Detection of Quantized Sparse Signals Using Locally Most Power Full Detector in Wireless Sensor NetworkS
Subject Areas : electrical and computer engineeringAbdolreza Mohammadi 1 * , Amin Jajarmi 2
1 -
2 - عضو هیات علمی
Keywords: wireless sensor network, locally most powerful, sparse signal, non-ideal channel, quantization,
Abstract :
This paper addresses the problem of distributed detection of stochastic sparse signals in a wireless sensor network. Observations/local likelihood ratios in each sensor node are quantized into 1-bit and sent to a fusion center (FC) through non-ideal channels. At the FC, we propose two sub-optimal detectors after that the data are fused based on the locally most powerful test (LMPT). We obtain the quantization threshold for each sensor node via an asymptotic analysis of the performance of the detector. It is realized that the quantization threshold depends on the bit error probability of the channels between the sensor nodes and the FC. Simulation results are carried out to confirm our theoretical analysis and to depict the performance of the proposed detectors.
[1] S. H. Javadi, "Detection over sensor networks: a tutorial," IEEE Aerosp. Elect. Syst. Mag., vol. 31, no. 3, pp. 2-18, Mar. 2016.
[2] A. Mohammadi, S. H. Javadi, D. Ciuonzo, V. Persico, and A. Pescap, "Distributed detection with fuzzy censoring sensors in the presence of noise uncertainty," Neurocomputing, vol. 351, pp. 196-204, 25 Jul. 2019.
[3] A. Mohammadi, S. H. Javadi, and D. Ciuonzo, "Bayesian fuzzy hypothesis test in wireless sensor networks with noise uncertainty," Applied Soft Computing, vol. 77, no. C, pp. 218-224, Apr. 2019.
[4] D. Ciuonzo, S. H. Javadi, A. Mohammadi, and P. S. Rossi, "Bandwidth constrained decentralized detection of an unknown vector signal via multi-sensor fusion," IEEE Trans. Signal Inf. Process. Netw, vol. 6, pp. 744-758, 2020.
[5] M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, "Signal processing with compressive measurements," IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 445-460, Apr. 2010.
[6] D. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
[7] T. Wimalajeewa and P. K. Varshney, "Compressive sensing-based detection with multimodal dependent data," IEEE Trans. Signal Process., vol. 66, no. 3, pp. 627-640, Feb. 2018.
[8] B. Kailkhura, T. Wimalajeewa, and P. K. Varshney, "Collaborative compressive detection with physical layer secrecy constraints," IEEE Trans. Signal Process., vol. 65, no. 4, pp. 1013-1025, Feb. 2017.
[9] T. Wimalajeewa and P. K. Varshney, "Sparse signal detection with compressive measurements via partial support set estimation," IEEE Trans. Signal Inf. Process. Netw., vol. 3, no. 1, pp. 46-60, Mar. 2017.
[10] A. Hariri and M. Babaie-Zadeh, "Compressive detection of sparse signals in additive white Gaussian noise without signal reconstruction," Signal Process., vol. 131, pp. 376-385, Feb. 2017.
[11] X. Wang, G. Li, and P. K. Varshney, "Detection of sparse signals in sensor networks via locally most powerful tests," IEEE Signal Process. Lett., vol. 25, no. 9, pp. 1418-1422, Sept. 2018.
[12] S. Kassam, "Optimum quantization for signal detection," IEEE Trans. Commun., vol. 25, no. 5, pp. 479-484, May 1977.
[13] D. Ciuonzo and P. S. Rossi, "Distributed detection of a non-cooperative target via generalized locally-optimum approaches," Inf. Fusion, vol. 36, pp. 261-274, Jul. 2017.
[14] D. Ciuonzo, G. Papa, G. Romano, P. S. Rossi, and P. Willett, "One-bit decentralized detection with a Rao test for multisensor fusion," IEEE Signal Process. Lett., vol. 20, no. 9, pp. 257-260, Sept. 2013.
[15] J. Fang, Y. Liu, H. Li, and S. Li, "One-bit quantizer design for multisensor GLRT fusion," IEEE Signal Process. Lett., vol. 20, no. 3, pp. 257-260, Mar. 2013.
[16] F. Gao, L. Guo, H. Li, J. Liu, and J. Fang, "Quantizer design for distributed GLRT detection of weak signal in wireless sensor networks," IEEE Trans. Wirel. Commun., vol. 14, no. 4, pp. 2032-2042, Apr. 2015.
[17] X. Wang, G. Li, and P. K. Varshney, "Detection of sparse stochastic signals with quantized measurements in sensor networks," IEEE Trans. Signal Process., vol. 67, no. 8, pp. 2210-2220, Apr. 2019.
[18] C. Li, Y. He, X. Wang, G. Li, and P. K. Varshney, "Distributed detection of sparse stochastic signals via fusion of 1-bit local likelihood ratios," IEEE Signal Process. Lett., vol. 26, no. 12, pp. 1738-1742, Dec. 2019.
[19] X. Wang, G. Li, C. Quan, and P. K. Varshney, "Distributed detection of sparse stochastic signals with quantized measurements: the generalized Gaussian case," IEEE Trans. Signal Process., vol. 67, no. 18, pp. 4886-4898, Sep. 2019.
[20] H. Zayyani, F. Haddadi, and M. Korki, "Double detector for sparse signal detection from one-bit compressed sensing measurements," IEEE Signal Process. Lett., vol. 23, no. 11, pp. 1637-1641, Nov. 2016.
[21] C. Li, G. Li, and P. K. Varshney, "Distributed detection of sparse signals with censoring sensors via locally most powerful test," IEEE Signal Process. Lett., vol. 27, pp. 346-350, 2020.
[22] M. Duarte, S. Sarvotham, D. Baron, M. Wakin, and R. Baraniuk, "Distributed compressed sensing of jointly sparse signals," in Proc. of the 39th Asilomar Conf. on Signals, Systems and Computers, pp. 1537-1541, Pacific Grove, CA, USA, 30 Oct.-2 Nov. 2005.
[23] J. Meng, H. Li, and Z. Han, "Sparse event detection in wireless sensor networks using compressive sensing," in Proc. of the 43rd Annual Conf. on Information Sciences and Systems, pp. 181-185, Baltimore, MD, USA, 18-20 Mar. 2009.
[24] C. Soussen, J. Idier, D. Brie, and J. Duan, "From Bernoulli-Gaussian deconvolution to sparse signal restoration," IEEE Trans. Signal Process., vol. 59, no. 10, pp. 4572-4584, Oct. 2011.
[25] M. Korki, J. Zhang, C. Zhang, and H. Zayyani, "Iterative Bayesian reconstruction of non-IID block-sparse signals," IEEE Trans. Signal Process., vol. 64, no. 13, pp. 3297-3307, Jul. 2016.
[26] C. Li, G. Li, and P. K. Varshney, "Distributed detection of sparse stochastic signals with 1-bit data in tree-structured sensor networks," IEEE Trans. Signal Process., vol. 68, pp. 2963-2976, 2020.
[27] S. M. Kay, Fundamentals of Statistical Signal Processing, vol. 2, Detection Theory, Prentice Hall PTR, Jan. 1998.
[28] A. Slowik and H. Kwasnicka, "Nature inspired methods and their industry applications-swarm intelligence algorithms," IEEE Trans. Industrial Informatics, vol. 14, no. 3, pp. 1004-1015, Mar. 2018.
[29] E. H. Houssein, A. G. Gad, K. Hussain, and P. N. Suganthan, "Major advances in particle swarm optimization: theory, analysis, and application," Swarm and Evolutionary Computation, vol. 63, Article ID:.100868, Jun. 2021.
[30] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of the IEEE Int. Conf. on Neural Networks, vol. 4, pp. 1942-1948, Perth, Australia, 27 Nov.-1 Dec.1995.
[31] S. Shirvani Moghaddam and A. Habibzadeh, "Cooperative spectrum sensing based on generalized likelihood ratio test for cognitive radio channels with unknown primary user's power and colored noise," International J. of Sensors, Wireless Communications and Control (SWCC), vol. 8, no. 3, pp. 204-216, Sep. 2018.
[32] F. Hoseiniamin, H, Zayyani, M, Korki, M. Bekrani, "A low complexity proportionate generalized correntropy-based diffusion LMS algorithm with closed-form gain coefficients," IEEE Trans. Circuits and Systems II: Express Briefs, vol. 70, no. 7, pp. 2690-2694, Jul. 2023.