Base station (BS) coordination with respect to data and energy cooperation has recently emerged as a potential solution for enhancing the energy efficiency (EE) of multi-cell multi-tier cellular network architecture. This work studies the EE maximization problem in a hybrid-powered (grid and renewable energy source) heterogeneous network (HetNet) where the data and energy are jointly coordinated among the BSs. We propose a combinatorial optimization algorithm to maximize the system EE with the aim to reduce grid power consumption (GPC). Due to the complexity of the formulation, Lagrange dual decomposition and metaheuristic method are incorporated to solve the problem. Furthermore, the nonfractional programming EE problem is solved using the Dinkelbach's method which converges faster with a lower complexity. Simulation results show that cooperation among the BSs to share the channel information and energy reduces the GPC by nearly 20% and increases EE around 10% during harvested energy scarcity among the BSs.

With the rapid proliferation of wireless traffic and the surge of various data-intensive applications, the energy consumption of wireless networks has tremendously increased in the last decade, which not only leads to more CO2 emission, but also results in higher operating expenditure. Consequently, energy efficiency (EE) has been regarded as an essential design criterion for future wireless networks. This paper investigates the problem of EE maximisation for a cooperative heterogeneous network (HetNet) powered by hybrid energy sources via joint base station (BS) switching (BS-Sw) and power allocation using combinatorial optimisation. The cooperation among the BSs is achieved through a coordinated multi-point (CoMP) technique. Next, to overcome the complexity of combinatorial optimisation, Lagrange dual decomposition is applied to solve the power allocation problem and a sub-optimal distance-based BS-Sw scheme is proposed. The main advantage of the distance-based BS-Sw is that the algorithm is tuning-free as it exploits two dynamic thresholds, which can automatically adapt to various user distributions and network deployment scenarios. The optimal binomial and random BS-Sw schemes are also studied to serve as benchmarks. Further, to solve the non-fractional programming component of the EE maximisation problem, a low-complexity and fast converging Dinkelbach’s method is proposed. Extensive simulations under various scenarios reveal that in terms of EE, the proposed joint distance-based BS-Sw and power allocation technique applied to the cooperative and harvesting BSs performs around 15–20% better than the non-cooperative and non-harvesting BSs and can achieve near-optimal performance compared to the optimal binomial method.