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

TL;DR

This paper introduces a novel deep learning framework for joint localization and sensing in MIMO-OFDM systems, utilizing mixed-integer bilevel optimization to improve spatial resolution and system efficiency.

Contribution

It formulates the joint localization and sensing as a mixed-integer bilevel deep learning problem and proposes a new SPG-MIBO algorithm with convergence guarantees.

Findings

Effective joint localization and sensing demonstrated on multiple datasets.

Significant performance improvements over existing methods.

Algorithm is scalable to high-dimensional, large-scale data.

Abstract

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.