Master thesis scope
MIMO detection is the problem of recovering user’s transmitted symbols from received signals at antennas. The detection can be tackled by linear estimators (e.g. LMMSE) which involve matrix inversion, or tackled by more computationally-involved near-optimal algorithms. Recent advances in physics give arise to ideas of how to revised those algorithms. These include new physics-inspired algorithms for classical computers [1,2] and algorithms for new computing hardware (e.g. quantum computing). Part of these algorithms have been experimentally verified for some scenarios in the recent literature [2,3,4,5]. This master thesis project targets the following:
- A unified review of the methods
- More comprehensive experimental study on when the physics-inspired algorithms outperforms the classical baseline in the scope of MIMO detection
- Propose new heuristics that could further speed up the algorithms
 Nadiia Chepurko, Kenneth Clarkson, Lior Horesh, Honghao Lin, David Woodruff, “Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra”, Proceedings of the 39th International Conference on Machine Learning, 2022
 Minsung Kim, Salvatore Mandrà, Davide Venturelli, Kyle Jamieson, “Physics-Inspired Heuristics for Soft MIMO Detection in 5G New Radio and Beyond”, Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, 2021
 Abhishek Kumar Singh, Davide Venturelli, Kyle Jamieson, “A Finite-Range Search Formulation of Maximum Likelihood MIMO Detection for Coherent Ising Machines”, arXiv:2205.05020, under review for IEEE Globecom 2022
 Abhishek Kumar Singh, Kyle Jamieson, Davide Venturelli, Peter McMahon, “Ising Machines’ Dynamics and Regularization for Near-Optimal MIMO Detection”, IEEE Transactions on Wireless Communications, 2022
 Masaya Norimoto, Ryuhei Mori, Naoki Ishikawa, “Quantum Speedup for Higher-Order Unconstrained Binary Optimization and MIMO Maximum Likelihood Detection”, arXiv:2205.15478, 2022
- Master student in Wireless Communications, Electrical Engineering or equivalent.
- A solid theoretical background in areas such as signal processing or linear algebra.
- Experience in modeling and simulation.
Send your questions to: Gunnar Peters