| Paper ID | D2-S7-T3.3 |
| Paper Title |
Model-based Neural Networks for Massive and Sporadic Connectivity |
| Authors |
Jeremy Johnston, Xiaodong Wang, Columbia University, United States |
| Session |
D2-S7-T3: Neural Networks II |
| Chaired Session: |
Wednesday, 14 July, 00:00 - 00:20 |
| Engagement Session: |
Wednesday, 14 July, 00:20 - 00:40 |
| Abstract |
We present two model-based neural network architectures purposed for sporadic user activity detection and channel estimation in the massive connectivity regime. In the considered scenario, the set of pilot sequences assigned to users is linearly dependent; but assuming user activity is sporadic, the detection/estimation problem is amenable to sparse recovery algorithms. We apply the deep unfolding framework to unroll two such algorithms, (1) linearized alternating direction method of multipliers and (2) vector approximate message passing, into a set of custom neural network layers. The networks thus inherit domain knowledge encapsulated in the signal model, plus suitable layer operations informed by the algorithms. The networks, trained on randomly generated data, offer improved complexity and accuracy relative to their iterative counterparts, and are a potential boon to cell-free massive MIMO systems.
|
