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The continuous traffic increase of mobile communication systems has the collateral effect of higher energy consumption, affecting battery lifetime in the user equipment (UE). An effective solution for energy saving is to implement a discontinuous reception (DRX) mode. However, guaranteeing a desired quality of experience (QoE) while simultaneously saving energy is a challenge; but undoubtedly both energy efficiency and the QoE have been essential aspects for the provision of real‐time services, such as voice over Internet protocol (VoIP), voice over LTE, and mobile broadband in 4G networks and beyond. This paper focuses on human voice communications and proposes a Gaussian process regression algorithm that is capable of recognizing patterns of silence and predicts its duration in human conversations, with a prediction error as low as 1.87%. The proposed machine learning mechanism saves energy by switching OFF/ON the radio frequency interface, in order to extend the UE autonomy without harming QoE. Simulation results validate the effectiveness of the proposed mechanism compared with the related literature, showing improvements in energy savings of more than 30% while ensuring a desired QoE level with low computational cost.

Massive multi-input–multi-output systems promise high data rates by using a large number of antennas, which also increases the power usage of the system as a consequence. This creates an optimization problem that specifies how many antennas the system should use to operate with maximal energy efficiency (EE). Our main goal is to consider a base station with a fixed number of antennas, such that the system can operate with a smaller subset of antennas according to the number of active user terminals, which may vary over time. Thus, in this paper, we propose an antenna selection algorithm, which selects the best antennas according to the better channel conditions with respect to the users, aiming at improving the overall EE. Then, due to the complexity of the mathematical formulation, a tight approximation for the consumed power is presented, using the Wishart theorem, and it is used to find a deterministic formulation for the EE. Simulation results show that the approximation is quite tight and that there is significant improvement in terms of EE when antenna selection is used.

The design of efficient wireless fronthaul connections for future heterogeneous networks incorporating emerging paradigms such as cloud radio access network has become a challenging task that requires the most effective utilisation of fronthaul network resources. In this paper, we propose to use distributed compression to reduce the fronthaul traffic in uplink Coordinated Multi-Point for cloud radio access network. Unlike the conventional approach where each coordinating point quantises and forwards its own observation to the processing centre, these observations are compressed before forwarding. At the processing centre, the decompression of the observations and the decoding of the user message are conducted in a successive manner. The essence of this approach is the optimisation of the distributed compression using an iterative algorithm to achieve maximal user rate with a given fronthaul rate. In other words, for a target user rate the generated fronthaul traffic is minimised. Moreover, joint decompression and decoding is studied and an iterative optimisation algorithm is devised accordingly. Finally, the analysis is extended to multi-user case and our results reveal that, in both dense and ultra-dense urban deployment scenarios, the usage of distributed compression can efficiently reduce the required fronthaul rate and a further reduction is obtained with joint operation.

This chapter focuses on the 5G key concepts and how they can be extremely beneficial in supporting the advanced smart grid services. It introduces the smart grid environment and discusses some of the future services that will be supported in the future smart grids. These services are broadly classified into two categories, namely data collection and management services that target enhanced grid monitoring capabilities, and control and operation services that deal with demand side management and electric vehicle charging and discharging coordination. The chapter illustrates how the 5G novel concepts such as software‐defined networking, network virtualization, and cloud computing offer enhanced services for grid monitoring, data processing, demand‐side management, and electric vehicle charging and discharging coordination. It also illustrates a summary of the application of these concepts in supporting the smart grid services. Future research directions are discussed to deal with the open challenging issues.