[Paper Review] A Survey on Integrated Sensing and Communication with Intelligent Metasurfaces: Trends, Challenges, and Opportunities

Ahmed Magbool, Vaibhav Kumar+3 more|arXiv (Cornell University)|Jan 28, 2024

Energy Harvesting in Wireless Networks4 citations

TL;DR

This survey presents a comprehensive analysis of integrated sensing and communication (ISAC) systems enhanced by intelligent metasurfaces, focusing on reconfigurable intelligent surfaces (RIS) and reconfigurable holographic surfaces (RHS). It reviews state-of-the-art techniques, highlights challenges in channel estimation and beamforming, and demonstrates through experiments that RHS-based ISAC achieves comparable performance to phased arrays with 30× lower power consumption and supports 5 Mbit/s data rates while enabling accurate sensing.

ABSTRACT

The emergence of technologies demanding high data rates and precise sensing, such as autonomous vehicles and IoT devices, has driven the popularity of integrated sensing and communication (ISAC) in recent years. ISAC provides a framework for communication and sensing, where both functionalities are performed simultaneously or in a coordinated manner. There are two levels of integration in ISAC: radio-communications coexistence (RCC), where communication and radar systems use distinct hardware, waveforms, and signal processing but share the spectrum; and dual-function radar-communications (DFRC), where communication and sensing share the same hardware, waveform, and signal processing. At the architectural level, intelligent metasurfaces are a key enabler for the sixth-generation (6G) of wireless communication due to their ability to control the propagation environment efficiently. With the potential to enhance communication and sensing performance, numerous studies have explored the gains of metasurfaces for ISAC. Moreover, certain ISAC frameworks address limitations associated with reconfigurable intelligent surfaces (RIS) for communication. Thus, integrating ISAC with metasurfaces enhances both technologies. This survey reviews the literature on metasurface-assisted ISAC, detailing challenges and opportunities. To provide a comprehensive overview, we begin with fundamentals of ISAC and metasurfaces. The paper summarizes state-of-the-art studies on metasurface-assisted ISAC, focusing on metasurfaces as separate entities between the transmitter and receiver (known as RIS) and emphasizing RCC and DFRC. We also review work on holographic ISAC, where metasurfaces are part of the transmitter and receiver. For each category, lessons learned, challenges, opportunities, and research directions are highlighted.

Motivation & Objective

• To provide a unified overview of integrated sensing and communication (ISAC) systems enhanced by intelligent metasurfaces, particularly reconfigurable intelligent surfaces (RIS) and reconfigurable holographic surfaces (RHS).
• To identify and analyze key challenges in metasurface-assisted ISAC, including channel estimation, beamforming complexity, and hardware efficiency.
• To explore the dual integration levels of ISAC: radio-communications co-existence and dual-function radar-communications (DFRC).
• To evaluate experimental results demonstrating the feasibility and performance advantages of RHS-based ISAC over traditional phased arrays.
• To outline open research directions for future ISAC systems in mmWave and THz bands, focusing on energy efficiency and scalability.

Proposed method

• Conducting a systematic literature review on metasurface-assisted ISAC, covering standalone communication, sensing, and ISAC systems.
• Classifying existing works into two main categories: RIS-assisted ISAC (with RIS as a separate reflect-reflect relay) and RHS-assisted ISAC (with RHS as an active transmitter or receiver).
• Analyzing the radio-communications co-existence and dual-function radar-communications (DFRC) integration levels in RIS-based ISAC systems.
• Evaluating holographic ISAC using a full-wave simulation setup with a 2D metasurface (LB-75-20-C-SF) operating at 10–15 GHz to validate beamforming and sensing performance.
• Implementing a time-division pilot-based channel estimation method for RHS, with analysis of overhead and scalability issues.
• Applying maximum likelihood-based direction-of-arrival (AoA) estimation to handle signal modulation via holographic patterns in RHS systems.

Figure 1: Applications of metasurface-assisted ISAC.

Experimental results

Research questions

• RQ1 How can intelligent metasurfaces enhance both communication and sensing performance in integrated ISAC systems?
• RQ2 What are the key technical challenges in channel estimation and beamforming for large-scale reconfigurable holographic surfaces (RHS) in ISAC?
• RQ3 How does RHS-based ISAC compare to traditional phased array systems in terms of energy efficiency, beamforming gain, and data rate?
• RQ4 What are the performance trade-offs in using RHSs as transmitters or receivers in holographic ISAC systems?
• RQ5 What future research directions are needed to enable scalable, low-overhead, and high-accuracy ISAC in mmWave and THz bands?

Key findings

• The experimental results show that the reconfigurable holographic surface (RHS) achieves slightly higher beamforming gains in desired directions compared to a conventional phased array.
• The power consumption of the RHS is approximately 30 times lower than that of the phased array, demonstrating significant energy efficiency improvements.
• The estimated range in the holographic ISAC setup closely matches the actual target range, confirming the system's viability for high-accuracy sensing.
• A data rate of 5 Mbit/s was successfully achieved between the base station and user, confirming that communication performance is maintained during concurrent sensing.
• Channel estimation in RHS-based ISAC incurs high time and pilot overhead due to the need to activate elements sequentially, making it impractical for large-scale systems.
• The multi-dimensional search required for AoA estimation in RHS systems leads to high computational complexity, especially for large-sized surfaces, necessitating efficient algorithms.

Figure 2: The organization of this paper.