Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

• URL: http://arxiv.org/abs/2101.10254v2
• Date: Sat, 20 Feb 2021 21:14:59 GMT
• Title: Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification
• Authors: Anu Jagannath, Jithin Jagannath
• Abstract summary: We exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. We release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.
• Score: 1.827510863075184
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.

Related papers

• Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission [68.69108124451263]
  Federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices. Each link is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator. Channel state information-based multiple-input multiple-output transmission module designed to combat channel fading and noise.
  arXiv  Detail & Related papers  (2023-08-07T16:32:14Z)
• Multi-task Learning for Radar Signal Characterisation [48.265859815346985]
  This paper presents an approach for tackling radar signal classification and characterisation as a multi-task learning (MTL) problem. We propose the IQ Signal Transformer (IQST) among several reference architectures that allow for simultaneous optimisation of multiple regression and classification tasks. We demonstrate the performance of our proposed MTL model on a synthetic radar dataset, while also providing a first-of-its-kind benchmark for radar signal characterisation.
  arXiv  Detail & Related papers  (2023-06-19T12:01:28Z)
• Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects [78.84264189471936]
  NextG communication systems are beginning to explore shifting this design paradigm to reliably executing a given task such as in task-oriented communications. Wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label. Task-oriented communications is considered by jointly training the transmitter, receiver and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB.
  arXiv  Detail & Related papers  (2022-12-19T17:54:36Z)
• Enabling the Wireless Metaverse via Semantic Multiverse Communication [82.47169682083806]
  Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems. We propose a novel semantic communication framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs) An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI)
  arXiv  Detail & Related papers  (2022-12-13T21:21:07Z)
• Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals [98.95383921866096]
  We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. We show that capturing high-resolution temporal structures (nonstationarities) leads to substantial performance gains. We propose a domain-informed neural network (NN) design that is able to improve upon both "off-the-shelf" NNs and classical detection and interference rejection methods.
  arXiv  Detail & Related papers  (2022-09-11T14:10:37Z)
• Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning [5.624291722263331]
  Self-supervised learning enables the learning of useful representations from Radio Frequency (RF) signals themselves. We show that the sample efficiency (the number of labeled samples required to achieve a certain accuracy performance) of AMR can be significantly increased by learning signal representations with self-supervised learning.
  arXiv  Detail & Related papers  (2022-07-07T02:07:03Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting [0.0]
  We present complex-valued Convolutional Neural Networks (CNNs) for RF fingerprinting. We focus on the problem of fingerprinting wireless IoT devices in-the-wild using Deep Learning (DL) techniques.
  arXiv  Detail & Related papers  (2021-05-08T03:03:21Z)
• Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks [0.0]
  Beamforming is an essential technology in the 5G massive multiple-input-multiple-output (MMIMO) communications. The inter-cell interference (ICI) is one of the main impairments faced by 5G communications due to frequency-reuse technologies. We propose a reinforcement learning (RL) assisted full dynamic beamforming for ICI mitigation in 5G downlink.
  arXiv  Detail & Related papers  (2021-01-27T07:18:07Z)
• 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.