[Paper Review] Integrated Sensing and Communication with mmWave Massive MIMO: A Compressed Sampling Perspective

Zhen Gao, Ziwei Wan+5 more|arXiv (Cornell University)|Jan 15, 2022

Sparse and Compressive Sensing Techniques4 citations

TL;DR

This paper proposes a compressed sensing (CS)-based integrated sensing and communication (ISAC) framework for mmWave massive MIMO systems with hybrid beamforming, enabling high-resolution radar imaging and low-pilot-overhead channel estimation. By leveraging a widely spaced array (WSA) and a novel orthogonal matching pursuit with support refinement (OMP-SR) algorithm, the framework achieves accurate joint estimation of angles, delays, and Doppler frequencies, with simulation results showing near-CRB performance and 10 dB BER gain under Doppler compensation.

ABSTRACT

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.

Motivation & Objective

• To address the high computational and pilot overhead in mmWave massive MIMO ISAC systems by leveraging compressive sensing (CS) for sparse signal recovery.
• To enhance radar angular resolution using an energy-efficient widely spaced array (WSA) architecture while managing angular ambiguity.
• To design a time-varying ISAC frame structure with hardware-aware pilot waveforms under hybrid beamforming (HBF) constraints.
• To enable joint estimation of channel state information and Doppler frequencies for reliable high-mobility communication.
• To develop a dedicated dictionary and OMP-SR algorithm that effectively handle angular ambiguity in radar imaging and channel estimation.

Proposed method

• Adopt a widely spaced array (WSA) at the radar receiver to increase angular resolution, albeit introducing angular ambiguity.
• Design a novel ISAC frame structure that accommodates different timescales for channel estimation, radar imaging, and Doppler estimation.
• Formulate ISAC processing as sparse signal recovery problems using a dedicated dictionary tailored for WSA, enabling compressed sampling.
• Propose the orthogonal matching pursuit with support refinement (OMP-SR) algorithm to resolve angular ambiguity and improve recovery accuracy over conventional OMP.
• Integrate a Doppler estimation framework during payload transmission to maintain communication performance in time-varying channels.
• Optimize pilot waveforms under HBF hardware constraints to ensure pilot diversity and reduce overhead.

Experimental results

Research questions

• RQ1 How can compressive sensing be effectively applied to mmWave ISAC systems to reduce pilot overhead while maintaining high-resolution sensing and accurate channel estimation?
• RQ2 What is the impact of angular ambiguity introduced by widely spaced arrays on radar imaging and channel recovery, and how can it be mitigated?
• RQ3 How can pilot waveforms be designed under hybrid beamforming constraints to enable both effective channel estimation and radar sensing?
• RQ4 To what extent can the proposed OMP-SR algorithm outperform traditional OMP in resolving ambiguous angles and improving recovery performance?
• RQ5 How effective is the Doppler estimation framework in maintaining communication reliability under high-mobility conditions?

Key findings

• The proposed OMP-SR algorithm significantly improves recovery performance over conventional OMP, especially in the presence of angular ambiguity.
• The Doppler estimation framework achieves near-ideal performance, with BER results under Doppler compensation matching those with perfect knowledge, achieving approximately 10 dB gain at BER = 1e-4 for 16-QAM.
• For low transmit power (P_D = 2 dBm), the Doppler estimation MSE approaches the Cramér-Rao Bound (CRB), indicating high estimation accuracy.
• At higher transmit power (P_D = 4 dBm), an error floor appears in MSE due to inter-user interference, but this has negligible impact on actual BER performance.
• The pilot waveform design is effective: increasing the number of codebooks (M^CB) significantly improves spectral efficiency, with M^CB = 16 yielding near-optimal performance.
• The WSA architecture enhances angular resolution without requiring additional RF chains, making it energy-efficient for radar sensing in ISAC systems.