Resource Allocation Algorithms In Wireless Networks

In recent years, wireless communication networks develops rapidly. In such a development momentum, technological advances greatly affect every aspect of people’s lives,work, study and entertainment. Therefore, it needs a higher require-ment of the performance of wireless communication, such as the rate and quality of service guarantees (Qos). These years, wireless sensor got a great development. However, in many cases there are not conditions to deploy sink nodes, at this time, it becomes a challenge problem to collect and transmit data.Orthogonal Frequency Division Multiplexing (OFDM) as a multi-carrier mod-ulation technique has now been widely used in such as Wimax, LTE and LTE-Advanced networks. In order to provide the multicast users better quality of service, the service provider is required to efficiently schedule the radio resources. In order to achieve the requirements of ITU’s rate, many new technologies are written into LTE-Advanced standards, such as carrier aggregation technology. It needs the appropriate resource scheduling algorithms to make those technologies work properly. To be able to collect the data of some special areas (disaster ar-eas) in real time, Unmanned Aircraft Vehicles (UAVs) and satellite are be used to collect data. In order to effectively collect more data, it requires efficient resource allocation and scheduling algorithms. Based on the above point of view, the above wireless network resource allocation problems are studied, which mainly includes the following aspects:1. An optimal margin adaptive resource allocation algorithm in OFDM mul-ticast systems is proposed. We model the margin adaptive resource allocation in OFDM multicast systems. This model is to minimize the total transmit power to reduce the power consumption of the system while limits the total rate of users. To analyze the model, An algorithm based on dynamic programming is proposed. The proposed algorithm can achieve the global optimum as long as a proper rate step for dynamic programming. Finally, the simulation results show that the algorithm can get the optimal value, as same as the brute force algorithm.2. For the carrier aggregation problem in LTE-Advanced network, a gen- eral resource allocation system model for carrier aggregation is proposed, and an efficient resource allocation algorithm which jointly considers transmit power allocation, carrier allocation and resource block allocation is proposed. In our real life, different users have different technology hardwares, thus the number of carriers supported by every user may be different. Therefore, the issue is how to assign carriers and resource blocks to users, and how to allocate transmit power to each resource block. We model the above research problem to maximize the total rate. We prove that the formulated problem is NP-hard. First, we propose an optimal resource block allocation and transmit power allocation algorithm based on convex optimization, then an efficient joint carrier allocation, resource block allocation and power allocation algorithm is proposed. Simulation results show that the proposed algorithm outperforms the existing algorithm.3. For the data gathering problem in the special areas (disaster areas, et al.), unmanned aircraft vehicle (UAV) and satellite are used to collect data. We model three corresponding problems step by step. First, we model bandwidth resource allocation between collection nodes and UAV, this model is to maximize the total utility of the system. Second, we model bandwidth allocation between collection nodes and UAV, and energy allocation for nodes to maximize the total utility. Third, based on the model second, we add the flow constraint between UAVs and satellite, our objective is also to maximize the total utility. Then the corresponding algorithms are proposed. First, a greedy algorithm is proposed, simulation results show that the proposed algorithm outperforms the equal bandwidth allocation algorithm. Second, we achieve bandwidth allocation according to the algorithm in model one, then we get energy allocation for each node, simulation results show our algorithm can achieve better results. Third, we achieve bandwidth allocation and energy allocation according to the algorithm in model two, then according to the flow constraint of the link between UAVs and satellite, we readjust bandwidth allocation and energy allocation. Simulation results show that this algorithm can get better performance.4. For the data collection problem with UAVs under LTE network, the fol-lowing two models and the corresponding algorithms are proposed. First, we model the resource block allocation, Modulation and Coding Scheme(MCS) selec-tion problem, then propose a dynamic programming algorithm. Second, we model the resource block allocation, MCS selection, and energy allocation problem, then propose a two-step algorithm, this algorithm first gets the resource block alloca- tion according to the model one, then achieves the energy allocation for each node based on the achieved resource block allocation. Finally, simulation results show the proposed algorithm can get better results.