利用先进的线性化技术优化多天线M-MIMO DM通信系统,进行射频前端非线性补偿的综合设计和性能评估研究

M. S. Jabbar, Israa Ibraheem Al_Barazanchi, Ahmed L. Khalaf, Poh Soon JosephNg, Ahmed Dheyaa Radhi
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引用次数: 0

摘要

本研究的重点是在多天线M-MIMO OFDM通信系统中补偿RF前端非线性的线性化策略。该研究包括模拟预失真(APD)、峰值因子降低(CFR)、多天线削波噪声消除(M-CNC)和多削波噪声抵消(MCNC)等技术的设计和评估。射频前端的非线性会导致信号失真,从而降低系统性能。为了解决这个问题,已经提出了各种线性化方法。本研究考察了天线相关性对使用这些方法传输的功率放大器效率和误码率(BER)的影响。在高信噪比(SNR)状态下进行的仿真研究表明,与其他技术相比,M-CNC和MCNC方法在BER性能和PA效率方面有显著改进。此外,该研究还探讨了削波电平和天线相关性对这些方法有效性的影响。研究结果表明,应根据天线数量、信噪比和系统的削波电平等因素选择适当的线性化策略。
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Optimizing multi-antenna M-MIMO DM communication systems with advanced linearization techniques for RF front-end nonlinearity compensation in a comprehensive design and performance evaluation study
The study presented in this research focuses on linearization strategies for compensating for nonlinearity in RF front ends in multi-antenna M-MIMO OFDM communication systems. The study includes the design and evaluation of techniques such as analogue pre-distortion (APD), crest factor reduction (CFR), multi-antenna clipping noise cancellation (M-CNC), and multi-clipping noise cancellation (MCNC). Nonlinearities in RF front ends can cause signal distortion, leading to reduced system performance. To address this issue, various linearization methods have been proposed. This research examines the impact of antenna correlation on power amplifier efficiency and bit error rate (BER) of transmissions using these methods. Simulation studies conducted under high signal-to-noise ratio (SNR) regimes reveal that M-CNC and MCNC approaches offer significant improvement in BER performance and PA efficiency compared to other techniques. Additionally, the study explores the influence of clipping level and antenna correlation on the effectiveness of these methods. The findings suggest that appropriate linearization strategies should be selected based on factors such as the number of antennas, SNR, and clipping level of the system.
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来源期刊
CiteScore
1.90
自引率
0.00%
发文量
140
审稿时长
7 weeks
期刊介绍: *Industrial Engineering: 1 . Ergonomics 2 . Manufacturing 3 . TQM/quality engineering, reliability/maintenance engineering 4 . Production Planning 5 . Facility location, layout, design, materials handling 6 . Education, case studies 7 . Inventory, logistics, transportation, supply chain management 8 . Management 9 . Project/operations management, scheduling 10 . Information systems for production and management 11 . Innovation, knowledge management, organizational learning *Mechanical Engineering: 1 . Energy 2 . Machine Design 3 . Engineering Materials 4 . Manufacturing 5 . Mechatronics & Robotics 6 . Transportation 7 . Fluid Mechanics 8 . Optical Engineering 9 . Nanotechnology 10 . Maintenance & Safety *Computer Science: 1 . Computational Intelligence 2 . Computer Graphics 3 . Data Mining 4 . Human-Centered Computing 5 . Internet and Web Computing 6 . Mobile and Cloud computing 7 . Software Engineering 8 . Online Social Networks *Electrical and electronics engineering 1 . Sensor, automation and instrumentation technology 2 . Telecommunications 3 . Power systems 4 . Electronics 5 . Nanotechnology *Architecture: 1 . Advanced digital applications in architecture practice and computation within Generative processes of design 2 . Computer science, biology and ecology connected with structural engineering 3 . Technology and sustainability in architecture *Bioengineering: 1 . Medical Sciences 2 . Biological and Biomedical Sciences 3 . Agriculture and Life Sciences 4 . Biology and neuroscience 5 . Biological Sciences (Botany, Forestry, Cell Biology, Marine Biology, Zoology) [...]
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