MiMo project from debre tabor in enhancing channel capacity in my mimo ofdma system

Debts tabor thesis presentation

1. Gafat Institute of Technology Electrical and computer Engineering Electronics Communication stream Title: Enhancing Channel Capacity in Multi-User MIMO-OFDMA Systems FEBRUARY 4, 2017 E.C Debre Tabor, Ethiopia Prepared by: Birhan Bayu & Saleamlak Mitiku Advisor: Mr. Molla B .

2. Outline Introduction Problem Statements Objectives General Objective Specific Objective Scope and Significance of the Study Literature Review Research Approach and Techniques Result and Discussion Conclusion and Recommendation Reference 2

3. Introduction MU-MIMO OFDMA Multi-User MIMO-OFDMA systems improve spectral efficiency and data rates. Challenges: Antenna selection, noise interference Goal: Enhance channel capacity using RLS-based noise cancellation and optimal antenna selection. 3

4. Problem of Statement Multi-user MIMO-OFDMA systems face challenges in antenna selection, noise interference that limit system performance. Existing noise cancellation techniques (LMS, MMSE) have drawbacks in efficiency and computational complexity. Optimization Need: Enhancing system capacity through RLS-based noise cancellation and antenna selection to improve spectral efficiency and system robustness. 4

5. Research Objectives General Objective Enhance channel capacity of MU MIMO-OFDMA system using RLS-based noise cancellation & antenna selection. Specific objective Analyze impact of antenna selection. Compare performance of MIMO, OFDMA, and MIMO-OFDMA Compare RLS, LMS, MMSE noise cancellation techniques. Develop an optimized RLS-based noise cancellation technique. 5

6. Scope and Significance of the Study Scope Focus on enhancing channel capacity in Multi-User MIMO-OFDMA systems. Optimization of antenna selection and RLS-based noise cancellation. Comparison of RLS, LMS, and MMSE algorithms for performance evaluation. MATLAB-based simulations for analyzing system efficiency and capacity. Significance Introduces antenna selection for capacity improvement. Develops optimized RLS-based adaptive filtering for noise suppression. Evaluates RLS, LMS, and MMSE to determine the most efficient filtering method. Highlights the benefits of integrating MIMO and OFDMA for enhanced spectral efficient. 6

7. Literature Review N o Author and year of publication Title Achievements Drawbacks 1 Chakrabarti et al.,2016 Analysis of Channel Capacity using MIMO-OFDM for 4G Applications Demonstrates that increasing the number of antennas improves capacity but introduces higher computational complexity. Focuses primarily on channel capacity improvements but does not address the impact of adaptive noise cancellation or antenna selection in multi-user MIMO-OFDMA 2 Nunez Cuadrado et al. (2019) Antenna Selection in MIMO-OFDM Systems Shows that antenna selection can reduce computational overhead while maintaining system performance. Proposes static antenna selection techniques that do not adapt dynamically to varying channel conditions or optimize spectral efficiency. 3 Mohammed et al. (2024) MIMO Channel Estimation Using the LS and MMSE Algorithm Achievement: Demonstrates that MMSE outperforms LS in channel estimation but at a higher computational cost. Drawback: The study compares Least Squares (LS) and Minimum Mean Square Error (MMSE) algorithms but does not explore the benefits of Recursive Least Squares (RLS) for adaptive noise cancellation. Table 1 7

8. Cont … N o Author and year of publication Title Achievements Drawback 4 Sonwane & Bhawar (2019) Channel Capacity Enhancement Using MIMO Systems: A Survey Highlights how beamforming reduces interference and improves link reliability but does not optimize for dynamic multi-user environments The survey focuses on beamforming and precoding techniques but does not integrate noise cancellation methods such as RLS for interference mitigation. Table 2 8

9. Research Approach and Techniques System Model: Multi-User MIMO-OFDMA with dynamic subcarrier allocation. 9

10. Cont... Key Techniques Recursive Least Squares (RLS) for noise cancellation. This algorithm Minimizes squared error for faster convergence. RLS, LMS and MMSE comparison. Antenna selection for spectral efficiency. 10

11. Cont… Simulation MATLAB-based analysis of system performance. We implement Multi-user MIMO-OFDMA system with adaptive filtering and also antenna selection MATLAB algorithm Key Equations Received Signal: Y = HX + N Channel Capacity: 11

12. Cont… Performance Metrics Channel Capacity Bit Error Rate (BER) Signal-to-Noise Ratio (SNR) Mean Squared Error (MSE) 12

13. Result and Discussion Capacity Enhancement with MIMO-OFDMA MIMO-OFDMA outperforms both standalone MIMO and OFDMA in spectral efficiency and channel capacity. At an SNR of 20 dB, capacity results are: MIMO-OFDMA: 27.0 bps/Hz MIMO: 17.0 bps/Hz OFDMA: 12.0 bps/Hz So, The integration of MIMO and OFDMA results in 2.25× higher capacity than OFDMA alone​ 13

14. Cont… 14

15. Cont… Effect of Antenna Selection Increasing the number of antennas significantly improves capacity. At 20 dB SNR, a 4-antenna configuration achieves 42.0 bps/Hz, a 2.4× gain over single-antenna systems 15

16. Cont… Comparison of Noise Cancellation Techniques (RLS, LMS, MMSE): RLS-based adaptive filtering provides the best performance: At BER = 1.0 RLS: 160 bits/sec/Hz Adaptive MMSE: 100 bits/sec/Hz LMS: 80 bits/sec/Hz 16

17. Cont… RLS outperforms both MMSE and LMS,. particularly at higher SNR levels, due to its ability to track time-varying noise and interference more effectively At 30 dB SNR RLS reaches 14.5 bps/Hz LMS is the weakest performer, reaching only 9.0 bps/Hz. MMSE provides stable but limited capacity growth, maxing out at 6.5 bps/Hz 17

18. Cont… Reduction with RLS Before RLS: 0.52 and After RLS: 0.25 (significant reduction across various SNR levels) 18

19. Cont… MIMO-OFDMA achieves up to 2.25× capacity gain over conventional methods. RLS-based noise cancellation reduces BER from 0.52 to 0.25. Antenna selection improves capacity 1 Antenna: 17.5 bps/Hz and 4 Antennas: 42.0 bps/Hz RLS outperforms LMS & MMSE in terms of capacity & noise reduction. MIMO-OFDMA performs best due to spatial diversity and efficient frequency allocation. Antenna selection optimizes system capacity. RLS adaptive filtering proves superior over MMSE and LMS in noise cancellation, minimizing BER and improving system reliability. 19

20. Conclusion and Recommendations MIMO-OFDMA improves channel capacity significantly than standalone MIMO and OFDMA Antenna selection boosts capacity by 2.4× over single-antenna systems. Antenna selection optimizes resource utilization RLS filtering achieves 160 bits/sec/Hz and reduces BER from 0.52 to 0.25. RLS outperforms MMSE and LMS in noise suppression and efficiency. RLS and Antenna selection effectively enhances capacity and minimizes interference. 20

21. Cont… Use AI for better adaptive noise cancellation. Optimize hardware for low-power and real-time processing. 21

22. Reference [1] Chakrabarti, P., Yadav, S. K., Dutta, K., Shome , P. P., & Datta, A. (2016). Analysis of Channel Capacity using MIMO-OFDM for 4G Applications . International Journal of Engineering Research & Technology (IJERT), 4(28), Special Issue - IC3S 2016. [2]. Nunez Cuadrado, D., Cal-Braz, J., & Sampaio-Neto, R. (2019). Antenna Selection in MIMO-OFDM systems. Proc. XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT), Petrópolis, Brazil. [3]. Sonwane, M., & Bhawar, M. S. (2019). Channel Capacity Enhancement using MIMO Systems: A Survey . International Journal of Innovative Research in Technology, 6(1), 452-457. [4]. Mohammed Ali, M., Wangdong, & Zaid Ali, A. (2024). MIMO Channel Estimation Using the LS and MMSE Algorithm. IOSR Journal of Electronics and Communication. 22

23. Thank you!