Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System

Ultra-reliable low-latency communication (URLLC) is an important application scenario in fifth generation (5G) communication. With the increasing deployment of 5G, spectrum resources are becoming increasingly scarce, and millimeter wave (mmWave) operating in a high frequency has garnered significant...

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Vydané v:Applied sciences Ročník 13; číslo 12; s. 6883
Hlavní autori: Liu, Zhiren, Shen, Yuan, Qiang, Mengye, Wang, Gang, Wang, Jun-Bo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.06.2023
Predmet:
Algorithms
Codes
Communications systems
Convex analysis
Efficiency
hybrid precoding
millimeter wave
Optimization algorithms
spectral efficiency
ultra-reliable and low-latency communication (URLLC)
Wireless telecommunications equipment
Canada
ISSN:2076-3417, 2076-3417
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Shrnutí:Ultra-reliable low-latency communication (URLLC) is an important application scenario in fifth generation (5G) communication. With the increasing deployment of 5G, spectrum resources are becoming increasingly scarce, and millimeter wave (mmWave) operating in a high frequency has garnered significant attention. However, the short wavelength of the mmWave makes the signals susceptible to fading. Precoding has emerged as a promising solution for mitigating the severe path loss in mmWave, which can enhance the system capacity and communication performance. In our system, an achievable rate maximization problem is established by jointly optimizing the analog and digital precoders at the base station, subject to the constraint of the total power and the constant modulus constraint of the subarrays. The simulation results demonstrate that the algorithm employed in our system can successfully solve the optimization problem in this new scenario. Furthermore, the subarray-structure hybrid precoder has a higher energy efficiency than that of a fully connected hybrid precoder or all-digital precoder. The proposed algorithm also outperforms the MM (Majorization–Minimization) algorithm.
Bibliografia:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13126883