DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio

In wireless communication, signal demodulation under non-ideal conditions is one of the important research topic. In this paper, a novel non-coherent binary phase shift keying demodulator based on deep neural network, namely DeepDeMod, is proposed. The proposed scheme makes use of neural network to decode the symbols from the received sampled signal. The proposed scheme is developed to demodulate signal under fading channel with additive white Gaussian noise along with hardware imperfections, such as phase and frequency offset. The time varying nature of hardware imperfections and channel poses a additional challenge in signal demodulation. In order to address this issue, additionally we propose transfer learning based DeepDeMod scheme. Pilot symbols along with data is transmitted in a packet which is used to learn the time varying parameters from the pilot reception followed by data demodulation. Results show that compared with the conventional demodulators and other machine learning based demodulators, our proposed DeepDeMod provides significantly better performance in term of bit error rate. We also implement the proposed DeepDeMod on software defined radio and present the experimental results

Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms

In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN based demodulator contains three restricted Boltzmann machines (RBMs) to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k-nearest neighbor (KNN) algorithm. These three demodulators are trained and tested by our online open dataset. Experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance

Deep learning for wireless communications : flexible architectures and multitask learning

Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML.Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML

A Novel Graph Neural Network-based Framework for Automatic Modulation Classification in Mobile Environments

Automatic modulation classification (AMC) refers to a signal processing procedure through which the modulation type and order of an observed signal are identified without any prior information about the communications setup. AMC has been recognized as one of the essential measures in various communications research fields such as intelligent modem design, spectrum sensing and management, and threat detection. The research literature in AMC is limited to accounting only for the noise that affects the received signal, which makes their models applicable for stationary environments. However, a more practical and real-world application of AMC can be found in mobile environments where a higher number of distorting effects is present. Hence, in this dissertation, we have developed a solution in which the distorting effects of mobile environments, e.g., multipath, Doppler shift, frequency, phase and timing offset, do not influence the process of identifying the modulation type and order classification. This solution has two major parts: recording an emulated dataset in mobile environments with real-world parameters (MIMOSigRef-SD), and developing an efficient feature-based AMC classifier. The latter itself includes two modules: feature extraction and classification. The feature extraction module runs upon a dynamic spatio-temporal graph convolutional neural network architecture, which tackles the challenges of statistical pattern recognition of received samples and assignment of constellation points. After organizing the feature space in the classification module, a support vector machine is adopted to be trained and perform classification operation. The designed robust feature extraction modules enable the developed solution to outperform other state-of-the-art AMC platforms in terms of classification accuracy and efficiency, which is an important factor for real-world implementations. We validated the performance of our developed solution in a prototyping and field-testing process in environments similar to MIMOSigRef-SD. Therefore, taking all aspects into consideration, our developed solution is deemed to be more practical and feasible for implementation in the next generations of communication systems. Advisor: Hamid R. Sharif-Kashan

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

L'abstract è presente nell'allegato / the abstract is in the attachmen

Meta-learning applications for machine-type wireless communications

Abstract. Machine Type Communication (MTC) emerged as a key enabling technology for 5G wireless networks and beyond towards the 6G networks. MTC provides two service modes. Massive MTC (mMTC) provides connectivity to a huge number of users. Ultra-Reliable Low Latency Communication (URLLC) achieves stringent reliability and latency requirements to enable industrial and interactive applications. Recently, data-driven learning-based approaches have been proposed to optimize the operation of various MTC applications and allow for obtaining the desired strict performance metrics. In our work, we propose implementing meta-learning alongside other deep-learning models in MTC applications. First, we analyze the model-agnostic meta-learning algorithm (MAML) and its convergence for regression and reinforcement learning (RL) problems. Then, we discuss uncrewed aerial vehicles (UAVs) trajectory planning as a case study in mMTC and RL, illustrating the system model and the main challenges. Hence, we propose the MAML-RL formulation to solve the UAV path learning problem. Moreover, we address the MAML-based few-pilot demodulation problem in massive IoT deployments. Finally, we extend the problem to include the interference cancellation with Non-Orthogonal Multiple Access (NOMA) as a paradigm shift towards non-orthogonal communication thanks to its potential to scale well in massive deployments. We propose a novel, data-driven, meta-learning-aided NOMA uplink model that minimizes the channel estimation overhead and does not require perfect channel knowledge. Unlike conventional deep learning successive interference cancellation (SICNet), Meta-Learning aided SIC (meta-SICNet) can share experiences across different devices, facilitating learning for new incoming devices while reducing training over- head. Our results show the superiority of MAML performance in addressing many problems compared to other deep learning schemes. The simulations also prove that MAML can successfully solve the few-pilot demodulation problem and achieve better performance in terms of symbol error rates (SERs) and convergence latency. Moreover, the analysis confirms that the proposed meta-SICNet outperforms classical SIC and conventional SICNet as it can achieve a lower SER with fewer pilots