An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks

The paradigm of wireless sensor networks (WSNs) has gained a lot of popularity in the recent years due to the proliferation of wireless devices. This is evident as WSNs find numerous application areas in fields such as agriculture, infrastructure monitoring, transport, and security surveillance. Traditionally, most deployments of WSNs operate in the unlicensed industrial scientific and medical (ISM) band and more specifically, the globally available 2.4 GHz frequency band. This band is shared with several other wireless technologies such as Bluetooth, Wi-Fi, near field communication and other proprietary technologies thus leading to overcrowding and interference problems. The concept of dynamic spectrum access alongside cognitive radio technology can mitigate the coexistence issues by allowing WSNs to dynamically access new spectrum opportunities. Furthermore, cognitive radio technology addresses some of the inherent constraints of WSNs thus introducing a myriad of benefits. This justifies the emergence of cognitive radio sensor networks (CRSNs). The selection of a spectrum sensing technique plays a vital role in the design and implementation of a CRSN. This dissertation sifts through the spectrum sensing techniques proposed in literature investigating their suitability for CRSN applications. We make amendments to the conventional energy detector particularly on the threshold selection technique. We propose an adaptive threshold energy detection model with noise variance estimation for implementation in CRSN systems. Experimental work on our adaptive threshold technique based on the recursive one-sided hypothesis test (ROHT) technique was carried out using MatLab. The results obtained indicate that our proposed technique is able to achieve adaptability of the threshold value based on the noise variance. We also employ the constant false alarm rate (CFAR) threshold to act as a defence mechanism against interference of the primary user at low signal to noise ratio (SNR). Our evaluations indicate that our adaptive threshold technique achieves double dynamicity of the threshold value based on the noise variance and the perceived SNR