اثر اطلاعات جانبی بر تعداد اندازه‌گیری‌ها و ردگیری کانال تنک متغیر با زمان با حسگری فشرده

نویسندگان

دانشکده مهندسی برق – دانشگاه علم و صنعت ایران

چکیده

در این مقاله، مساله تخمین کانال‌های تنک متغیر با زمان در سیستم‌های مالتی‌پلکس تقسیم فرکانسی متعامد (OFDM) با روش نرم-پایه مورد مطالعه قرار گرفته است. در این روش، ترکیب نرم‌ها بر اساس بازیابی تنک با استفاده از اطلاعات جانبی، جهت استخراج همزمان دو ویژگی تنکی و تغییرات زمانی کانال صورت می‌گیرد. بدین منظور همبستگی زمانی کانال‌های متغیر با زمان به‌عنوان اطلاعات جانبی در نظر گرفته می‌شود و اثر این اطلاعات بر تعداد اندازه‌گیری‌ها و ردگیری کانال بررسی می‌شود.  نتایج شبیه‌سازی افزایش دقت تخمین و ردگیری کانال‌های تنک متغیر با زمان را تا چند دسی‌بل و همچنین کاهش تعداد اندازه‌گیری‌ها را در مقایسه با بعضی الگوریتم‌های متداول بازیابی نشان می‌دهد. 

کلیدواژه‌ها


عنوان مقاله [English]

Effect of Side Information on the Number of Measurements and Dynamic Sparse Channel Tracking using Compressed Sensing

نویسندگان [English]

  • H. Khaledian
  • M. H. Kahaei
School of Electrical Engineering, Iran University of Science and Technology , Tehran, Iran
چکیده [English]

In this paper, the problem of dynamic sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) systems is studied using norm-based methods. In this method, a mixture of norms is performed based on sparse recovery using side information for simultaneous extraction of both sparseness and dynamic features. For this purpose, temporal correlation of dynamic channels is considered as the side information whose effect on the number of measurements and dynamic channel tracking is investigated. Simulation results show an increase on both estimation accuracy and tracking of dynamic sparse channels for some decibels and also reduction of the number of measurements compared to some conventional reconstruction algorithms.

کلیدواژه‌ها [English]

  • Dynamic sparse channel estimation
  • sparse recovery with side information
  • measurements
  • mixed norms
  • OFDM systems
[1] D. Hu, X. Wang and L. He, “A new sparse channel estimation and tracking method for time-varying OFDM systems,” IEEE Trans. Veh. Technol., vol. 62, no. 9, pp. 4648–4653, 2013.
[2] M. Morelli and U. Mengali, “A comparison of pilot-aided channel estimation methods for OFDM systems,” IEEE Trans. signal Process., vol. 49, no. 12, pp. 3065–3073, 2001.
[3] L. Tong, G. Xu and T. Kailath, “Blind identification and equalization based on second-order statistics: A time domain approach,” IEEE Trans. Inf. Theory, vol. 40, no. 2, pp. 340–349, 1994.
[4] محمود آتشبار و محمدحسین کهایی، «جهت‌یابی چند گوینده با استفاده از نمونه‌برداری فشرده مبتنی بر فاز،» مجله مهندسی برق دانشگاه تبریز، جلد 40، شماره 2، صفحات 1-11، 1390.
[5] E. J. Candes and M. B. Wakin, “An Introduction To Compressive Sampling,” IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21–30, 2008.
[6] C. R. Berger, S. Zhou, J. C. Preisig and P. Willett, “Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication : From Subspace Methods to Compressed Sensing,” IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1708-1721, 2010.
[7] K. Manolakis, C. Oberli, L. Herrera and V. Jungnickel, “Analytical Models for Channel Aging and Synchronization Errors for Base Station Cooperation,” in Signal Processing Conference (EUSIPCO), Proceedings of the 21th European, pp. 3–7, 2013.
[8] N. Jing and L. Wang, “Dynamic Sparse Channel Estimation Using -constrained Kalman Filter in OFDM Systems,” in International Conference on Big Data Computing and Communications, pp. 28–42, 2015.
[9] R. Prasad, C. R. Murthy and B. D. Rao, “Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning,” Signal Process. IEEE Trans., vol. 62, no. 14, pp. 3591–3603, 2014.
[10] S. Cotter and B. Rao, “The Adaptive Matching Pursuit Algorithm for Estimation and Equalization of Sparse Time-Varying Channels,” In Signals, Systems and Computers, Conference Record of the Thirty Fourth Asilomar Conference on, vol. 2, pp. 1772-1776, 2000.
[11] A. Yongac, “Estimation of Time-Varying Channels with Orthogonal Matching Pursuit Algorithm,” Advances in Wired and Wireless Communication, IEEE/Sarnoff Symposium, no. 1, pp. 141-144, 2005.
[12] X. Zhu, L. Dai, W. Dai, Z. Wang and M. Moonen, “Tracking a dynamic sparse channel via differential orthogonal matching pursuit,” in Military Communications Conference, MILCOM IEEE, pp. 792–797, 2015.
[13] X. Zhu, L. Dai, G. Gui, W. Dai, Z. Wang, and F. Adachi, “Structured matching pursuit for reconstruction of dynamic sparse channels,” in IEEE Global Communications Conference, (GLOBECOM), pp. 1-5, 2015.
[14] S. C. S. Chen and D. Donoho, “Basis pursuit,” Proc. 28th Asilomar Conf. Signals, Syst. Comput., vol. 1, pp. 41–44, 1994.
[15] W. Jakes, Microwave Mobile Communications. 1974.
[16] M. L. Jakobsen, K. Laugesen, C. N. Manchón, G. E. Kirkelund, C. Rom and B. Fleury, “Parametric modeling and pilot-aided estimation of the wireless multipath channel in OFDM systems,” in Communications (ICC), IEEE International Conference, pp. 1–6, 2010.
[17] E. Candes and T. Tao, “Near Optimal Signal Recovery From Random Projections : Universal Encoding Strategies,” IEEE Transactions on Information Theory, vol. 52, no.12, pp. 5406-5425, 2006.
[18] H. Van Luong, J. Seiler, A. Kaup and S. Forchhammer, “Sparse signal reconstruction with multiple side information using adaptive weights for multiview sources,” in Image Processing (ICIP), IEEE International Conference, pp. 2534-2538, 2016.
[19] J. F. C. Mota, N. Deligiannis and M. R. D. Rodrigues, “Compressed Sensing with Prior Information : Optimal Strategies , Geometry , and Bounds,” IEEE Transaction on Information Theory, no. 2, pp. 1–21, 2017.
[20] S. Oymak, A. Jalali, M. Fazel, Y. C. Eldar, and B. Hassibi, “Simultaneously structured models with application to sparse and low-rank matrices,” IEEE Trans. Inf. Theory, vol. 61, no. 5, pp. 2886–2908, 2015.
[21] هادی شکری و محمدحسین کهایی، «حسگری فشرده تصاویر ابرطیفی و بازسازی با تنظیم‌کننده تغییرات کلی طیفی-مکانی،» مجله مهندسی برق دانشگاه تبریز، پذیرفته شده برای چاپ.
[22] D. Needell and R. Ward, “Near-optimal compressed sensing guarantees for total variation minimization,” IEEE Transaction on Image Processing, vol. 22, no. 10, pp. 3941-3949, 2013.
[23] D. Needell and R. Ward, “Stable image reconstruction using total variation minimization,” in SIAM Journal on Image Sciences, vol. 6, no. 2, pp. 1035–1058, 2013.
[24] J. Haupt, W. U. Bajwa, G. Raz and R. Nowak, “Toeplitz compressed sensing matrices with applications to sparse channel estimation,” IEEE Trans. Inf. Theory, vol. 56, no. 11, pp. 5862–5875, 2010.