Interference-aware power allocation in soft decision fusion (SDF) based cooperative spectrum sensing

Spectrum sensing is very crucial in cognitive radio networks (CRNs) for ensuring reliable data transmission. Additionally, it helps in providing sufficient protection to the licensed user against the interference. In CRNs, throughput maximization is becoming a key challenge in the presence of interference or collision to the primary user (PU). This directly affects the spectrum efficiency, and hence spectrum access opportunities get reduced. In this paper, we model an adaptive interference aware framework in soft decision fusion (SDF) based cooperative spectrum sensing (CSS) with power and sensing time optimization which jointly maximizes the system throughput under the constraint of interference power results from imperfect spectrum sensing. In this study, status of the PU during the data transmission period is considered as an exponential transition probability, and accordingly we formulate the interference constraint throughput maximization model. Simulation results validate the proposed adaptive algorithm framework for maximizing the system throughput by jointly optimizing the sensing time and transmitted power allocation to the CR users.