sixth-generation (6G) communication systems, the utilization of spectrum resources faces incremental challenges. Integrated sensing and communication (ISAC) technology, as a crucial element in 6G technology, is expected to enhance energy efficiency and spectrum utilization efficiency by integrating radar and communication signals, achieving environmental awareness, and enabling scene interconnection. Simultaneously, to realize the vision of seamless coverage in 6G, research on integrated satellite-terrestrial communication has been prioritized. To integrate the advantages, ISAC for integrated satellite–terrestrial networks (ISTNs) in 6G has emerged as a potential research direction. This paper offers an extensive overview of the present state of key technologies for ISAC and the development of ISTNs. Meanwhile, with a focus on the ISTN-oriented 6G ISAC system, several hotspot topics, including future application scenarios and key technological developments, are outlined and demonstrated. Full article
(This article belongs to the Special Issue Energy-Efficient Wireless Solutions for 6G/B6G)
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25 pages, 4224 KiB  
Review
A Comprehensive Survey on Wi-Fi Sensing for Human Identity Recognition
by Pengsong Duan, Xianguang Diao, Yangjie Cao, Dalong Zhang, Bo Zhang and Jinsheng Kong
Electronics 2023, 12(23), 4858; https://doi.org/10.3390/electronics12234858 - 1 Dec 2023
Cited by 2 | Viewed by 2577
Abstract
In recent years, Wi-Fi sensing technology has become an emerging research direction of human–computer interaction due to its advantages of low cost, contactless, illumination insensitivity, and privacy preservation. At present, Wi-Fi sensing research has been expanded from target location to action recognition and [...] Read more.
In recent years, Wi-Fi sensing technology has become an emerging research direction of human–computer interaction due to its advantages of low cost, contactless, illumination insensitivity, and privacy preservation. At present, Wi-Fi sensing research has been expanded from target location to action recognition and identity recognition, among others. This paper summarizes and analyzes the research of Wi-Fi sensing technology in human identity recognition. Firstly, we overview the history of Wi-Fi sensing technology, compare it with traditional identity-recognition technologies and other wireless sensing technologies, and highlight its advantages for identity recognition. Secondly, we introduce the steps of the Wi-Fi sensing process in detail, including data acquisition, data pre-processing, feature extraction, and identity classification. After that, we review state-of-the-art approaches using Wi-Fi sensing for single- and multi-target identity recognition. In particular, three kinds of approaches (pattern-based, model-based, and deep learning-based) for single-target identity recognition and two kinds of approaches (direct recognition and separated recognition) for multi-target identity recognition are introduced and analyzed. Finally, future research directions are discussed, which include transfer learning, improved multi-target recognition, and unified dataset construction. Full article
(This article belongs to the Special Issue Energy-Efficient Wireless Solutions for 6G/B6G)
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