ML-Aided Cross-Band Channel Prediction in MIMO Systems

Wireless communications technologies have experienced an exponential development during the last decades. 5G is a prominent exponent whose one of its crucial component is the Massive MIMO technology. By supporting multiple streams of signals it allows a revamped signal reconstruction in terms of mobile traffic size, data rate, latency, and reliability. In this thesis work, we isolated this technology into a SIMOapproach (Single-Input Multiple-Output) to explore a Machine Learning modeling to address the so-called Channel Prediction problem. Generally, the algorithms available to perform Channel Estimation in FDD and TDD deployments incur computational complexity downsides and require explicit feedback from client devices, which is typically prohibitive. This thesis work focuses on Channel Prediction by aims of employing Machine and deep Learning models in order to reduce the computational complexity by further relying in statistical modeling/learning. We explored the cross-Frequency Subband prediction intra-TTI (Transmission Time Interval) by means of proposing 3 three models. These intended to leverage frequency Multipath Components dependencies along TTIs. The first two ones are Probabilistic Principal Components Analysis (PPCA) and its Bayesiancounterpart, Bayesian Principal Components Analysis (BPCA). Then, we implemented Deep Learning Variational Encoder-Decoder (VED) architecture. These three models are intended to deal with the hugely high-dimensional space of the 4 datasets used by its intrinsic dimensionality reduction. The PPCA method was on average five times better than the VED alternative in terms of MSE accounting for all the datasets used