无线电信号在噪声波动情形下的检测性能有待提高.该文提出了认知用户根据无线电环境变化自动调整检测阈值的感知方法.融合中心应用坐标搜索算法为认知用户提供最优控制参数,认知用户依据最优参数设定检测阈值,并自主学习特定无线电环境下的最佳阈值.此外,该算法充分考虑了各认知用户的个体特征及其感知贡献,并提出了一种基于能量值的加权算法体现用户特征.实验结果说明该算法对噪声波动具有卓越的鲁棒性,在信噪比低于-15 dB时的检测概率远高于传统方法.
In the case of noise fluctuation, the performance of the radio signal detection needs to be improved. In this paper, a method for cognitive users to automatically adjust the detection threshold according to the changes of the radio environment is proposed. The fusion center applies coordinate search algorithm to provide the optimal control parameters to cognitive users. Cognitive users set the detection threshold according to the optimal parameters and autonomously learn the optimal threshold for a specific radio environment. In addition, by taking a full consideration of the distinctions and sensing contributions of cognitive users, a new weight calculation method to reflect the distinctions is designed. Simulation results show that the spectrum sensing method has excellent robustness to noise fluctuation. It performs a much higher detection probability than the traditional sensing methods as signal-to-noise ratio (SNR) is below -15 dB.
[1] Wang F, Ma J. Investigating factors influencing moment tensor inversion of induced seismicity in virtual IoT[J]. IEEE Access, 2019(7):34238-34251.
[2] Liu S, Zhang Z. Wavelength-spacing adjustable dual-wavelength dissipative soliton resonance thulium-doped fiber laser[J]. IEEE Photonics Journal, 2019, 2(11):1-9.
[3] Sobron I, Diniz P S R. Energy detection technique for adaptive spectrum sensing[J]. IEEE Transactiond on Communications, 2015, 63(3):617-627.
[4] Arslan H U S. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys and Tutorials, 2016, 11(1):116-130.
[5] 董莉,宋晓勤,韩杰.基于遗传粒子群优化的认知OFDM网络资源分配算法[J].应用科学学报,2017, 35(3):288-298. Dong L, Song X Q, Han J. Resource allocation based on genetic algorithm and particle swarm optimization for cognitive OFDM network[J]. Journal of Applied Sciences, 2017, 35(3):288-298.(in Chinese)
[6] Li S, Hu B J, Wu X Y. Hierarchical cooperative spectrum sensing based on double thresholds energy detection[J]. IEEE Communications Letters, 2012, 16(7):1096-1099.
[7] Zhao N. A novel two-stage entropy-based robust cooperative spectrum sensing scheme with two-bit decision in cognitive radio[J]. Wireless Personal Communications, 2016, 5(3):1-15.
[8] Salman A, Qureshi I M, Sultan K. Joint spectrum sensing for detection of primary users using cognitive relays with evolutionary computing[J]. IET Communications, 2015, 9(13):1643-1648.
[9] Chen X, Zhang S. A joint scheduling and beamforming scheme for RoF-Aided MC-SSN[J]. IEEE Access, 2019(7):29245-29252.
[10] Hamza D, Aissa S, Aniba G. Equal gain combining for cooperative spectrum sensing in cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2017, 13(8):4334-4345.
[11] Mousavifar S A, Leung C. Transient analysis for a trust-based cognitive radio collaborative spectrum sensing scheme[J]. IEEE Wireless Communications Letters, 2015, 4(4):377-380.
[12] Zhang S, Dong X, Bao Z. Adaptive spectrum sensing algorithm in cognitive ultra-wideband systems[J]. Wireless Personal Communications, 2013, 68(3):789-810.
[13] Khalid L, Anpalagan A. Reliability-based decision fusion scheme for cooperative spectrum sensing[J]. IET Communications, 2014, 8(14):2423-2432.
[14] Kieu X T, Koo I. A cooperative spectrum sensing scheme using adaptive fuzzy system for cognitive radio networks[J]. Information Sciences, 2016, 220:102-109.
[15] Ding G, Wu Q. Decentralized sensor selection for cooperative spectrum sensing based on unsupervised learning[C]//IEEE International Conference on Communications (ICC), Shanghai, China, 2012:1576-1580.