# Synergistic Localization and Sensing in MIMO-OFDM Systems via Mixed-Integer Bilevel Learning

arXiv: https://arxiv.org/abs/2507.07118 · HTML: https://mlsanity.com/paper/2507.07118/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 69 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization](https://mlsanity.com/paper/2112.12376/) (2021)
- [A Single Time-Scale Stochastic Approximation Method for Nested Stochastic Optimization](https://mlsanity.com/paper/1812.01094/) (2018)
- [EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression](https://mlsanity.com/paper/2204.04138/) (2022)
- [Gradient-based Bi-level Optimization for Deep Learning: A Survey](https://mlsanity.com/paper/2207.11719/) (2022)
- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [Distributed Distributionally Robust Optimization with Non-Convex Objectives](https://mlsanity.com/paper/2210.07588/) (2022)
- [Asynchronous Distributed Bilevel Optimization](https://mlsanity.com/paper/2212.10048/) (2022)
- [Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey](https://mlsanity.com/paper/2301.03857/) (2023)
- [Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization](https://mlsanity.com/paper/2410.07018/) (2024)
- [WiFi-based Real-time Breathing and Heart Rate Monitoring during Sleep](https://mlsanity.com/paper/1908.05108/) (2019)
- [A Branch-and-Cut Algorithm for Mixed Integer Bilevel Linear Optimization Problems and Its Implementation](https://mlsanity.com/paper/2104.09010/) (2021)
- [RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing](https://mlsanity.com/paper/2109.04819/) (2021)
- [Neur2BiLO: Neural Bilevel Optimization](https://mlsanity.com/paper/2402.02552/) (2024)
- [Robust Beamforming for Downlink Multi-Cell Systems: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2401.11409/) (2024)
- [Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence](https://mlsanity.com/paper/2405.02372/) (2024)
- [Breathfinding: A Wireless Network that Monitors and Locates Breathing in a Home](https://mlsanity.com/paper/1302.3820/) (2013)

## Abstract (arXiv)

Wireless localization and sensing technologies are essential in modern wireless networks, supporting applications in smart cities, the Internet of Things (IoT), and autonomous systems. High-performance localization and sensing systems are critical for both network efficiency and emerging intelligent applications. Integrating channel state information (CSI) with deep learning has recently emerged as a promising solution. Recent works have leveraged the spatial diversity of multiple input multiple output (MIMO) systems and the frequency granularity of orthogonal frequency division multiplexing (OFDM) waveforms to improve spatial resolution. Nevertheless, the joint modeling of localization and sensing under the high-dimensional CSI characteristics of MIMO-OFDM systems remains insufficiently investigated. This work aims to jointly model and optimize localization and sensing tasks to harness their potential synergy. We first formulate localization and sensing as a mixed-integer bilevel deep learning problem and then propose a novel stochastic proximal gradient-based mixed-integer bilevel optimization (SPG-MIBO) algorithm. SPG-MIBO is well-suited for high-dimensional and large-scale datasets, leveraging mini-batch training at each step for computational and memory efficiency. The algorithm is also supported by theoretical convergence guarantees. Extensive experiments on multiple datasets validate its effectiveness and highlight the performance gains from joint localization and sensing optimization.