Sparse Random Linear Network coding (RLNC) reduces the computational complexity of the RLNC decoding through a low density of the non-zero coding coefficients, which can be achieved through sending uncoded (systematic...
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Sparse Random Linear Network coding (RLNC) reduces the computational complexity of the RLNC decoding through a low density of the non-zero coding coefficients, which can be achieved through sending uncoded (systematic) packets. However, conventional recoding of sparse RLNC coded packets at an intermediate node in a multi-hop network increases the density of non-zero coding coefficients. We develop and evaluate sparsity-preserving recoding (SpaRec) strategies that preserve the low density of non-zero coding coefficients of sparse RLNC with systematic packets. We develop SpaRec strategies with and without decoding at the intermediate nodes, with and without a specified coding rate, as well as with finite and infinite recodingwindow lengths. We evaluate the SpaRec strategies in multi-hop networks in terms of packet loss, packet delivery delay, as well as recoding and decoding (computation) throughput. We find that the SpaRec strategies substantially improve the RLNC performance compared to conventional recoding.
The modular power of large integer is a research hotspot in the public-key cryptosystem. To improve the efficiency, many modified algorithms were proposed, among them, the algorithms based on sliding window coding are...
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ISBN:
(纸本)9781424453023
The modular power of large integer is a research hotspot in the public-key cryptosystem. To improve the efficiency, many modified algorithms were proposed, among them, the algorithms based on sliding window coding are popular. In this paper, a novel length limited run-length coding method was proposed by modifying sliding window coding. In this method, the large exponent was recoded, so the modular power operation was changed into simple multiplication, addition and displacement operation. Then, a modular power algorithm based on the novel coding method was designed, and its time and space complexity was analyzed. Finally, its efficiency was compared with which based on the current optimal sliding window coding. Analysis shows that the both algorithms are equivalent in the space complexity, whereas the former is superior to the later in the time complexity. Tested under the same environment, the proposed algorithm's efficiency increase 40%.
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