Distributed Learning for Energy-Efficient Resource Management in Self-Organizing Heterogeneous Networks.

In heterogeneous networks, a dense deployment of base stations (BSs) leads to increased total energy consumption, and, consequently, increased cochannel interference (CCI). In this paper, to deal with this problem, self-organizing mechanisms are proposed, for joint channel and power allocation procedures, which are performed in a fully distributed manner. A dynamic channel allocation mechanism is proposed, in which the problem is modeled as a noncooperative game, and a no-regret learning algorithm is applied for solving the game. In order to improve the accuracy and reduce the effect of shadowing, we propose another channel allocation algorithm executed at each user equipment (UE). In this algorithm, each UE reports the channel with minimum CCI to its associated BS. Then, the BS selects its channel based on these received reports. To combat the energy consumption problem, BSs choose their transmission power by employing an <sc>on</sc>–<sc>off</sc> switching scheme. Simulation results show that the proposed mechanism, which is based on the second proposed channel allocation algorithm and combined with the <sc>on–off</sc> switching scheme, balances load among BSs. Furthermore, it yields significant performance gains up to about $40.3\%$, $44.8\%$ , and $70.6\%$ in terms of average energy consumption, UE's rate, and BS's load, respectively, compared to a benchmark based on an interference-aware dynamic channel allocation algorithm. [ABSTRACT FROM PUBLISHER]