MIMO OTFS Matlab Projects

The abbreviation of MIMO-OTFS is “Multiple-Input Multiple-Output Orthogonal Time Frequency Space”. This technology combines two techniques in order to enhance the performance of the system. To further know in detail about this technology you can read deep into this research paper on MIMO-OTFS.

  1. Define MIMO-OTFS

This is a technology used for communication which integrates two advanced technologies called OTFS and MIMO. This technique uses multiple antennas in transmitting end as well as receiving end. This helps to increase the reliability and capacity by providing multiple paths for the wireless link present in transmitting side and receiving side. The OTFS technology used here is modulation technique which is used for representing data from time frequency space. For transmitting symbols, it uses time frequency grid which is two dimensional. This technique is used for highly mobile and high speed communication for improving performance. The combination of MIMO and OTFS in the wireless communication provides better efficiency and performance. The significance of this technique is increased in terms of more spectral efficiency, resistance to fading and interference also increased link reliability with the use of data representation in OTFS and multiple antennas.

  1. What is MIMO-OTFS?

The MIMO-OTFS technique increases the performance in the communication system which could not be done by the conventional method because they have channel impairments due to dispersive propagation and high mobility.

  1. Where MIMO-OTFS is used?

This technology is very important in 5G network. It is mostly used in conditions where high mobility, channels of multipath fading and selection of frequency for channel are preferred. On comparing with the earlier MIMO techniques the advanced MIMO-OTFS offers high spectral efficiency, increased throughput and more reliability.

  1. Why MIMO-OTFS is proposed? Previous Technology Issues

This technology was proposed in order to provide: strength to Multipath fading, Compatibility to existing networks, more spectral efficiency and Low latency.

The issues faced by previous technologies are: Limited standardization, Channel estimation complexity, Interference management and Computational complexity.

  1. Algorithms / Protocols

The algorithms provided for MIMO-OTFS to overcome the previous issues faced by it are: “Entropy Based Adaptive Filtering Algorithm” (EAFA), “Improved Naïve Bayes” (INB), “Soft Actor Critic (SAC) algorithm” and “Dynamic Orthogonal Matching Pursuit” (DOMP).

  1. Comparative study / Analysis

The comparison study of this technique is done in order to analyze its techniques. To improve the quality of signal, data detection and channel estimation, noise suppression has been used along with adaptive filtering based on entropy. To increase the signal quality and to minimize the pilot overhead, this uses “Zeros added Superimposed Sequence Pilot”. To strengthen the channel estimation “Dynamic Orthogonal Matching Pursuit” (DOMP) is used, by considering time delay, DOA, uplink or downlink angles and Doppler frequency. In order to reduce latency computation and complexity in receiver “optimal MAP detection” is used with the help of “Improved naïve Bayes” (INB). Creating a hybrid algorithm by combining “Linear Minimum Mean Square” (LMMS) with “Approximate Message Passing” (AMP), we can detect data accurately. By making use of three-stage equalizer with “Rock Hyraxes Swarm Optimization”, the channel noise, ISI is reduced and also receiver performance can be improved.

  1. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by MIMO-OTFS are tested using different methodologies to analyze its performance. The comparison is done by using metrics like SNR (dB) vs. MSE, SNR vs. MSE of DL, SNR vs. BER, SNR vs. MSE of UL, SNR vs. Detection probability, Throughput vs. SNR, Pilot overhead vs. NMSE, BER vs. Number of users, Latency vs. Number of users, Throughput vs. Number of users and User velocity vs. NMSE.

  1. Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about MIMO-OTFS which can be useful for you:

  1. MIMO-OTFS Applications

MIMO-OTFS can be applied in various fields where it requires Mission-Critical Communications, Automation and Wireless Robotics, Mobile Broadband with Ultra-High-Speed.

  1. Topology

MIMO-OTFS topology is structured in order to increase the spectral efficiency, reliability and data rate by making use of “Joint time-frequency domain transmission” and multiple antennas.

  1. Environment

The MIMO-OTFS technique is mostly used in sectors where there are availability of various user interface and high multipath fading. The application of this can be done in other environments also, based on the requirements.

  1. Simulation Tools

Here we provide some simulation software for MIMO-OTFS, which is established with the usage of MATLAB and NS tool with version 3.36 or above version.

  1. Results

After going through this research paper for MIMO-OTFS you can completely understand about this technique and the ideology behind it. You can also know about the applications of it the algorithms used in it and the issues faced by it previously.

MIMO OTFS Topics and Ideas

  1. Design and Optimization of Downlink Massive MIMO System Based on OTFS Modulation Enabling Modified 3D-SOMP Channel Estimation
  2. Low Overhead Pilot Design for Channel Estimation in MIMO-OTFS Systems
  3. DSC-FeedNet Based CSI Feedback in Massive MIMO OTFS Systems
  4. Spatially Correlated MIMO-OTFS for LEO Satellite Communication Systems
  5. 28 GHz Over-the-Air Measurement using an OTFS Multi-User Distributed MIMO
  6. Precoding Design for Uplink MU-MIMO-OTFS with Statistical Information of Doppler Shift
  7. A High-Performance Block LMMSE Equalizer for OTFS-MIMO Diversity and Multiplexing
  8. Sensing Aided OTFS Massive MIMO Systems: Compressive Channel Estimation
  9. Efficient Channel Equalization and Symbol Detection for MIMO OTFS Systems
  10. Cell-Free Massive MIMO with OTFS Modulation: Statistical CSI-Based Detection
  11. On the Spectral Efficiency of MMSE-based MIMO OTFS Systems
  12. Performance Analysis of MIMO-OTFS with Selective Decode and Forward Relaying
  13. Performance Analysis of MIMO-OTFS with Decode and Forward Relaying
  14. Random Access with Massive MIMO-OTFS in LEO Satellite Communications
  15. Low-Complexity Linear Diversity-Combining Detector for MIMO-OTFS
  16. Mitigating Spatial Correlation in MIMO-OTFS
  17. Generalized Index Modulation for MIMO-OTFS Transmission
  18. Parameter Estimation for MIMO OTFS via the SAGE Algorithm
  19. Beam-Space MIMO Radar with OTFS Modulation for Integrated Sensing and Communications
  20. OTFS Transceiver Design and Sparse Doubly-Selective CSI Estimation in Analog and Hybrid Beamforming Aided mm Wave MIMO Systems
  21. Delay-Doppler Domain Tomlinson-Harashima Precoding for OTFS-Based Downlink MU-MIMO Transmissions: Linear Complexity Implementation and Scaling Law Analysis
  22. Simultaneous Localization and Communications with Massive MIMO-OTFS
  23. OTFS without CP in Massive MIMO: Breaking Doppler Limitations with TR-MRC and Windowing
  24. Channel Estimation for Massive MIMO-OTFS System in Asymmetrical Architecture
  25. Delay-Doppler and Angular Domain 4D-Sparse CSI Estimation in OTFS Aided MIMO Systems
  26. near Optimal Hybrid Digital-Analog Beamforming for Point-to-Point MIMO-OTFS Transmissions
  27. Deep-Learning Based Signal Detection for MIMO-OTFS Systems
  28. Block Sparse Bayesian Learning-Based Channel Estimation for MIMO-OTFS Systems
  29. Cell-Free Massive MIMO Meets OTFS Modulation
  30. MIMO OTFS with Arbitrary Time-Frequency Allocation for Joint Radar and Communications
  31. Channel Estimation for Massive MIMO-OTFS Systems via Sparse Bayesian Learning with 2-D Local Beta Process
  32. Online Bayesian Learning Aided Sparse CSI Estimation in OTFS Modulated MIMO Systems for Ultra-High-Doppler Scenarios
  33. Beam-Space MIMO Radar for Joint Communication and Sensing With OTFS Modulation
  34. Low-Complexity Memory AMP Detector for High-Mobility MIMO-OTFS SCMA Systems
  35. Low-Complexity ZF/MMSE MIMO-OTFS Receivers for High-Speed Vehicular Communication
  36. Low-Complexity LMMSE Receiver Design for Practical-Pulse-Shaped MIMO-OTFS Systems
  37. 3D-IPRDSOMP Algorithm for Channel Estimation in Massive MIMO with OTFS Modulation
  38. Compressive Sensing-Based Channel Estimation for MIMO OTFS Systems
  39. When Cell-Free Massive MIMO Meets OTFS Modulation: The Downlink Case
  40. Low-Complexity LMMSE Receiver for Practical Pulse-Shaped MIMO-OTFS Systems
  41. Low-Complexity MMSE Receiver Design for Massive MIMO OTFS Systems
  42. LEO Satellite-Enabled Grant-Free Random Access with MIMO-OTFS
  43. OTFS-Based Massive MIMO with Fractional Delay and Doppler Shift: The URLLC Case
  44. Uplink-Aided Downlink Channel Estimation for a High-Mobility Massive MIMO-OTFS System
  45. Rectangular Pulse-Shaped OTFS with Fractional Delay and Doppler Shift for MU-MIMO Systems
  46. Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO Systems
  47. Delay-Doppler Domain Tomlinson-Harashima Precoding for Downlink MU-MIMO OTFS Transmissions
  48. Cell-Free Massive MIMO with OTFS Modulation: Power Control and Resource Allocation
  49. Basis Expansion Extrapolation Based DL Channel Prediction with UL Channel Estimates for TDD MIMO-OTFS Systems
  50. Iterative channel estimation and data detection algorithm for MIMO-OTFS systems
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