Thesis description

Distributed multi-antenna systems play a crucial role in the development of 5G wireless communication standards and beyond, due to their capabilities to be pervasive and power efficient in serving multiple users spread over a coverage area. Such systems rely on base station cooperation, where multiple base stations work jointly to transmit data streams to the users in an effective and reliable way. In this context, an essential signal processing technique is beamforming (or precoding), which allows to handle the multi-user interference and ultimately to orient the direction of the transmitted beams towards the specific user terminal locations.

While traditional beamforming algorithms operate in a fully digital fashion, thus digitally calculating the transmit coefficients at the base stations for each single antenna, the latest development of multi-antenna architectures, incorporating an ever-increasing number of antennas, makes it difficult to employ fully-digital processing, due to cost and complexity limitations. Instead, hybrid beamforming (HBF) architectures are considered, which are composed of a digital low-dimensional part followed by an analog part driving the antennas with low-complexity and low-cost components. While the utilization of HBF has been widely studied in single-cell systems, current research is focused towards developing next generation HBF schemes for distributed systems, aiming at scalable algorithms with reduced latency and complexity.

The objective of this thesis project is to perform a literature review of relevant HBF algorithms in the context of distributed multi-antenna wireless systems, and to select relevant ones for further development, implementation and performance assessment in an existing simulation environment. The ultimate goal is to outperform the existent solutions in terms of overall performance, flexibility and/or complexity.

Qualifications

• Master student in Electrical Engineering, Computer Science or equivalent.
• A solid theoretical background in areas such as information theory and signal processing.
• Experience in modeling, link level simulation and optimization.

Location: Kista, Stockholm

Preferred starting date: Jan. 2023

Extent: 1 student, 30hp.

Contact person: Li Wang, Leo.Li.Wang@huawei.com