TY - GEN
T1 - Adaptive multicast beamforming
T2 - 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
AU - Gopalakrishnan, Balasubramanian
AU - Sidiropoulos, Nicholas D.
PY - 2015/8/4
Y1 - 2015/8/4
N2 - Multicast beamforming is a part of the Evolved Multimedia Broadcast Multicast Service (eMBMS) in the Long-Term Evolution (LTE) standard for efficient audio and video streaming. The associated beamformer design problem has drawn considerable attention over the last decade, but existing solutions are not quite satisfactory. The core problem is NP-hard, and the available approximations leave much to be desired in terms of achieving favorable performance-complexity trade-offs, especially for online implementation. This paper introduces a new class of adaptive multicast beamforming algorithms that simultaneously cover all bases - featuring guaranteed convergence and state-of-art performance at low complexity. Each update takes a step in the direction of an inverse Signal to Noise Ratio (SNR) weighted linear combination of the SNR-gradient vectors of all users. Convergence is established by recourse to proportional fairness. Simulation results show that the proposed algorithms outperform Semi-Definite Relaxation (SDR) and Successive Linear Approximation (SLA - the prior state-of-art) at an order of magnitude lower complexity.
AB - Multicast beamforming is a part of the Evolved Multimedia Broadcast Multicast Service (eMBMS) in the Long-Term Evolution (LTE) standard for efficient audio and video streaming. The associated beamformer design problem has drawn considerable attention over the last decade, but existing solutions are not quite satisfactory. The core problem is NP-hard, and the available approximations leave much to be desired in terms of achieving favorable performance-complexity trade-offs, especially for online implementation. This paper introduces a new class of adaptive multicast beamforming algorithms that simultaneously cover all bases - featuring guaranteed convergence and state-of-art performance at low complexity. Each update takes a step in the direction of an inverse Signal to Noise Ratio (SNR) weighted linear combination of the SNR-gradient vectors of all users. Convergence is established by recourse to proportional fairness. Simulation results show that the proposed algorithms outperform Semi-Definite Relaxation (SDR) and Successive Linear Approximation (SLA - the prior state-of-art) at an order of magnitude lower complexity.
KW - LTE
KW - Multicast beamforming
KW - eMBMS
KW - max-min
KW - proportional fairness
UR - http://www.scopus.com/inward/record.url?scp=84946036046&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946036046&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2015.7178465
DO - 10.1109/ICASSP.2015.7178465
M3 - Conference contribution
AN - SCOPUS:84946036046
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2719
EP - 2723
BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 April 2014 through 24 April 2014
ER -