Spectrum sensing in cognitive vehicular networks for uniform mobility model

Computation‐constrained spectrum sensing in IoT‐based scenarios

“…As a result, the obtained optimal sampling points provide a tradeoff between sensing performance and sensing complexity. On the other hand, this paper is different from the work in [15–19] in terms of application. In [15], the outage probability is explored with EH and reinforcement learning in CR networks, which is to find the optimal values of sensing duration, secondary power allocation fraction on SS and data transmission.…”

“…The proposed CR4S in [18] aims at CSS by employing real‐valued fast Fourier transform (FFT) and sparse FFT with minimum computational complexity. The uniform velocity motion of PUs and secondary users is considered in [19]. However, this paper contributes to a tradeoff between sensing performance and sensing complexity considering different energy resource states.…”

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

“…It should be noted that increasing the relative speed between the PU and the IoT node results in time-selective channel fading, which degrades both the transceiver link and the spectrum sensing performance, and in turn, increases the bit error rate probability P BER at the IoT receiver as well as the probabilities of false-alarm P f and miss-detection P m [58], [88], [89]. To see this, it can be inferred from (8) and (10) that IoT mobility may severely degrade the packet reliability, which is due to the increase in P m and P BER , ultimately (and intuitively) requiring more packet replicas N rep to be transmitted to improve P R .…”

“…Note that many studies have considered cooperative spectrum sensing (CSS)[89]-[91] or proposed enhanced sensing techniques[88],[92] to overcome the effect of mobility and improve sensing performance. Such strategies can be used when mobile IoT nodes are considered (e.g.…”

Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks

“…Considering the imperfect channel state information in a Rayleigh fading environment, Gahane and Sharma [19] derived the expression for the received signal at destination and the expression for the probability of false alarm and miss detection using an arbitrary power p-based improved energy detector and analysed the error performance of the system. Considering the uniform velocity motion of PU and SU nodes, Paul et al [20] explored an energy detection-based cooperative spectrum-sensing scheme in CR enabled vehicular networks, furthermore, the authors developed a distance-dependent distribution function to find the probability of a SU resides in a specific coverage of the PU transmission zone. This distribution function was then used in deriving the expression of the probability of detection and probability of false alarm using the majority rule at the FC.…”