Intelligent Metamaterials/Metasurfaces for 5G/6G Wireless Networks

About the Project

Background and Motivation:

Within the framework of 5G/6G infrastructure, advancements in network architectures and service integration are enabling emerging technologies such as Intelligent Metamaterials and Metasurface antenna systems. These systems can dynamically manipulate electromagnetic waves to enhance connectivity, sensing, and adaptive coverage within smart radio environments. They are particularly relevant to dense indoor networks, vehicular communication systems, and Internet of Things (IoT) platforms, where efficient spectrum utilization, low latency, and adaptability are crucial. Despite rapid progress, current metamaterial and metasurface technologies face key challenges in scalability, autonomous control, and energy efficiency. Many existing designs lack self-optimization capabilities and remain costly or complex to integrate into practical communication infrastructures.

This project seeks to address these limitations through a focused investigation into adaptable, autonomous, and energy-efficient Intelligent Metamaterials/Metasurfaces, ensuring they remain practical, compact, and cost-effective for large-scale deployment.

Research Aim and Objectives:

The overall aim of this project is to refine the design and control of Intelligent Metamaterials/Metasurfaces to achieve enhanced adaptability, autonomy, and energy efficiency in next-generation wireless systems.

Specific objectives include:

1. Design and Optimization: Develop and model compact, reconfigurable unit cells for both passive and active operation modes, targeting measurable improvements in radiation efficiency, adaptive response, and structural compactness.
2. AI-Driven Control: Design and implement AI-based algorithms for real-time reconfiguration and environmental adaptation, enabling autonomous operation under 5G/6G channel conditions.
3. Simulation and Analysis: Utilize CST, ADS, and MATLAB for electromagnetic modeling, system-level simulation, and multi-physics analysis to validate design efficiency and control mechanisms.
4. Experimental Validation: Fabricate and test prototypes in representative 5G/6G environments such as indoor dense networks and IoT scenarios to confirm adaptability and scalability performance.

Methodology:

The research will integrate electromagnetic design, AI-driven optimization, and multi-physics modeling within a unified framework. Parametric studies will guide unit-cell design to optimize reflection and transmission characteristics. Reinforcement learning and adaptive control strategies will enable real-time tuning of metasurface parameters. Simulation results will be validated through experimental testing on fabricated prototypes under various communication and sensing scenarios.

Metrics and Evaluation:

Performance will be assessed through quantitative and verifiable metrics, including:

• ≥ 20% reduction in physical footprint compared with existing designs;
• Beam-steering accuracy ≥90% under variable channel conditions;
• Energy consumption reduction ≥10% relative to conventional active surfaces; and
• Adaptation latency ≤10 ms during reconfiguration.

These metrics will provide measurable evidence of improvements in efficiency, responsiveness, and scalability, ensuring the proposed solutions meet practical 5G/6G deployment requirements.

Expected Outcomes and Impact:

This research will deliver new methodologies for developing adaptive, autonomous, and cost-effective Intelligent Metamaterials/Metasurfaces. The outcomes will advance both theoretical understanding and practical implementation of intelligent electromagnetic structures, contributing to the realization of energy-efficient, reconfigurable, and high-performance 5G/6G smart wireless environments.

Academic qualifications

A first degree (at least a 2.1) ideally in Electrical or Computer Engineering

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Fundamental knowledge of Antennas and Microwave Engineering
• Experience of fundamental Antennas and Metasurfaces.
• Competent in Signal Processing and CAD tools
• Knowledge of Microwave Engineering
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:

• Solid experience in RF circuits, programming, metamaterials and antenna systems with a track record of publishing in high-quality journals and international conferences

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact n.OjaroudiParchin@napier.ac.uk

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc

PhD Start Date: October 2026

Funding Notes

International applicants should note that visa application costs and the NHS health surcharge are additional costs to be taken into consideration, and successful applicants will need to cover these expenses themselves.