Novel Deep Learning Framework for Wideband Spectrum Characterization at Sub-Nyquist Rate

TL;DR

This paper introduces a deep learning framework that directly reconstructs and characterizes wideband spectrum from sub-Nyquist samples, outperforming existing methods and simplifying the process for reconfigurable 5G networks.

Contribution

A novel unified deep learning approach that combines spectrum reconstruction and characterization from sub-Nyquist samples without complex signal conditioning.

Findings

Outperforms existing SNS-based spectrum reconstruction methods.

Approaches Nyquist sampling performance with increased SNR.

Effective across various modulation schemes and channel conditions.

Abstract

Introduction of spectrum-sharing in 5G and subsequent generation networks demand base-station(s) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.) of occupied bands. Such characterization at Nyquist sampling is area and power-hungry due to the need for high-speed digitization. Though sub-Nyquist sampling (SNS) offers an excellent alternative when the spectrum is sparse, it suffers from poor performance at low signal to noise ratio (SNR) and demands careful design and integration of digital reconstruction, tunable channelizer and characterization algorithms. In this paper, we propose a novel deep-learning framework via a single unified pipeline to accomplish two tasks: 1)~Reconstruct the signal directly from sub-Nyquist samples, and 2)~Wideband spectrum characterization. The proposed approach eliminates the need for complex signal conditioning between reconstruction and characterization and does not need complex tunable channelizers. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based approaches and characterization performance approaches to Nyquist sampling-based framework with an increase in SNR. Easy to design and integrate along with a single unified deep learning framework make the proposed architecture a good candidate for reconfigurable platforms.