Network Coding In Wireless Communication

In 1956, Elias, Feinstein and Shannon draw a conclusion that the communication network end-to-end maximum flow is decided by the network minimum cut.But storing-forwarding (SF) of traditional router cannot achieve the upper bound of information flow in maximum flow minimum cut theorem. Network coding is introduced by R.Ahlswede in the "Network information flow" published by IEEE Transactions on Information Theory in 2000, which is based on the conception of network in formation flow. The max-flow bound of network multicast can be achieved if the nodes encode ifferent information flow, which can not always be achieved by traditional SF mode.To research the performance of network coding in wireless communications networks, starting from the basic principles and the structure of network coding, analyses linear network coding, random network coding and algebraic network coding.Especially studies the design of random linear network coding.Basic on above network coding theory, clarifies gain of network coding applying in wireless communication, provides system models, demonstrates several algorithm, sets up different simulation parameters, proves the application performance, concludes the theory results and simulation analysis.The main work and innovation of the dissertation includes the following:In order to study the performance of network coding in relay and cooperative communication networks,three stuctures of Turbo-network coding are provided, and compares the BER gain of system with network coding and that of without network coding.On the basis of Turbo-network coding,puts forward the application of joint channel and network coding in cooperative diversity communication. System performance of BER and throughput can be further improved via joint LDPC,convolutional code, or space-time code and network coding, and confirms the results via simulation diagrams.The performance of network coding in distributed storage is studied. An important parameter-source node degree is emphased, develops the tradeoff relationshio between source node degree and the number of queried nodes brought out as theorem and formula. This theory formula reflects the tradeoff betweem network coding cost and gain, which has practical guiding value.Erasure code, such as RS code, LT code and Raptor code, is the most commonly used in distributed storage. According to the limitations of erasure code and the advantages of network coding, research the application of network coding in distributed storage. Devoloping a sensor network model as an instance, analyzes network coding gain on energy consumption and system throughput. Furtherly, combines with node degree, sets up several network conditions, demonstrates the performance and confirms the potential future of network coding applying in distributed storage network.To analyze the gain of down-load time and success rate brought by random linear network coding in wireless mesh nework, set up three simulation conditions:upload-limited but MAC layer be ideal conflict-free, MAC layer be conflict and mobile nodes and MAC be conflict. Simulation results compare the performance between the system with random network coding and the system with traditional route.