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karbalai Ghareh, Alireza | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44794 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Nasiri Kenari, Masoumeh
  7. Abstract:
  8. In the recent years, the cooperative communications based on Network Coding have received significant attentions to simultaneously improve the diversity order and the network throughput. In this thesis, a new cooperative transmission scheme for the cooperative relay-based networks called “Convolutional Network-Coded Cooperation” (CNCC) is proposed, in which a systematic convolutional code is used in the network level. In this scheme, the systematic packets are directly transmitted by the sources, and the parity packets, calculated based on the received sources’ packets, are transmitted by the relays. The proposed CNCC scheme is considered in the cooperative networks including N sources, M relays (or one M-antenna relay), and one common destination. The source-destination and the relay-destination channels are assumed the Rayleigh fading channels. Furthermore, due to the proximity of the relays and the sources, both the ideal and the Nakagami-m fading channels are considered for the source-relay channels. The performance of the CNCC scheme is analyzed in terms of the Bit Error Rate (BER) of the network’s sources, and it is shown that the performance is improved in compared to the traditional schemes such as AF and DF , and also, the cooperative schemes based on the Linear Network Coding (LNC). Moreover, in the networks with an M-antenna relay, the CNCC scheme is combined with the “Best Antenna Selection” (BAS) technique to further improve the network performance in terms of the diversity order. Finally, in the last part of the thesis, a more practical case has been considered, in which instead of considering having the perfect channel coefficients at the related receivers, the coefficients are estimated using the training bits. To this end, the optimal Maximum Likelihood (ML) estimator is used to estimate the channel coefficients in the receivers. Then, the network performance is evaluated taking into account the channel estimation errors. It is verified while the channel estimation errors can lead to the degradation of the BER, it has no effect on the achieved diversity order
  9. Keywords:
  10. Linear Network Coding ; Convolutional Network-Coded Cooperation (CNCC) ; Cooperative Network ; Network Throughput ; Diversity Theory

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