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Joint Channel and CFO Estimation From Beam-Swept Synchronization Signal Under Strong Inter-Cell Interference

Bowen Li, Junting Chen, Nikolaos Pappas

Abstract

Complete awareness of the wireless environment, crucial for future intelligent networks, requires sensing all transmitted signals, not just the strongest. A fundamental barrier is estimating the target signal when it is buried under strong co-channel interference from other transmitters, a failure of which renders the signal unusable. This work proposes a maximum likelihood (ML)-based cross-preamble estimation framework that exploits carrier frequency offset (CFO) constancy across beam-swept synchronization signals (SS), coherently aggregating information across multiple observations to reinforce the desired signal against overwhelming interference. Cramer-Rao lower bound (CRLB) analysis and simulation demonstrate reliable estimation even when the signal is over a thousand times weaker than the interference. A low-altitude radio-map case study further verifies the framework's practical effectiveness.

Joint Channel and CFO Estimation From Beam-Swept Synchronization Signal Under Strong Inter-Cell Interference

Abstract

Complete awareness of the wireless environment, crucial for future intelligent networks, requires sensing all transmitted signals, not just the strongest. A fundamental barrier is estimating the target signal when it is buried under strong co-channel interference from other transmitters, a failure of which renders the signal unusable. This work proposes a maximum likelihood (ML)-based cross-preamble estimation framework that exploits carrier frequency offset (CFO) constancy across beam-swept synchronization signals (SS), coherently aggregating information across multiple observations to reinforce the desired signal against overwhelming interference. Cramer-Rao lower bound (CRLB) analysis and simulation demonstrate reliable estimation even when the signal is over a thousand times weaker than the interference. A low-altitude radio-map case study further verifies the framework's practical effectiveness.
Paper Structure (11 sections, 3 theorems, 25 equations, 6 figures, 1 algorithm)

This paper contains 11 sections, 3 theorems, 25 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Preamble Structure
  4. Transmission Model
  5. Cross-Preamble Estimation
  6. Channel Estimator
  7. Separate Analysis and Separate Estimator
  8. Cross-Preamble Estimator
  9. Joint and Channel Estimation Algorithm
  10. Simulation Results
  11. Conclusion

Key Result

Lemma 1

For any $k\in\mathcal{K}$, $p\in\mathcal{P}$, and $i\in\{0,1\}$, if $\tau=\tau_k$, the cross-correlation $r_{y,k}^{p,i}\left[\tau_k\right]$ follows where the $\sigma_{k,p,i}^{2}$ is the averaged interference plus noise

Figures (6)

  • Figure 1: Preamble Structure.
  • Figure 2: $\omega$ estimation NMAE over , obtained from 200 Monte-Carlo simulations, with $K=12$ , and $P=12$ preambles.
  • Figure 3: Channel $\alpha$ estimation over , obtained from 200 Monte-Carlo simulations, with $K=12$ , and $P=12$ preambles.
  • Figure 4: Aerial sampling campaign over the CUHK-Shenzhen campus. A DJI M300 RTK equipped with an on-board Latte Panda 3 Delta computer, and a USRP B210 (calibrated) software-defined radio flies at an altitude of $150\text{ m}$ to collect measurements within the area outlined by the dashed red polygon. Red triangles with yellow labels indicate the locations and Cell ID of a subset of known terrestrial .
  • Figure 5: Field test: Radio maps for beam 0 transmitted by a ground ; the location is indicated by a red triangle.
  • ...and 1 more figures

Theorems & Definitions (5)

  • Lemma 1
  • proof
  • Proposition 2
  • Proposition 3
  • proof