Energy-Efficient Spectrum Sensing for IoT Devices

Abstract

Device-to-device communications have been considered as an indispensable enabler, which reduces the traffic burden associated with fifth-generation (5G) mobile networks. In such communications, cognitive spectrum sensing identifies the available spectrum resources for direct interconnections among user devices. Although various sensing techniques have been proposed during the last decade, improving the sensing efficiency (SE), such as energy reduction and positive sensing ratio, remains an open challenge. The problem becomes severe in 5G networks, wherein battery-constrained Internet-of-things devices (IoTDs) are densely interconnected. In this paper, we optimize the SE based on adaptive medium learning with a probabilistic decay feature. The wireless channels that are potentially available for IoTDs are sorted and sensed in the descending order of their probabilities, which indicate the estimated percentage of the availability of the sensed channels. The probabilities learn from the preceding sensing-results, and they decay with time. Numerical results show that the proposed sensing approach achieves significant SE improvement compared to existing algorithms.

Selection Square Combining: A Fully-Distributed Radio Source Detector for Fast Fading Channels Based on Energy Measurements

“…2]. The source to be detected is assumed to be a uniformly distributed 64-QAM communication signal, as can be found in applications of spectrum sensing for IoT devices [34], [56], [57]. Its kurtosis is then κ s = 7×64−13 5(64−1) = 1.381.…”

Recent advances in energy management for Green-IoT: An up-to-date and comprehensive survey

“…These techniques are expected to have a leading role in the realization of 5G-IoT networks. In [95], the authors proposed a probabilistic decay feature-based solution which focused on the energy consumption of spectrum sensing for IoT devices in 5G networks. In [96], the authors proposed an integrating and energy efficient system model for 5G-IoT that uses a massive MIMO array to replace the single remote antenna and cellular partition zooming (CPZ) mechanism as a select-and-sleep mechanism, shortening consequently the distance between the components and reducing the number of routers.…”

“…Energy Saving In 5G-IoT [92], [93], [94], [95], [96] Energy Efficient Cognitive Radio IoT [97], [98], [99], [100], [101] Energy efficient network softwarization [102], [103], [104], [105], [106], [107], [108] Energy Saving In Social IoT [109], [110], [111], [112], [113], [114], [115] AI-based solutions Machine learning [87], [116], [117], [118], [119], [120], [121], [122],…”

Enhanced capsule generative adversarial network for spectrum and energy efficiency of cooperative spectrum prediction framework in cognitive radio network

“…[7][8][9] In higher traffic CRNs, the probability of primary user channel is higher in busy state. [10][11][12] To enable spectrum sensing results in higher SU waiting times (lower SU rate of transmission) and energy consume, the channels are randomly selected, which lessens the spectrum and energy efficiency. To this intention, spectrum occupancy prediction is suggested for predicting the primary user channel occupancy depending upon historical sensing data.…”