The document presents an efficient and low complexity uplink detection and channel estimation method for next generation massive MIMO systems. It begins with an introduction to massive MIMO and outlines the block diagram and components of the proposed system. It then reviews relevant literature on the topic and discusses the motivation for using the orthogonal matching pursuit algorithm for its low complexity. A comparison table evaluates performance and complexity of different detection methods. The document concludes with potential future research directions such as machine learning techniques and cooperative detection to further improve spectral efficiency and detection accuracy in 5G massive MIMO systems.

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
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 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
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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
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N
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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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