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Switching off base stations is a popular approach to improving the energy efficiency a network. The challenge is how to determine which and how many base stations to switch off given a network configuration while ensuring that the network coverage is not compromised. Different algorithms have been formulated to solve this challenge ranging from load-aware algorithms to random selection. Results show that load-aware algorithms have better performance. Most algorithms assume universal frequency reuse by all base stations even in the case of heterogeneous networks. In this paper we assume an indoor, femtocell network where subcarrier allocation is based on an existing subcarrier allocation technique which ensures maximum reuse. A load-aware base station switch-off algorithm is developed that uses the allocation matrix when all base stations are active and an estimate of required subcarriers by each base station to determine which base stations to switch off. It will be shown that even without any power control, the proposed technique can result in significant energy savings for lower femtocell densities.

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This paper proposes an adaptive large scale antenna system (ALSAS) for enhancing energy efficiency in low density wireless network scenarios. The proposed ALSAS comprises of two stages, a novel adaptive discontinuous transmission (ADTx) stage and an antenna array optimization (AAO) one. The basic idea is to utilize prior knowledge of the users' quality of service (QoS) requirements as well as precoding selection in the ADTx stage to maximize the transmitter hibernation periods subject to a certain complexity constraint. In the AAO stage, further power saving is achieved by reducing the number of active antenna elements subject to a certain QoS requirement. It is shown that, relative to conventional large scale antenna system (LSAS), the proposed ALSAS system achieves significant energy efficiency improvements under various scenarios. The results show that the proposed technique can provide energy efficiency improvement between 125% and 1124% in the suburban scenario, and between 196% and 952% in the rural scenario. It is also demonstrated that for rural environments with relatively small short inter-site-distance (ISD) values, ALSAS can provide up to 500% power saving for the fixed bit rate requirement case. INDEX TERMS Massive MIMO, discontinuous transmission, adaptive precoding energy efficiency, QoS, suburban and rural scenarios.