Energy and Traffic Aware Full-Duplex Communications for 5G Systems

In this paper, we consider the problem of resource allocation in a dense small-cell network. Each small-cell base station is powered by a renewable energy source and operates in the full-duplex mode. We account for the rate-dependent energy term for data decoding into the total energy consumption at the small-cell base station. Owing to this new energy term, the transmitter and receiver operations now draw the energy from a common source. For a new energy consumption model and high interference scenario, which arises due to full-duplex communications, we formulate an energy and load aware resource management optimization problem under the energy causality and total transmit power constraints of the small-cell base station and uplink user equipments. In particular, the problem minimizes the data queue length of each network user equipment by jointly designing the beamformers, power, and sub-carrier allocation and their scheduling. Owing to the non-convexity of the problem, a global solution is inefficient; thus, we opt for the successive parametric convex approximation method to obtain a sub-optimal solution. This method solves for the convex approximate of the non-convex problem in each iteration and leads to faster convergence. For practical implementation, we further develop a distributed algorithm by using the dual decomposition framework, which relies on limited exchange of information between the involved base stations. Numerical simulations compare the network scenario which accounts for uplink channel rate-dependent energy consumption with that which ignores it. Results advocate the need for redesigning of the resource allocation scheme. In addition, numerical simulations also validate the usefulness of full-duplex communications over the half-duplex communications in terms of minimizing the sum data queue length of the users.