Research On Key Techniques Of Radio Resource Allocation In Green Wireless Networks

During the past decades, the design of wireless communication networks has been focused on how to improve the speed of the data transmission over the limited bandwidth to meet the growing bandwidth requirement of users. With the explosive growth of wireless communication networks, the tremendous energy consumption is becoming a serious problem which cannot be ignored. Therefore, the green network with lower energy consumption and carbon footprints is a trend for the evolution of the future wireless communication networks. As one of the key technologies in wireless communication networks, radio resource allocation certainly will play an important role in energy saving and emission reduction.In order to improve the spectral efficiency, many advanced technologies such as orthogonal frequency division multiple access, carrier aggregation, and relaying have been applied in wireless networks. The application of the new technologies makes the radio resource have higher dimension and finer granularity. Not only does it open the door to energy efficient design, but it also imposes more new and complicated constraints and hence making the problem more challenging. Moreover, the change of power supply, especially the utilization of harvested energy, is another issue to be faced in the radio resource allocation. With the development of energy harvesting technology, networks can be powered by not only the power grid but also the ambient energy, such as wind energy, solar energy, vibrational energy, and etc. The future wireless networks powered by harvested energy are energy self-sufficiency and perpetual operation. However, the randomness of the available harvested energy is a new problem to be solved. Hence, in this dissertation, applying the cross-layer design method, under the guidance of nonlinear optimization theory, the radio resource allocation problem with the objective of energy saving, subject to the multidimensional constraints is intensively studied.The OFDMA wireless networks with carrier aggregation are the typical networks wherein the resources have multiple dimensions and fine granularity. The radio resources including two-dimensional resource blocks, transmission power, and component carriers impose much more complicated constraints on the allocation of both homogeneous resources and heterogeneous resources. In this dissertation, the methods of modeling, analyzing, and solving for the tightly coupled resource allocation problem in OFDMA networks is first investigated, which are the important foundations for the energy-efficient design. The joint CC, RB and power allocation problem is formulated as a mathematical programming with the objective of the Network Utility Maximization(NUM). Distinguished from many existing methods, our approach, i.e., JCRPA algorithm, combines dynamic spectrum resources assignment and adaptive power allocation technology. With the iterative update, the wireless diversity of the system can be fully exploited. Furthermore, with the cooperation of the JCRPA scheme, a CC switch control mechanism is devised to reduce the overhead due to the frequent CC switching.The focus of our research is on the energy-efficient resource allocation. Because the objective function of the energy efficiency is not concave, the convex methods which are widely used in the spectral efficiency optimization cannot be directly applied to solve the energy efficiency optimization. However, the general concavity of the energy efficiency function guarantees that the KKT conditions are necessary and sufficient conditions for the global optimal solutions, which is the theoretical basis of energy-efficient resource allocation. Different from the existing approaches utilizing the KKT conditions only as necessary conditions, we exploit the KKT sufficient optimality theorem to solve the problem and obtain the optimal solution in closed-form. Specifically, the energy-efficient water-filling approach is proposed to obtain the optimal solution by constructing the decision variables and Lagrange multipliers to meet the KKT conditions. This approach provides a new effective way to find the energy efficient solutions. Based on the theoretical analysis, a joint energy-efficient resource allocation(JERA) algorithm is proposed to maximize the energy efficiency under the under the minimum data rate requirements with significantly low computational complexity.The OFDMA-based radio resource allocation model is further extended to a relay-aided wireless communication system. The energy efficiency maximization problem is formulated as a fractional programming with the consideration of individual power constraints at base station and relay stations. Due to the general concavity of the energy efficiency function, this type of the fractional programming can be effectively solved. Based on convex optimization and non-linear fractional programming theory, and by exploiting the properties of decode-and-forward(DF) relaying communication, a green resource allocation algorithm is proposed to solve the joint transmission mode selection, subcarrier allocation and power control problem.Energy harvesting will have a profound impact on the design for the future wireless communication networks. From the views of resource allocation, the first issue to be handled is how to meet the new constraints imposed by harvested energy, i.e., the energy causality constraint and the battery capacity constraint. The additional constraints make the problem cannot be solved by using the water-filling method whose effectiveness is demonstrated in the spectral efficiency problem. Following the approach which utilizes the optimality sufficiency of the KKT conditions, a novel energy-transferring approach is proposed. By using the transfer energy, the power allocation expression which meets the energy causality constraint is first obtained, and the sufficient condition for the optimal transfer energy is also derived. Based on the sufficient condition, the transfer energy equations are provided and the closed-form solutions are obtained as well. Combining expansion and contraction of the transfer energy equations, an energy-transferring power allocation(ETPA) algorithm is proposed to obtain the accurate optimal power allocation with low computation complexity.