Adaptive Uplink Data Compression in Spectrum Crowdsensing Systems

Abstract

Understanding spectrum activity is challenging when attempted at scale. The wireless community has recently risen to this challenge in designing spectrum monitoring systems that utilize many low-cost spectrum sensors to gather large volumes of sampled data across space, time, and frequencies. These crowdsensing systems are limited by the uplink bandwidth available for transmitting to the backhaul network raw in-phase and quadrature (IQ) samples and power spectrum density (PSD) measurements needed to run a variety of applications. This paper presents FlexSpec, a framework based on the Walsh-Hadamard transform to compress spectrum data collected from distributed and low-cost sensors for real-time applications. We show that this transformation allows sensors to greatly save uplink bandwidth thanks to its inherent properties both when it is applied to IQ and PSD measurements. Additionally, by leveraging a feedback loop between an edge device and the sensor, FlexSpec carefully adapts the compression ratio over time, such that data size, application’s performance, and spectrum variations are all considered. We experimentally evaluate FlexSpec in several applications. Our results show that FlexSpec is particularly suitable for IoT transmissions and for signals close to the noise floor. Compared with prior work, FlexSpec provides up to 7× more reduction of uplink data size for signal detection based on PSD data, and reduces up to 6× to 8× the number of undecodable messages for IQ sample decoding.