TY - GEN
T1 - Parallelizable First-Order Fast Algorithm for Symbol-Level Precoding in Lage-Scale Systems
AU - Yang, Junwen
AU - Li, Ang
AU - Liao, Xuewen
AU - Masouros, Christos
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We investigate constructive interference (CI)-based symbol-level precoding (SLP) in large-scale systems with massive connectivity of users to minimize the transmit power subject to the instantaneous signal-to-interference-plus-noise-ratio (SINR) and CI constraints. By converting the considered problem into a novel separable formulation, we reveal the existence of separability in SLP, which is therefore well-suited for decomposition. The proximal Jacobian alternating direction method of multipliers (PJ-ADMM) framework is adopted to decompose the reformulated problem into multiple subproblems, which can be solved in parallel with closed-form solutions. We further linearize the second-order terms by approximation, which leads to a parallelizable first-order fast solution to SLP. Our derivations are validated by simulation results, which also show that our algorithm can provide optimal performance with substantially lower computational complexity than state-of-the-art algorithms.
AB - We investigate constructive interference (CI)-based symbol-level precoding (SLP) in large-scale systems with massive connectivity of users to minimize the transmit power subject to the instantaneous signal-to-interference-plus-noise-ratio (SINR) and CI constraints. By converting the considered problem into a novel separable formulation, we reveal the existence of separability in SLP, which is therefore well-suited for decomposition. The proximal Jacobian alternating direction method of multipliers (PJ-ADMM) framework is adopted to decompose the reformulated problem into multiple subproblems, which can be solved in parallel with closed-form solutions. We further linearize the second-order terms by approximation, which leads to a parallelizable first-order fast solution to SLP. Our derivations are validated by simulation results, which also show that our algorithm can provide optimal performance with substantially lower computational complexity than state-of-the-art algorithms.
KW - ADMM
KW - Massive MU-MISO
KW - constructive interference
KW - parallel and distributed computing
KW - separability
KW - symbol-level precoding
UR - https://www.scopus.com/pages/publications/85169800662
U2 - 10.1109/VTC2023-Spring57618.2023.10199658
DO - 10.1109/VTC2023-Spring57618.2023.10199658
M3 - 会议稿件
AN - SCOPUS:85169800662
T3 - IEEE Vehicular Technology Conference
BT - 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Y2 - 20 June 2023 through 23 June 2023
ER -