Multi-source Transfer Learning for Signal Detection over a Fading Channel with Co-channel Interference

For signal detection tasks in wireless communications, most of the existing algorithms either ignore the co-channel interference or treat it as Gaussian noise, which may result in unsatisfactory accuracy when the interference is non-negligible and complex-distributed. When neural networks are motivated in the design of the detectors, difficulty arises in the training due to the fact that there are few accessible pilots in each packet. In this paper, we consider a data-driven detector based on multi-source transfer learning (MSTL) for signal detection in a fading channel with interference. The MSTL detector transfers channel knowledge of previous packets into the latest detection. In particular, we consider a linear combination of the pilots and historical symbols in the distribution space, and design the optimal combination coefficients based on the number of those symbols as well as distributions similarity. Numerical simulations on a Gauss-Markov flat Rayleigh fading channel with co-channel interference validate the advantages of our algorithms, compared with several existing training schemes including directly applying the fully connected deep neural network (FCDNN) and conventional linear minimum mean square error (LMMSE) detector.