Duc Thinh Vu , Ba Cao Nguyen , Danh Khoa Nguyen , Taejoon Kim , Bui Vu Minh , Anh Vu Le
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引用次数: 0
Abstract
This article examines the capacity performance of millimeter-wave (mmWave) communication systems utilizing multiple reconfigurable intelligent surfaces (RISs). Specifically, transceiver hardware impairments (THI) at both source and destination are taken into account. To dramatically reduce the effects of THI, transmit antenna selection (TAS) is exploited at the source. The mathematical formulas of achievable rate (AR) and energy efficiency (EE) of the considered multi-RIS-mmWave systems with TAS are derived over Nakagami-m channels under the THI conditions. Numerical results clarify the big effects of THI on the AR and EE of the multi-RIS-mmWave systems. It is because the AR and EE with THI are greatly lower than those without THI (without THI is perfect transceiver hardware (PTH)). In this circumstance, utilizing TAS as well as RISs can achieve many benefits. In addition to the combined advantages of TAS and RISs, their individual advantages are also verified. In particular, the AR and EE achieved with TAS are significantly greater than those obtained without TAS. Then, other solutions such as increasing the number of RISs/reflecting elements (REs), rising the number of transmission antennas, and using suitable frequencies and bandwidths are provided to improve the AR and EE of the considered multi-RIS-mmWave systems with TAS and THI. Finally, the AR and EE expressions are verified by Monte-Carlo simulations.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,