ارائه روشی مناسب جهت تخمین کانال موج میلیمتری با استفاده از حسگری فشرده

دلیری, علیرضا; رییس دانایی, میثم; سلیمانی, حسین

ارائه روشی مناسب جهت تخمین کانال موج میلیمتری با استفاده از حسگری فشرده

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ کارشناسی ارشد،دانشگاه جامع امام حسین (ع)، تهران، ایران

² استادیار،دانشگاه جامع امام حسین (ع)، تهران، ایران

چکیده

با افزایش روزافزون تقاضا برای نرخ بالای انتقال داده و همچنین قابلیت‌های فراوان پهپاد‌ها، ایجاد لینک‌هایی بر بستر امواج میلیمتری می‌توانند بسیار سودمند باشند. به دلیل افت زیاد مسیر، در این‌گونه کانال‌ها تعداد خوشه-مسیر دریافتی از فرستنده در گیرنده کم است و کانال تنک است و می‌توان برای تخمین آن از الگوریتم های حسگری فشرده استفاده نمود. در این مقاله ابتدا به مدل‌سازی کانال موج میلیمتری پهپادی با سیستم چند ورودی-چند خروجی انبوه و ویژگی‌های مخصوص آن پرداخته خواهد شد، سپس عملکرد دو الگوریتم از مجموعه روش‌های حسگری فشرده برای تخمین کانال بررسی خواهد گشت. اولین الگوریتم مبتنی بر تئوری بهینه‌سازی و دومین الگوریتم از خانواده حریص می‌باشد. همان‌طور که در شبیه‌سازی‌ها نشان داده خواهد شد، الگوریتم SPC که از روش‌های حریص است، نسبت بهAGDAR که از الگوریتم‌های بهینه‌سازی است، ۱۵۹۸ برابر عملکرد سریع‌تری برای تخمین کانال پهپادی از خود نشان می‌دهد در حالی که عملکرد آن اندکی نسبت به AGDAR افت دارد. به عبارتی، با مقایسه میزان خطای دو الگوریتم این نتیجه حاصل می‌شود که می‌توان با یک مصالحه قابل‌قبول از SPC جهت تخمین کانال استفاده کرد.

کلیدواژه‌ها

20.1001.1.23224347.1404.13.1.4.9

موضوعات

پردازش سیگنال حوزه های مرتبط

عنوان مقاله [English]

Providing a suitable method for estimating the millimeter wave channel using compact sensing

نویسندگان [English]

A. Daliri ¹
Meysam RaeesDanaee ²
H. Soleimani ²

¹ Master's degree, Imam Hussein (AS) University, Tehran, Iran

² Assistant Professor, Imam Hussein (AS) University, Tehran, Iran

چکیده [English]

With the increasing demand for high data rate and the abundant capabilities of drones, creating links based on millimeter waves can be very beneficial. Due to the high path loss, in such channels, the number of cluster-paths received from the transmitter at the receiver is low and the channel is sparse and can be estimated using compressed sensing algorithms. In this paper, first, the modeling of the millimeter wave drone channel with a massive multiple-input multiple-output system and its specific features will be discussed, then the performance of two algorithms from the set of compressed sensing methods for channel estimation will be investigated. The first algorithm is based on optimization theory and the second algorithm is from the greedy family. As will be shown in the simulations, the SPC algorithm, which is one of the greedy methods, shows 1598 times faster performance for estimating the drone channel than AGDAR, which is one of the optimization algorithms, while its performance is slightly lower than AGDAR. In other words, by comparing the error rate of the two algorithms, it is concluded that SPC can be used for channel estimation with an acceptable trade-off.

کلیدواژه‌ها [English]

mm-wave
massive MIMO
5G
Beamforming
compressive sensing
channel model

مراجع

[1] W. U. Bajwa, J. Haupt, et al., “Compressed channel sensing :A new approach to estimating sparse multipath channels, ” PROC IEEE, vol. 98, no. pp. 1058–1076 6, 2010. DOI: 10.1109/JPROC.2010.2042415

[2] X. Rao and V. K. N. Lau, “Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems,” IEEE TRANS SIGNAL PROC, vol. 62, no. 12, pp. 3261-3271, June15, 2014. DOI: 10.1109/TSP.2014.2324991

[3] N. Sadeghi and M. Azghani, “Channel Estimation using Block Sparse Joint Orthogonal Matching Pursuit in Massive MIMO Systems,” in 26th CSICC, pp. 1-5 , 2021. DOI: 10.1109/CSICC52343.2021.9420624

[4] W. Shen, L. Dai, Y. Shi, Z. Gao and Z. Wang, “Massive MIMO channel estimation based on block iterative support detection,” IEEE Wirel. Commun. Netw. Conf., pp. 1-6, 2016. DOI: 10.1109/WCNC.2016.7564735

[5] Y. Li, Y. Zhu, T. Zhang and D. Fan, “Joint Doppler shift and channel estimation for UAV mmWave system with massive ULA,” China Commun, vol. 19, no. 4, pp. 67-82, April 2022, doi: 10.23919/JCC.2022.04.006.

[6] X. Li, J. Fang and H. Li, “Millimeter wave channel estimation via exploiting joint sparse and low-rank structures,” IEEE TRANS WIREL COMMUN, vol. 17, no. 2, pp. 1123-1133, 2017. DOI: 10.1109/TWC.2017.2776108

[7] S. Sun and T. S. Rappaport, “Millimeter wave MIMO channel estimation based on adaptive compressed sensing,” IEEE INT CONF COMMUN WORKSH, 2017. DOI: 10.1109/ICCW.2017.7962632

[8] O. Oyerinde, A. Flizikowski and T. Marciniak, “Compressive sensing-based channel estimation schemes for wideband millimeter wave wireless communication systems,” COMPUT ELECTR ENG, vol. 104, p. 108452, 2022. https://doi.org/10.1016/j.compeleceng.2022.108452

[9] J. Gao, C. Zhong, G. Y. Li and J. B. Soriaga, “Deep learning-based channel estimation for wideband hybrid mmWave massive MIMO,” IEEE TRANS COMMUN, 2023. DOI: 10.1109/TCOMM.2023.3258484

[10] H. Wang, P. Xiao and X. Li, “Channel parameter estimation of mmWave mimo system in urban traffic scene: a training channel-based method,” IEEE TRANS INTELL TRANSP, 2022. DOI: 10.1109/TITS.2022.3145363

[11] H. Soleimani, D. De Donno, S. Tomasin, “mm-Wave channel estimation with accelerated gradient descent algorithms,” J. Wirel. Commun. Netw. vol. 1, p. 1-17, 2018.

[12] S. Zhang, L. Xu and S. Yan, "A low complexity OMP sparse channel estimation algorithm in OFDM system," IEEE INT CONF SIGNAL PROC COMMUN COMPUT, 2021, pp. 1-5. DOI: 10.1109/ICSPCC52875.2021.9565070

[13] H. T. Friis, “A note on a simple transmission formula,” PROC IRE, vol. 34, no. 5, pp. 254–256, May 1946.

[14] T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N.Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter wave mobile communications for 5G cellular: It will work! ” IEEE Access, vol. 1, pp. 335–349, 2013. DOI: 10.1109/ACCESS.2013.2260813

[15] Q. Wu, Y. Zeng, and R. Zhang, “Joint trajectory and communication design for multi-UAV enabled wireless networks,” IEEE TRANS WIREL COMMUN, vol. 17, no. 3, pp. 2109–2121, 2018. DOI: 10.1109/TWC.2017.2789293

[16] G. R. MacCartney, S. Deng, S. Sun, and T. S. Rappaport, “Millimeterwave human blockage at 73 GHz with a simple double knife-edge diffraction model and extension for directional antennas,” PROC IEEE VEH TECHNOL CONF, Sep. 2016, pp. 1–6. DOI: 10.1109/VTCFall.2016.7881087

[17] 3GPP TR 38.901: “Study on channel model for frequencies from 0.5 to 100 GHz”, V16.1.0, 3rd Generation Partnership Project, Dec. 2019.

[18] W. Khawaja, O. Ozdemir, and I. Guvenc, “UAV air-to-ground channel characterization for mmWave systems,” PROC IEEE VEH TECHNOL CONF FALL, Sep. 2017, pp. 1–5. DOI: 10.1109/VTCFall.2017.8288376

[19] A. A. M. Saleh and R. Valenzuela, “A statistical model for indoor multipath propagation,” IEEE J. Sel. Areas Commun., vol. 5, no. 2, pp. 128–137, Feb. 1987. DOI: 10.1109/JSAC.1987.1146527

[20] S. Montagner, N. Benvenuto, S. Tomasin, “Taming the complexity of mm-wave massive MIMO systems: Efficient channel estimation and beamforming”, PROC IEEE INT CONF COMMUN WORKSH. (2015), pp. 1251–1256. DOI: 10.1109/ICCW.2015.7247349

[21] Y. Nesterov, “A method of solving a convex programming problem with convergence rate o(1/k 2 )”. Sov. Math. Dokl. 27, pp. 372–376 .1983.

[22] B. O’Donoghue, E. Candès, “Adaptive restart for accelerated gradient schemes”. Found. Comput. Math. vol. 15,pp 715–732 .2015. https://doi.org/10.1007/s10208-013-9150-3

[23] M. Anarfarhad, "Evaluation and proof of routing algorithm in Tor anonymity network". Electronic and Cyber Defense. vol. 11, N.3, pp 15-24. 2023. (in Persian).https://dor.isc.ac/dor/20.1001.1.23224347.1402.11.3.2.7

[24] M. S. Alamdari, and M. Fatemi, "Applying weighted smoothed norm in sparse representation classification for face recognition".Electronic and Cyber Defense. vol. 11, N.3, pp 57-65. 2023. (in Persian).https://dor.isc.ac/dor/20.1001.1.23224347.1402.11.3.6.1

[25] T. Motedaien and M. Yaghoobi and M. kheirabadi, "Improving mobile mass monitoring in the IoT environment based on Chaotic Fog Algorithm". Electronic and Cyber Defense. vol. 11, N.3, pp 77-88. 2023. (in Persian).https://dor.isc.ac/dor/20.1001.1.23224347.1402.11.3.8.3

[26] R. janani and R. fatemi mofrad, "A New Method in Wide Band Adaptive Beam Forming By Deep Learning Method In An Array System". Electronic and Cyber Defense. vol. 11, N.3, pp 35-47. 2023. (in Persian).https://dor.isc.ac/dor/20.1001.1.23224347.1402.11.3.4.9

[27] D. Needell and J. A. Tropp, ‘‘CoSaMP: Iterative signal recovery from incomplete and inaccurate samples,’’ Appl. Comput. Harmon. Anal., vol. 26, no. 3, pp. 301–321, May 2009

[28] T. JIANG, M. SONG, X. ZHAO, AND X.LIU, “Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing,” IEEE access 9 (2021): 49738-49749.. DOI: 10.1109/ACCESS.2021.3069335