Energy-aware resource allocation in next generation wireless networks : application in large-scale MIMO Systems

In this thesis, we investigate the resource allocation problem for wireless networks that incorporate large-scale multiple-input multiple-output (MIMO) systems. These systems are considered as key technologies for future 5G wireless networks and are based on using few hundreds of antennas simultaneously to serve tens of users in the same time-frequency resource. The gains obtained by large-scale MIMO systems cannot be fully exploited without adequate resource allocation strategies. Hence, the aim of this thesis is to develop energy-aware resource allocation solutions for large-scale MIMO systems that take into consideration network power cost. Firstly, this thesis investigates the downlink of a base station equipped with large-scale MIMO system while taking into account a non-negligible transmit circuit power consumption. This consumption involves that activating all RF chains does not always necessarily achieve the maximum sum-rate. Thus, we derive the optimal number of activated RF chains. In addition, efficient antenna selection, user scheduling and power allocation algorithms in term of instantaneous sum-rate are proposed and compared. Also, fairness is investigated by considering equal receive power among users. Secondly, this thesis investigates a large-scale MIMO system that incorporates energy harvesting that is a promising key technology for greening future wireless networks since it reduces network operation costs and carbon footprints. Hence, we consider distributed large-scale MIMO systems made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Optimal on-line and off-line energy management strategies are developed. In addition, on-line energy management algorithm based on energy prediction is devised. The feasibility problem is addressed by proposing an efficient link removal algorithm and for better energy efficiency, RRH on/off operation is investigated. Thirdly, wireless backhauling was proposed as an alternative solution that enable low-cost connection between the small base stations and the macro base station in heterogeneous networks (HetNets). The coexistence of massive MIMO, HetNets and wireless backhauling is a promising research direction since massive MIMO is a suitable solution to enable wireless backhauling. Thus, we propose a new transmission technique that is able to efficiently manage the interference in heterogeneous networks with massive MIMO wireless backhaul. The optimal time splitting parameter and the allocated transmit power are derived. The proposed transmission technique is shown to be more efficient in terms of transmit power consumption than the conventional reverse time division duplex with bandwidth splitting. In this thesis, we developed efficient resource allocation solutions related to system power for wireless networks that incorporate large-scale MIMO systems under different assumptions and network architectures. The results in this thesis can be expanded by investigating the research problems given at the end of the dissertation

Edge Caching in Dense Heterogeneous Cellular Networks with Massive MIMO Aided Self-backhaul

This paper focuses on edge caching in dense heterogeneous cellular networks (HetNets), in which small base stations (SBSs) with limited cache size store the popular contents, and massive multiple-input multiple-output (MIMO) aided macro base stations provide wireless self-backhaul when SBSs require the non-cached contents. Our aim is to address the effects of cell load and hit probability on the successful content delivery (SCD), and present the minimum required base station density for avoiding the access overload in an arbitrary small cell and backhaul overload in an arbitrary macrocell. The massive MIMO backhaul achievable rate without downlink channel estimation is derived to calculate the backhaul time, and the latency is also evaluated in such networks. The analytical results confirm that hit probability needs to be appropriately selected, in order to achieve SCD. The interplay between cache size and SCD is explicitly quantified. It is theoretically demonstrated that when non-cached contents are requested, the average delay of the non-cached content delivery could be comparable to the cached content delivery with the help of massive MIMO aided self-backhaul, if the average access rate of cached content delivery is lower than that of self-backhauled content delivery. Simulation results are presented to validate our analysis.Comment: Accepted to appear in IEEE Transactions on Wireless Communication

Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-latency

In this paper, we study the coexistence and synergy between edge and central cloud computing in a heterogeneous cellular network (HetNet), which contains a multi-antenna macro base station (MBS), multiple multi-antenna small base stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are empowered by edge clouds offering limited computing services for UEs, whereas the MBS provides high-performance central cloud computing services to UEs via a restricted multiple-input multiple-output (MIMO) backhaul to their associated SBSs. With processing latency constraints at the central and edge networks, we aim to minimize the system energy consumption used for task offloading and computation. The problem is formulated by jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices, which is {a mixed-integer and non-convex optimization problem}. Based on methods such as decomposition approach and successive pseudoconvex approach, a tractable solution is proposed via an iterative algorithm. The simulation results show that our proposed solution can achieve great performance gain over conventional schemes using edge or central cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO backhaul can significantly reduce the complexity of the proposed algorithm and obtain even better performance.Comment: Accepted in IEEE Transactions on Wireless Communication