Learning Based Relay and Antenna Selection in Cooperative Networks

We investigate a cross-layer relay selection scheme based on Q-learning algorithm. For the study, we consider multi-relay adaptive decode and forward (DF) cooperative diversity networks over multipath time-varying Rayleigh fading channels. The proposed scheme selects relay subsets that maximizes the link layer transmission efficiency without having knowledge of channel state information (CSI). Results show that the proposed scheme outperforms the capacity based cooperative transmission with the same number of reliable relays in terms of transmission efficiency gain. Furthermore, a Q-learning based cross-layer antenna selection for the multiple antenna relay networks is proposed, where multiple antennas allow more links from the relays to the destination under time varying Rayleigh fading channel. We studied the performance of multi-antenna relay networks and compared with single antenna case. Both schemes are shown to offer high bandwidth efficiency from low to high signal-to-noise ratios (SNRs). Finally, we conclude that cooperative diversity with learning offers improved performance enhancement and bandwidth efficiency for the communication network

A Simple Cooperative Diversity Method Based on Network Path Selection

Cooperative diversity has been recently proposed as a way to form virtual antenna arrays that provide dramatic gains in slow fading wireless environments. However most of the proposed solutions require distributed space-time coding algorithms, the careful design of which is left for future investigation if there is more than one cooperative relay. We propose a novel scheme, that alleviates these problems and provides diversity gains on the order of the number of relays in the network. Our scheme first selects the best relay from a set of M available relays and then uses this best relay for cooperation between the source and the destination. We develop and analyze a distributed method to select the best relay that requires no topology information and is based on local measurements of the instantaneous channel conditions. This method also requires no explicit communication among the relays. The success (or failure) to select the best available path depends on the statistics of the wireless channel, and a methodology to evaluate performance for any kind of wireless channel statistics, is provided. Information theoretic analysis of outage probability shows that our scheme achieves the same diversity-multiplexing tradeoff as achieved by more complex protocols, where coordination and distributed space-time coding for M nodes is required, such as those proposed in [7]. The simplicity of the technique, allows for immediate implementation in existing radio hardware and its adoption could provide for improved flexibility, reliability and efficiency in future 4G wireless systems.Comment: To appear, IEEE JSAC, special issue on 4

Splitting algorithm for DMT optimal cooperative MAC protocols in wireless mesh networks

A cooperative protocol for wireless mesh networks is proposed in this paper. The protocol implements both on-demand relaying and a selection of the best relay terminal so only one terminal is relaying the source message when cooperation is needed. Two additional features are also proposed. The best relay is selected with a splitting algorithm. This approach allows fast relay selection within less than three time-slots, on average. Moreover, a pre-selection of relay candidates is performed prior to the splitting algorithm. Only terminals that are able to improve the direct path are pre-selected. So efficient cooperation is now guaranteed. We prove that this approach is optimal in terms of diversity-multiplexing trade-off. The protocol has been designed in the context of Nakagami-mfading channels. Simulation results show that the performance of the splitting algorithm does not depend on channel statistics