Distributed Energy and Resource Management for Full-Duplex Dense Small Cells for 5G

TL;DR

This paper presents a distributed resource management framework for full-duplex dense small cell 5G networks powered by renewable energy, optimizing energy and traffic to reduce user data buffers efficiently.

Contribution

It introduces a joint beamforming, power, and sub-carrier allocation algorithm that accounts for energy sharing and uplink decoding energy, with a distributed solution for improved convergence.

Findings

The proposed algorithm reduces user data buffer lengths effectively.

Energy sharing between downlink and uplink improves network performance.

Distributed implementation achieves fast convergence with limited information exchange.

Abstract

We consider a multi-carrier and densely deployed small cell network, where small cells are powered by renewable energy source and operate in a full-duplex mode. We formulate an energy and traffic aware resource allocation optimization problem, where a joint design of the beamformers, power and sub-carrier allocation, and users scheduling is proposed. The problem minimizes the sum data buffer lengths of each user in the network by using the harvested energy. A practical uplink user rate-dependent decoding energy consumption is included in the total energy consumption at the small cell base stations. Hence, harvested energy is shared with both downlink and uplink users. Owing to the non-convexity of the problem, a faster convergence sub-optimal algorithm based on successive parametric convex approximation framework is proposed. The algorithm is implemented in a distributed fashion, by using the alternating direction method of multipliers, which offers not only the limited information exchange between the base stations, but also fast convergence. Numerical results advocate the redesigning of the resource allocation strategy when the energy at the base station is shared among the downlink and uplink transmissions.