Master Thesis: Adaptive Mixed Periodic and Aperiodic SRS Allocation for MU-MIMO

Technology, Data & Digital · Software & Web Development · Software Engineering

In short

This Master Thesis project focuses on efficiently allocating periodic and aperiodic SRS resources for MU-MIMO under varying conditions like traffic load and mobility. The work aims to improve SRS utilization, MU-MIMO layers, throughput, and spectral efficiency through simulation and data analysis.

Responsibilities

Evaluate sharing strategies for periodic and aperiodic SRS resources under different traffic loads, UE capabilities, mobility conditions, and TDD configurations.
Study when mixed P-SRS/AP-SRS operation outperforms using either mode alone.
Investigate prioritization strategies when SRS capacity is exhausted.
Analyze the impact on SRS utilization, RAT MU-MIMO layers, throughput, and spectral efficiency.
Design and evaluate experiments using relevant simulation, trace, counter, or log data.

Requirements

MSc studies in wireless communications, telecommunications, signal processing, computer engineering, or a related field.
Solid understanding of 4G/5G NR radio concepts, TDD, uplink and downlink channels, MIMO, beamforming, precoding, CSI, channel estimation, SRS, and DMRS.
Understanding of link adaptation and channel measurement parameters such as CQI, RI, MCS, SINR, and BLER.
Ability to read technical specifications and internal design documents.
Good Python skills for data processing, visualization, and experimentation.
Excellent Java skills if Redhawk is used for simulation.
Linux and Git experience.
Ability to design experiments, analyze logs, and document results clearly.
Familiarity with GenAI tools for coding and verification.

Skills

PythonJavaLinuxGit

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