Closed-form solution for minimizing power consumption in coordinated transmissions

The growth in the demand of energy, and its consequent contribution to the greenhouse effect, gives rise to new challenges in the design of future wireless networks. Keeping in mind these requirements, in this article we study the power allocation problem in the downlink of an orthogonal frequency division multiple access (OFDMA) system, where two (or more) coordinated transmission points (CTPs) should find the best way to allocate their transmit power through the multiple orthogonal sub-channels of the system. The ultimate goal of the power allocation scheme is to minimize the joint power consumption of the system, but verifying at the same time the target throughput and the individual power constraint per CTP. The power allocation problem is formulated as a constrained optimization problem, and a group of closed-form power allocation solutions are derived. Based on the derived solutions (that take the form of the traditional water-filling but demanding cooperation among CTPs), a novel power allocation algorithm with joint minimization power consumption (JMPC-PA) is proposed. Numerical results are presented to verify the optimality of the results that were obtained by the JMPC-PA scheme. It is important to note that, due to the flexibility that exist in the definition of CTPs in this article, the derived power allocation scheme is valid for any kind of network that incorporates the coordinated multipoint transmission feature in its design.Peer reviewe

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