Name
Abstract | ||
|---|---|---|
In this paper, we introduce a new efficient method based on reinforcement learning to construct linear network codes, referred to as the Reinforcement Learning Network Code (RLNC) design. We will make use of the market theory concepts in the learning section of the algorithm. The learning approach results in a smart random search and we demonstrate that the proposed algorithm is decentralized, and polynomial time complex. The RLNC algorithm constructs network codes faster than the random method of (4) with the same complexity order, especially in large networks with small field sizes. It is also shown that the algorithm can re-construct the network code in the presence of link failures with a small complexity and without previously being aware of error patterns even in very large networks. Further extension to achieve the objective of decreasing the number of encoding nodes in a network is discussed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ITWITWN.2007.4318025 | Solstrand |
Keywords | Field | DocType |
computational complexity,directed graphs,learning (artificial intelligence),linear codes,multicast communication,acyclic directed graph,decentralized approach,intermediate nodes,linear coding,market theory concept,multicast network capacity,network coding,polynomial time complex,reinforcement learning,sink nodes,source nodes | Linear network coding,Network delay,Computer science,Computer network,Network simulation,Theoretical computer science,Robustness (computer science),Directed acyclic graph,Time complexity,Distributed computing,Reinforcement learning,Computational complexity theory | Conference |
ISBN | Citations | PageRank |
978-1-4244-1200-6 | 2 | 0.38 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Jabbarihagh | 1 | 2 | 0.38 |
| Lahouti, F. | 2 | 133 | 11.22 |