A Parallel ADMM Approach for PAPR Reduction in Mixed-Numerology Systems

• Published in: IEEE Vehicular Technology Conference pp. 1 - 7
• Main Authors: Zhou, Yuhang, Li, Jiaxuan, Liu, Xiaoran, Xiong, Jun, Zhao, Haitao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.06.2024
• Subjects: ADMM; Baseband; Distortion; Jacobian matrices; Linear programming; Minimization; Mixed-Numerology; PAPR Reduction; Peak to average power ratio; Vehicular and wireless technologies
• ISSN: 2577-2465
• DOI: 10.1109/VTC2024-Spring62846.2024.10683507

Mixed-numerology transmission still suffers from a large peak-to-average power ratio (PAPR) and the conventional PAPR reduction methods cannot be applied straightforwardly due to its multiple baseband processing units. In this paper, we develop a novel parallel PAPR reduction approach for the decentralized baseband processing architecture. By considering the in-band distortion minimization problem subject to the PAPR constraint, we find that this problem is separable in both the objective function and the constraints. Based on the "decomposition-coordination" mode of alternating direction method of multipliers (ADMM), original problem can be divided into several subproblems which can be easily solved in each subbands with its dedicated numerology. Because of the independence between each subbands, the proximate Jacobian method is also applied so that the subproblems can be updated parallelly and are suitable to the decentralized baseband processing architecture. Analysis corroborated by simulations demonstrate that the proposed approach is convergent. Numerical results illustrate that the proposed approach is less time-consuming than existing benchmark when the same PAPR reduction performance is achieved.