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for Quantum-enhanced, distributed radar signal processing techniques that maximise target Signal-to-Noise Ratio (SNR). The algorithms we will derive will be applied and tested in the context of target detection
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action discretization, exploration parameters, and decision intervals significantly affects algorithmic convergence and system performance. Distributional RL techniques, such as quantile regression and
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
resources. To fill this gap, this proposal aims to design novel distributed and lightweight LLMs for spectrum management in aerial 6G networks. Specifically, the project will design wireless-aware data
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algorithms * Parallel algorithms and distributed computing * Parameterized complexity and structural graph theory * Random structures and randomized algorithms * Sublinear and streaming algorithms
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-cases of classical supercomputers, the development of quantum CFD algorithms will be of widespread benefit upon the arrival of fault-tolerant quantum computing. This project involves the adaptation
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-generation 6G wireless networks. Cell-free massive MIMO represents a significant advancement in wireless communications, where a large number of distributed access points cooperate to serve users without
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as