Weak Signal Detection in 5G+ Systems: A Distributed Deep Learning Framework

Internet connected mobile devices in 5G and beyond (simply 5G+) systems are penetrating all aspects of people's daily life, transforming the way we conduct business and live. However, this rising trend has also posed unprecedented traffic burden on existing telecommunication infrastructure including cellular systems, consistently causing network congestion. Although additional spectrum resources have been allocated, exponentially increasing traffic tends to always outpace the added capacity. In order to increase the data rate and reduce the latency, 5G+ systems have heavily relied on hyperdensification and higher frequency bands, resulting in dramatically increased interference temperature, and consequently significantly more weak signals (i.e., signals with low Signal-to-Noise-plus-Interference (SINR) ratio). With traditional detection mechanisms, a large number of weak signals will not be detected, and hence be wasted, leading to poor throughput in 5G+ systems.

To this end, in this paper, we develop an online weak-signal detection scheme to recover weak signals for mobile users so as to significantly boost their data rates without adding additional spectrum resource. Specifically, we first formulate weak signal detection as a high dimensional user-time signal matrix factorization problem and solve this problem by devising a novel learning model, called Dual-CNN Deep Matrix Factorization (DCDMF). Then, we design an online distributed learning framework to collaboratively train and update our proposed DCDMF model between an edge network and mobile users, with correctly decoded signals at users only. By conducting simulations on real-world traffic datasets, we demonstrate that our proposed weak signal detection scheme can achieve throughput gain of up to 3.12 times, with computing latency of 0.84 ms per KB signals on average.