# Sensor Selection via GFlowNets: A Deep Generative Modeling Framework to Navigate Combinatorial Complexity

arXiv: https://arxiv.org/abs/2407.19736 · HTML: https://mlsanity.com/paper/2407.19736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation](https://mlsanity.com/paper/2106.04399/) (2021)
- [GFlowNet Foundations](https://mlsanity.com/paper/2111.09266/) (2021)
- [Trajectory balance: Improved credit assignment in GFlowNets](https://mlsanity.com/paper/2201.13259/) (2022)
- [Multi-Objective GFlowNets](https://mlsanity.com/paper/2210.12765/) (2022)
- [6G White Paper on Localization and Sensing](https://mlsanity.com/paper/2006.01779/) (2020)
- [Cognitive Radar Antenna Selection via Deep Learning](https://mlsanity.com/paper/1802.09736/) (2018)
- [Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems](https://mlsanity.com/paper/2101.06951/) (2021)
- [Seventy Years of Radar and Communications: The Road from Separation to Integration](https://mlsanity.com/paper/2210.00446/) (2022)
- [Sparse Antenna Array Design for MIMO Radar Using Softmax Selection](https://mlsanity.com/paper/2102.05092/) (2021)
- [Greedy Selection for Heterogeneous Sensors](https://mlsanity.com/paper/2307.00840/) (2023)

## Abstract (arXiv)

The performance of sensor arrays in sensing and wireless communications improves with more elements, but this comes at the cost of increased energy consumption and hardware expense. This work addresses the challenge of selecting k sensor elements from a set of m to optimize a generic Quality-of-Service metric. Evaluating all binommk possible sensor subsets is impractical, leading to prior solutions using convex relaxations, greedy algorithms, and supervised learning approaches. The current paper proposes a new framework that employs deep generative modeling, treating sensor selection as a deterministic Markov Decision Process where sensor subsets of size k arise as terminal states. Generative Flow Networks (GFlowNets) are employed to model an action distribution conditioned on the state. Sampling actions from the aforementioned distribution ensures that the probability of arriving at a terminal state is proportional to the performance of the corresponding subset. Applied to a standard sensor selection scenario, the developed approach outperforms popular methods which are based on convex optimization and greedy algorithms. Finally, a multiobjective formulation of the proposed approach is adopted and applied on the sparse antenna array design for Integrated Sensing and Communication (ISAC) systems. The multiobjective variation is shown to perform well in managing the trade-off between radar and communication performance.