A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 3، دوره 9، شماره 2 - شماره پیاپی 17، بهمن 2023، صفحه 35-49 اصل مقاله (2.25 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2024.8730.1167 | ||
| نویسندگان | ||
| Hamid Amiriara* 1؛ Mohammadreza Zahabi2 | ||
| 1Electrical Engineering, Sharif University of Technology, Tehran, Iran. | ||
| 2Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran | ||
| چکیده | ||
| Despite the advantages of massive multi-input multi-output (MIMO) technology, traditional multi-antenna detection algorithms are not suitable for systems with large-scale antennas, and the use of this technology requires a significant increase in computational costs. In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. The TLBO algorithm, as one of the advanced methods of intelligence, is very useful for large-scale problems. Therefore, we use this method to search for the optimal solution vector in the modulation alphabet. In order to prove the accuracy and efficiency of the detector, it was suggested to simulate the system with different dimensions from 64×64 to 1024×1024. The proposed TLBO detector, in a limited time, achieves a bit error rate (BER) 10^(-5) in the average signal-to-noise ratio of 12 dB, which is approximately equal to the optimal detector performance, and maximum likelihood. As a result, the proposed detector is very efficient for use in massive MIMO systems. | ||
| کلیدواژهها | ||
| 5G wireless communication؛ Detection algorithm؛ Massive Multi Input Multi Output؛ Teaching-Learning based optimization | ||
| عنوان مقاله [English] | ||
| آشکارساز نزدیک به بهینه با پیچیدگی کم مبتنی بر الگوریتم آموزش-یادگیری برای سیستم چندآنتنه انبوه | ||
| نویسندگان [English] | ||
| حمید امیری آرا1؛ محمدرضا ذهابی2 | ||
| 1دانشکده مهندسی برق، دانشگاه صنعتی شریف، تهران، ایران. | ||
| 2دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران | ||
| چکیده [English] | ||
| با وجود مزایای فنّاوری چندآنتنه انبوه، الگوریتمهای آشکارساز سنتی چندآنتنه برای سیستمها با آنتنهای مقیاس بزرگ مناسب نیستند و بهکارگیری این فنّاوری مستلزم افزایش چشمگیر هزینههای محاسباتی میباشد. در این مقاله، یک گیرنده با پیچیدگی کم با استفاده از الگوریتم فراابتکاری آموزش-یادگیری (TLBO) برای سیستم چندآنتنه انبوه طراحی میگردد. الگوریتم TLBO به عنوان یکی از روشهای پیشرفته هوش جمعی، برای مسئله بهینهسازی عددی با مقیاس بزرگ بسیار کاربردی است؛ بنابراین، ما ازاینروش برای جستجوی بردار راه حل بهینه در الفبای مدولاسیون استفاده میکنیم. به جهت اثبات صحت و کارایی آشکارساز پیشنهادی شبیهسازی سیستم با ابعاد متفاوتی از 64×64 تا 1024×1024 انجام گردید. آشکارساز TLBO پیشنهادی در مدت زمان محدود، به میزان خطای بیت نزدیک به10^(-5) در نسبت متوسط سیگنال به نویز دریافتی 12 دسیبل دست مییابد که تقریباً برابر با عملکرد خطای بیت آشکارساز بهینه، درستنمایی بیشینه، است. در نتیجه آشکارساز پیشنهادی برای بهکارگیری در سیستمهای چندآنتنه انبوه بسیار کارا میباشد. | ||
| کلیدواژهها [English] | ||
| الگوریتم آشکارسازی, ارتباطات بیسیم نسل 5, بهینهسازی مبتنی بر آموزش-یادگیری, سیستم چند ورودی چند خروجی انبوه | ||
| مراجع | ||
|
Elijah, S. K. Abdul Rahim, W. K. New, C. Y. Leow, K. Cumanan, and T. Kim Geok, “Intelligent massive MIMO systems for Beyond 5G Networks: An overview and future trends,” IEEE Access, vol. 10, pp. 102532–102563, 2022. DOI:10.1145/2480362.2480474 Tang, J. Xia, L. Fan, X. Lei, W. Xu, and A. Nallanathan, “Dilated convolution based CSI feedback compression for massive MIMO Systems,” IEEE Transactions on Vehicular Technology, pp. 1–6, 2022. DOI:10.1145/2480362.2480474 A. Ayidh, Y. Sambo, and M. Imran, “Mitigation pilot contamination based on matching technique for uplink cell-free massive MIMO Systems,” Scientific Reports, vol. 12(1), pp. 1-14, 2022. DOI:10.1145/2480362.2480474 Yang, A.-A. Lu, Y. Chen, X. Gao, X.-G. Xia, and D. T. Slock, “Channel estimation for MASSIVE MIMO: An information geometry approach,” IEEE Transactions on Signal Processing, vol. 70, pp. 4820–4834, 2022. DOI:10.1145/2480362.2480474 Ö. Özdemir and A. Ö. Yilmaz, “ML performance analysis of digital relaying in bi-directional relay channels,” Wireless DOI:10.1145/2480362.2480474 Communications and Mobile Computing, vol. 12, no. 8, pp. 676–688, 2020. DOI:10.1145/2480362.2480474 Li and E. Ayanoglu, “Reduced complexity sphere decoding,” Wireless Communications and Mobile Computing, vol. 11, no. 12, pp. 1518–1527, 2018. DOI:10.1145/2480362.2480474 J.-S. Kim, S.-H. Moon, and I. Lee, “A new reduced complexity ML detection scheme for MIMO Systems,” IEEE Transactions on Communications, vol. 58, no. 4, pp. 1302–1310, 2019. Tang, Y. Xiao, P. Yang, Q. Yu, and S. Li, “A new low-complexity near-ML detection algorithm for spatial modulation,” IEEE Wireless Communications Letters, vol. 2, no. 1, pp. 90–93, 2020. DOI:10.1145/2480362.2480474 Bai and J. Choi, “Lattice reduction-based MIMO iterative receiver using Randomized Sampling,” IEEE Transactions on Wireless Communications, vol. 12, no. 5, pp. 2160–2170, 2019. DOI:10.1145/2480362.2480474 Lyu, J. Wen, J. Weng, and C. Ling, “On low-complexity lattice reduction algorithms for large-scale MIMO detection: The blessing of sequential reduction,” IEEE Transactions on Signal Processing, vol. 68, pp. 257–269, 2020. DOI:10.1145/2480362.2480474 Vishnu Vardhan, S. K. Mohammed, A. Chockalingam, and B. Sundar Rajan, “A low-complexity detector for large MIMO systems and Multicarrier CDMA systems,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 3, pp. 473–485, 2018. DOI:10.1145/2480362.2480474 R. Challa and K. Bagadi, “Design of near-optimal local likelihood search-based detection algorithm for coded large-scale MU-MIMO system,” International Journal of Communication Systems, vol. 33, no. 12, 2020. DOI:10.1145/2480362.2480474 Datta, N. Srinidhi, A. Chockalingam, and B. S. Rajan, “Random-restart reactive tabu search algorithm for detection in large-MIMO systems,” IEEE Communications Letters, vol. 14, no. 12, pp. 1107–1109, 2018. DOI:10.1145/2480362.2480474 Shaoshi Yang, Tiejun Lv, R. G. Maunder, and L. Hanzo, “From nominal to true a posteriori probabilities: An exact bayesian theorem based Probabilistic Data Association approach for iterative MIMO detection and decoding,” IEEE Transactions on Communications, vol. 61, no. 7, pp. 2782–2793, 2013. DOI:10.1145/2480362.2480474 Yoon, S., & Chae, C. B. Low-complexity MIMO detection based on belief propagation over pairwise graphs. IEEE transactions on vehicular technology, Vol.63, No.5, pp. 2363-2377, 2013. DOI:10.1145/2480362.2480474 Som, P., Datta, T., Srinidhi, N., Chockalingam, A., & Rajan, B. S. (2011). Low-complexity detection in large-dimension MIMO-ISI channels using graphical models. IEEE journal of selected topics in signal processing, Vol.5, No.8, pp. 1497-1511, 2011. DOI:10.1145/2480362.2480474 Haselmayr, W., Etzlinger, B., & Springer, A. Factor-graph-based soft-input soft-output detection for frequency-selective MIMO channels. IEEE communications letters, Vol.16, No.10, pp. 1624-1627, 2012. DOI:10.1145/2480362.2480474 Goldberger, J., & Leshem, A. MIMO detection for high-order QAM based on a Gaussian tree approximation. IEEE transactions on information theory, Vol. 57, No.8, pp. 4973-4982, 2011. DOI:10.1145/2480362.2480474 Chen, J., Hu, J., & Sobelman, G. E. Stochastic MIMO detector based on the Markov chain Monte Carlo algorithm. IEEE Transactions on Signal Processing, Vol.62, No.6, pp. 1454-1463, 2014. DOI:10.1145/2480362.2480474 Yen, K., & Hanzo, L. Genetic-algorithm-assisted multiuser detection in asynchronous CDMA communications. IEEE Transactions on Vehicular Technology, Vol.53, No.5, pp. 1413-1422, 2004. DOI:10.1145/2480362.2480474 Khurshid, K., Irteza, S., & Khan, A. A. Application of ant colony optimization based algorithm in MIMO detection. In IEEE Congress on Evolutionary Computation, pp. 1-7, 2010. DOI:10.1145/2480362.2480474 Yao, W., Chen, S., & Hanzo, L. (2012). Particle swarm optimisation aided MIMO multiuser transmission designs. Journal of Computational and Theoretical Nanoscience, Vol.9, No.2, pp. 266-275, 2012. DOI:10.1145/2480362.2480474 Rao, R. V., Savsani, V. J., & Vakharia, D. P. Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, Vol.183, No.1, pp. 1-15, 2012. DOI:10.1145/2480362.2480474 Siddiqui, M. H., Khurshid, K., Rashid, I., Khan, A. A., & Ahmed, K. Optimal Massive MIMO Detection for 5G Communication Systems via Hybrid n-Bit Heuristic Assisted-VBLAST. IEEE Access, 7, pp. 173646-173656, 2019. DOI:10.1145/2480362.2480474 Proakis, J. G., & Salehi, M. Digital communications New York: McGraw-hill, Vol. 4, 2001. DOI:10.1145/2480362.2480474 Alizadeh, E., Maleki, A., & Amiriara, H. Modify Teaching-Learning Based Optimization, International Congress of Interdisciplinary studies in science and Engineering, Tehran, 2017. DOI:10.1145/2480362.2480474 Hassani, A., Amiriara, H., & Zahabi, M., Energy Efficiency Optimization in Energy Harvesting Wireless Sensor Networks Using TLBO Algorithm. 2th International Conference on Electrical Engineering, Tehran, 2017. DOI:10.1145/2480362.2480474 Das, A. Abraham, U. K. Chakraborty, and A. Konar, ‘‘Differential evolution using a neighborhood-based mutation operator,’’ IEEE Trans. Evol. Comput., vol. 13, no. 3, pp. 526–553, Jun. 2009. DOI:10.1145/2480362.2480474
| ||
|
آمار تعداد مشاهده مقاله: 781 تعداد دریافت فایل اصل مقاله: 458 |
||