Achieving the Maximum Sum Rate Using D.C. Programming in Cellular Networks

Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancellation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 60; no. 3; pp. 1331 - 1341
Main Authors: Al-Shatri, H., Weber, T.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.2012
Institute of Electrical and Electronics Engineers
Subjects:
Applied sciences
Approximation algorithms
Branch-and-bound algorithm
D.C. programming
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
interference channel
Interference channels
Optimization
power allocation
Programming
Resource management
Signal and communications theory
Signal to noise ratio
Signal, noise
Telecommunications and information theory
Performance evaluation
Mobile radiocommunication
Lower bound
Noise reduction
Branch and bound method
Wireless telecommunication
interference channel
Cell network
Algorithm
Power allocation
Convex programming
Optimization
Joint detection
Interference suppression
D.C. programming
Upper bound
Simulation
Telecommunication network
Recursive method
Signal processing
Objective function
Branch-and-bound algorithm
Intercell interference
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancellation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2011.2177824