Edge-Native Environmental Sensing through SDR and TinyML-MEC Framework

About the Project

Project Summary

The successful candidates will redefine SDR reuse by merging environmental sensing with TinyML and MEC to a novel sensing-communication framework, enabling autonomous adaptiveness to RF fragility and uncertainties.

In addition to tuition coverage, a 20-hour weekly part-time associate position will be available.

Project Details

Within the 6G paradigm, Integrated Sensing and Communications (ISAC) represents a transformative leap in wireless evolution. As communication networks expand into higher frequency bands (e.g., millimeter-wave and sub-terahertz), signals become vulnerable to dynamic environmental changes. Consequently, environmental sensing becomes critical as it shifts networks from passive data conduits into cognitive infrastructures capable of perceiving physical surroundings, supporting advanced applications like urban digital twins and autonomous vehicular navigation.

The primary challenges of this research lie in extracting environmental monitoring information through light-weight/distributed AI, as well as designing a hybrid AI architecture to intelligently partition and offload complex computational tasks into hardware-constraint platforms. Specifically, the objective is to compress heavy deep learning processes for environmental awareness into ultra-lightweight TinyML modules deployable directly on Software-Defined Radios (SDRs). Seamlessly integrated with Mobile Edge Computing (MEC), this hybridised architecture will enable rapid, low-latency environmental sensing to facilitate the next-gen wireless communication paradigms.

To succeed this project, you will be required to tackle the following/relevant technical bottlenecks:

• Efficiently acquiring and processing complex, high-dimensional environmental sensing data and user physical parameters from both SDR front-ends and complementary sensor networks.
• Architecting hybrid modules to execute high-granularity sensing tasks, e.g. user tracking and identification, dynamic obstacle recognition, and predictive channel estimation, within strict edge resource bounds.
• Robustly decoupling task-specific physical signatures from entangled wireless metrics, including Channel State Information (CSI), amidst severe multipath fading and environmental noises.

Person Specification

Candidates should have been awarded, or expect to achieve, EITHER:

a] a First or Upper Second Class award in their undergraduate degree, in a relevant subject.

OR

b] a First or Upper Second Class award in their undergraduate degree, and a Merit or Distinction in a Masters degree, both in a relevant subject.

Qualifications from overseas institutions will be considered, but performance must be equivalent to that described above, and the University reserves the right to ascertain this equivalence according to its own criteria.

Desirable / Essential Skills or Experience

Preferred qualifications and hands-on experience include exceptional technical expertise in software-defined radio (SDR), embedded systems, FPGA development, and deployable AI, among other related fields. Candidates with extensive industrial experience are especially welcome to apply.

Submitting an application

We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected.Your application must include:

1. English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
2. A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
3. A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
4. A Curriculum Vitae (Resume) which details your education and work history.
5. Two academic refereeswho can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
6. Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
7. A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

Contact Information

For enquiries about this project, contact Dr. Zhengjia Xu at x.zhengjia@aston.ac.uk or Dr. Jose Maria Alcaraz Calero at j.alcarazcalero@aston.ac.uk.

Location

This position will be based on the Aston Campus in Birmingham, UK. The successful candidate will need to be located within a reasonable distance of the campus, and will be expected to visit in person regularly.

Interviews

Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Key words

RF Environmental Sensing, SDR, TinyML, MEC

Funding Notes

This project covers all tuition fees.

Please note that the successful candidate will be responsible for any costs relating to moving to Birmingham and/or visiting the Aston campus. International students must meet the financial requirements for the visa, flights, and NHS Surcharge. Applicants should be confident that they can meet these costs before applying.

Further information can be found here: Financial Requirements | Aston University

Project supervisors

Dr Zhengjia Xu

Dr Zhengjia Xu's profile is coming soon

Prof Jose Alcaraz Calero

Prof Jose Alcaraz Calero's profile is coming soon