Continual Learning-Based Channel Estimation for 5G Millimeter-Wave Systems

Accurate channel estimation in the millimeter-wave (mmWave) based wireless communication systems is challenging and involves a lot of computational costs. The mmWave frequency band has its advantages and disadvantages. At higher frequency mmWave bands, due to smaller wavelengths, we can pack a large number of antennas compared to lower frequency bands. However, the main disadvantages of the mmWave system are computing accurate channel estimation, smaller coverage, and high signal absorption. Besides, when multiple-input multiple-output (MIMO) systems operated over mmWave frequencies, it makes the channel estimation even more intricate in terms of computational complexity and estimation accuracy. In this paper, we plan to address these limitations and improve channel accuracy; we proposed a Continual Learning (CL)-based method for channel estimation in mmWave MIMO systems. Besides, we also proposed an activation function that is numerically stable and robust against early saturation. We discussed several channel estimation algorithms from the literature, also evaluated and compared their performances via numerical simulations. Our simulation results show that the proposed CL-based method outperforms the existing minimum mean squared error (MMSE)-based channel estimators in terms of precision. Furthermore, based on our experiments, we give insight into spectral efficiency with respect to the number of available channel observations.