This gives us an opportunity to apply the results in the literature of compressed sensing and sparse recovery [20, 37] to our QNC scenario with near-sparse messages.
The chapter shows the performance of such a combination (sparse graph code and message-passing decoding) analyzed and discussed, with respect to the relationship of the performance under message-passing decoding to the performance of the optimal decoder.
Details are, of course, still sparse about how threaded messaging will look like.
For sparse networks when, the messaging overhead is low since not that many nodes are localized to be broadcasted in the first place.
Moreover, the protocol employs implicit acknowledgements to guarantee robustness in message delivery under sparse scenarios.
In this paper, we propose high-performance GPU implementations for a set of important irregular EDA computing patterns including sparse matrix, graph algorithms and message-passing algorithms.
To tackle these problems, we propose a novel Data dissemination pRotocol In VEhicular networks (DRIVE) that relies exclusively on local one-hop neighbor information to deliver messages under dense and sparse networks.
Maybe for a given node in a sparse environment, there is no HELLO message from neighbors during an interval, so the NHFL table can be null.
This approach results in a large geographical gain per message retransmission, while ensuring that in sparse networking conditions all neighbors get the opportunity to relay the message when necessary.
Robustness is achieved by assigning message delivery responsibility to multiple vehicles in sparse networks.
Short text messages such as tweets are very noisy and sparse in their use of vocabulary.
