In this study, the energy efficient joint antenna scheduling, subcarrier assignment and power allocation are studied in an orthogonal frequency division multiple access‐based distributed antenna system. A new power consumption model is proposed to the consideration of the backhauling power, which is appropriate for the architecture of distributed antenna deployment and centralised signal processing. Under the maximum transmit power constraint per distributed antenna unit and the minimum rate requirement per user, the authors formulate a mixed‐integer optimisation problem to maximise the system energy efficiency. Due to the intractability, at first, a heuristic antenna scheduling strategy is proposed based on the received pilot signal power strength. Then, the remaining joint subcarrier and power allocation is equivalently turned into a parametric problem, which is solved by employing the Lagrange duality method and the Dinkelbach's algorithm. Simulation results show that the authors’ proposed joint resource allocation algorithm greatly improves the system energy efficiency.

This study studies the joint antenna selection and transmit beamforming optimisation in the downlink of a single‐cell multi‐user distributed antenna system, assuming that all users are delay sensitive and only partial channel state information is available at the transmitter side. Under the constraint of the maximum transmit power of each distributed antenna unit, the authors formulate an optimisation problem to minimise the total power consumption while maintaining the rate outage ratio of each user below a threshold. To mitigate the signalling overhead, they firstly propose an adaptive antenna selection strategy based on the received pilot signal power strength. Then, considering that the remaining transmit beamforming problem may be infeasible and non‐convex, a series of relaxation and convex approximation are applied. Finally, they propose a transmit beamforming algorithm based on successive convex approximation and user admission control. Simulation results show that the authors proposed algorithm can achieve a near optimal system performance in both the satisfied user ratio and total power consumption.

In this study, the authors study the downlink joint user scheduling and power allocation in a single‐cell distributed antennas system. Considering that all users are delay sensitive and without any assumption on the feasibility of the system, they formulate an optimisation problem to minimise the total power consumption while maximising the number of satisfied users. Given a user scheduling result, a deflation‐based joint user removal and power allocation algorithm and a conservative power allocation algorithm are proposed. Owing to the complexity in full enumeration of all user scheduling results, they further propose a joint heuristic user scheduling and power allocation algorithm, in which a priority parameter is defined to determine the user scheduling order and the conservative power allocation algorithm is applied to execute the power allocation. The user with the worst quality of experience is iteratively removed away from the active user set until all the remaining users are guaranteed with their quality of service requirements. Simulation results show that the authors proposed heuristic algorithm obtains a near optimal system performance in both user outage ratio and total power consumption.

Emergence of various radio access networks enables users to communicate with each other anytime and anywhere. Handover management strategy directly influences the user's service continuity performance and quality of service. In this paper, we propose a handover trigger time selection strategy based on the prediction of received signal strength. With auto-regression model, the mobile terminal predicts both the earliest and the latest handover trigger time threshold, and then determines the optimal trigger time. Simulation results show that our proposed method can almost eliminate handover failure and has a good adaptability to the varying network overlapping environment.