Orthogonal frequency division multiplexing-(OFDM-) based amplify-and-forward (AF) cooperative communication is an effective way for single-antenna systems to exploit the spatial diversity gains in frequency-selective fading channels, but the receiver usually requires the knowledge of the channel state information to recover the transmitted signals. In this paper, a training-sequences-aided linear minimum mean square error (LMMSE) channel estimation method is proposed for OFDMbased cooperative diversity systems with multiple AF relays over frequency-selective fading channels. The mean square error (MSE) bound on the proposed method is derived and the optimal training scheme with respect to this bound is also given. By exploiting the optimal training scheme, an optimal low-rank LMMSE channel estimator is introduced to reduce the computational complexity of the proposed method via singular value decomposition. Furthermore, the Chu sequence is employed as the training sequence to implement the optimal training scheme with easy realization at the source terminal and reduced computational complexity at the relay terminals. The performance of the proposed low-complexity channel estimation method and the superiority of the derived optimal training scheme are verified through simulation results.
Game theory has emerged as a brand new approach to model and analyse several problems of wireless sensor networks, such as routing, data collection, and topology control. Recently, a novel clustering mechanism called clustered routing for selfish sensors (CROSS) has been proposed based on game theory. The sensor nodes, which are modelled as players, join in a clustering game to campaign for cluster heads with an equilibrium probability. However, the CROSS algorithm needs the global information of how many nodes participate in the game at every round. Considering that this global way introduces much more packets exchange and energy consumption, we present a Localized game theoretical clustering algorithm (LGCA). In our protocol, each node selfishly plays a localized clustering game only with its neighbours within a communication radius . Moreover, exactly one node can successfully bid for a position of the cluster head in one district, thus achieving an optimal payoff. Simulation results show that our method achieves a better result compared with CROSS and LEACH in terms of network lifetime.
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