Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Convex Optimization
Hot topic: physical-layer network coding
Proceedings of the 12th annual international conference on Mobile computing and networking
Optimal beamforming for two-way multi-antenna relay channel with analogue network coding
IEEE Journal on Selected Areas in Communications - Special issue on network coding for wireless communication networks
Distributed space-time coding for two-way wireless relay networks
IEEE Transactions on Signal Processing
Optimal channel estimation and training design for two-way relay networks
IEEE Transactions on Communications
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals
IEEE Transactions on Signal Processing
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks
IEEE Transactions on Information Theory
Cooperative diversity in wireless networks: Efficient protocols and outage behavior
IEEE Transactions on Information Theory
Computation Over Multiple-Access Channels
IEEE Transactions on Information Theory
On the Capacity and Diversity-Multiplexing Tradeoff of the Two-Way Relay Channel
IEEE Transactions on Information Theory
MIMO two-way relay channel with superposition coding and imperfect channel estimation
Journal of Network and Computer Applications
LDPC-Coded Cooperation with Receive Multi-Antenna and Unknown CSI in the Destination
Wireless Personal Communications: An International Journal
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In this paper, we propose a new channel estimation prototype for the amplify-and-forward (AF) two-way relay network (TWRN). By allowing the relay to first estimate the channel parameters and then allocate the powers for these parameters, the final data detection at the source terminals could be optimized. Specifically, we consider the classical three-node TWRN where two source terminals exchange their information via a single relay node in between and adopt the maximum likelihood (ML) channel estimation at the relay node. Two different power allocation schemes to the training signals are then proposed to maximize the average effective signal-to-noise ratio (AESNR) of the data detection and minimize the meansquare-error (MSE) of the channel estimation, respectively. The optimal/sub-optimal training designs for both schemes are found as well. Simulation results corroborate the advantages of the proposed technique over the existing ones.