Abstract
The conventional millimeter wave systems are mostly designed to operate only for the Gaussian noise model. In many physical channels, such as urban and indoor radio channels, the ambient noise is known through experimental measurements to be non-Gaussian. Hence, recent research findings state that a mixture noise model with additive impulsive noise is a more realistic approximation for millimeter wave channels. In this paper, we propose a novel approach to suppress the impulsive noise effects on single-user millimeter wave massive multiple-input-multiple-output system using an adaptive fuzzy logic filter. Hence, a fuzzy median filter is applied to the system and it is aimed to minimize the effects of the impulsive noise by ordering samples based on fuzzy rank. Simulation results show that the proposed filter successfully suppresses the impulsive noise effects and achieves a better bit error rate and spectral efficiency performance than the competing methods in the literature while also working efficiently in Gaussian noise.
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Data Availability
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
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Code is available from the corresponding author, upon reasonable request.
References
Alsharif, M. H., & Nordin, R. (2017). Evolution towards fifth generation (5G) wireless networks: Current trends and challenges in the deployment of millimetre wave, massive MIMO, and small cells. Telecommunication Systems, 64(4), 617–637. https://doi.org/10.1007/s11235-016-0195-x
Shariat, M., et al. (2016). 5G radio access above 6 GHz. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.3076
Su, L., Niu, Y., Vasilakos, A. V., Li, Y., & Jin, D. (2015). A survey of millimeter wave communications (mmWave) for 5G: Opportunities and challenges. Wireless Networks, 21(8), 2657–2676. https://doi.org/10.1007/s11276-015-0942-z
Rappaport, T. S., et al. (2013). Millimeter wave mobile communications for 5G cellular: It will work! IEEE Access, 1, 335–349. https://doi.org/10.1109/ACCESS.2013.2260813
Pi, Z., & Khan, F. (2011). An introduction to millimeter-wave mobile broadband systems. IEEE Communications Magazine, 49(6), 101–107. https://doi.org/10.1109/MCOM.2011.5783993
Vu, M., & Paulraj, A. (2007). MIMO wireless linear precoding. IEEE Signal Processing Magazine, 24(5), 86–105. https://doi.org/10.1109/MSP.2007.904811
Alkhateeb, A., Mo, J., González-Prelcic, N., & Heath, R. W. (2014). MIMO precoding and combining solutions for millimeter-wave systems. IEEE Communications Magazine. https://doi.org/10.1109/MCOM.2014.6979963
El Ayach, O., Rajagopal, S., Abu-Surra, S., Pi, Z., & Heath, R. W. (2014). Spatially sparse precoding in millimeter wave MIMO systems. IEEE Transactions on Wireless Communications, 13(3), 1499–1513. https://doi.org/10.1109/TWC.2014.011714.130846
Vuong, B. Q., Huynh, H. T., Do, H. N. (2018) Monte-Carlo performance analysis of OFDM system in the presence of multi-path fading environment and non-Gaussian noise. In International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom), doi: https://doi.org/10.1109/SIGTELCOM.2018.8325796
Middleton, D. (2009). An introduction to: Statistical communication theory. Wiley-IEEE Press.
Wang, X., & Poor, H. V. (1999). Robust multiuser detection in non-gaussian channels. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.740103
Rouissi, F., Vinck, A. J. H., & Ghazel, A. (2021). On the simulation of the Middleton Class-A noise model for single- and multi-carrier modulation in power line communication. Telecommunication Systems. https://doi.org/10.1007/s11235-020-00746-x
Alam, M. S., Selim, B., & Kaddoum, G. (2019). Analysis and comparison of several mitigation techniques for middleton class-A noise. IEEE Latin-American Conference on Communications (LATINCOM). https://doi.org/10.1109/LATINCOM48065.2019.8938020
Hagglund, K., & Axell, E. (2019) Adaptive demodulation in class a impulse noise channels. In IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM), doi: https://doi.org/10.1109/GLOBECOM38437.2019.9013968.
Moaveninejad, S., Kumar, A., Elgenedy, M., Magarini, M., Al-Dhahir, N., Tonello, A. M. (2019) Gaussian-Middleton Classification of cyclostationary correlated noise in Hybrid MIMO-OFDM WiNPLC. In IEEE International Conference on Communications (ICC), doi: https://doi.org/10.1109/ICC.2019.8761152.
Liu, L., & Amin, M. G. (2008) Performance analysis of GPS receivers in non-Gaussian noise incorporating precorrelation filter and sampling rate. In IEEE Transactions Signal Processing, doi: https://doi.org/10.1109/TSP.2006.890827.
Maeda, K., Nakata, H., & Fujito, K. (1993). Analysis of BER of 16QAM signal in AM/16QAM hybrid optical transmission system. Electronics Letters, 29(7), 640–642.
R. I. P, “RECOMMENDATION ITU-R P.372-9 Radio noise,” Group, 2007.
Katkovnik, V. (2000). New concept of adaptive beamforming for moving sources and impulse noise environment. Signal Processing. https://doi.org/10.1016/S0165-1684(00)00094-3
Lee, M. S., Katkovnik, V., & Kim, Y. H. (2004). Robust approximate median beamforming for phased array radar with antenna switching. Signal Processing. https://doi.org/10.1016/j.sigpro.2004.05.007
Abu Hilal, H. (2019). Error rate analysis of ZF and MMSE decoders for massive multi cell MIMO systems in impulsive noise channels. International Journal of Wireless Information Networks. https://doi.org/10.1007/s10776-019-00422
Juwono, F. H., Reine, R., Liu, L. (2016) Performance of impulsive noise blanking in precoded OFDM-based PLC systems. In IEEE International Conference on Communication Systems (ICCS), pp. 1–6, doi: https://doi.org/10.1109/ICCS.2016.7833562.
Rabie K. M., & Alsusa, E. (2015) Performance analysis of adaptive hybrid nonlinear preprocessors for impulsive noise mitigation over power-line channels. In International Conference on Communications (ICC), pp. 728–733, doi: 10.1109/ ICC.2015. 7248408.
Sarabchi, F., & Nerguizian, C. (2014) Impulsive noise mitigation for OFDM-based systems using enhanced blanking nonlinearity. In IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile RadioCommunication (PIMRC), pp. 841–845, doi: https://doi.org/10.1109/PIMRC.2014.7136282.
Shhab, L., Rizaner, A., Ulusoy, A. H., & Amca, H. (2020). Suppressing the effect of impulsive noise on millimeter-wave communications systems. Radioengineering. https://doi.org/10.13164/RE.2020.0376
Abu Hilal, H. (2017). Neural networks applications for CDMA systems in non-Gaussian multi-path channels. AEU—International Journal of Electronics and Communications, 73, 150–156. https://doi.org/10.1016/j.aeue.2017.01.006
Barazideh, R., Niknam, S., Natarajan, B. (2019) Impulsive noise detection in OFDM-based systems: A deep learning perspective. In IEEE Annual Computing and Communication Workshop and Conference (CCWC), doi: https://doi.org/10.1109/CCWC.2019.8666489.
Delgado, O., & Labeau, F. (2020) Deep learning decoder for MIMO communications with impulsive noise. In IEEE Consumer Communications and Networking Conference (CCNC), doi: https://doi.org/10.1109/CCNC46108.2020.9045329.
Hmidat, A. M., Sharif, B. S., & Woo, W. L. (2004). Fuzzy decorrelating detector for non-Gaussian CDMA channel. Electronics Letters. https://doi.org/10.1049/el:20040595
Ulusoy, A. H., & Rizaner, A. (2008). Adaptive path selective fuzzy decorrelating detector under impulsive noise for multipath fading CDMA systems. IEEE Communications Letters. https://doi.org/10.1109/LCOMM.2008.072012
Ulusoy, A. H., & Rizaner, A. (2011). RBF network assisted adaptive path selective decorrelating detector under impulsive noise for multipath fading CDMA systems. Annals of Telecommunications-annales des Télécommunications. https://doi.org/10.1007/s12243-010-0212-0
Crosby, T., Iglewicz, B., & Hoaglin, D. C. (1994). How to detect and handle outliers. Technometrics. https://doi.org/10.2307/1269377
Alkhateeb, A., Leus, G., & Heath, R. W. (2015). Limited feedback hybrid precoding for multi-user millimeter wave systems. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2015.2455980
Alkhateeb, A., El Ayach, O., Leus, G., & Heath, R. W. (2014). Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE Journal of Selected Topics in Signal Processing. https://doi.org/10.1109/JSTSP.2014.2334278
Yu, X., Shen, J. C., Zhang, J., & Letaief, K. B. (2016). Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 10(3), 485–500. https://doi.org/10.1109/JSTSP.2016.2523903
Mulla, M., Ulusoy, A. H., Rizaner, A., & Amca, H. (2020). Barzilai-borwein gradient algorithm based alternating minimization for single user millimeter wave systems. IEEE Wireless Communications Letters. https://doi.org/10.1109/LWC.2019.2960691
Saleh, A. A. M., & Valenzuela, R. A. (1987). A statistical model for indoor multipath propagation. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/JSAC.1987.1146527
Raghavan, V., & Sayeed, A. M. (2011). Sublinear capacity scaling laws for sparse MIMO channels. IEEE Transactions on Information Theory. https://doi.org/10.1109/TIT.2010.2090255
Rizaner, A., Ulusoy, A. H., & Amca, H. (2016). Adaptive fuzzy assisted detector under impulsive noise for DVB-T systems. Optik (Stuttg), 127(13), 5196–5199. https://doi.org/10.1016/j.ijleo.2016.02.079
Rusu, C., Mèndez-Rial, R., González-Prelcic, N., & Heath, R. W. (2016). Low complexity hybrid precoding strategies for millimeter wave communication systems. IEEE Transactions on Wireless Communications, 15(12), 8380–8393. https://doi.org/10.1109//TWC.2016.2614495
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MM, AR and AHU. The first draft of the manuscript was written by MM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Mulla, M., Rizaner, A. & Ulusoy, A.H. Fuzzy Logic Based Decoder for Single-User Millimeter Wave Systems Under Impulsive Noise. Wireless Pers Commun 124, 1883–1895 (2022). https://doi.org/10.1007/s11277-021-09435-7
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DOI: https://doi.org/10.1007/s11277-021-09435-7