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 IDSPCOM-6.2
Paper Title STOCHASTIC SUCCESSIVE WEIGHTED SUM-RATE MAXIMIZATION FOR MULTIUSER MIMO SYSTEMS WITH FINITE-ALPHABET INPUTS
Authors Xin Guan, Xiaotong Zhao, Qingjiang Shi, Tongji University, China
SessionSPCOM-6: System Design and Optimization
LocationGather.Town
Session Time:Friday, 11 June, 11:30 - 12:15
Presentation Time:Friday, 11 June, 11:30 - 12:15
Presentation Poster
Topic Signal Processing for Communications and Networking: [SPC-MIMO] Multiple-Input Multiple-Output
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Weighted sum-rate maximization (WSRM) is a fundamental problem for multiuser multiple-input-multiple-output (MU-MIMO) systems with finite-alphabet inputs. However, solving this problem is challenging because of the intractable expectation involved in rate functions. The state-of-art WSRM methods for the case of finite-alphabet inputs suffer from high computational complexity due to the issue of complicated numerical integrals for expectation calculation. Inspired by the stochastic successive upper-bound minimization (SSUM) method [1], this paper proposes a stochastic successive inexact lower-bound maximization (SSILM) algorithm for the WSRM problem with finite-alphabet inputs. Our algorithm significantly differs from SSUM in that we use an inexact lower bound of the objective function which is skillfully devised based on an exact but extremely loose lower bound of the objective function. Simulation results show that the proposed algorithm exhibits much faster convergence than state-of-art algorithms.