Monotonic Optimization: Problems and Solution Approaches
SIAM Journal on Optimization
Fundamentals of wireless communication
Fundamentals of wireless communication
MAPEL: achieving global optimality for a non-convex wireless power control problem
IEEE Transactions on Wireless Communications
Capacity bounds for the Gaussian interference channel
IEEE Transactions on Information Theory
A new outer bound and the noisy-interference sum-rate capacity for Gaussian interference channels
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Capacity regions and sum-rate capacities of vector Gaussian interference channels
IEEE Transactions on Information Theory
Complete Characterization of the Pareto Boundary for the MISO Interference Channel
IEEE Transactions on Signal Processing - Part II
On the Achievable Sum Rate for MIMO Interference Channels
IEEE Transactions on Information Theory
Competition Versus Cooperation on the MISO Interference Channel
IEEE Journal on Selected Areas in Communications
Competitive Design of Multiuser MIMO Systems Based on Game Theory: A Unified View
IEEE Journal on Selected Areas in Communications
Sum Capacity of MIMO Interference Channels in the Low Interference Regime
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
Resource allocation and transmit optimization for the multiple-antenna Gaussian interference channel are important but difficult problems. The spatial degrees of freedom can be exploited to avoid, align, or utilize the interference. In recent literature, the upper boundary of the achievable rate region has been characterized. However, the resulting programming problems for finding the sum-rate, proportional fair, and minimax (egalitarian) operating points are non-linear and non-convex. In this paper, we develop a non-convex optimization framework based on monotonic optimization by outer polyblock approximation. First, the objective functions are represented in terms of differences of monotonic increasing functions. Next, the problems are reformulated as maximization of increasing functions over normal constraint sets. Finally, the idea to approximate the constraint set by outer polyblocks is explained and the corresponding algorithm is derived. Numerical examples illustrate the advantages of the proposed framework compared to an exhaustive grid search approach.