Spectrum Sensing Using Optimized Deep Learning Techniques in Reconfigurable Embedded Systems

Abstract

The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users. In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication. IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics. Implementing the above learning methods on the embedded chip leads to high latency, high power consumption and more chip area utilisation. To overcome the problems mentioned above, we present DEEP HOLE Radio systems, the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency (RF) environment. DEEP HOLE Radio provides (i) an optimized deep learning framework with a good trade-off between latency, power and utilization. (ii) Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance. The experimentation has been carried out using GNURADIO software interfaced with Zynq- 7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni directional antennas. The whole spectrum of knowledge has been extracted using GNU radio. These extracted features are used to train the proposed optimized deep learning models, which run parallel on Zynq-SoC 7000, consuming less area, power, latency and less utilization area. The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn, Long Term Short Memory (LSTM), Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN). The outcome shows that the proposed framework has outperformed the existing framework regarding the area, power and time. Moreover, the experimental results show that the proposed framework decreases the delay, power and area by 15%, 20% 25% concerning the existing RFlearn and other hardware constraint frameworks.