ISAC: A Technical Overview with RIS

Summary

This blog discusses the technical basics of ISAC and the promising roles of multiple antennas and RIS.

Integrated Sensing and Communication (ISAC) aims at the joint design of wireless communication and sensing functionalities within a single system, reusing the same hardware, spectrum, and signal resources. Instead of treating communication and sensing (e.g., radar or localization) as independent subsystems, ISAC formulates both as coupled signal processing tasks that operate on shared waveforms and channels.

Signal model and principle

In a typical ISAC setup, a multi-antenna transmitter emits a communication signal that simultaneously illuminates the surrounding environment. Besides reaching the intended users, the transmitted waveform is reflected by objects and scatterers, giving rise to sensing-relevant signal components. The received signal therefore contains:

• a communication component, conveying user data through multipath propagation, and
• a sensing component, encoding parameters such as delay, Doppler shift, angle of arrival, or object location.

From a signal-model perspective, ISAC can be interpreted as a superposition of data-bearing channels and structured reflections, which must be jointly processed at the receiver.

Trade-offs and design challenges

A central challenge in ISAC lies in the fundamental trade-off between communication performance and sensing accuracy. For example:

• Highly directive beams improve angular resolution but may reduce multi-user communication flexibility.
• Waveforms optimized for data rate or spectral efficiency may be suboptimal for parameter estimation.

These trade-offs motivate joint design and optimization across multiple dimensions, including waveform and pilot design, beamforming, and time–frequency resource allocation.

Role of MIMO and advanced processing

Multi-antenna (MIMO) systems play a key role in ISAC. Large antenna arrays enable spatial multiplexing for communication while simultaneously improving spatial resolution for sensing. In this context, ISAC closely interacts with classical problems such as channel estimation and tracking, localization, and joint detection–estimation. A key research question is to what extent communication-oriented channel state information (CSI) can be reused or enriched to support sensing tasks with limited additional overhead.

Integration of Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RIS) extend the ISAC concept by introducing controllable reflections within the propagation environment. An RIS consists of a large number of nearly passive elements whose phase responses can be adjusted, allowing the wireless channel itself to become a design variable. In ISAC systems, RIS can be used to:

• shape the illumination of the environment for improved sensing observability,
• enhance coverage or signal strength for communication users, and
• introduce additional spatial diversity through structured reflected paths.

From a modeling perspective, RIS give rise to cascaded channels that depend on both the physical environment and the chosen RIS configuration. These additional degrees of freedom can improve sensing resolution and robustness, but they also increase the complexity of channel estimation and system optimization. As a result, RIS-assisted ISAC typically requires joint optimization of beamforming and RIS configurations, often under practical constraints such as discrete phase shifts or limited reconfiguration rates.

Outlook toward 6G

ISAC is widely regarded as a core functionality of future 6G networks, particularly for applications such as joint localization and communication, autonomous systems, and smart environments. The integration of RIS further strengthens this vision by enabling environment-aware and programmable propagation, while raising new research challenges related to scalability, robustness, and learning-based adaptation.