145 research outputs found
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
Spectrum coexistence is essential for next generation (NextG) systems to
share the spectrum with incumbent (primary) users and meet the growing demand
for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service
(CBRS) band, where the 5G and beyond communication systems need to sense the
spectrum and then access the channel in an opportunistic manner when the
incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity
classifier based on a deep neural network is needed for low misdetection (to
protect incumbent users) and low false alarm (to achieve high throughput for
NextG). In a dynamic wireless environment, the classifier can only be used for
a limited period of time, i.e., coherence time. A portion of this period is
used for learning to collect sensing results and train a classifier, and the
rest is used for transmissions. In spectrum sharing systems, there is a
well-known tradeoff between the sensing time and the transmission time. While
increasing the sensing time can increase the spectrum sensing accuracy, there
is less time left for data transmissions. In this paper, we present a
generative adversarial network (GAN) approach to generate synthetic sensing
results to augment the training data for the deep learning classifier so that
the sensing time can be reduced (and thus the transmission time can be
increased) while keeping high accuracy of the classifier. We consider both
additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this
GAN-based approach can significantly improve both the protection of the
high-priority user and the throughput of the NextG user (more in Rayleigh
channels than AWGN channels)
Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition
Deep neural networks have been widely used in communication signal
recognition and achieved remarkable performance, but this superiority typically
depends on using massive examples for supervised learning, whereas training a
deep neural network on small datasets with few labels generally falls into
overfitting, resulting in degenerated performance. To this end, we develop a
semi-supervised learning (SSL) method that effectively utilizes a large
collection of more readily available unlabeled signal data to improve
generalization. The proposed method relies largely on a novel implementation of
consistency-based regularization, termed Swapped Prediction, which leverages
strong data augmentation to perturb an unlabeled sample and then encourage its
corresponding model prediction to be close to its original, optimized with a
scaled cross-entropy loss with swapped symmetry. Extensive experiments indicate
that our proposed method can achieve a promising result for deep SSL of
communication signal recognition
Ieee access special section editorial: Cloud and big data-based next-generation cognitive radio networks
In cognitive radio networks (CRN), secondary users (SUs) are required to detect the presence of the licensed users, known as primary users (PUs), and to find spectrum holes for opportunistic spectrum access without causing harmful interference to PUs. However, due to complicated data processing, non-real-Time information exchange and limited memory, SUs often suffer from imperfect sensing and unreliable spectrum access. Cloud computing can solve this problem by allowing the data to be stored and processed in a shared environment. Furthermore, the information from a massive number of SUs allows for more comprehensive information exchanges to assist the
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