Intelligent Reflecting Surfaces

An Intelligent Reflecting Surface (IRS) is a new technology that can significantly improve the performance of wireless communication networks in 6G and beyond. An IRS is a controllable reflect array with a massive number of low-cost passive elements which introduce a phase shift to the incoming signals from the sources before the propagation towards the destination. In fact, IRS enables the control of the wireless propagation environment.

Throughput and capacity gain in IRS-assisted wireless communication systems is highly dependent on the availability of channel state information (CSI) at both user and base station (BS). However, the acquisition of CSI is quite challenging since the IRS is generally not equipped with any radio frequency (RF) chains and thus not capable of performing any baseband signal processing functionality. Thus, the user-IRS and IRS-BS channels cannot be separately estimated via conventional training-based approaches. Instead, only the concatenated user- IRS-BS channels can be estimated based on the training signals sent from the users to the BS. Hence, new statistical signal processing-based approaches are required to be developed to estimate user-IRS and IRS-BS channels individually. In addition to new estimation techniques, new transmission protocols for channel estimation in IRS-assisted systems are required to be developed to facility channel estimation. Channel estimation for mobile users and multiple users becomes even more challenging and requires the development of more advanced techniques.

Assignment

In this master thesis, we plan to study IRS-assisted wireless communication systems for for 6G and beyond. Our focus will be on channel estimation. Specifically, we are interested in channel estimation for multiple static users or a single mobile user. Besides channel estimation problems, we are also interested in other signal processing problems in IRS-assisted systems, such as precoding and decoding, IRS-assisted millimeter wave communications, power allocation, non-orthogonal multiple access (NOMA), and more.

Requirements

The project requires a self-motivated student who has a strong linear algebra and statistical signal processing background, and is interested in theoretical and practical aspects of wireless communication systems. Furthermore, you are required to respect deadlines in delivering the results. You should have advanced coding skills in some computer language, such as Matlab, Python, or R.

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Last modified: 2022-10-04