← Back to all news
arXiv Machine Learning By Melih Can Zerin

Mean Spatial Frequency Decoupling for Learning-Based Uplink-to-Downlink Covariance Conversion in FDD Massive MIMO

Read the original on arXiv Machine Learning →

The paper tackles the challenge of converting uplink to downlink channel covariance matrices in FDD massive MIMO systems, where learning‑based methods lose accuracy as the antenna count grows. It identifies that the mean angle of arrival creates a phase ramp whose oscillation rate increases with array size, making fixed‑size datasets sparse. The authors propose a deramping technique that estimates and removes this ramp before learning, reducing estimation error across three different learners and maintaining superiority over model‑based benchmarks at large array sizes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 7

EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.

By Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen
More like this →
arXiv AI
Aug 20

Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition

The paper introduces a hybrid estimator for pilot‑limited MIMO channel estimation that frames the problem as low‑rank tensor completion from sparse pilot data. It compares Canonical Polyadic (CP) and Tucker decompositions, showing CP excels for specular channels while Tucker offers stability under extreme pilot scarcity. A lightweight 3D U‑Net is added to capture residual components, and the combined Tensor–NN approach achieves significant NMSE improvements over conventional methods across various pilot densities and channel models.

By Alexandre Barbosa de Lima
More like this →
arXiv AI
Sep 25

Multi-Agent Orchestration of 3GPP Channel Estimators

The paper evaluates eight existing pilot‑aided channel estimators for 5G‑NR and LTE across various 3GPP channel models and numerologies, showing that no single estimator dominates under all conditions. It introduces a condition‑adaptive multi‑agent orchestrator that selects the best estimator per operating scenario, achieving performance within 1.07 dB of an oracle and improving NMSE by up to 3.6 dB at high SNR. The orchestrator runs agents in parallel, yielding near‑single‑estimator latency while scaling wall‑clock time roughly inversely with the number of workers.

By I. Zakir Ahmed, Hamid Sadjadpour
agents
More like this →
arXiv AI
Aug 25

DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks

The paper introduces DRIFT, a lightweight framework for joint channel estimation and prediction in low Earth orbit non-terrestrial networks, aiming to reduce pilot overhead by using data-driven processing after the initial slot. DRIFT refines data-aided channel estimates and forecasts future channel responses with low computational cost, offering two variants based on convolutional and LSTM layers. Simulations show up to 12% spectral efficiency gain over conventional pilot-based systems, with under 200k multiply-accumulate operations suitable for on-board satellite implementation.

By Bruno De Filippo, Carla Amatetti, Alessandro Vanelli-Coralli
More like this →