Sum rate maximization for RSMA aided small cells edge users using meta-learning variational quantum algorithm

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Deepak Gupta, Ishan Budhiraja, Bireshwar Dass Mazumdar
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引用次数: 0

Abstract

This study aims to enhance wireless communication efficiency by maximizing the sum rate through optimized rate allocation and power control for edge users in small cell networks. Small cells improve coverage and bandwidth in congested networks but face challenges such as interference and limited resources, particularly for users at the cell edge. This article introduces a Meta-LVQA technique to boost system throughput by optimizing rate allocation and power control, ensuring equitable resource distribution among users, and managing in-cell interference using Rate Splitting Multiple Access (RSMA). The problem is initially framed using classical methods. However, this manuscript employs the Meta-Learning Variational Quantum Algorithm (Meta-LVQA) to optimize the sum rate. Therefore, it is necessary to transform the classical equation into an equivalent quantum equation using a quantum circuit. Numerical results demonstrate that RSMA with Meta-LVQA consistently outperforms all other methods. Specifically, RSMA with Meta-LVQA surpasses RSMA with Variational Quantum Algorithm (VQA), NOMA with Meta-LVQA, and NOMA with VQA by 3.91%,10.11%, and 31.99%, respectively, when the sum rate is measured against a minimum rate requirement of 1.15 Mbps at SCEU1. When computing the sum rate using four SCEUs, RSMA with Meta-LVQA outperforms RSMA with VQA, NOMA with Meta-LVQA, and NOMA with VQA by 13.91%,18.63%, and 43.06%, respectively.
基于元学习变分量子算法的RSMA辅助小蜂窝边缘用户和速率最大化
本研究旨在通过优化速率分配和功率控制,使小蜂窝网络边缘用户的总速率最大化,从而提高无线通信效率。小型基站改善了拥塞网络中的覆盖范围和带宽,但也面临着干扰和资源有限等挑战,尤其是对基站边缘的用户而言。本文介绍了一种Meta-LVQA技术,通过优化速率分配和功率控制、确保用户之间公平的资源分配以及使用速率分割多址(RSMA)管理小区内干扰来提高系统吞吐量。这个问题最初是用经典方法提出的。然而,本文采用元学习变分量子算法(Meta-LVQA)来优化求和速率。因此,有必要利用量子电路将经典方程转化为等效量子方程。数值结果表明,基于Meta-LVQA的RSMA方法优于其他方法。具体来说,当在SCEU1上以1.15 Mbps的最低速率要求测量总和速率时,使用Meta-LVQA的RSMA分别超过使用变分量子算法(VQA)的RSMA、使用Meta-LVQA的NOMA和使用VQA的NOMA,分别高出3.91%、10.11%和31.99%。当使用4种SCEUs计算总和率时,Meta-LVQA的RSMA分别比VQA的RSMA、Meta-LVQA的NOMA和VQA的NOMA分别高出13.91%、18.63%和43.06%。
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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