Skip to content
New
Latest News || 7 October 2026: Quiz and Pizza Night — Titanic Crew placed fourth. || 3 October 2026: Serving as a Student Ambassador at Queen's University Belfast, welcoming prospective students and visitors and supporting EEECS tours and event activities. || 9 September 2026: Published at AP-S/URSI 2026: “Near Field-Aware UE Localization in RIS-Aided Wireless Networks Through ML-Regressor.” || 25 August 2026: Poster presentation at UCMMT 2026 in Birmingham — “Experimental Validation of Localisation in RIS-Assisted mmWave Networks.” || 18 August 2026: Published at EuCAP 2026: “Impact of RIS Size on Machine Learning-Enabled Beam Sweeping for User Localization.”
Knowledge Hubris assisted isac system4. What are the Recent Trends?

ris assisted isac system learning note

4. What are the Recent Trends?

Recent research trends in RIS-assisted ISAC, including active RIS, STAR-RIS, NOMA, learning-based control, near-field sensing, and multifunctional surfaces.

1. Active RIS for ISAC

Passive RIS suffers from multiplicative path loss. Active RIS integrates amplification into the surface and can improve radar SINR or communication coverage. Recent IEEE work has studied active RIS-aided ISAC beamforming under BS and RIS power constraints.

References for this section3

2. STAR-RIS and Full-Space ISAC

STAR-RIS can serve and sense users on both sides of a surface. This is attractive for building facades, windows, and indoor/outdoor environments.

References for this section3

3. Multifunctional and Hybrid RIS

A major 2025 IEEE Communications Surveys & Tutorials direction is multifunctional RIS. These surfaces may integrate reflection, transmission, sensing, receiving, energy harvesting, or active elements.

The motivation is simple: a purely passive RIS is hard to estimate and control. Adding limited sensing or receiving capability can make the RIS more network-aware.

References for this section3

4. RIS + NOMA + ISAC

Recent IEEE TVT work has considered hybrid NOMA-assisted ISAC via RIS. The idea is to support more communication users while preserving sensing performance. This introduces additional tradeoffs from user pairing, power-domain decoding, and radar constraints.

References for this section3

5. Learning-Based Control

RIS phase optimization is often nonconvex and high-dimensional. Machine learning can help with:

  • Fast beam prediction.
  • RIS codebook selection.
  • User/target tracking.
  • Online adaptation.
  • Digital-twin-assisted training.
References for this section3

6. Near-Field RIS-ISAC

Large RIS panels and high frequencies create near-field propagation. Near-field ISAC can focus energy on a point rather than only a direction, which is promising for high-resolution localization and target sensing.

References for this section3

7. Experimental Validation

The field needs more real measurements. Many results still depend on idealized simulations. Important validation topics include:

  • Real RIS hardware response.
  • Phase-dependent amplitude.
  • Mutual coupling.
  • Calibration.
  • Mobility.
  • Outdoor deployment.
References for this section3

Takeaway

The trend is moving from ideal passive reflecting panels toward active, STAR, hybrid, multifunctional, learning-controlled, and experimentally validated RIS-assisted ISAC systems.

References for this section3

Complete references and further reading