Qos-based Resource Allocation And Cooperation Technology

User cooperative transmissions take advantage of the broadcasting nature of wireless networks, as well as exploiting the inherent spatial and multiuser diversity, to directly combat the effects of channel fading and enhance the system capacity. So it has gained much attention as an emerging transmission strategy for future wireless networks. Resource allocation technology makes full use of the differences of various QoS requirements and the nature of wireless channels to improve the resources utilization, while meeting users'QoS. Therefore, allocating resource among cooperative users reasonably has been a hot point of research. We also research the resource allocation strategy in the user cooperation system.In user cooperative system, subchannel resource is one kind of wireless resources which shared by the users'terminals. Through allocating and choosing reasonable subchannels for users, channel allocation technology can make the system obtain stable and high quality communication. We focus in this paple, on leveraging cooperation for channel allocation. The aim is to improve the system throughput besides meeting the QoS requirements. By introducing QoS price, we proposed a suboptimal heuristic algorithm utilizing virtual nodes to solve the joint channel allocation and cooperation problem. This algorithm simplifies the joint problem by allowing the consideration of only direct transmissions.Power is another important kind of wireless resources which shared by the users'terminals. Most terminals in user cooperative system are small hand-held devices containing a little energy. So, power control technology is significant. In this paper, we research the resource allocation algorithms aims at minimize the system total transmission power in multiuser uplink TDMA cooperation network. We propose several heuristic algorithms based on two-step politic and decomposition theory. These algorithms allocate the channel and power in different stages, and in this way, they reduce the computational complexity, while meeting users'QoS.