2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSAM-4.2
Paper Title ADMM-BASED FAST ALGORITHM FOR ROBUST MULTI-GROUP MULTICAST BEAMFORMING
Authors Niloofar Mohamadi, Min Dong, Shahram ShahbazPanahi, Ontario Tech University, Canada
SessionSAM-4: MIMO and Massive MIMO Array Processing
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Sensor Array and Multichannel Signal Processing: [SAM-CAMS] Computational advances for multi-sensor systems
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We consider robust multi-group multicast beamforming design in massive MIMO large-scale systems. The goal is to minimize the transmit power subject to the minimum signal-to-interference-plus-noise-ratio (SINR) under channel uncertainty. Using the exact worst-case SINR constraint, we transform the problem into a non-convex optimization problem. We develop an alternating direction method of multipliers (ADMM)-based fast algorithm to solve this problem directly with convergence guarantee. Our two-layer ADMM-based algorithm decomposes the non-convex problem into a sequence of convex subproblems, for which we obtain the semi-closed-form or closed-form solutions. Simulation studies show that our algorithm provides a considerable computational advantage over the conventional interior-point method non-convex solver with nearly identical performance.