Hybrid Parallel Model of Semi-Blind Joint Timing-Offset and Channel Estimation for AF-TWRNs

Timing offset and channel estimation plays an important role in the performance of amplify-and-forward two-way relay networks. The state-of-the art approach models the system as a Hidden Markov Model (HMM) and performs semi-blind estimation using expectation maximization (EM). The HMM formulation allows the use of the Baum Welch (BW) forward-backward algorithm to calculate the posterior probabilities of the data. One of the main challenges of this algorithm is reducing the computational complexity to allow fast real-time implementation. In this work, the opportunities of parallelization are studied based on the time and performance analysis. We propose different parallelization techniques of compute-intensive tasks in semi-blind joint timing-offset and channel estimation. Multi-threading, message passing interface (MPI) and hybrid (multi-threading/ MPI) models are implemented and tested. The results show that the hybrid model is superior to the other models. In particular, the hybrid parallel model can achieve 10.4X speedup over the serial implementation of semi-blind joint timing-offset and channel estimation, while multithreading and MPI achieve only 6.5X and 1.5X speedup, respectively. Moreover, our proposed hybrid parallel model achieved better performance than other existing models.