Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction

Automatic Modulation Recognition (AMR) plays a crucial role in wireless communication systems. Deep learning AMR strategies have achieved tremendous success in recent years. Modulated signals exhibit long temporal dependencies, and extracting global features is crucial in identifying modulation schemes. Traditionally, human experts analyze patterns in constellation diagrams to classify modulation schemes. Classical convolutional-based networks, due to their limited receptive fields, excel at extracting local features but struggle to capture global relationships. To address this limitation, we introduce a novel hybrid deep framework named TLDNN, which incorporates the architectures of the transformer and long short-term memory (LSTM). We utilize the self-attention mechanism of the transformer to model the global correlations in signal sequences while employing LSTM to enhance the capture of temporal dependencies. To mitigate the impact like RF fingerprint features and channel characteristics on model generalization, we propose data augmentation strategies known as segment substitution (SS) to enhance the model's robustness to modulation-related features. Experimental results on widely-used datasets demonstrate that our method achieves state-of-the-art performance and exhibits significant advantages in terms of complexity. Our proposed framework serves as a foundational backbone that can be extended to different datasets. We have verified the effectiveness of our augmentation approach in enhancing the generalization of the models, particularly in few-shot scenarios. Code is available at \url{https://github.com/AMR-Master/TLDNN}.Comment: submitted to IEEE Transactions on Vehicular Technology, 14 pages, 11 figure

Deep Learning Designs for Physical Layer Communications

Wireless communication systems and their underlying technologies have undergone unprecedented advances over the last two decades to assuage the ever-increasing demands for various applications and emerging technologies. However, the traditional signal processing schemes and algorithms for wireless communications cannot handle the upsurging complexity associated with fifth-generation (5G) and beyond communication systems due to network expansion, new emerging technologies, high data rate, and the ever-increasing demands for low latency. This thesis extends the traditional downlink transmission schemes to deep learning-based precoding and detection techniques that are hardware-efficient and of lower complexity than the current state-of-the-art. The thesis focuses on: precoding/beamforming in massive multiple-inputs-multiple-outputs (MIMO), signal detection and lightweight neural network (NN) architectures for precoder and decoder designs. We introduce a learning-based precoder design via constructive interference (CI) that performs the precoding on a symbol-by-symbol basis. Instead of conventionally training a NN without considering the specifics of the optimisation objective, we unfold a power minimisation symbol level precoding (SLP) formulation based on the interior-point-method (IPM) proximal ‘log’ barrier function. Furthermore, we propose a concept of NN compression, where the weights are quantised to lower numerical precision formats based on binary and ternary quantisations. We further introduce a stochastic quantisation technique, where parts of the NN weight matrix are quantised while the remaining is not. Finally, we propose a systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. Furthermore, we investigate performance complexity tradeoffs via regularisation constraints on the layer weights such that, at inference, parts of network layers can be removed with minimal impact on the detection accuracy. Simulation results show that our proposed learning-based techniques offer better complexity-vs-BER (bit-error-rate) and complexity-vs-transmit power performances compared to the state-of-the-art MIMO detection and precoding techniques

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

Information Processing Using Circulant Matrices

Circulant matrices may be used to process certain kinds of signals in computer science applications. Specifically, they can be used as signal transforms. In this thesis several new applications of circulant matrices are described. New results have been obtained in number theoretic Hilbert transform (NHT), which is a generalization of discrete Hilbert transform (DHT).The NHT matrix generates ideal orthogonal sequences named as random residue sequences, since the NHT matrix with its transpose computes all correlation in the block. Random residue sequences can be used as carriers for wireless communications. We also investigate applications of circulant matrices to store and reproduce patterns as neural memories.Computer Scienc

A Memory-Efficient Learning Framework for Symbol Level Precoding with Quantized NN Weights

This paper proposes a memory-efficient deep neural network (DNN) framework-based symbol level precoding (SLP). We focus on a DNN with realistic finite precision weights and adopt an unsupervised deep learning (DL) based SLP model (SLP-DNet). We apply a stochastic quantization (SQ) technique to obtain its corresponding quantized version called SLP-SQDNet. The proposed scheme offers a scalable performance vs memory trade-off, by quantizing a scalable percentage of the DNN weights, and we explore binary and ternary quantizations. Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield ~3.46× and ~2.64× model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer ~20× and ~10× computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively