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Belief propagation (BP) is a powerful algorithm to decode low- density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates expectation propagation (EP) into the BP based LDPC decoding process. The proposed...
In this work, an optimization model of load balancing in P2P network is proposed to make full use of each node resource, and to consider the coordination of network resource allocation between the other network area and this area, making the whole network resource can be used as fully as possible. based on the load information of the different nodes, we layer the nodes and use an undirected graph...
This paper addresses the problem of distributed source coding (DSC) of binary sources with unknown varying correlation statistics over erasure channels. We propose an adaptive asymmetric Slepian-Wolf (SW) decoding scheme using particle-based belief propagation (BP) based on Raptor codes. We show through the experiments that the proposed algorithm can simultaneously reconstruct the compressed sources...
In this paper we propose an adaptive Wyner-Ziv (WZ) coding scheme for non-binary correlated sources with side information at the decoder. At the encoder, a low density parity check (LDPC) code is employed to implement Slepian-Wolf (SW) coding for each bit-plane independently. The joint "source-channel" decoder, which combines SW decoding and dequantizing in a single step, preserves the symbol-domain...
Recent research efforts in the fields of data stream processing show the increasing importance of processing data streams, e.g., in the e-science domain. Together with the advent of peer-to-peer (P2P) networks and grid computing, this leads to the necessity of developing new techniques for distributing and processing continuous queries over data streams in such networks. These systems often have to...
Data streaming systems are becoming essential for monitoring applications such as financial analysis, network intrusion detection and sensor network. These systems often have to process multiple similar but different continuous aggregation queries simultaneously. Since executing each query separately can lead to significant scalability and performance problems, it is vital to share resources by exploiting...
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