A deep learning framework for blockage mitigation and duration prediction in mmWave wireless networks

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ahmed Almutairi , Alireza Keshavarz-Haddad , Ehsan Aryafar
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引用次数: 0

Abstract

Millimeter-Wave (mmWave) communication can be highly affected by blockages, which can drastically decrease the signal strength at the receiver side. To overcome the impact of blockages, predicting the optimal mitigation technique and accurately estimating the duration of the blockage events are crucial for maintaining reliable and high-performance mmWave communication systems. Prior works on mitigating blockages have proposed a variety of model and protocol-based blockage mitigation solutions that concentrate on a singular technique at a time, like switching the current beam to an alternative beam at the current base station or client. In this paper, we tackle the overarching question: what blockage mitigation technique should be employed? and what is the optimal sub-selection within that technique? We also address the blockage duration estimation problem. We solve these problems by developing a Gated Recurrent Unit (GRU) model, trained on data from periodic message exchanges in mmWave systems. We tested our neural network models by utilizing a mmWave simulator that is commercially available and widely used in wireless communication to compile a large amount of dataset for this purpose. Our findings reveal that our proposed method introduces no extra communication overhead, while achieving remarkable accuracy, exceeding 91%, in predicting the optimal blockage mitigation technique. Moreover, the blockage duration estimation model achieves a very high accuracy with a residual mean error of less than 0.04 s. Finally, we demonstrate that our proposed blockage mitigation method substantially boosts the volume of data transferred in comparison to various other blockage mitigation strategies.

用于毫米波无线网络阻塞缓解和持续时间预测的深度学习框架
毫米波(mmWave)通信会受到阻塞的严重影响,这会大大降低接收端的信号强度。为了克服阻塞的影响,预测最佳缓解技术和准确估计阻塞事件的持续时间对于维护可靠和高性能的毫米波通信系统至关重要。之前有关缓解阻塞的研究提出了各种基于模型和协议的阻塞缓解解决方案,这些方案每次只集中使用一种技术,如在当前基站或客户端将当前波束切换为替代波束。在本文中,我们要解决的首要问题是:应采用哪种阻塞缓解技术? 该技术中的最佳子选择是什么?我们还解决了阻塞持续时间估计问题。为了解决这些问题,我们开发了一个门控递归单元(GRU)模型,并根据毫米波系统中周期性信息交换的数据进行了训练。我们利用市场上广泛应用于无线通信领域的毫米波模拟器,为此编制了大量数据集,从而测试了我们的神经网络模型。我们的研究结果表明,我们提出的方法没有引入额外的通信开销,同时在预测最佳阻塞缓解技术方面取得了显著的准确性,准确率超过 91%。最后,我们证明,与其他各种阻塞缓解策略相比,我们提出的阻塞缓解方法大大提高了数据传输量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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