E-HFWN:增强型5G毫米波通信和传感集成网络的设计和性能测试

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS Array Pub Date : 2023-07-01 DOI:10.1016/j.array.2023.100289
Chaoyi Zhang , Zhangchao Ma , Xiangna Han , Jianquan Wang
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引用次数: 0

摘要

通信与传感集成网络是指同时具备物理数字空间感知和泛在智能通信的能力。这些网络通过通信和传感资源的协同工作,实现了对多维资源的感知和协同通信,具有智能交互和处理新信息流的能力。首先,本研究提出了增强型CSIN(E-HFWN)的技术架构,研究了其关键技术和性能指标,并解释了空中接口技术,包括帧结构设计、载波聚合、信道检测、物理天际线映射、波束形成和管理、资源分配和调度。在资源分配方案中,使用行动者-评论家强化学习(RL)框架来划分无线资源。目标是最大化互信息量(MI)并最小化感测终端的端到端延迟。然后,对E-HFWN的性能进行了测试,包括无线资源管理、系统峰值速率、容量、端到端延迟和通信感知波形旁瓣比的数值模拟。最后,从E-HFWN指数测试的结果来看,在5G毫米波的基础上进一步增强了E-HFWN。增强的感知功能可以为分布式计算能力的优化和快速调度提供先验信息,并为人工智能(AI)服务和应用提供更丰富的数据源,以增强训练模型的稳健性。E-HFWN可以为6G通感计算集成网络相关技术的发展做出贡献,促进学术界和工业界的共识。
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E-HFWN: Design and performance test of a communication and sensing integrated network for enhanced 5G mmWave

Communication and sensing integrated networks (CSINs) refer to the ability of physical digital space perception and ubiquitous intelligent communication at the same time. These networks realize the perception and cooperative communication of multidimensional resources through the cooperative work of communication and sensing resources and have the ability of intelligent interaction and processing of new information flow. First, this study proposes the technical architecture of an enhanced CSIN (E-HFWN), studies its key technologies and performance indicators, and explains the air interface technology, including frame structure design, carrier aggregation, channel detection, physical skyline mapping, beamforming and management, resource allocation and scheduling. In the resource allocation scheme, an actor-critic reinforcement learning (RL) framework is used to divide the wireless resources. The goal is to maximize the amount of mutual information (MI) and minimize the end-to-end delay of the sensing terminal. Then, the performance of the E-HFWN is tested, including numerical simulation of wireless resource management, system peak rate, capacity, end-to-end delay and communication perception waveform sidelobe ratio. Finally, from the results of the E-HFWN index test, the E-HFWN is further enhanced on the basis of 5G mmWave. The enhanced sensing function can provide a priori information for the optimal and rapid scheduling of distributed computing power and provide richer data sources for artificial intelligence (AI) services and applications to enhance the robustness of the training model. The E-HFWN can contribute to the development of technologies related to 6G synaesthesia computing integrated networks, promote the consensus between academia and industry.

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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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