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An Efficient Wireless Channel Estimation Model for Environment Sensing

Zainab Zaidi, Tansu Alpcan, Christopher Leckie, Sarah Efrain

TL;DR

This work tackles environment sensing with wireless channels by moving beyond conventional single-tap CSI to a detailed, time-varying multipath model. It proposes a gray-box CE method, TDL-NN, a shallow neural network that learns tap delays and gains from pilot signals within a tapped-delay-line framework. Validation using Matlab ray-tracing data shows RMS estimation error below $10^{-4}$ (−40 dB) for SNR ≥ $60$ dB and demonstrates dynamic tracking and drone-detection capabilities. Compared with ML-based and classical CE methods, the approach offers interpretable, real-time capable environment sensing and can enable new applications such as UAV detection and anomaly identification.

Abstract

This paper presents a novel and efficient wireless channel estimation scheme based on a tapped delay line (TDL) model of wireless signal propagation, where a data-driven machine learning approach is used to estimate the path delays and gains. The key motivation for our novel channel estimation model is to gain environment awareness, i.e., detecting changes in path delays and gains related to interesting objects and events in the field. The estimated channel state provides a more detailed measure to sense the field than the single-tap channel state indicator (CSI) in current OFDM systems. Advantages of this approach also include low computation time and training data requirements, making it suitable for environment awareness applications. We evaluate this model's performance using Matlab's ray-tracing tool under static and dynamic conditions for increased realism instead of the standard evaluation approaches that rely on classical statistical channel models. Our results show that our TDL-based model can accurately estimate the path delays and associated gains for a broad-range of locations and operating conditions. Root-mean-square estimation error was less than $10^{-4}$, or $-40$dB, for SNR $\geq 60$dB in all of our experiments. Our results show that interference of a flying drone on signal multipaths, in a preliminary experiment, can be detected in estimated channel states which, otherwise, remains obscured in conventional CSI.

An Efficient Wireless Channel Estimation Model for Environment Sensing

TL;DR

This work tackles environment sensing with wireless channels by moving beyond conventional single-tap CSI to a detailed, time-varying multipath model. It proposes a gray-box CE method, TDL-NN, a shallow neural network that learns tap delays and gains from pilot signals within a tapped-delay-line framework. Validation using Matlab ray-tracing data shows RMS estimation error below (−40 dB) for SNR ≥ dB and demonstrates dynamic tracking and drone-detection capabilities. Compared with ML-based and classical CE methods, the approach offers interpretable, real-time capable environment sensing and can enable new applications such as UAV detection and anomaly identification.

Abstract

This paper presents a novel and efficient wireless channel estimation scheme based on a tapped delay line (TDL) model of wireless signal propagation, where a data-driven machine learning approach is used to estimate the path delays and gains. The key motivation for our novel channel estimation model is to gain environment awareness, i.e., detecting changes in path delays and gains related to interesting objects and events in the field. The estimated channel state provides a more detailed measure to sense the field than the single-tap channel state indicator (CSI) in current OFDM systems. Advantages of this approach also include low computation time and training data requirements, making it suitable for environment awareness applications. We evaluate this model's performance using Matlab's ray-tracing tool under static and dynamic conditions for increased realism instead of the standard evaluation approaches that rely on classical statistical channel models. Our results show that our TDL-based model can accurately estimate the path delays and associated gains for a broad-range of locations and operating conditions. Root-mean-square estimation error was less than , or dB, for SNR dB in all of our experiments. Our results show that interference of a flying drone on signal multipaths, in a preliminary experiment, can be detected in estimated channel states which, otherwise, remains obscured in conventional CSI.
Paper Structure (10 sections, 5 equations, 5 figures, 3 tables)

This paper contains 10 sections, 5 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. TDL-NN Channel Estimation Approach
  4. Experimental Results
  5. Ray-tracing Simulations
  6. Static Multipath Scenario
  7. Mobile Receiver Scenario
  8. BER Comparison with other Channel Estimation Methods
  9. Sensing Example: Drone Detection
  10. Conclusion

Figures (5)

  • Figure 1: TDL-NN channel estimation for a scenario with multiple transmitters and one receiver.
  • Figure 2: Static multipath scenario, (a) scene with one transmitter and receiver, (b) actual and estimated path delays and gains, tap 4 has 2 different rays and the estimation shows the combined effect, (c) scene of (a) with two interferers, (d) estimation error versus SNR for scenarios of one transmitter with no interferer (1 tx), one interferer (2 txs), and 2 interferers (3 txs).
  • Figure 3: Changes in signal multipaths as the mobile user moves around a city block, (a) scene with changing rays when the receiver is at different locations, and (b) actual and (c) estimated taps for each observation.
  • Figure 4: (a) Scene with a mobile user transmitting to a base-station, which is continuously monitoring channel states. (b) A drone flying on the street impacts the signal multipaths.
  • Figure 5: Detection of anomalous channel state when drone impacts the signal. (a, b) static scenario, (a) TDL-NN based channel state and (b) conventional CSI. (c, d) dynamic scenario, (c) TDL-NN and (d) conventional CSI.