8. ISAC-Aided 6G Networks

ISAC as a 6G Pillar

6G networks are expected to support not only communication but also sensing, localization, intelligence, and control. ISAC is central to that vision because it makes the network aware of the physical world.

6G ISAC may support:

• Centimeter-level positioning.
• Device-free tracking.
• High-resolution environmental sensing.
• Beam prediction.
• Smart mobility.
• Industrial automation.
• Digital twins.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• A. Tishchenko et al., "The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications", IEEE Communications Surveys & Tutorials, early access, 2025.

Sensing as a Service

In sensing-as-a-service, the network provides sensing information to applications. For example, a factory may subscribe to a private 6G network that tracks robots and workers. A smart city may use network sensing to monitor traffic flow.

This raises important nontechnical issues:

• Privacy.
• Data ownership.
• Security.
• Regulation.
• Trust and explainability.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• A. Tishchenko et al., "The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications", IEEE Communications Surveys & Tutorials, early access, 2025.

Networked Sensing: Many Eyes on One Target

A single base station measures range well but angle less precisely, so its uncertainty about a distant target is a long, thin ellipse. When several nodes observe the same target, each adds a measurement from a different direction. This is multi-static or networked sensing.

The information from independent measurements simply adds up. For range measurements along unit directions $\mathbf{u}_i$ with error $\sigma_i$, the Fisher information and the best achievable position error are

$$\mathbf{J} = \sum_i \frac{\mathbf{u}_i \mathbf{u}_i^{T}}{\sigma_i^{2}}, \qquad \sigma_{\text{pos}} \ge \sqrt{\operatorname{tr}\!\left(\mathbf{J}^{-1}\right)} .$$

Two nodes in the same direction add the same information twice; two nodes at right angles pin the target in both directions. Geometry matters as much as the number of nodes. Drag the target in the lab below and add nodes to see it.

Networked sensing: many eyes on one target

1. Formula and symbols

$$J=\sum_iJ_i,\quad P=J^{-1}$$

$$\mathrm{PEB}=\sqrt{\operatorname{tr}P}$$

• $J_i$: independent node's position-information matrix derived from range/bearing uncertainty
• $J$: fused information
• $P$: covariance bound
• $\mathrm{PEB}$: position-error bound

Geometry and independence assumptions matter.

Sensing nodes

1234

Bandwidth

400 MHz

Angle noise per node

2°

2. Work with the current inputs

$$\mathbf{J} = \sum_{i} \left( \frac{\mathbf{u}_i\mathbf{u}_i^{T}}{\sigma_r^{2}} + \frac{\mathbf{v}_i\mathbf{v}_i^{T}}{(r_i\sigma_\theta)^{2}} \right), \qquad \sigma_{\text{pos}} = \sqrt{\operatorname{tr}\left(\mathbf{J}^{-1}\right)} = 7.3\ \text{cm}$$

3. Result

Position error (CRB)7.3 cm2 nodes

Node 1 alone1.67 m

Improvement22.9×

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• A. Tishchenko et al., "The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications", IEEE Communications Surveys & Tutorials, early access, 2025.

AI-Native ISAC

ISAC generates high-dimensional data: channel estimates, echoes, Doppler maps, point clouds, user trajectories, and beam histories. AI can help convert this data into decisions.

Examples:

• Neural networks for target detection.
• Reinforcement learning for beam scheduling.
• Federated learning for privacy-preserving sensing.
• Digital twins for simulation and planning.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• A. Tishchenko et al., "The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications", IEEE Communications Surveys & Tutorials, early access, 2025.

RIS and ISAC Together

RIS helps ISAC when the target or user is blocked. The RIS can create a controllable sensing/communication path and add geometry for localization.

This is why RIS-assisted ISAC has become a major research direction.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• A. Tishchenko et al., "The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications", IEEE Communications Surveys & Tutorials, early access, 2025.

Open 6G Research Problems

• Joint waveform and protocol design.
• Multi-user and multi-target ISAC.
• Sensing privacy and security.
• Standardized sensing interfaces.
• Edge processing for low latency.
• RIS-assisted NLoS sensing.
• Experimental validation beyond simulations.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• A. Tishchenko et al., "The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications", IEEE Communications Surveys & Tutorials, early access, 2025.

Takeaway

ISAC makes 6G networks context-aware. Its value is not only that one waveform can do two jobs, but that sensing information can continuously improve communication, mobility, and network intelligence.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• A. Tishchenko et al., "The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications", IEEE Communications Surveys & Tutorials, early access, 2025.