The cognitive radio wireless sensor network (CR-WSN) has gained worldwide attention in recent years for its potential applications. Reliable spectrum sensing is the premise for opportunistic access to sensor nodes. However, as a result of the radio frequency (RF) front-end nonlinearity of sensor nodes, distortion products can easily degrade the spectrum sensing performance by causing false alarms and degrading the detection probability. Given the limitations of the widely-used adaptive interference cancellation (AIC) algorithm, this paper develops several details to avoid these limitations and form a new mitigation architecture to alleviate nonlinear distortions. To demonstrate the efficiency of the proposed algorithm, verification tests for both simulations and actual RF front-end measurements are presented and discussed. The obtained results show that distortions can be suppressed significantly, thus improving the reliability of spectrum sensing. Moreover, compared to AIC, the proposed algorithm clearly shows better performance, especially at the band edges of the interferer signal.
Keywords: adaptive interference cancellation (AIC); cognitive radio wireless sensor network (CR-WSN); nonlinear distortion; singular value decomposition (SVD); spectrum sensing.