Using sequence to sequence learning for digital BPSK and QPSK demodulation

In the last few years Machine Learning (ML) has seen explosive growth in a wide range of research fields and industries. With the advancements in Software Defined Radio (SDR), which allows more intelligent, adaptive radio systems to be built, the wireless communications field has a number of opportunities to apply ML techniques. In this paper, a novel approach to demodulation using a Sequence to Sequence (Seq2Seq) model is proposed. This type of model is shown to work effectively with PSK data and also has a number of useful properties that are not present in other machine learning algorithms. A basic Seq2Seq implementation for BPSK and QPSK demodulation is presented in this paper, and learned properties such as Automatic Modulation Classification (AMC), and ability to adapt to different length input sequences, are demonstrated. This is an exciting new avenue of research that provides considerable potential for application in next generation 5G networks

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

Telecommunication Education Environment and its Optimal Usage

Students in introductory communication theory classes can benefit from a well-planned laboratory component. Such that TIMS lap equipments which can handle a wide variety of experiments ranging from analog baseband to pass-band digital communications. These papers describe three advanced laboratory tasks with their optimized manuals designed by Al Salman Ahmed at Brno University of technology. Two are based on the simulation software TutorTIMS to implement Eye patterns, Signal Constellations, and the third is based on the Biskit hardware to implement Quadrature phase shift keying (QPSK).Students in introductory communication theory classes can benefit from a well-planned laboratory component. Such that TIMS lap equipments which can handle a wide variety of experiments ranging from analog baseband to pass-band digital communications. These papers describe three advanced laboratory tasks with their optimized manuals designed by Al Salman Ahmed at Brno University of technology. Two are based on the simulation software TutorTIMS to implement Eye patterns, Signal Constellations, and the third is based on the Biskit hardware to implement Quadrature phase shift keying (QPSK).

Artificial Intelligence Aided Receiver Design for Wireless Communication Systems

Physical layer (PHY) design in the wireless communication field realizes gratifying achievements in the past few decades, especially in the emerging cellular communication systems starting from the first generation to the fifth generation (5G). With the gradual increase in technical requirements of large data processing and end-to-end system optimization, introducing artificial intelligence (AI) in PHY design has cautiously become a trend. A deep neural network (DNN), one of the population techniques of AI, enables the utilization of its ‘learnable’ feature to handle big data and establish a global system model. In this thesis, we exploited this characteristic of DNN as powerful assistance to implement two receiver designs in two different use-cases. We considered a DNN-based joint baseband demodulator and channel decoder (DeModCoder), and a DNN-based joint equalizer, baseband demodulator, and channel decoder (DeTecModCoder) in two single operational blocks, respectively. The multi-label classification (MLC) scheme was equipped to the output of conducted DNN model and hence yielded lower computational complexity than the multiple output classification (MOC) manner. The functional DNN model can be trained offline over a wide range of SNR values under different types of noises, channel fading, etc., and deployed in the real-time application; therefore, the demands of estimation of noise variance and statistical information of underlying noise can be avoided. The simulation performances indicated that compared to the corresponding conventional receiver signal processing schemes, the proposed AI-aided receiver designs have achieved the same bit error rate (BER) with around 3 dB lower SNR

SORTING OF RADIO SIGNALS USING ADVERSARIAL MACHINE LEARNING

Signals Intelligence depends on signal classification accuracy. Artificial intelligence is a tool that allows for the fast and accurate identification of communications signals. Neural networks utilize a set of training data to learn patterns in datasets for recognition and classification. This learning is pivotal to the performance of the neural network and is dependent on the accuracy of the training data used to train. In this thesis, a strong and realistic communications training dataset is developed using MATLAB. It incorporates realistic and real-world factors that more accurately represent a radio frequency (RF) communication signal, then tests the neural network against the newly developed signals to prove the accuracy of the technology. The dataset is also varied in modulation type to fully represent the spectrum of signals to be analyzed by the neural networks.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Overcoming CubeSat downlink limits with VITAMIN: a new variable coded modulation protocol

Thesis (M.S.) University of Alaska Fairbanks, 2013Many space missions, including low earth orbit CubeSats, communicate in a highly dynamic environment because of variations in geometry, weather, and interference. At the same time, most missions communicate using fixed channel codes, modulations, and symbol rates, resulting in a constant data rate that does not adapt to the dynamic conditions. When conditions are good, the fixed date rate can be far below the theoretical maximum, called the Shannon limit; when conditions are bad, the fixed data rate may not work at all. To move beyond these fixed communications and achieve higher total data volume from emerging high-tech instruments, this thesis investigates the use of error correcting codes and different modulations. Variable coded modulation (VCM) takes advantage of the dynamic link by transmitting more information when the signal-to-noise ratio (SNR) is high. Likewise, VCM can throttle down the information rate when SNR is low without having to stop all communications. VCM outperforms fixed communications which can only operate at a fixed information rate as long as a certain signal threshold is met. This thesis presents a new VCM protocol and tests its performance in both software and hardware simulations. The protocol is geared towards CubeSat downlinks as complexity is focused in the receiver, while the transmission operations are kept simple. This thesis explores bin-packing as a way to optimize the selection of VCM modes based on expected SNR levels over time. Working end-to-end simulations were created using MATLAB and LabVIEW, while the hardware simulations were done with software defined radios. Results show that a CubeSat using VCM communications will deliver twice the data throughput of a fixed communications system