Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest energy from environmental sources whereas the macrocell base station (MBS) uses conventional power supply. Given a stochastic energy arrival process for the SBSs, we derive a power control policy for the downlink transmission of both MBS and SBSs such that they can achieve their objectives [e.g., maintain the signal-to-interference-plus-noise ratio (SINR); at an acceptable level] on a given transmission channel. We consider a centralized energy harvesting mechanism for SBSs, i.e., there is a central energy storage (CES) where energy is harvested and then distributed to the SBSs. When the number of SBSs is small, the game between the CES and the MBS is modeled as a single-controller stochastic game and the equilibrium policies are obtained as a solution of a quadratic programming problem. However, when the number of SBSs tends to infinity (i.e., a highly dense network), the centralized scheme becomes infeasible, and therefore, we use a mean field stochastic game to obtain a distributed power control policy for each SBS. By solving a system of partial differential equations, we derive the power control policy of SBSs given the knowledge of mean field distribution and the available harvested energy levels in the batteries of the SBSs.

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest their energy from environment sources whereas the macro base station (MBS) uses conventional power supply. Given a stochastic energy arrival process, this paper derives a power control policy for the downlink transmission of both MBS and SBSs such that they can obtain an equilibrium of their own objectives on a long-term basis (e.g., maximizing the transmission rate for SBSs while maintaining the target signal-to-interference-plus-noise ratio (SINR) at the macro users) on a given transmission channel. To this end, we propose a single controller stochastic game and develop a power control policy as a solution of a quadratic programming problem. Numerical results demonstrate the significance of the developed optimal power control policy over the conventional fixed and random power control policies.

Bandwidth allocation is important for a mesh network to meet quality of service (QoS) requirements of the users. This bandwidth allocation has to ensure that the maximum throughput is achieved. However, this problem becomes more challenging when the information about a network (e.g., channel quality and hence link capacity) is not accurately known, especially, in a real system whose parameters are uncertain. Therefore, in this paper, we apply the robust optimization technique to develop a model that can tolerate the uncertainty of the system parameters. First, we formulate the bandwidth allocation problem as a constrained optimization problem and its robust counterpart is obtained. Then, we suggest an approach to choose the suitable system parameters so that with a certain high probability, the feasibility of the solution from optimization formulation is ensured for all meaningful realizations of the system parameters. Moreover, we introduce the reduced conservativeness approach for the bandwidth allocation. In this approach, the tradeoff between throughput and robustness to the uncertainty of a system parameter is explored. Specifically, the decrease in throughput will be minimized given the target robustness.