Spectrum Segmentation Techniques for Edge-RAN Decoding in Telemetry-Based IoT Networks

The possible fields of application for small sensor nodes are tremendous and still growing fast. Concepts like the Internet of Things (IoT), Smart City or Industry 4.0 adopt wireless sensor networks for environmental interaction or metering purposes. As they commonly operate in license-exempt frequency bands, telemetry transmissions of sensors are subject to strong interferences and possible shadowing. Especially in the scope of Low Power Wide Area (LPWA) communications, this scenario results in high computational effort and complexity for the receiver side to perceive the signals of interest. Therefore, this paper investigates means to an adequate segmentation of receive spectra for a partial spectrum exchange between base stations of telemetry-based IoT sensor networks. The distinct interchange of in-phase and quadrature (IQ) data could facilitate stream combining techniques to mask out interferences amongst other approaches. This shall improve decoding rates even under severe operation conditions and simultaneously limit the required data volume. We refer to this approach of a reception network as Edge-RAN (Random Access Network). To cope with the high data rates and still enable a base station collaboration, especially in wirelessly connected receiver mesh networks, different filter bank techniques and block transforms are examined, to divide telemetry spectra into distinct frequency sub-channels. Operational constraints for the spectral decomposition are given and different filter methodologies are introduced. Finally, suitable metrics are established. These metrics shall assess the performance of the presented spectrum segmentation schemes for the purpose of a selective partial interchange between sensor network receivers.