MIMO OTFS Research Topics

Multi-Input Multi-Output Orthogonal Time Frequency Space (MIMO-OTFS) research topic is now widely employed to improves the capacity and reliability of the wireless network. Varies technologies and parameters are examined in this research to get a best outcome. Below we have provided some MIMO-OTFS based concepts, applications, technique and parameters.

  1. Define MIMO-OTFS?

At the beginning we first take a look at the definition of MIMO-OTFS it is the full form of Multi Input Multi-Output Orthogonal Time Frequency Space. It integrates two innovative methods of communication methodology called MIMO-OTFS. OTFS is data in the time-frequency space it is a regulation method. It is permitted to enhance the mobile communication consequences and presentation in high-speed it is used to two dimensional time-frequency network to transfer symbols. MIMO is made reference to the wireless communication structure method and it is used for the transmitting and receiving ends of multiple antennas. This method enhances the consistency and capability of the wireless connection by manipulating several routs among the receiver and transmitter. This integrates methods to improve efficiency and best performance in wireless communication structures. Exploiting several antennas and orthogonally encoding time-frequency data, in these methods to offer fading, best resistance to interference, enhanced connection reliability, and also maximized spectral efficiency.

  1. What is MIMO-OTFS?

Next to the definition we see the detailed explanation of MIMO-OTFS it is an innovative thought with the objective or goal of forcing the margin of communication structure presentation, mainly in circumstances where traditional communication methodologies can difficult due to channel damages produced by dispersive broadcast conditions or high mobility.

  1. Where MIMO-OTFS is used?

After the explanation of MIMO-OTFS, we discuss where to incorporate MIMO-OTFS.It is used in 5G wireless communication structures. It is particularly beneficial in time varying several routing fading channels, channel frequency-selectivity, and high mobility. This method offers maximized spectral efficiency, high throughput, and enhanced reliability compared with traditional MIMO methods.

  1. Why MIMO-OTFS proposed? Previous technology issues

Next, to the explanation of the MIMO-OTFS are utilized to enhance the robustness to several route fading, high spectral efficiency; minimize latency, and compatibility with current wireless systems. The major limitations can be standardization limit, channel complexity estimation, complicated computation, and management interference.

  1. Algorithms/ protocols

The MIMO-OTFS recommended in this work it overcomes the issues; we provide some methods or techniques to be employed for MIMO-OTFS Entropy Based Adaptive Filtering Algorithm (EAFA), Soft Actor Critic (SAC) algorithm, Improved Naïve Bayes (INB), Dynamic Orthogonal Matching Pursuit (DOMP)

  1. Comparative study / Analysis

Following the algorithms or protocols to be utilized in our work, we have to compare several techniques to analyze the corresponding outputs; here we provide some technologies to be compared

  • For noise suppression we introduce entropy-based adaptive filtering. This method to enhance the data detection, channel estimation and, improve the signal quality. To enhance overall signal quality, and minimize pilot overhead using Zeros added Superimposed Sequence Pilot method. Dynamic Orthogonal Matching Pursuit (DOMP) method used for Doppler frequency, downlink/uplink positions, time delay, DOA and best channel estimation. To reduce computation latency, receiver complexity, and optimal MAP detection using improved naïve bayes algorithm. Accurate data detection using integrated Linear Minimum Mean Square (LMMS) and Approximate Message Passing (AMP) algorithm. For three stage equalizer we suggest Rock Hyraxes Swarm Optimization. To minimize noise in the channel, minimize ISI, and improving receiver presentation.
  1. Simulation results / Parameters

Succeeding the comparative analysis, we have to compare dissimilar parameters for the MIMO-OTFS to find the corresponding outcome.

For MIMO-OTFS we compare the parameter like MSE of different users, MSE of UL, MSE of DL, BER (perfect CSI), detection probability, pilot overhead, user velocity, BER, throughput, latency to find the better outputs.

  1. Dataset LINKS / Important URL

In this the parameters we selected are compared to obtain the best outcomes, and then afterwards here we have provide some important links that is useful to overview the MIMO-OTFS, application and some additional references for any clarification we go through the following links:

  1. MIMO-OTFS Applications

We provide some application for MIMO-OTFS like Ultra-High-Speed Mission-critical communications, wireless robotics and automation, and mobile broadband are some of the applications to be employed in MIMO-OTFS.

  1. Topology for MIMO-OTFS

We provide some topology for tracking systems like optimize the communication system’s spectral efficiency, data rate, and reliability by utilizing multiple antennas and joint time-frequency domain transmission which employ service to activate in tracking systems.

  1. Environment in MIMO-OTFS

We discuss the MIMO-OTFS environment might differ, but it is adapted for multipath user interface and multipath fading conditions.

  1. Simulation tools

The suggested system needs the subsequent software requirements. We require that the MIMO-OTFS are to be implemented by the tool Matlab-R2020a (or and above version). The operating system required for the work is Windows-10 (64-bit).

MIMO-OTFS Research 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 mmWave 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