Weighted Sum Rate Optimization for Downlink Multiuser MIMO Systems with per Antenna Power Constraint: Downlink-Uplink Duality Approach
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This document proposes a downlink-uplink duality approach to solve the weighted sum rate maximization problem for multiuser MIMO systems with per antenna power constraints. The duality is established by modifying the input covariance matrix of the dual uplink problem and formulating the noise covariance matrix as a fixed point function. Simulation results show that the proposed duality-based solution utilizes less total base station power than existing algorithms.
![Weighted Sum Rate Optimization for Downlink Multiuser
MIMO Systems with per Antenna Power Constraint:
Downlink-Uplink Duality Approach
Tadilo Endeshaw Bogale, Luc Vandendorpe
ICTEAM Institute, Place du Levant, 2, 1348 Louvain-la-Neuve
{tadilo.bogale, luc.vandendorpe}@uclouvain.be
ABSTRACT
This work proposes downlink-uplink duality approach to solve weighted sum rate
maximization problem for multiuser MIMO systems. The duality is established by
modifying the input covariance matrix of the dual uplink problem, and formulating
the noise covariance matrix of the uplink channel as a fixed point function. In our
simulation results, we have observed that the proposed duality based solution
utilizes less total BS power than that of existing algorithms.
PROBLEM FORMULATION
PROPSED SOLUTION CONTINUED
In this work we propose new duality based iterative solution to solve per antenna power constrained WSR
maximization problem. For our simulation setup, we have observed that the proposed duality based solution
requires less total BS power compared to the existing approach.
TWO STEP PROPOSED DUALITY SOLUTION
Difficult to ensure Per ant power const
The per antenna power constraint issue of Algorithm I at step 4 can be resolved as follows
SYSTEM MODEL
CONCLUSIONS
REFERENCES
[1] T. E. Bogale and L.Vandendorpe,”Weighted sum rate optimization for downlink multiuser MIMO
coordinated base station systems: Centralized and distributed algorithms,” IEEE Tran. Sig. Proc., Dec. 2011.
[2] S.Shi, M. Schubert and H. Boche, “Per antenna power constrained rate optimization for multiyser MIMO
systems,” ITG WSA, Berlin, Germany, 26 – 27 Feb., 2008, pp 270 -- 277
SIMULATION RESULTS
IMPROVING THE CONVERGENCE SPEED OF ALGORITHM I](https://image.slidesharecdn.com/120137poster-140612100020-phpapp01/85/Weighted-Sum-Rate-Optimization-for-Downlink-Multiuser-MIMO-Systems-with-per-Antenna-Power-Constraint-Downlink-Uplink-Duality-Approach-1-320.jpg)
Weighted Sum Rate Optimization for Downlink Multiuser MIMO Systems with per Antenna Power Constraint: Downlink-Uplink Duality Approach
- 1. Weighted Sum Rate Optimization for Downlink Multiuser MIMO Systems with per Antenna Power Constraint: Downlink-Uplink Duality Approach Tadilo Endeshaw Bogale, Luc Vandendorpe ICTEAM Institute, Place du Levant, 2, 1348 Louvain-la-Neuve {tadilo.bogale, luc.vandendorpe}@uclouvain.be ABSTRACT This work proposes downlink-uplink duality approach to solve weighted sum rate maximization problem for multiuser MIMO systems. The duality is established by modifying the input covariance matrix of the dual uplink problem, and formulating the noise covariance matrix of the uplink channel as a fixed point function. In our simulation results, we have observed that the proposed duality based solution utilizes less total BS power than that of existing algorithms. PROBLEM FORMULATION PROPSED SOLUTION CONTINUED In this work we propose new duality based iterative solution to solve per antenna power constrained WSR maximization problem. For our simulation setup, we have observed that the proposed duality based solution requires less total BS power compared to the existing approach. TWO STEP PROPOSED DUALITY SOLUTION Difficult to ensure Per ant power const The per antenna power constraint issue of Algorithm I at step 4 can be resolved as follows SYSTEM MODEL CONCLUSIONS REFERENCES [1] T. E. Bogale and L.Vandendorpe,”Weighted sum rate optimization for downlink multiuser MIMO coordinated base station systems: Centralized and distributed algorithms,” IEEE Tran. Sig. Proc., Dec. 2011. [2] S.Shi, M. Schubert and H. Boche, “Per antenna power constrained rate optimization for multiyser MIMO systems,” ITG WSA, Berlin, Germany, 26 – 27 Feb., 2008, pp 270 -- 277 SIMULATION RESULTS IMPROVING THE CONVERGENCE SPEED OF ALGORITHM I