Throughput analysis of movable antenna systems with mobility exploitation models

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication Pub Date : 2025-03-03 DOI:10.1016/j.phycom.2025.102638

Pedro Candeias, Rodolfo Oliveira

{"title":"Throughput analysis of movable antenna systems with mobility exploitation models","authors":"Pedro Candeias, Rodolfo Oliveira","doi":"10.1016/j.phycom.2025.102638","DOIUrl":null,"url":null,"abstract":"<div><div>Movable antenna systems offer a promising approach to enhancing wireless communications by dynamically adjusting antenna positions to optimize signal reception. This paper explores the performance of a movable antenna at the receiver side considering a multi-tap propagation scenario in both Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. We introduce and evaluate four distinct mobility patterns that dictate antenna movement. The mobility patterns are part of the exploitation phase, aimed at probing the achievable capacity at different antenna locations. Additionally, we account for the time required to physically move the antenna in the system throughput, considering the worst-case scenario where the transmission is temporarily paused during the antenna motion. Our analysis assesses the performance of mobility pattern heuristics by examining their ability to balance the tradeoff between capacity gains from exploring new antenna positions and the downtime due to antenna movement. Simulation results show that specific antenna mobility patterns can achieve up to 70% of the SISO’s optimal throughput or 77% of the MIMO’s optimal throughput. The results reported in this paper show that simple mobility patterns more than double or triple the MIMO’s or SISO’s throughput compared to a scenario where the antenna remains fixed in a random position, respectively, underscoring the significant potential of the antenna mobility patterns in enhancing the MA system performance.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102638"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490725000412","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

Movable antenna systems offer a promising approach to enhancing wireless communications by dynamically adjusting antenna positions to optimize signal reception. This paper explores the performance of a movable antenna at the receiver side considering a multi-tap propagation scenario in both Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. We introduce and evaluate four distinct mobility patterns that dictate antenna movement. The mobility patterns are part of the exploitation phase, aimed at probing the achievable capacity at different antenna locations. Additionally, we account for the time required to physically move the antenna in the system throughput, considering the worst-case scenario where the transmission is temporarily paused during the antenna motion. Our analysis assesses the performance of mobility pattern heuristics by examining their ability to balance the tradeoff between capacity gains from exploring new antenna positions and the downtime due to antenna movement. Simulation results show that specific antenna mobility patterns can achieve up to 70% of the SISO’s optimal throughput or 77% of the MIMO’s optimal throughput. The results reported in this paper show that simple mobility patterns more than double or triple the MIMO’s or SISO’s throughput compared to a scenario where the antenna remains fixed in a random position, respectively, underscoring the significant potential of the antenna mobility patterns in enhancing the MA system performance.

查看原文本刊更多论文

基于机动性开发模型的可移动天线系统吞吐量分析

移动天线系统通过动态调整天线位置来优化信号接收，为增强无线通信提供了一种很有前途的方法。本文探讨了在单输入单输出（SISO）和多输入多输出（MIMO）系统中考虑多分接传播场景的接收器侧移动天线的性能。我们介绍和评估四种不同的移动模式，决定天线的运动。移动模式是开发阶段的一部分，旨在探测不同天线位置的可实现容量。此外，考虑到在天线运动期间传输暂时暂停的最坏情况，我们考虑了在系统吞吐量中物理移动天线所需的时间。我们的分析评估了移动模式启发式的性能，通过检查它们在探索新天线位置带来的容量收益和由于天线移动造成的停机时间之间的平衡能力。仿真结果表明，特定的天线迁移模式可以达到SISO最佳吞吐量的70%或MIMO最佳吞吐量的77%。本文报告的结果表明，与天线保持固定在随机位置的情况相比，简单迁移模式的吞吐量分别是MIMO或SISO的两倍或三倍以上，强调了天线迁移模式在增强MA系统性能方面的巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

期刊介绍： PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.