Resource Allocation in Energy Cooperation Enabled 5G Cellular Networks

PhD thesisIn fifth generation (5G) networks, more base stations (BSs) and antennas have been deployed to meet the high data rate and spectrum efficiency requirements. Heterogeneous and ultra dense networks not only pose substantial challenges to the resource allocation design, but also lead to unprecedented surge in energy consumption. Supplying BSs with renewable energy by utilising energy harvesting technology has became a favourable solution for cellular network operators to reduce the grid energy consumption. However, the harvested renewable energy is fluctuating in both time and space domains. The available energy for a particular BS at a particular time might be insufficient to meet the traffic demand which will lead to renewable energy waste or increased outage probability. To solve this problem, the concept of energy cooperation was introduced by Sennur Ulukus in 2012 as a means for transferring and sharing energy between the transmitter and the receiver. Nevertheless, resource allocation in energy cooperation enabled cellular networks is not fully investigated. This thesis investigates resource allocation schemes and resource allocation optimisation in energy cooperation enabled cellular networks that employed advanced 5G techniques, aiming at maximising the energy efficiency of the cellular network while ensuring the network performance. First, a power control algorithm is proposed for energy cooperation enabled millimetre wave (mmWave) HetNets. The aim is to maximise the time average network data rate while keeping the network stable such that the network backlog is bounded and the required battery capacity is finite. Simulation results show that the proposed power control scheme can reduce the required battery capacity and improve the network throughput. Second, resource allocation in energy cooperation enabled heterogeneous networks (Het- Nets) is investigated. User association and power control schemes are proposed to maximise the energy efficiency of the whole network respectively. The simulation results reveal that the implementation of energy cooperation in HetNets can improve the energy efficiency and the improvement is apparent when the energy transfer efficiency is high. Following on that, a novel resource allocation for energy cooperation enabled nonorthogonal multiple access (NOMA) HetNets is presented. Two user association schemes which have different complexities and performances are proposed and compared. Following on that, a joint user association and power control algorithm is proposed to maximise the energy efficiency of the network. It is confirmed from the simulation results that the proposed resource allocation schemes efficiently coordinate the intra-cell and inter-cell interference in NOMA HetNets with energy cooperation while exploiting the multiuser diversity and BS densification. Last but not least, a joint user association and power control scheme that considers the different content requirements of users is proposed for energy cooperation enabled caching HetNets. It shows that the proposed scheme significantly enhances the energy efficiency performance of caching HetNets

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments