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Design and Optimization of Cooperative Sensing With Limited Backhaul Capacity

Wenrui Li, Min Li, An Liu, Tony Xiao Han

TL;DR

This work tackles cooperative target localization in ISAC cellular networks with limited backhaul capacity. It develops an advanced design where each sensing receiver locally estimates the time delay \\tau_n and the effective reflection coefficient \\alpha_n, then quantizes signal samples around the estimated delay using a Karhunen-Loève Transform (KLT) plus Lloyd scalar quantization, with bit allocation optimized via an iterative greedy algorithm to minimize the Expected Cramér-Rao Bound. The fusion center performs ML estimation of the target location using quantized data and local estimates, while a baseline scheme uses only locally estimated values. Numerical results show substantial improvements over the baseline across topologies and backhaul budgets, and demonstrate near-infinite-backhaul performance with modest bit budgets when using the proposed KLT-based quantization.

Abstract

This paper introduces a cooperative sensing framework designed for integrated sensing and communication cellular networks. The framework comprises one base station (BS) functioning as the sensing transmitter, while several nearby BSs act as sensing receivers. The primary objective is to facilitate cooperative target localization by enabling each receiver to share specific information with a fusion center (FC) over a limited capacity backhaul link. To achieve this goal, we propose an advanced cooperative sensing design that enhances the communication process between the receivers and the FC. Each receiver independently estimates the time delay and the reflecting coefficient associated with the reflected path from the target. Subsequently, each receiver transmits the estimated values and the received signal samples centered around the estimated time delay to the FC. To efficiently quantize the signal samples, a Karhunen-Loève Transform coding scheme is employed. Furthermore, an optimization problem is formulated to allocate backhaul resources for quantizing different samples, improving target localization. Numerical results validate the effectiveness of our proposed advanced design and demonstrate its superiority over a baseline design, where only the locally estimated values are transmitted from each receiver to the FC.

Design and Optimization of Cooperative Sensing With Limited Backhaul Capacity

TL;DR

This work tackles cooperative target localization in ISAC cellular networks with limited backhaul capacity. It develops an advanced design where each sensing receiver locally estimates the time delay \\tau_n and the effective reflection coefficient \\alpha_n, then quantizes signal samples around the estimated delay using a Karhunen-Loève Transform (KLT) plus Lloyd scalar quantization, with bit allocation optimized via an iterative greedy algorithm to minimize the Expected Cramér-Rao Bound. The fusion center performs ML estimation of the target location using quantized data and local estimates, while a baseline scheme uses only locally estimated values. Numerical results show substantial improvements over the baseline across topologies and backhaul budgets, and demonstrate near-infinite-backhaul performance with modest bit budgets when using the proposed KLT-based quantization.

Abstract

This paper introduces a cooperative sensing framework designed for integrated sensing and communication cellular networks. The framework comprises one base station (BS) functioning as the sensing transmitter, while several nearby BSs act as sensing receivers. The primary objective is to facilitate cooperative target localization by enabling each receiver to share specific information with a fusion center (FC) over a limited capacity backhaul link. To achieve this goal, we propose an advanced cooperative sensing design that enhances the communication process between the receivers and the FC. Each receiver independently estimates the time delay and the reflecting coefficient associated with the reflected path from the target. Subsequently, each receiver transmits the estimated values and the received signal samples centered around the estimated time delay to the FC. To efficiently quantize the signal samples, a Karhunen-Loève Transform coding scheme is employed. Furthermore, an optimization problem is formulated to allocate backhaul resources for quantizing different samples, improving target localization. Numerical results validate the effectiveness of our proposed advanced design and demonstrate its superiority over a baseline design, where only the locally estimated values are transmitted from each receiver to the FC.
Paper Structure (9 sections, 1 theorem, 30 equations, 6 figures, 1 algorithm)

This paper contains 9 sections, 1 theorem, 30 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Statement
  3. An Advanced Cooperative Sensing Design
  4. ML Estimation of Individual Time Delay and Reflecting Coefficient
  5. Quantization of Received Signal Samples at Each Receiver
  6. Target Location Estimator at the FC
  7. Optimization for the Proposed Advanced Design
  8. Numerical Results
  9. Conclusion

Key Result

Proposition 1

Signal vector ${{\bf{r}'}_{n}}$ is Gaussian distributed with its mean and covariance matrix given as: where ${{\bf{q}}_{{{\bf{r}}_{n}}}}=[\left\{\alpha _n^R\frac{{\partial s(t)}}{{\partial t}}{|_{t = {k_{n}}{T_s} - {{\hat{\tau} }_n}}},{k_{n}} \in {{\bf{K}}_{n}}\right\},\nonumber \\ \left\{\alpha _n^I\frac{{\partial s(t)}}{{\partial t}}{|_{t = {k_{n}}{T_s} - {{\hat{\tau} }_n}}},{k_{n}} \in {{\bf{K

Figures (6)

  • Figure 1: An illustration of the cooperative sensing network considered.
  • Figure 2: A case with circular and linear topology of the sensing receivers.
  • Figure 3: Performance evaluation for the two topologies considered assuming infinite (sufficiently large) backhaul capacity.
  • Figure 4: Comparison between the two designs under different RSNR.
  • Figure 5: Comparison between the two designs under different backhaul capacity $C_n$.
  • ...and 1 more figures

Theorems & Definitions (3)

  • Remark 1
  • Proposition 1
  • Remark 2