# PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication

arXiv: https://arxiv.org/abs/2503.21242 · HTML: https://mlsanity.com/paper/2503.21242/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation](https://mlsanity.com/paper/2401.01738/) (2024)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications](https://mlsanity.com/paper/2405.01816/) (2024)
- [MIMO-OFDM Joint Radar-Communications: Is ICI Friend or Foe?](https://mlsanity.com/paper/2103.15694/) (2021)
- [A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems](https://mlsanity.com/paper/2308.03387/) (2023)
- [Multiple Signal Classification Based Joint Communication and Sensing System](https://mlsanity.com/paper/2211.04064/) (2022)
- [Joint Range-Velocity-Azimuth Estimation for OFDM-Based Integrated Sensing and Communication](https://mlsanity.com/paper/2312.13154/) (2023)
- [DOA Estimation for Transmit Beamspace MIMO Radar via Tensor Decomposition with Vandermonde Factor Matrix](https://mlsanity.com/paper/2101.12666/) (2021)
- [Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation](https://mlsanity.com/paper/2207.05337/) (2022)
- [Integrated Super-Resolution Sensing and Communication with 5G NR Waveform: Signal Processing with Uneven CPs and Experiments](https://mlsanity.com/paper/2305.05142/) (2023)
- [Estimating Channels With Hundreds of Sub-Paths for MU-MIMO Uplink: A Structured High-Rank Tensor Approach](https://mlsanity.com/paper/2409.00723/) (2024)

## Abstract (arXiv)

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.