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Compressed Sensing Inspired User Acquisition for Downlink Integrated Sensing and Communication Transmissions

Yi Song, Fernando Pedraza, Shuangyang Li, Siyao Li, Han Yu, Giuseppe Caire

TL;DR

This work tackles radar-assisted user acquisition in downlink ISAC with multi-user MIMO-OFDM by modeling each user with a delay $\\tau_p$ and a beamspace response ${\\bf q}_p$. It introduces a two-stage approach—MUSIC-based delay estimation followed by LASSO-based beamspace recovery—and analyzes performance through pairwise error probability, showing that the rank and the geometric mean of nonzero eigenvalues of the squared beamspace difference matrix govern error behavior. The paper compares beam sweeping and random multi-beam probing, finding that random multi-beam achieves faster acquisition under tight symbol budgets while sweeping can outperform with larger time budgets, guided by the PEP insights. Numerical results validate the method and offer practical design guidance for ISAC systems, highlighting gains from probing multiple beams simultaneously under a power constraint.

Abstract

This paper investigates radar-assisted user acquisition for downlink multi-user multiple-input multiple-output (MIMO) transmission using Orthogonal Frequency Division Multiplexing (OFDM) signals. Specifically, we formulate a concise mathematical model for the user acquisition problem, where each user is characterized by its delay and beamspace response. Therefore, we propose a two-stage method for user acquisition, where the Multiple Signal Classification (MUSIC) algorithm is adopted for delay estimation, and then a least absolute shrinkage and selection operator (LASSO) is applied for estimating the user response in the beamspace. Furthermore, we also provide a comprehensive performance analysis of the considered problem based on the pair-wise error probability (PEP). Particularly, we show that the rank and the geometric mean of non-zero eigenvalues of the squared beamspace difference matrix determines the user acquisition performance. More importantly, we reveal that simultaneously probing multiple beams outperforms concentrating power on a specific beam direction in each time slot under the power constraint, when only limited OFDM symbols are transmitted. Our numerical results confirm our conclusions and also demonstrate a promising acquisition performance of the proposed two-stage method.

Compressed Sensing Inspired User Acquisition for Downlink Integrated Sensing and Communication Transmissions

TL;DR

This work tackles radar-assisted user acquisition in downlink ISAC with multi-user MIMO-OFDM by modeling each user with a delay and a beamspace response . It introduces a two-stage approach—MUSIC-based delay estimation followed by LASSO-based beamspace recovery—and analyzes performance through pairwise error probability, showing that the rank and the geometric mean of nonzero eigenvalues of the squared beamspace difference matrix govern error behavior. The paper compares beam sweeping and random multi-beam probing, finding that random multi-beam achieves faster acquisition under tight symbol budgets while sweeping can outperform with larger time budgets, guided by the PEP insights. Numerical results validate the method and offer practical design guidance for ISAC systems, highlighting gains from probing multiple beams simultaneously under a power constraint.

Abstract

This paper investigates radar-assisted user acquisition for downlink multi-user multiple-input multiple-output (MIMO) transmission using Orthogonal Frequency Division Multiplexing (OFDM) signals. Specifically, we formulate a concise mathematical model for the user acquisition problem, where each user is characterized by its delay and beamspace response. Therefore, we propose a two-stage method for user acquisition, where the Multiple Signal Classification (MUSIC) algorithm is adopted for delay estimation, and then a least absolute shrinkage and selection operator (LASSO) is applied for estimating the user response in the beamspace. Furthermore, we also provide a comprehensive performance analysis of the considered problem based on the pair-wise error probability (PEP). Particularly, we show that the rank and the geometric mean of non-zero eigenvalues of the squared beamspace difference matrix determines the user acquisition performance. More importantly, we reveal that simultaneously probing multiple beams outperforms concentrating power on a specific beam direction in each time slot under the power constraint, when only limited OFDM symbols are transmitted. Our numerical results confirm our conclusions and also demonstrate a promising acquisition performance of the proposed two-stage method.
Paper Structure (16 sections, 18 equations, 2 figures)

This paper contains 16 sections, 18 equations, 2 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. OFDM Signaling for Target Detection
  4. Problem Formulation
  5. The User Acquisition Method
  6. Beam Probing Strategies
  7. Beam sweeping strategy
  8. Random multi-beam strategy
  9. The Two-stage User Acquisition Method
  10. Stage I: delay estimation using the MUSIC algorithm for $P$ targets
  11. Stage II: beamspace estimation using the LASSO algorithm
  12. Performance Analysis Using Pairwise Error Probability
  13. Simulation Results
  14. Evaluation for the Squared Beamspace Difference Matrix
  15. Missed detection rate and false alarm rate vs. time slots
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Left:Evaluation on ranks. Right:Evaluation on geometric means.
  • Figure 2: Left: Evaluation on missed detection. Right: Evaluation on false alarm.