Game theoretic framework for power control in intercell interference coordination

Abstract

International audienceInter-Cell Interference Coordination (ICIC) is commonly identified as a key radio resource management mechanism to enhance system performance of 4G networks. This paper addresses the problem of ICIC in the downlink of cellular OFDMA systems where the power level selection process of resource blocks (RB) is apprehended as a sub-modular game. The existence of Nash equilibriums (NE) for that type of games shows that stable power allocations can be reached by selfish Base Stations (BS). We put forward a semi distributed algorithm based on best response dynamics to attain the NEs of the modeled game. Based on local knowledge conveyed by the X2 interface in LTE (Long Term Evolution) networks [1], each BS will first select a pool of favorable RBs with low interference. Second, each BS will strive to fix the power level adequately on those selected RBs realizing performances comparable with the Max Power policy that uses full power on selected RBs while achieving substantial power economy. Finally, we compare the obtained results to an optimal global solution to quantify the efficiency loss of the distributed game approach. It turns out that even though the distributed game results are sub-optimal, the low degree of system complexity and the inherent adaptability make the decentralized approach promising especially for dynamic scenarios

Interference Mitigation Through Inter-Cell Interference Coordination Using Virtual PRB Allocation in 4G Networks

“…However, radio resource allocation problem in dynamic ICIC schemes is known to be NP-hard [29,30]. Accordingly, several heuristics have been proposed to solve the optimization problem such as: game theory [31][32][33][34], graph coloring [26,35], genetic algorithms [36], fuzzy logic [37], etc.…”

Performance evaluation of JT CoMP approach: Tractable model using spatial fluid modeling

“…Several works dealt recently with the interference issue in both homogeneous and heterogeneous cellular networks, using different modeling approaches and methodologies, for example : game theory [22], simulation and trials [9], [13], and stochastic geometry [5]- [7]. A concise overview on stochastic geometry can be found in [20], [23].…”