Machine Intelligence on Wireless Edge Networks

• URL: http://arxiv.org/abs/2506.12210v2
• Date: Thu, 04 Sep 2025 05:14:04 GMT
• Title: Machine Intelligence on Wireless Edge Networks
• Authors: Sri Krishna Vadlamani, Kfir Sulimany, Zhihui Gao, Tingjun Chen, Dirk Englund,
• Abstract summary: Machine intelligence on edge devices enables low-latency processing and improved privacy.<n>Current systems frequently avoid local model storage by sending queries to a server.<n>We present the opposite approach: broadcasting model weights to clients that perform inference locally.
• Score: 4.593295337659598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine intelligence on edge devices enables low-latency processing and improved privacy, but is often limited by the energy and delay of moving and converting data. Current systems frequently avoid local model storage by sending queries to a server, incurring uplink cost, network latency, and privacy risk. We present the opposite approach: broadcasting model weights to clients that perform inference locally using in-physics computation inside the radio receive chain. A base station transmits weights as radio frequency (RF) waveforms; the client encodes activations onto the waveform and computes the result using existing mixer and filter stages, RF components already present in billions of edge devices such as cellphones, eliminating repeated signal conversions and extra hardware. Analysis shows that thermal noise and nonlinearity create an optimal energy window for accurate analog inner products. Hardware-tailored training through a differentiable RF chain preserves accuracy within this regime. Circuit-informed simulations, consistent with a companion experiment, demonstrate reduced memory and conversion overhead while maintaining high accuracy in realistic wireless edge scenarios.

Related papers

• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• Over-the-Air Multi-Sensor Inference with Neural Networks Using Memristor-Based Analog Computing [13.5346836945515]
  This study proposes a multi-sensor wireless inference system with memristor-based analog computing.<n>Given the sensors' limited computational capabilities, the features from the network's front end are transmitted to a central device.<n>We also introduce a trainable over-the-air sensor fusion method based on $L_p$-norm inspired combining function.
  arXiv  Detail & Related papers  (2025-01-17T15:14:58Z)
• A Fully Hardware Implemented Accelerator Design in ReRAM Analog Computing without ADCs [5.6496088684920345]
  ReRAM-based accelerators process neural networks via analog Computing-in-Memory (CiM) for ultra-high energy efficiency.<n>This work explores the hardware implementation of the Sigmoid and SoftMax activation functions of neural networks with crossbarally binarized neurons.<n>We propose a complete ReRAM-based Analog Computing Accelerator (RACA) that accelerates neural network computation by leveraging inferenceally binarized neurons.
  arXiv  Detail & Related papers  (2024-12-27T09:38:19Z)
• PCA-Featured Transformer for Jamming Detection in 5G UAV Networks [0.5999777817331317]
  Unmanned Aerial Vehicles (UAVs) face significant security risks from jamming attacks, which can compromise network functionality.<n>Traditional detection methods often fall short when confronting AI-powered jamming that dynamically modifies its behavior.<n>We introduce a novel U-shaped transformer architecture to refine feature representations for improved wireless security.
  arXiv  Detail & Related papers  (2024-12-19T16:13:04Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis [8.805162150763847]
  This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder. Our approach is grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoencoders.
  arXiv  Detail & Related papers  (2024-11-05T07:01:15Z)
• Deep Learning for Low-Latency, Quantum-Ready RF Sensing [2.478795575797574]
  Recent work has shown the promise of applying deep learning to enhance software processing of radio frequency (RF) signals.<n>In this paper, we describe our implementations of quantum-ready machine learning approaches for RF signal classification.
  arXiv  Detail & Related papers  (2024-04-27T17:22:12Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication [9.571774189070531]
  We introduce a novel open-set detection approach based on the patterns of the hidden state values within a Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) model. Our approach greatly improves the Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets.
  arXiv  Detail & Related papers  (2023-05-16T16:47:02Z)
• Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses [46.000685134136525]
  We show that magnetic tunnel junctions can process RF inputs with multiple frequencies in parallel. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals. These results are a key step for embedded radiofrequency artificial intelligence.
  arXiv  Detail & Related papers  (2022-11-02T14:09:42Z)
• Reliability-Aware Deployment of DNNs on In-Memory Analog Computing Architectures [0.0]
  In-Memory Analog Computing (IMAC) circuits remove the need for signal converters by realizing both MVM and NLV operations in the analog domain. We introduce a practical approach to deploy large matrices in deep neural networks (DNNs) onto multiple smaller IMAC subarrays to alleviate the impacts of noise and parasitics.
  arXiv  Detail & Related papers  (2022-10-02T01:43:35Z)
• Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design [68.86220939532373]
  The finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. The proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm.
  arXiv  Detail & Related papers  (2022-07-19T16:37:24Z)
• A Photonic-Circuits-Inspired Compact Network: Toward Real-Time Wireless Signal Classification at the Edge [3.841495731646297]
  Large size of machine learning models can make them difficult to implement on edge devices for latency-sensitive downstream tasks. In wireless communication systems, ML data processing at a sub-millisecond scale will enable real-time network monitoring. We propose a novel compact deep network that consists of a photonic-hardware-inspired recurrent neural network model.
  arXiv  Detail & Related papers  (2021-06-25T19:55:41Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)
• Noise-robust classification of single-shot electron spin readouts using a deep neural network [2.9123559461323016]
  Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. We present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN) In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN)
  arXiv  Detail & Related papers  (2020-12-20T04:26:40Z)
• Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning [56.94644428312295]
  Wireless connectivity is instrumental in enabling federated learning (FL) Channel randomnessperturbs each worker inversions model update while multiple workers updates incur significant interference on bandwidth. In A-FADMM, all workers upload their model updates to the parameter server using a single channel via analog transmissions. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper.
  arXiv  Detail & Related papers  (2020-07-03T16:31:15Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.