Real-time energy trading with grid in green cloud-RAN

With enormously increasing demand for mobile data and high data rates, the aggregated power requirements by user terminals may exceed the amount of power budget at the remote radio heads (RRHs). Hence, the retailer has to purchase additional energy from the real-time market, i.e., the grid, and may take a risk of losing the profit. To address the aforementioned issue, this paper studies the real-time energy management for green cloud radio access network (C-RAN) using the local renewable energy generation at RRHs. We develop three different cooperative real-time energy trading strategies, namely, power-shortage management by partial cooperation, power-shortage management by full cooperation, and overall network energy management by full cooperation, to jointly minimize the energy consumption and the real-time energy trading under the constraints of demand and supply power balancing at RRHs and quality of service satisfaction at user terminals. For the first strategy, we formulate a sparse beamforming problem as an ℓ0-norm optimization problem and solve it using semidefinite relaxation (SDR) and iterative reweighted ℓ1-norm approximation of ℓ0-norm. We formulate the second and the third strategies as numerically tractable optimization problems and solve them using the SDR approach. Our simulation results show that the second and the third strategies perform closely up to medium signal-to-interference-plus-noise ratio range and both outperform the first strategy in terms of reducing the total energy cost of the retailer. Furthermore, in terms of cost reduction, all of our three joint cooperative transmission and energy trading strategies achieve significant performance gain, as compared to a baseline scheme that separately optimize the cooperative transmission and energy trading.