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Potential Game based Energy Efficient Resource Allocation in HeNB Networks

Powering an individual LTE femtocell base station, or Home eNodeB (HeNB) in the LTE jargon, requires relatively very little energy. It is only when HeNBs are deployed massively, as has happened in the past few years, that energy efficiency becomes an important issue. With large numbers of co-located HeNBs and the increased inter-cell interference resource utilization becomes highly inefficient resulting in a high unnecessary energy consumption. To tackle this problem, coordination techniques could be invoked by the network operator to consolidate the offered workload to the network on as few HeNBs as possible and power down idle ones. Recognizing, however, that such techniques usually impair user-perceived quality of service (QoS), especially with bursty traffic, more sophisticated methods need to be investigated to also consider QoS. Despite the volume of prior work, the key issue -- viz. simultaneously reducing energy waste, increasing bandwidth utilization while guaranteeing user-perceived QoS -- has not been properly considered so far. In this paper, we model the trade-off between energy efficiency and QoS preservation by manipulating user equipment (UE) association and OFDMA scheduling in controlled networks of HeNBs. The problem being NP-hard, we propose two distributed learning algorithms, within a potential game-based framework, to obtain good and fast solutions to the problem. We demonstrate via numerical results the effectiveness of the proposed algorithms in achieving better performance in terms of utility, power, energy efficiency, convergence speed, and complexity compared to other alternatives.