Special Issue on 6G Wireless TechnologyMobile Communications, 2024, Issue 6Table of Contents | Special Topic: 6G Wireless Technology01
Research on Nonlinear Precoding Algorithms in Multi-Beam Satellite Communication Systems
Wang Ke, Zhang Xincheng, Lin Wenliang, Deng Zhongliang
(School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876)
AbstractNon-Terrestrial Networks (3GPP NTN) are an important component of the evolution of 5G and 6G networks. To enhance satellite capacity and spectral efficiency, dense multi-beam, co-frequency deployment based on phased arrays has gradually become a research hotspot. Due to the severe inter-beam interference caused by sidelobe power leakage as the number of beams increases, low-complexity multi-user interference cancellation schemes are urgently needed. From the perspective of precoding technology, this paper discusses the application and optimization methods of nonlinear precoding in satellite communication systems, and verifies the effectiveness and advancement of this scheme in the 5G NR baseband link through tests on actual hardware platforms.
Keywords3GPP NTN; Multi-Beam Satellite; Inter-Beam Interference; Nonlinear Precoding
☞ Research on Nonlinear Precoding Algorithms in Multi-Beam Satellite Communication Systems
02
Beyond Backscatter Communication: A New Paradigm for Passive IoT Sensing Based on Metamaterials
Liu Xu, Zhang Hongliang, Song Lingyang
(School of Electronics, Peking University, Beijing 100871)
AbstractWith the increasingly complex and diverse demands for IoT applications, the number of networked sensors is expected to grow exponentially. Among existing sensor technologies, passive sensing tags based on backscatter mechanisms show greater potential for large-scale deployment due to their extremely low cost and maintenance-free characteristics. However, the existing passive sensing tags are limited by insufficient transmission distance, which restricts their widespread deployment in practical applications. To address this issue, a passive IoT sensing system based on metamaterial tags is proposed. As a backscatter tag, it can change its scattering coefficient for wireless signals according to the state of the sensed target. The wireless transceiver can then process the scattering signals from the tags to simultaneously achieve sensing and data transmission. The metamaterial tags are composed of multiple units arranged at intervals smaller than half the wavelength, effectively concentrating signal energy for easier detection by receivers, thus increasing the system’s transmission distance. To optimize the performance of the proposed system, a novel sensing transmission joint model is constructed based on the metamaterial equivalent circuit model and wireless transmission model. Furthermore, the structure of the metamaterial tags is analyzed and optimized based on this model, and a deep learning-based sensing algorithm is proposed to achieve sensing of environmental state distributions. Prototype experiments based on an open resonator ring structure realize a humidity-sensitive tag and demonstrate the system’s capability to achieve passive sensing of humidity distribution within a 2 m range, effectively enhancing the transmission distance of passive sensing from the decimeter level to the meter level.
KeywordsIoT; Metamaterials; Wireless Sensing; Backscatter
☞ Beyond Backscatter Communication: A New Paradigm for Passive IoT Sensing Based on Metamaterials
03
Distributed Ultra-Massive MIMO Technology for Sub-7 GHz Bands
Suo Shiqiang, Chen Li, Song Lei, Su Xin, Huang Ruihua, Wang Ha
(CITIC Mobile Communication Technology Co., Ltd., Beijing 100000)
AbstractThe next generation of mobile communication systems will further enhance overall performance in new frequency bands. Facing potential challenges such as continuous coverage, capacity enhancement, and deployment difficulties for future 6G systems in Sub-7 GHz bands, an innovative overall solution is proposed. This solution combines a user-centric access network architecture with distributed ultra-massive MIMO technology, aiming to achieve more efficient communication performance and user experience. In the proposed user-centric network architecture, the system can break through centralization limitations to construct a user-centric end-to-end distributed autonomous network that combines central networks with edge distributed networks, intelligently adapting to end-to-end user demands. Based on this architecture, the coverage and capacity of cells can be dynamically adjusted to better meet user demands in different scenarios. By optimizing connection management strategies, connection interruptions and delays can be effectively reduced, enhancing system stability and user satisfaction. For the aforementioned access network architecture, flexible cell construction, link management, and access process schemes in distributed ultra-massive systems are provided, along with an analysis of underlying core issues such as CSI measurement reporting and distributed antenna calibration.
KeywordsDistributed Ultra-Massive MIMO; User-Centric Intelligent Access Network; Flexible Cells; 6G
☞ Distributed Ultra-Massive MIMO Technology for Sub-7 GHz Bands
04
Research on Maximizing Energy Efficiency of Dual-Function Radar Communication Based on RF Chain Selection and Hybrid Precoding Design
Liu Jianing1, Zhao Qianxi1, Wang Diwen2, Tian Feng1
(1. Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Jiangsu Nanjing 210003; 2. Unicom IoT Co., Ltd., Jiangsu Nanjing 210001)
AbstractTo address the low energy efficiency and high hardware costs of millimeter-wave large-scale multi-antenna dual-function radar communication systems, a new model for dual-function radar communication is established, and the problem of maximizing system energy efficiency is proposed and solved. First, considering hardware loss interference and multi-user interference, new communication and radar models are proposed. Second, an energy efficiency maximization problem with sensing and power constraints is constructed, and this non-convex problem is decomposed into two sub-problems. Finally, using approximate optimization methods such as fractional programming and weighted orthogonal matching pursuit algorithms, optimization solutions for the RF chain selection sub-problem and hybrid precoding design sub-problem are achieved. Simulation results show that the proposed mechanism not only maximizes system energy efficiency but also achieves a trade-off optimization between communication performance and sensing performance.
KeywordsDual-Function Radar Communication; Energy Efficiency; RF Chain; Hybrid Precoding
☞ Research on Maximizing Energy Efficiency of Dual-Function Radar Communication Based on RF Chain Selection and Hybrid Precoding Design
05
Design of Pilot Insertion and Channel Estimation for End-to-End Systems
Zhuo Nana1,2, Xiang Leping2, Hu Jie1,2, Yang Kun3
(1. Yangtze River Delta Research Institute of Electronic Science and Technology, Huzhou, Zhejiang 313000; 2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731; 3. School of Intelligent Software and Engineering, Nanjing University (Suzhou Campus), Suzhou, Jiangsu 215000)
AbstractSince the introduction of end-to-end communication system solutions driven by deep learning technology, the architecture of communication systems has undergone tremendous changes. Although this technology performs well in AWGN channels, its performance is not satisfactory in varying wireless channel environments. Therefore, the current common strategy is to introduce pilot signals, inserting them at the head position or fixed intervals into the data stream, while adding a channel estimation module outside the end-to-end system to supplement channel information. However, this practice contradicts the concept of end-to-end integration. This paper proposes a comprehensive end-to-end system that utilizes deep neural networks for pilot insertion position selection and receiver design, enabling the system to effectively collect channel information and successfully recover transmitted data. Compared with traditional pilot insertion schemes, this system demonstrates significant performance advantages, with a reduction in bit error rate of 1-2 dB. Moreover, the pilot insertion algorithm within the system can provide targeted and efficient pilot insertion solutions for various channel environments, thus exhibiting high adaptability to communication environments.
KeywordsEnd-to-End System; Channel Estimation; Deep Neural Networks; Pilot Insertion; Dynamic Communication Environment
☞ Design of Pilot Insertion and Channel Estimation for End-to-End Systems
06
Location-Aided Beam Selection Based on Graph Neural Networks
Lei Yuzhu, Xiao Qiqi, He Yinghui, Yu Guanding
(School of Information and Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang 310013)
AbstractThe method of location-aided beam selection based on deep neural networks faces issues of high training costs and significant accuracy decline as codebooks increase. To address this problem, a beam selection method based on GNN is proposed. This method first constructs a graph structure based on the correlation between beams in a predefined codebook, and then uses GNN to capture graph structure information and perform message passing, thereby predicting the optimal transmitting and receiving beams based on the location information of user devices. Simulation results show that the proposed GNN-based method reduces training costs and improves beam alignment accuracy and stability.
KeywordsBeam Selection; Graph Neural Networks; Location-Aided; Millimeter Wave
☞ Location-Aided Beam Selection Based on Graph Neural Networks
07
Research on Multi-Base Station Cooperative Sensing and Communication Integration Architecture and Key Technologies
Lin Zhiqiu, Zhang Jiupeng, Yan Shi
(School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876)
AbstractAs one of the key technologies for 6G, sensing and communication integration aims to enhance network intelligence and efficiency by integrating wireless communication and sensing functions, achieving coexistence and mutual collaborative gains. Limited by the bottlenecks of single-base station schemes in communication area coverage interference and insufficient sensing accuracy, multi-base station cooperative sensing and communication integration schemes significantly improve network capacity and coverage through joint data transmission and information sharing between base stations, meeting the demands for network intelligence and new services. This paper first outlines the research status and related concepts of multi-base station cooperative sensing and communication integration, summarizes existing network architectures and advantages, and reviews key enabling technologies such as multi-antenna technology and its coherence design, multi-base station resource management strategies and interference suppression control, as well as intelligent clustering strategies and signal synchronization schemes under integrated sensing access technology. Finally, the technical challenges and future development directions of multi-base station cooperative sensing and communication integration are discussed from the perspectives of computing power support for sensing systems and performance evaluation indicators.
KeywordsMulti-Base Station Cooperation; Sensing and Communication Integration; Multi-Antenna Technology; Resource Management; Integrated Access Technology
☞ Research on Multi-Base Station Cooperative Sensing and Communication Integration Architecture and Key Technologies
08
Design of Multi-Band Signal Recovery System for 6G Sensing and Communication Integration
Wang Xue1, Liu Huacong1, Jia Min2
(1. Key Laboratory of Measurement and Control Technology and Instrumentation, Harbin University of Science and Technology, Heilongjiang Harbin 150080; 2. Harbin Institute of Technology, Heilongjiang Harbin 150001)
AbstractSensing and communication integration (ISAC) is seen as a core technology for achieving space-ground integration based on 6G. The Modulation Wideband Converter (MWC) can simultaneously complete spectrum localization and information communication tasks during signal processing, thereby improving spectrum efficiency, alleviating pressure on hardware devices, and enhancing information processing efficiency to meet the functional requirements of ISAC. However, difficulties exist in determining the number of signal processing channels, leading to the proposal of an adaptive wideband spectrum sensing system capable of automatically adjusting the number of channels. This system also introduces an Exponential Variable Step Size Adaptive Matching Pursuit (ES-EVssAMP) algorithm based on sparsity estimation, combined with an Aliased MWC (AMWC) system to achieve adaptive wideband spectrum sensing. Simulation results confirm the advantages of this improved structure.
Keywords6G; Sensing and Communication Integration; ES-EVssAMP Algorithm; Adaptive Wideband Spectrum Sensing System; Number of Channels
☞ Design of Multi-Band Signal Recovery System for 6G Sensing and Communication Integration
09
Channel Estimation Technology for RIS-Assisted Communication Systems Aimed at 6G
Zhang Likang1, Du Qinghe1, Lu Chen2, Tang Hui2
(1. School of Information and Communication Engineering, Xi’an Jiaotong University, Shaanxi Xi’an 710049; 2. School of Information and Communication, Shenzhen Institute of Information Technology, Guangdong Shenzhen 518172)
AbstractWith the ongoing exploration of future 6G technologies, RIS has emerged as one of the potentially revolutionary technologies, demonstrating great potential in enhancing communication system capacity, expanding coverage, and reducing energy consumption. In RIS-assisted communication systems, channel estimation is one of the key links to realize the advantages of RIS technology. This paper systematically reviews the technical characteristics of RIS and its application prospects in modern communication. It then summarizes and categorizes key channel estimation technologies in RIS-assisted communication scenarios based on the characteristics of different application scenarios, clarifying the objectives and methods of various research hotspots. Finally, the technical challenges faced by channel estimation technologies in practical applications of RIS-assisted communication scenarios are discussed.
KeywordsReconfigurable Intelligent Surface; Channel Estimation; 6G; Wireless Communication
☞ Channel Estimation Technology for RIS-Assisted Communication Systems Aimed at 6G
10
Research on Joint Resource Allocation of Wireless Bandwidth and Power Based on Reinforcement Learning
Sun Wanfei1,2,3,4, Wu Licheng1,2,3, Zhang Xiaokang1,2,3, Wang Hucheng1,2,3, Xu Hui1,2,3
(1. CITIC Mobile Communication Technology Co., Ltd., Beijing 100083; 2. National Key Laboratory of Wireless Mobile Communication, Telecom Science and Technology Research Institute, Beijing 100191; 3. Datang Mobile Communication Equipment Co., Ltd., Beijing 100083; 4. Beihang University, Beijing 100191)
AbstractWireless resource scheduling is one of the core issues in mobile communication network research. Efficient and reliable wireless resource scheduling plays a key role in service quality assurance and system efficiency. For the 6G intelligent endogenous network, a unified scheduling method for PRB allocation and power allocation based on double delay deep deterministic policy gradient reinforcement learning is proposed. Compared with traditional resource scheduling and single-user resource scheduling based on reinforcement learning, this algorithm considers user demands and network resources uniformly during the scheduling cycle, achieving more than a 10% increase in single PRB rate compared to bandwidth polling allocation and average power allocation, effectively improving the utilization efficiency of wireless resources and system throughput.
KeywordsTD3; Wireless Resources; PRB Allocation; Power Allocation; Joint Resource Scheduling
☞ Research on Joint Resource Allocation of Wireless Bandwidth and Power Based on Reinforcement Learning
11
Method for Computing Offloading in Smart Medical Networks Based on Multi-Agent Deep Reinforcement Learning
Fang Xin, Qin Zijian, Gao Xinping, Wen Yuchao, Su Xin
(School of Information Science and Engineering, Hohai University, Changzhou, Jiangsu 213022)
AbstractThe significant breakthroughs in 6G and mobile edge computing technologies have propelled the prosperous development of smart medical services. To meet the low-latency and high-reliability requirements of medical services, a computation offloading method based on multi-agent deep reinforcement learning is proposed. Considering latency and energy consumption, a mixed integer nonlinear programming task offloading problem is established. Resource allocation problems are solved using traditional optimization algorithms, while offloading decision-making problems are addressed using multi-agent deep reinforcement learning algorithms. Simulation results show that the proposed algorithm can perform real-time task offloading in dynamic environments of smart medical networks compared to existing methods.
KeywordsSmart Medical Networks; Deep Reinforcement Learning; Mobile Edge Computing; Resource Allocation; Computation Offloading
☞ Method for Computing Offloading in Smart Medical Networks Based on Multi-Agent Deep Reinforcement Learning
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Highlights2024 Special Call for Papers – Issue 106G Wireless Air Interface Technology2024 Special Call for Papers – Issue 11 Marine Mobile Communication NetworksTable of Contents | 2024, Issue 7 Special Topic: 6G Endogenous Intelligent TechnologySummary of Mobile Communications 2024 (Issues 1-6)
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