利用LT 編碼增進網路通訊系統吞吐量之研究

Abstract

網路傳輸時所發生的瓶頸於接收端已造成嚴重的吞吐量降低。這篇論文中，我們介紹了一個將LT碼的編碼系統應用於傳輸環境之下來增進吞吐量。所提的LT編碼能改善吞吐量來逼近理論上的最大值。同時也能提供重要的資料保護能力來對抗於通道的封包遺失。在LT編碼系統中，相對於所有傳輸資料量，中間節點所需要的緩沖器變成不重要的因素而能大量地減少。在某些網路中，我們結合LT碼和特定的網路編碼機制來得到更進一步接收端吞吐量的改善，同時也對資料提供強大的保護。在整個傳輸過程，因編碼機制所造成的額外負擔也在一個合理的代價。 所提出的LT碼工作在封包數目從4K到64K，每個封包的大小為1KB。提出的方法能在兩種網路之下成功的減輕瓶頸：含edge-disjoint edges（EDPs）以及不含EDPs。同時也能給予強大的資料保護在封包遺失比率從0%到20%。我們提及的LT碼能改善20%到30%的吞吐量。於包含EDPs的網路之中，結合的LT-網路編碼能提供更進一步到達約50%的吞吐量改善。最後，編碼運算是在有限場GF(2)而所需的額外負擔為每一個碼字大約30個exclusive-or運算。

Bottlenecks occurring during the transmission in the network have caused serious consequences on the throughput degradation in receivers. In this thesis, a coding system that applies low degree LT codes in transmission nvironment to enhance the throughput is introduced. The proposed LT code can improve the throughput approach to the theoretical maximum. In the meanwhile, it provides a significant data protection ability against the packet loss in erasure channels. The required buffer size in the intermediates in the LT codes system becomes a inconsequential factor that it can be reduced considerably compared to the data size. In some network topology, we combine low degree LT codes with specified network coding mechanism to get further improvement in throughput of every receiver and also give strong protection of data. The overhead of the coding mechanism causes the reasonable computation cost in whole transmission. For each packet of 1KB, the proposed LT codes work under the entire packet number from 4K to 64K. The proposed method alleviates the bottlenecks successfully in two kinds of topologies, with edge-disjoint paths (EDPs) and without EDPs. It also gives strong protection of data in the erasure channels with different scale of loss rate from 0% to 20%. Our proposed low degree LT code can enhance the throughput in the range from 20% to 30%. In the network with edge-disjoint paths, the combined LT-network code offers advanced improvement up to 50% or so. Finally, the computation is over GF(2) and the coding overhead is about 30 XOR operations per codeword.