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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.”
Tarek Hassan
Knowledge Hubreconfigurable intelligent surface5. mmWave Localization Perspective

reconfigurable intelligent surface learning note

5. mmWave Localization Perspective

Why mmWave and RIS are naturally connected for high-resolution positioning, beam training, and near-field localization.

Why mmWave Helps Localization

Millimeter-wave signals usually refer to frequencies from about 30 GHz to 300 GHz. Their wavelengths are short, so large antenna arrays can fit into compact hardware. Large arrays provide narrow beams and high angular resolution.

High bandwidth also improves delay resolution:

larger bandwidth -> finer time resolution -> finer range resolution

This makes mmWave useful for both communication and positioning.

References for this section3

Why mmWave Needs RIS

The same properties that make mmWave powerful also make it fragile:

  • Severe blockage.
  • Higher penetration loss.
  • Narrow beams that require alignment.
  • Sensitivity to mobility and orientation.

RIS can provide a controllable reflected path when direct mmWave links fail.

mmWave + RIS positioning
narrow BS beam -> RIS reflection -> sparse path estimate -> angle/range/location
References for this section3

Beam Training

Before data transmission, mmWave systems must find good beams. With RIS, this becomes a joint problem:

  • BS transmit beam.
  • RIS phase pattern.
  • UE receive beam.

Naively testing every combination is too slow. Practical methods use hierarchical beam codebooks, compressive sensing, machine learning, or position-aided beam search.

References for this section3

Near-Field Effects

For large RIS apertures, the user may be in the near-field region. In this region, beams focus on points rather than only directions.

This enables:

  • Range-angle localization.
  • User-specific focusing.
  • More spatial degrees of freedom.
  • Improved separation of nearby users or targets.

But it also requires more accurate channel models.

References for this section3

RIS Orientation and Placement

RIS localization performance depends strongly on where the RIS is placed:

  • A wall-mounted RIS can help a blocked street or corridor.
  • A ceiling RIS can improve indoor positioning.
  • Multiple RIS panels can improve geometry.
  • Poorly placed RIS panels create weak or ambiguous measurements.

RIS orientation matters because a surface has a limited useful angular sector. A panel facing the wrong direction may provide little energy.

References for this section3

Main Research Problems

  • Fast RIS-aided beam training.
  • Sparse channel estimation with low pilot overhead.
  • Near-field spherical-wave modeling.
  • Joint communication-rate and localization-error optimization.
  • Learning-based fingerprinting using RIS beam patterns.
  • Robustness under blockage and mobility.
References for this section3

Takeaway

mmWave gives high-resolution localization, while RIS helps overcome mmWave blockage and geometry limitations. The combination is especially powerful for 6G positioning, but it requires careful beam training, channel estimation, and near-field modeling.

References for this section3

Complete references and further reading