Hu Lin, Ma Hong, Zhang Hua, Zhao Wen
School of Electronic Information and Communications, Huazhong University of Science & Technology, Wuhan 430074, China.
Sensors (Basel). 2016 Nov 25;16(12):1999. doi: 10.3390/s16121999.
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.
近年来，认知无线电无线传感器网络（CR-WSN）因其潜在应用而受到全球关注。可靠的频谱感知是传感器节点进行机会性接入的前提。然而，由于传感器节点的射频（RF）前端非线性，失真产物很容易通过引起误报和降低检测概率来降低频谱感知性能。鉴于广泛使用的自适应干扰消除（AIC）算法的局限性，本文阐述了几个细节以避免这些局限性，并形成一种新的缓解架构来减轻非线性失真。为了证明所提算法的有效性，给出并讨论了针对仿真和实际RF前端测量的验证测试。所得结果表明，失真可得到显著抑制，从而提高频谱感知的可靠性。此外，与AIC相比，所提算法表现出明显更好的性能，尤其是在干扰信号的频段边缘。
