This paper considers the (NP-)hard problem of joint multicast beamforming and antenna selection. Prior work has focused on using Semi-Definite relaxation (SDR) techniques in an attempt to obtain a high quality sub-optimal solution. However, SDR suffers from the drawback of having high computational complexity, as SDR lifts the problem to higher dimensional space, effectively squaring the number of variables. This paper proposes a high performance, low complexity Successive Convex Approximation (SCA) algorithm for max-min SNR 'fair' joint multicast beamforming and antenna selection under a sum power constraint. The proposed approach relies on iteratively approximating the non-convex objective with a series of non-smooth convex subproblems, and then, a first order-based method called Saddle Point Mirror-Prox (SP-MP) is used to compute optimal solutions for each SCA subproblem. Simulations reveal that the SP-MP SCA algorithm provides a higher quality and lower complexity solution compared to the one obtained using SDR.
|Original language||English (US)|
|Title of host publication||2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Aug 24 2018|
|Event||19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece|
Duration: Jun 25 2018 → Jun 28 2018
|Name||IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC|
|Other||19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018|
|Period||6/25/18 → 6/28/18|
Bibliographical noteFunding Information:
This work is supported by a Graduate Fellowship awarded by the Digital Technology Center (DTC) at the University of Minnesota.
© 2018 IEEE.