Self-learning cooperative transmission - Coping with unreliability due to mobility, channel estimation errors, and untrustworthy nodes
Date of Original Version
For cooperative transmission in wireless networks, a very challenging problem is to combine information from different paths that do not necessarily perfectly represent the source's information. The excessive channel information needs to be estimated at the destination, especially in the networks with mobile nodes. Channel estimation error is often inevitable. Moreover, there may be untrustworthy nodes that do not honestly forward messages. Additionally, good nodes may fail to forward messages because they move out of the transmission range. In all above scenarios, the message forwarded by a relay node may not be the same as the message broadcast by the source node. This could significantly degrade the performance of cooperative transmission. Another challenging problem is to study complicated cooperative relaying topologies with multihop, multiple relays. Currently, existing schemes only analyze the one-hop case possibly with multiple relays, or the multi-hop case in a single chain topology. In this paper, we propose a low-overhead self-learning cooperative transmission scheme that solves above problems. In particular, each node detects the events in which the received signal-to-noise-ratio (SNR) is greater than a threshold. The distribution of the events, which can describe reliability/trustworthiness of a relay node, is modeled as a Beta function. With a very low overhead, those Beta functions can propagate through the complicated cooperative relaying topology from the source to the destination. When the destination combines information from different paths, both mean and variance are taken into the consideration. The simulation results have shown that the proposed scheme can adapt to estimation errors, untrustworthy nodes, and node mobility. The BER performance of our proposed scheme is more than 4 times better than the traditional Maximal Ratio Combining (MRC) that does not consider unreliability in relays. © 2007 IEEE.
GLOBECOM - IEEE Global Telecommunications Conference
Han, Zhu, and Yan L. Sun. "Self-learning cooperative transmission - Coping with unreliability due to mobility, channel estimation errors, and untrustworthy nodes." GLOBECOM - IEEE Global Telecommunications Conference , (2007): 361-365. doi:10.1109/GLOCOM.2007.74.