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Compressed Multiband Sensing in FR3 Using Alternating Direction Method of Multipliers

Dexin Wang, Isha Jariwala, Ahmad Bazzi, Sundeep Rangan, Theodore S. Rappaport, Marwa Chafii

TL;DR

This paper tackles joint sensing and localization in FR3 multiband ISAC by formulating a structured sparse recovery problem across sub-bands and solving it with an adaptive ADMM algorithm (ADMM-CMS). By enforcing shared support across sub-bands and using a proximal gradient/ADMM framework with automatic stopping, it achieves sharper delay-angle localization and denoising compared to Bartlett beamforming and per-band compressed sensing. The results show a substantial $34$ dB gain in transmit power for SRP of $0.9$ and significant RMSE reductions when leveraging multiple sub-bands, demonstrating practical improvements for FR3 ISAC in 6G. The method promises efficient, scalable multiband sensing suitable for FR3 deployments in future wireless applications such as IoT and autonomous systems.

Abstract

Joint detection and localization of users and scatterers in multipath-rich channels on multiple bands is critical for integrated sensing and communication (ISAC) in 6G. Existing multiband sensing methods are limited by classical beamforming or computationally expensive approaches. This paper introduces alternating direction method of multipliers (ADMM)-assisted compressed multiband sensing (CMS), hereafter referred to as ADMM-CMS, which is a novel framework for multiband sensing using uplink quadrature amplitude modulation-modulated pilot symbols. To solve the CMS problem, we develop an adaptive ADMM algorithm that adjusts to noise and ensures automatic stopping if converged. ADMM combines the decomposability of dual ascent with the robustness of augmented Lagrangian methods, making it suitable for large-scale structured optimization. Simulations show that ADMM-CMS achieves higher spatial resolution and improved denoising compared to Bartlett-type beamforming, yielding a 34 dB gain in per-antenna transmit power for achieving a 0.9 successful recovery probability (SRP). Moreover, compared to performing compressed sensing separately on the constituent 7 GHz and 10 GHz sub-bands, ADMM-CMS achieves reductions in delay root mean squared error of 34.46% and 40.76%, respectively, at -41 dBm per-antenna transmit power, while also yielding improved SRP. Our findings demonstrate ADMM-CMS as an efficient enabler of ISAC in frequency range 3 (FR3, 7-24 GHz) for 6G systems.

Compressed Multiband Sensing in FR3 Using Alternating Direction Method of Multipliers

TL;DR

This paper tackles joint sensing and localization in FR3 multiband ISAC by formulating a structured sparse recovery problem across sub-bands and solving it with an adaptive ADMM algorithm (ADMM-CMS). By enforcing shared support across sub-bands and using a proximal gradient/ADMM framework with automatic stopping, it achieves sharper delay-angle localization and denoising compared to Bartlett beamforming and per-band compressed sensing. The results show a substantial dB gain in transmit power for SRP of and significant RMSE reductions when leveraging multiple sub-bands, demonstrating practical improvements for FR3 ISAC in 6G. The method promises efficient, scalable multiband sensing suitable for FR3 deployments in future wireless applications such as IoT and autonomous systems.

Abstract

Joint detection and localization of users and scatterers in multipath-rich channels on multiple bands is critical for integrated sensing and communication (ISAC) in 6G. Existing multiband sensing methods are limited by classical beamforming or computationally expensive approaches. This paper introduces alternating direction method of multipliers (ADMM)-assisted compressed multiband sensing (CMS), hereafter referred to as ADMM-CMS, which is a novel framework for multiband sensing using uplink quadrature amplitude modulation-modulated pilot symbols. To solve the CMS problem, we develop an adaptive ADMM algorithm that adjusts to noise and ensures automatic stopping if converged. ADMM combines the decomposability of dual ascent with the robustness of augmented Lagrangian methods, making it suitable for large-scale structured optimization. Simulations show that ADMM-CMS achieves higher spatial resolution and improved denoising compared to Bartlett-type beamforming, yielding a 34 dB gain in per-antenna transmit power for achieving a 0.9 successful recovery probability (SRP). Moreover, compared to performing compressed sensing separately on the constituent 7 GHz and 10 GHz sub-bands, ADMM-CMS achieves reductions in delay root mean squared error of 34.46% and 40.76%, respectively, at -41 dBm per-antenna transmit power, while also yielding improved SRP. Our findings demonstrate ADMM-CMS as an efficient enabler of ISAC in frequency range 3 (FR3, 7-24 GHz) for 6G systems.

Paper Structure

This paper contains 13 sections, 35 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Multiband Channel and Signal Model
  4. Problem Formulation
  5. Estimation Algorithm
  6. Target Localization
  7. Adaptive Learning Parameters
  8. Simulation Results
  9. Comparison Benchmark
  10. Metrics
  11. Simulation Implementation
  12. Results and Discussion
  13. Conclusion and Future Work

Figures (4)

  • Figure 1: The cellular ISAC scenario with one base station operating in bistatic mode and $N$ targets, including one user device during uplink channel transmission and $N-1$ scatterers within the range of the user device.
  • Figure 2: Sample normalized $X(\theta,\tau)$ of our ADMM-CMS and $P(\theta,\tau)$ of the Bartlett-type BF benchmark. The black lines correspond to the true AoA and ToA, and $P^{\tt{T}} = -25 \textrm{ dBm}$.
  • Figure 3: SRP vs. $P^{\tt{T}}$ for one LoS path and one scatterer for both ADMM-CMS and the Bartlett-type BF benchmark.
  • Figure 4: Delay and angle RMSE vs. $P^{\tt{T}}$ for one LoS path and one scatterer for ADMM-CMS.