Paper Info
Title
A Dynamic Pilot and Data Power Allocation for TDD Massive MIMO Systems.
Abstract
In this paper, we propose a joint dynamic pilot and data power allocation scheme for time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, so as to both adaptively mitigate pilot contamination and balance the mutual interference. Due to the unknown of instant channel state information before pilots, we exploit the Gauss-Markov process of temporally-correlated channels and use the Kalman filter to not only filter out the pilot contamination but also provide the priori estimation values. Subsequently, the deterministic approximation of the rate is derived as a function of the priori channel estimation and the priori estimate errors, and accordingly the rate-profile maximization to achieve max-min fairness is formulated. To deal with this optimization coupled across the pilot power and data power as well as the users, we give an iterative alternating rate-suboptimal algorithm composed of two sub-problems, both of which are further solved by introducing the successive convex approximation (SCA) methods and slack variables. Numerical results confirm the improved rate provided by the proposed scheme.
Year
DOI
Venue
2018
10.1109/GLOCOM.2018.8647568
IEEE Global Communications Conference
Field
DocType
ISSN
Mathematical optimization,Slack variable,Computer science,MIMO,Communication channel,Kalman filter,Real-time computing,Exploit,Interference (wave propagation),Maximization,Channel state information
Conference
2334-0983
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Ruizhe Yang1659.31
Bo Lin2116.82
Fei Yu35116335.58
yinglei teng49119.76
Yanhua Zhang514524.84