The ECE Department is proud to announce that the paper authored by Spilios Evmorfos and Zhaoyi Xu (under the supervision of Prof. Athina Petropulu) has received the Best Student Paper Award at the 2023 IEEE Workshop on Machine Learning for Signal Processing (IEEE MLSP), Rome Italy. The paper was presented at the MLSP workshop by Spilios. The abstract of the paper is given below.
Congratulations to Spilios, Zhaoyi, and Athina !
GFLOWNETS FOR SENSOR SELECTION
Spilios Evmorfos, Zhaoyi Xu, Athina Petropulu
Spilios Evmorfos, Zhaoyi Xu, Athina Petropulu
The efficacy of sensor arrays improves with more elements, yet increased number of elements leads to higher computational demands, cost and power consumption. Sparse arrays offer a cost-effective solution by utilizing only a subset of available elements. Each subset has a different effect on the performance properties of the array. This paper presents an unsupervised learning approach for sensor selection based on a deep generative modeling.
The selection process is treated as a deterministic Markov Decision Process, where sensor subarrays arise as terminal states. The Generative Flow Network (GFlowNet) paradigm is employed to learn a distribution over actions based on the current state. Sampling from the aforementioned distribution ensures that the cumulative probability of reaching a terminal state is proportional to the sensing performance of the corresponding subset. The approach is applied for transmit beamforming where the performance of a subset is inversely proportional to the error between its corresponding beampattern and a desired beampattern.
The method can generate multiple high-performing subsets by being trained on a small percentage of the possible subsets (less than 0.0001% of the possible subsets for the conducted experiments).