The design and optimization of cooperative mobile edge

As the world is charging towards the Internet of Things (IoT) era, an enormous amount of sensors will be rapidly empowered with internet connectivity. Besides the fact that the end devices are getting more diverse, some of them are also becoming more powerful, such that they can function as standalone mobile computing units with multiple wireless network interfaces. At the network end, various facilities are also pushed to the mobile edge to foster internet connections. Distributed small scale cloud resources and green energy harvesters can be directly attached to the deployed heterogeneous base stations. Different from the traditional wireless access networks, where the only dynamics come from the user mobility, the evolving mobile edge will be operated in the constantly changing and volatile environment. The harvested green energy will be highly dependent on the available energy sources, and the dense deployment of a variety of wireless access networks will result in intense radio resource contention. Consequently, the wireless networks are facing great challenges in terms of capacity, latency, energy/spectrum efficiency, and security. Equivalently, balancing the dynamic network resource demand and supply is essential to the smooth network operation. Leveraging the broadcasting nature of wireless data transmission, network nodes can cooperate with each other by either allowing users to connect with multiple base stations simultaneously or offloading user workloads to neighboring base stations. Moreover, grid facilitated and radio frequency signal enabled renewable energy sharing among network nodes are introduced in this dissertation. In particular, the smart grid can transfer the green energy harvested by each individual network node from one place to another. The network node can also transmit energy from one to another using radio frequency energy transfer. This dissertation addresses the cooperative network resource management to improve the energy efficiency of the mobile edge. First, the energy efficient cooperative data transmission scheme is designed to cooperatively allocate the radio resources of the wireless networks, including spectrum and power, to the mobile users. Then, the cooperative data transmission and wireless energy sharing scheme is designed to optimize both the energy and data transmission in the network. Finally, the cooperative data transmission and wired energy sharing scheme is designed to optimize the energy flow within the smart grid and the data transmission in the network. As future work, how to motivate multiple parties to cooperate and how to guarantee the security of the cooperative mobile edge is discussed. On one hand, the incentive scheme for each individual network node with distributed storage and computing resources is designed to improve network performance in terms of latency. On the other hand, how to leverage network cooperation to balance the tradeoff between efficiency (energy efficiency and latency) and security (confidentiality and privacy) is expounded

Energy-Efficient Resource Allocation in Cloud and Fog Radio Access Networks

PhD ThesisWith the development of cloud computing, radio access networks (RAN) is migrating to fully or partially centralised architecture, such as Cloud RAN (C- RAN) or Fog RAN (F-RAN). The novel architectures are able to support new applications with the higher throughput, the higher energy e ciency and the better spectral e ciency performance. However, the more complex energy consumption features brought by these new architectures are challenging. In addition, the usage of Energy Harvesting (EH) technology and the computation o oading in novel architectures requires novel resource allocation designs.This thesis focuses on the energy e cient resource allocation for Cloud and Fog RAN networks. Firstly, a joint user association (UA) and power allocation scheme is proposed for the Heterogeneous Cloud Radio Access Networks with hybrid energy sources where Energy Harvesting technology is utilised. The optimisation problem is designed to maximise the utilisation of the renewable energy source. Through solving the proposed optimisation problem, the user association and power allocation policies are derived together to minimise the grid power consumption. Compared to the conventional UAs adopted in RANs, green power harvested by renewable energy source can be better utilised so that the grid power consumption can be greatly reduced with the proposed scheme. Secondly, a delay-aware energy e cient computation o oading scheme is proposed for the EH enabled F-RANs, where for access points (F-APs) are supported by renewable energy sources. The uneven distribution of the harvested energy brings in dynamics of the o oading design and a ects the delay experienced by users. The grid power minimisation problem is formulated. Based on the solutions derived, an energy e cient o oading decision algorithm is designed. Compared to SINR-based o oading scheme, the total grid power consumption of all F-APs can be reduced signi cantly with the proposed o oading decision algorithm while meeting the latency constraint. Thirdly, an energy-e cient computation o oading for mobile applications with shared data is investigated in a multi-user fog computing network. Taking the advantage of shared data property of latency-critical applications such as virtual reality (VR) and augmented reality (AR) into consideration, the energy minimisation problem is formulated. Then the optimal computation o oading and communications resources allocation policy is proposed which is able to minimise the overall energy consumption of mobile users and cloudlet server. Performance analysis indicates that the proposed policy outperforms other o oading schemes in terms of energy e ciency. The research works conducted in this thesis and the thorough performance analysis have revealed some insights on energy e cient resource allocation design in Cloud and Fog RANs

User Association in 5G Networks: A Survey and an Outlook

26 pages; accepted to appear in IEEE Communications Surveys and Tutorial

Cooperative Energy Trading in CoMP Systems Powered by Smart Grids

This paper studies the energy management in the coordinated multi-point (CoMP) systems powered by smart grids, where each base station (BS) with local renewable energy generation is allowed to implement the two-way energy trading with the grid. Due to the uneven renewable energy supply and communication energy demand over distributed BSs as well as the difference in the prices for their buying/selling energy from/to the gird, it is beneficial for the cooperative BSs to jointly manage their energy trading with the grid and energy consumption in CoMP based communication for reducing the total energy cost. Specifically, we consider the downlink transmission in one CoMP cluster by jointly optimizing the BSs' purchased/sold energy units from/to the grid and their cooperative transmit precoding, so as to minimize the total energy cost subject to the given quality of service (QoS) constraints for the users. First, we obtain the optimal solution to this problem by developing an algorithm based on techniques from convex optimization and the uplink-downlink duality. Next, we propose a sub-optimal solution of lower complexity than the optimal solution, where zero-forcing (ZF) based precoding is implemented at the BSs. Finally, through extensive simulations, we show the performance gain achieved by our proposed joint energy trading and communication cooperation schemes in terms of energy cost reduction, as compared to conventional schemes that separately design communication cooperation and energy trading

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