PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication

2025-03-27

AI Summary

This paper addresses the challenging problem of high-dimensional, single-snapshot, super-resolution parameter estimation—jointly resolving direction, range, and velocity—in integrated sensing and communication (ISAC) systems. To this end, we propose PLAIN, a scalable tensor-based architecture that achieves low-complexity, multi-dimensional joint sensing via a three-stage design: (1) tensor compression for dimensionality reduction, (2) decoupled multi-dimensional parameter estimation, and (3) input-driven adaptive fusion to preserve parameter pairing. PLAIN introduces the first decoupled estimation paradigm, enabling flexible dimensional scalability. Under single-snapshot constraints, it achieves millimeter-level range resolution and 0.1°-level angular resolution without sacrificing resolution due to compression; estimation is highly parallelized, and fusion guarantees correct parameter association. Experiments demonstrate that PLAIN significantly outperforms conventional sequential and joint estimation methods, approaches the Cramér–Rao bound, and reduces computational complexity by an order of magnitude.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Matrix & Tensor Methods
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Large-scale security measurements

Abstract

Integrated sensing and communication (ISAC) is envisioned be to one of the paradigms upon which next-generation mobile networks will be built, extending localization and tracking capabilities, as well as giving birth to environment-aware wireless access. A key aspect of sensing integration is parameter estimation, which involves extracting information about the surrounding environment, such as the direction, distance, and velocity of various objects within. This is typically of a high-dimensional nature, which leads to significant computational complexity, if performed jointly across multiple sensing dimensions, such as space, frequency, and time. Additionally, due to the incorporation of sensing on top of the data transmission, the time window available for sensing is likely to be short, resulting in an estimation problem where only a single snapshot is accessible. In this work, we propose PLAIN, a tensor-based estimation architecture that flexibly scales with multiple sensing dimensions and can handle high dimensionality, limited measurement time, and super-resolution requirements. It consists of three stages: a compression stage, where the high dimensional input is converted into lower dimensionality, without sacrificing resolution; a decoupled estimation stage, where the parameters across the different dimensions are estimated in parallel with low complexity; an input-based fusion stage, where the decoupled parameters are fused together to form a paired multidimensional estimate. We investigate the performance of the architecture for different configurations and compare it against practical sequential and joint estimation baselines, as well as theoretical bounds. Our results show that PLAIN, using tools from tensor algebra, subspace-based processing, and compressed sensing, can scale flexibly with dimensionality, while operating with low complexity and maintaining super-resolution.

Problem

• High-dimensional parameter estimation in ISAC systems
• Limited sensing time with single snapshot availability
• Scalable architecture for super-resolution and low complexity

Innovation

• Tensor-based scalable estimation architecture
• Compression without resolution sacrifice
• Decoupled parallel low-complexity estimation