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PPT.pptx

1. Apr 2023
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PPT.pptx

  1. EFFICIENT AND LOW COMPLEX UPLINK DETECTION AND CHANNEL ESTIMATION FOR NEXT GENERATION MASSIVE MIMO SYSTEMS 1 Presented by B.Sri Harsha Reddy(19J41A04G1) C.Nagabhushan Goud(19J41A04D0) V.John Moses(20J45A0418) B.Sudharshan Raj(19J41A04C5) Under the guidance of Dr.B.Vasudeva Assistant Professor Department of ECE
  2. Outline  INTRODUCTION  LITERATURE SURVEY  MOTIVE  BLOCK DIAGRAM  COMPARISION TABLE  FUTURE SCOPE  PAPER PUBLISH  RESULTS  REFERENCE  THANK YOU 2
  3. Introduction of MIMO 3  It is mature wireless technology.  It incorporate all flavour of conventional MIMO with larger scale.  Current 4G standard incorporate only 8antenna at Tx –Rx end,but massive MIMO will have more flexibility. FEATURES:  It increases the degree of freedom.  Increases spectrum efficiency as well as energy efficiency.  It can support large number of user in same time-frequency slot.
  4. BLOCK DIAGRAM OF M-MIMO SYSTEM 4
  5. LITERATURE SURVEY  Zou, Qiuyun, et al. "A low-complexity joint user activity, channel and data estimation for grant-free massive MIMO systems." IEEE Signal Processing Letters 27 (2020): 1290-1294.  "Compressive Sensing Based Uplink Channel Estimation for Massive MIMO Systems" by Y. Liu et al. This paper presents a compressive sensing-based approach to uplink channel estimation that reduces the number of measurements required to estimate the channel while maintaining high detection accuracy.  Low-Complexity Detection Algorithms for Uplink Massive MIMO Systems" by S. Zhang et al. This paper proposes low-complexity detection algorithms for uplink massive MIMO systems that achieve near-optimal performance while reducing computational complexity.  "Joint Channel Estimation and Uplink Detection in Massive MIMO Systems" by S. Jin et al. This paper presents a joint channel estimation and uplink detection algorithm that exploits the correlation between the channel and the received signal to improve detection accuracy.
  6. M MOTIVE: Why OMP Algorithm  The Orthogonal Matching Pursuit (OMP) algorithm is a popular approach for uplink detection in massive MIMO systems because it provides an efficient and low-complexity solution.  The key advantage of the OMP algorithm is that it has a much lower computational complexity than other detection algorithms, such as maximum likelihood (ML) and sphere decoding (SD). This makes it a practical choice for implementation in real-world systems.  Additionally, the OMP algorithm has been shown to provide good performance in terms of detection accuracy, even in scenarios with a large number of UEs and high signal-to-noise ratio (SNR).  For example, ML and SD algorithms are known to provide optimal detection performance but may have much higher computational complexity than OMP. Therefore, they may not be practical for real-time implementation in systems with a large number of UEs.
  7. BLOCK DIAGRAM GROUP-1 GROUP - j OFDM- DETECTION DEMODULATION AND DECODING COMBINING OFDM- DETECTION DEMODULATION AND DECODING GROUP-N COMBINING OFDM- DETECTION DEMODULATION AND DECODING USER SELECTION OMP ALGORITHM PRE CODING COMBINING DATA - 1 DATA - 1 DATA - j DATA - N DATA - j DATA - N DATA - N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CHANNEL ESTIMATION CHANNEL ESTIMATION CHANNEL ESTIMATION
  8. COMPARISION TABLE
  9. FUTURE SCOPE  Machine Learning-based Techniques: Machine learning-based techniques can be explored to develop efficient uplink detection algorithms for 5G massive MIMO systems. These techniques can help in improving the detection accuracy and reducing the computational complexity of the system.  Hybrid Precoding and Detection: The combination of hybrid precoding and detection techniques can be explored to achieve a trade-off between detection accuracy and computational complexity. These techniques can help in achieving near-optimal detection performance with reduced complexity.  Cooperative Detection: Cooperative detection techniques can be used to improve the detection accuracy and reliability of 5G massive MIMO systems. By leveraging the spatial diversity of the system, cooperative detection techniques can help in mitigating the effects of fading and interference.  Joint Transmission and Detection: Joint transmission and detection techniques can be explored to further improve the spectral efficiency of 5G massive MIMO systems. These techniques can help in achieving better resource utilization and reducing the overall system complexity.  Practical Implementation: The practical implementation of uplink detection algorithms for 5G massive MIMO systems can be a challenging task. Future research can focus on developing practical implementation techniques that can be easily deployed in real-world scenarios
  10. PAPER PUBLISH Project title : “Efficient and low complex uplink detection and channel estimation for next generation massive mimo systems” Project Supervisor : Dr. B. Vasudeva Publications (Journals): Published – 1 nos, Under- review – 1 no, Under review – •C3 batch, “Efficient and low complex uplink detection and channel estimation for next generation massive mimo systems” , 2023. (under review). .
  11. RESULTS BER performance versus SNR NMSE performance versus SNR
  12. REFERENCES  T. L. Marzetta, Massive MIMO: An Introduction,, Bell Labs Technical Journal,vol. 20, pp. 1222, 2015.  T. L. Marzetta, Noncooperative cellular wireless with unlimited numbers of BS antennas,, IEEE Trans. Wireless Commun. vol. 9, no. 11, pp. 3590- 3600, November 2010.  X. Rao and V. K. N. Lau, ”Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems ”, IEEE Trans. Signal Process., vol. 62, no. 12, pp. 3261-3271, Jun. 2014.  N. G. Prelcic , K. T. Truong , C. Rusu and R.W. Heath, ”Compressive Channel Estimation in FDD Multi-Cell Massive MIMO Systems with Arbitrary Arrays”, IEEE GC Wkshps, December. 2016.  S. Noh, M. Zoltowski, Y. Sung, and D. Love, Pilot beam pattern design for channel estimation in massive MIMO systems,, IEEE J. Sel. Topic Signal Proess., vol. 8, no. 5, pp. 781-801, October. 2014.  J. Choi, D. Love, and P. Bidigare, Downlink training techniques for FDD massive MIMO systems: Open-loop and closed-loop training with memory,, IEEE J. Sel. Topic Signal Proess., vol. 8, no. 5, pp. 802-814, October. 2014.
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