AIR-LLM is an edge inference architecture that broadcasts large language model (LLM) weights over radio, allowing edge devices to perform matrix-vector multiplications directly in the RF domain without storing or loading the weights. The system uses MIMO spatial multiplexing and an energy‑efficient precoder‑postcoder pair to reduce airtime and calibrate the wireless channel, enabling a single broadcast to serve unlimited users. Experiments on real urban channel models show that AIR-LLM achieves only a 4.0% perplexity loss on LLaMA‑3.1‑8B while saving energy by up to 157.7× compared to FP16 and reducing airtime by over 100× for 20 users.
By Zhihui Gao, Tingjun Chen, Dirk Englund
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
arXiv:2609.09708v2 Announce Type: replace-cross
Abstract: 6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast band...
By Beier Li, Mai Vu
arXiv:2609.08312v1 Announce Type: cross
Abstract: To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) e...
By Haifeng Wen, Nicol\`o Michelusi, Osvaldo Simeone, Yang Yang, Hong Xing
arXiv:2603.03146v2 Announce Type: replace-cross
Abstract: \emph{Integrated communication and computation} (IC$^2$) has emerged as a new paradigm for enabling efficient edge inference in sixth-generat...
By Jierui Zhang, Jianhao Huang, Kaibin Huang
arXiv:2607. 04224v1 Announce Type: cross Abstract: AI-RAN aims to unify artificial intelligence and radio access network workloads on a shared compute substrate.
By Shilong Zhang, Luping Xiang, Jienan Chen, Kun Yang
The paper introduces Radio‑Frequency Convolutional Neural Networks (RF‑CNNs), which repurpose the frequency mixer in wireless radios to perform convolutional neural network inference directly on edge devices. By mapping multi‑channel convolutions onto frequency tones, the passive mixer can execute the entire operation in a single pass, enabling deep CNNs with up to 26.4 million parameters and nine layers to run on smartphones, wearables, and drones. Experimental results show near full‑precision performance while reducing energy consumption to 0.72 fJ per multiply‑accumulate—two orders of magnitude lower than adding a digital processor.
"whyItMatters":"The approach leverages existing radio hardware to deliver efficient, state‑of‑the‑art AI inference on billions of devices without increasing size, weight, power, or cost."
By Zhihui Gao, Shi-Yuan Ma, Yiran Chen, Dirk Englund, Tingjun Chen
arXiv:2601. 00549v2 Announce Type: replace-cross Abstract: The deployment of large-scale neural networks within the Open Radio Access Network (O-RAN) architecture is pivotal for enabling native edge intelligence.
By Zhiheng Guo, Zhaoyang Liu, Zihan Cen, Chenyuan Feng, Xinghua Sun, Xiang Chen, Tony Q. S. Quek, Xijun Wang
The paper introduces AirCPU, an extraction‑oriented approach to over‑the‑air computation that uses joint source‑channel coding and a multi‑layer nested lattice architecture to create a structured wireless superposition. Unlike traditional embedding‑oriented designs, AirCPU operates directly on continuous device data, avoiding separate quantization and enabling progressive resolution through hierarchically scaled components transmitted over a bounded digital constellation. The authors formalize decoupled resolution, showing that with low decoding error probability, channel noise and constellation limits have negligible distortion, and they extend the method to fading MACs with collective, successive, and direct computation mechanisms to broaden the reliable operating regime.
By Seyed Mohammad Azimi-Abarghouyi
arXiv:2507. 09627v3 Announce Type: replace-cross Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity.
By Muhammad Kamran Saeed, Ashfaq Khokhar, Shakil Ahmed
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
arXiv:2608. 08804v1 Announce Type: cross Abstract: With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks.
By Zhanyu Ju, Wenchi Cheng