Revisiting Semi-Blind Block-Term Decomposition-Based Receivers for Uniform Rectangular Arrays

Block-term tensor decomposition (BTD) was recently reconsidered in the context of array signal processing and shown to be the natural choice for modeling sensor arrays of increased dimensionality with channel multipath. Semi-blind joint channel estimation/data detection (JCD) was addressed in the special but important case of the uniform rectangular array (URA), via efficient algorithms that wed existing JCD schemes with BTD approximation. That prior work is revisited in this paper from a Bayesian perspective to help cope with the discrete nature of the input symbol constellation. This offers a new interpretation of the best-performing BTD JCD scheme among those previously developed, and results in additional JCD schemes for URAs that provide alternatives to our previous work in detection performance and computational complexity. All receivers are evaluated by simulation results at various noise levels and different constellation sizes and are compared favorably with the training-only-based solution.