Search arXiv⌕ Search

arXiv · 2610.08056

Noncoherent MIMO in the FR3 Upper Mid-Band Using Grassmannian Signaling: Prototype and Experimental Evaluation

Abstract

This work presents a prototype implementation of a noncoherent multiple-input multiple-output (MIMO) system based on Grassmannian signalling, where neither the transmitter nor receiver requires channel state information (CSI), operating at a carrier frequency of 15 GHz in the frequency range 3 (FR3) upper mid-band envisioned for 6G. To the best of our knowledge, this is the first prototype implementation of Grassmannian signaling reported in the literature beyond sub-6 GHz bands. We identify the motivation of applying Grassmannian signaling, a form of unitary space-time modulation (USTM), in this band for wireless and mobile applications with low spectral-efficiency requirements but strict reliability and power-efficiency demands. With this idea in mind, we present a system design integrated with orthogonal frequency-division multiplexing (OFDM) that addresses the challenges associated with the higher carrier frequencies of FR3 while providing reliable operation across a broad range of scenarios, including urban and rural environments as well as vehicular communications. We then present the prototype implementation of the proposed design, based on software-defined radio (SDR) devices integrated with radio-frequency (RF) mixers, filters, and power amplifiers to ensure adequate operation in the targeted band. Over-the-air (OTA) experiments based on symbol error rate (SER) measurements under non-line-of-sight (NLoS) and line-of-sight (LoS) scenarios, and across different data rates, show good agreement with software simulations, thereby validating the prototype implementation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Álvaro Pendás-Recondo, Enrique Pendás-Recondo, Álvaro F. Vaquero, Marcos R. Pino, Jesús Alberto López-Fernández. 2026-10-06. Noncoherent MIMO in the FR3 Upper Mid-Band Using Grassmannian Signaling: Prototype and Experimental Evaluation. https://arxiv.org/abs/2610.08056

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Semantic Sensing: Toward a Task-Oriented Paradigm

Sensing and communication are fundamental enablers of next-generation networks. While communication technologies have advanced significantly, the potential of sensing beyond conventional parameter estimation remains far from fully explored. To address these limitations, we propose semantic sensing (SemS), a framework that extends task-oriented sensing through explicit modeling and recovery of task-relevant environmental information. Specifically, we mathematically formulate how transmit waveforms and receiver processing jointly support the recovery of task-relevant semantics, thereby establishing SemS as a semantics-oriented transceiver design. Within this architecture, we formulate an information-theoretic objective that guides the sensing pipeline to maximize task-relevant information extraction under resource constraints. To solve this optimization problem, we develop a deep learning (DL)-based framework that jointly designs transmit waveform parameters and receiver representations. The framework is implemented in an orthogonal frequency division multiplexing (OFDM) system, featuring a semantic encoder that employs differentiable pilot selection to select informative time--frequency resources. At the receiver, we design a shared semantic decoder that estimates three semantic attributes, namely object category, proximity, and radial motion, to support target classification and approach warning through predefined task mappings. We also consider continuous target range estimation as a special case in which the semantic state and task output coincide. Numerical results demonstrate that the proposed semantic pilot design achieves superior classification accuracy and approach-warning Macro-F1 compared to reconstruction-based baselines, particularly under constrained resource budgets.

eess.SP↗

PyDPF: A Python Package for Differentiable Particle Filtering

State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common to employ particle filtering (PF), an efficient Monte Carlo method for estimating the hidden state corresponding to a sequence of observations. Applying particle filtering requires specifying both the parametric form and the parameters of the system, which are often unknown and must be estimated. Gradient-based optimisation techniques cannot be applied directly to standard particle filters, as the filters themselves are not differentiable. However, several recently proposed methods modify the resampling step to make particle filtering differentiable. In this paper, we present an implementation of several such differentiable particle filters (DPFs) with a unified API built on the popular PyTorch framework. Our implementation makes these algorithms easily accessible to a broader research community and facilitates straightforward comparison between them. We validate our framework by reproducing experiments from several existing studies and demonstrate how DPFs can be applied to address several common challenges with state space modelling.

eess.SP↗

Rigid Body Localization via Gaussian Belief Propagation with Quadratic Angle Approximation

Gaussian belief propagation (GaBP) is a technique that relies on linearized error and input output models to yield low-complexity solutions to complex estimation problems, which has been recently shown to be effective in the design of range-based GaBP schemes for stationary and moving rigid body localization (RBL) in three-dimensional (3D) space, as long as the relative rotation between the prior position and the target rigid body is sufficiently small. In this article we present a novel range-based RBL scheme via GaBP that relaxes the latter limitation significantly. To this end, the proposed method incorporates a quadratic angle approximation to linearize the relative orientation between the prior and the target rigid body, enabling high precision estimates of corresponding rotation angles even for large deviations. Leveraging the resulting linearized model, we derive the corresponding message-passing (MP) rules to obtain estimates of the translation vector and rotation matrix of the target rigid body, relative to a prior reference frame. Numerical results corroborate the good performance of the proposed angle approximation itself, as well as the consequent RBL performance in terms of root mean square errors (RMSEs) in comparison to the state-of-the-art (SotA), while maintaining a low computational complexity.

eess.SP↗