Real-time Range-Angle Estimation and Tag Localization for Multi-static Backscatter Systems

Multi-static backscatter networks (BNs) are strong candidates for joint communication and localization in the ambient IoT paradigm for 6G. Enabling real-time localization in large-scale multi-static deployments with thousands of devices require highly efficient algorithms for estimating key parameters such as range and angle of arrival (AoA), and for fusing these parameters into location estimates. We propose two low-complexity algorithms, Joint Range-Angle Clustering (JRAC) and Stage-wise Range-Angle Estimation (SRAE). Both deliver range and angle estimation accuracy comparable to FFT- and subspace-based baselines while significantly reducing the computation. We then introduce two real-time localization algorithms that fuse the estimated ranges and AoAs: a maximum-likelihood (ML) method solved via gradient search and an iterative re-weighted least squares (IRLS) method. Both achieve localization accuracy comparable to ML-based brute force search albeit with far lower complexity. Experiments on a real-world large-scale multi-static testbed with 4 illuminators, 1 multi-antenna receiver, and 100 tags show that JRAC and SRAE reduce runtime by up to 40X and IRLS achieves up to 500X reduction over ML-based brute force search without degrading localization accuracy. The proposed methods achieve 3 m median localization error across all 100 tags in a sub-6GHz band with 40 MHz bandwidth. These results demonstrate that multi-static range-angle estimation and localization algorithms can make real-time, scalable backscatter localization practical for next-generation ambient IoT networks.

Next-Generation Backscatter Networks for Integrated Communications and RF Sensing

This paper provides a comprehensive analysis and theoretical foundation for next-generation backscatter networks that move beyond communication and integrate RF location sensing capabilities. An end-to-end system model for wideband OFDM backscatter systems is derived, including detailed characterization of propagation channels, receiver chain impairments, RF tag operation, and unsynchronized network nodes. The theoretical system model is validated through experimental evaluation using actual hardware, demonstrating the detailed model's accuracy. A practical bistatic ranging method that can operate with unsynchronized nodes is presented, along with the Cram\'er-Rao Lower Bound (CRLB) derived to show the achievable performance limits. Our experimental results demonstrate the system performance for communication, RF sensing, and ranging, while also benchmarking against the derived theoretical limits. This analytical framework and experimental validation establish fundamental understanding of distributed, unsynchronized backscatter systems for future machine-type communication networks that are deployed in massive scale, while remaining energy-efficient.

Ambient IoT: A missing link in 3GPP IoT Devices Landscape

Ambient internet of things (IoT) is the network of devices which harvest energy from ambient sources for powering their communication. After decades of research on operation of these devices, Third Generation Partnership Project (3GPP) has started discussing energy harvesting technology in cellular networks to support massive deployment of IoT devices at low operational cost. This article provides a timely update on 3GPP studies on ambient energy harvesting devices including device types, use cases, key requirements, and related design challenges. Supported by link budget analysis for backscattering energy harvesting devices, which are a key component of this study, we provide insight on system design and show how this technology will require a new system design approach as compared to New Radio (NR) system design in 5G.