基于反向散射通信的非正交多址系统中的能效最大化：丁克巴赫法和连续凸近似法

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

IET Signal Processing Pub Date : 2024-08-22 DOI:10.1049/2024/4107801

Dingjia Lin, Tianqi Wang, Kaidi Wang, Zhiguo Ding

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引用次数: 0

摘要

本文研究了多入单出（MISO）场景下基于反向散射通信（BackCom）的非正交多址（NOMA）系统，其中采用了两种解码方法，包括总和容量法和 QR 分解法。目标是通过优化波束成形矩阵和 BackCom 设备的反射系数，最大限度地提高能效 (EE)。本文提出了两种算法，即基于惩罚半定松弛的 Dinkelbach 算法（SDR）和连续凸近似算法（SCA），分别作为高性能和低复杂度的解决方案。仿真结果表明，总和容量法与 Dinkelbach 的组合性能最佳，但复杂度最高；QR 分解与 SCA 的组合性能最低，但复杂度最小。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Energy-Efficiency Maximization in Backscatter Communication-Based Non-Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches

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Energy-Efficiency Maximization in Backscatter Communication-Based Non-Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches

This paper investigates a backscatter communication (BackCom) based non-orthogonal multiple access (NOMA) system in a multiple-input and single-output (MISO) scenario, where two decoding methods are deployed, including the sum-capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high-performance and low-complexity solutions, respectively. Simulation results indicate that the combination of the sum-capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity.

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来源期刊

IET Signal Processing 工程技术-工程：电子与电气

CiteScore

3.80

自引率

5.90%

发文量

审稿时长

9.5 months

期刊介绍： IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf