Research On Wireless Network Resource Optimization Models And Algorithms Based On Network Coding Technology

As a major departure from conventional store-and-forward transmission paradigm, network coding allows encoding operations on intermediate nodes. Network coding provides several advantages, such as increasing network throughput, reducing energy consumption, and so on. Network coding techniques can improve performances of wireless networks by taking advantage of the broadcast nature of wireless links, and there has recently been significant interest in wireless network coding. This dissertation focuses on energy optimization of multicast in wireless networks, rate allocation based on network coding in multipath wireless networks, and network coding-aware rate control.First, it is studied energy-efficient multicast algorithms based on network coding in wireless networks. Combining intersession network coding with minimum energy multicast based on intrasession network coding, this thesis proposes an improved energy-efficient algorithm for multi-source multicast. Simulation results show that this improved algorithm outperforms traditional network coding algorithm, and it significantly reduces power consumption.Second, this thesis study rate allocation for wireless multipath network coding. It is proposed a wireless multipath network coding algorithm based on potential game. The objective function is system cost which depends on the capacity of coded hyperlinks and rate on every path. Given the capacity of coded hyperlinks, this algorithm decomposed this problem into a number of rate-allocation subproblems, each of which can be solved independently at each source node. Simulation results show that this algorithm reduced network total cost, compared with multi-path COPE, single-path network coding and other network coding algorithms.Third, this thesis proposed a single-path coding-aware rate control algorithm. The goal is to find the optimal transmitting rate of each session that maximizes network utility. This thesis defines two types of capacity for each node, build the optimization model according to the capacity constraint, and then decompose it to algorithms solved in a distributed way. Simulation results show that this algorithm improves wireless network utility compared with traditional rate control algorithms.