With the rapid development of wireless transmission requirements, the wireless spectrum resources are becoming more and more limited. Due to frequency reuse among multi-cells, inter-cell interference is introduced inevitably. In order to mitigate the inter-cell interference, the centralized algorithm needs to exchange a large amount of information, which is not feasible for large-scale networks. Therefore, using distributed algorithms to settle the multi-cell interference problem is very practical. This thesis will focus on the distributed algorithm design for multi-cell networks. The main contributions are listed below. Chapter 2 considers the problems of minimizing sum power and maximizing sum rate for multi-cell networks with TDMA, where coupling relation occurs among cells due to inter-cell interference. This coupling relation is characterized by the SINR coupling model with cell load vector and cell power vector as the variables, where cell load measures the average proportion of resource usage in the cell. Due to the nonlinear SINR coupling model, the optimization problems for multi-cell networks with TDMA is nonconvex. To solve these nonconvex problems, we first consider the optimization problems for single-cell networks. Through variable transformation, the optimization problems can be equivalently transformed into convex problems. By solving KKT conditions, the optimal solutions to power minimization and rate maximization problems can be obtained in closed form. Based on the theoretical findings of optimization problems for single-cell networks, we develop a distributed resource allocation and power control algorithm with low complexity for sum power minimization in multi-cell networks. This algorithm is proved to be convergent and globally optimal by using the properties of standard interference function. For sum rate optimization in multi-cell networks, we also provide a distributed algorithm which yields locally optimal solution. Besides, the convergence for this distributed algorithm is proved. Numerical results illustrate the theoretical findings, showing the superiority of our solutions compared to the conventional solution of allocating uniform power for users in the same cell.
In Chapter 3, the power control and resource allocation problem is studied in downlink orthogonal frequency division multiplexing networks where mutual interference exists among cells. This mutual relation is characterized by the load coupling model. Both cell load and transmit power interact via the coupling model. We consider three kinds of problems, sum power minimization, sum rate maximization and sum energy efficiency maximization. For each problem, we develop a correspondingly distributed power control and resource allocation algorithm with low complexity.
Chapter 4 investigates the problems of sum power minimization and sum rate maximization for multi-cell networks with NOMA. Considering the sum power minimization, we obtain closed-form solutions to the optimal power allocation strategy and then successfully transform the original problem to a linear one with a much smaller size, which can be optimally solved by using the standard interference function. To solve the nonconvex sum rate maximization problem, we first prove that the power allocation problem for a single cell is a convex problem. By analyzing the KKT conditions, the optimal power allocation for users in a single cell is derived in closed form. Based on the optimal solution in each cell, a distributed algorithm is accordingly proposed to acquire efficient solutions. Numerical results verify our theoretical findings showing the superiority of our solutions compared to the orthogonal frequency division multiple access and broadcast channel.
Chapter 5 considers the network utility maximization problem with various user priorities via jointly optimizing user association, load distribution and power control in a load-coupled heterogeneous network. In order to tackle the nonconvexity of the problem, we first analyze the problem by obtaining the optimal resource allocation strategy in closed form and characterizing the optimal base station load distribution pattern. Both observations are shown essential in simplifying the original problem and making it possible to transform the nonconvex load distribution and power control problem into convex reformulation via exponential variable transformation. An iterative algorithm with low complexity is accordingly presented to obtain a suboptimal solution to the joint optimization problem. Simulation results show that the proposed algorithm achieves better performance than conventional approaches.