The year 2019 witnessed the rollout of 5G standard, which promises to offer significant data rate improvement over 4G. While is still in its infancy, there has been an increased shift research community for communication technologies beyond 5G. recent emergence machine learning approaches enhancing wireless communications and empowering them with much-desired intelligence holds immense potential redefining 6G. evolving systems will be bottlenecked terms latency, throughput, reliability by...

In this paper, we design and evaluate a practical AMC system that can be readily deployed to provide robust performance in various real-time commercial scenarios. Thus, our main goal is develop algorithm with low computational complexity for easy implementation deployment. To end, utilize recently revitalized machine learning based approaches used classification purposes. proposed architecture, first propose statistics serve as features of the signals; next, an artificial neural network...

Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems (CPS) using the vast amount data available by Internet Things (IoT) networks. In this position paper, we elucidate unique characteristics and capabilities DT framework that enables realization such promises online learning environment, monitoring assets, Monte Carlo heuristic search predictive prevention, on-policy, off-policy reinforcement in real-time. We establish...

A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional (Mbed-ATN) suitable for fingerprinting actual Its generalization capability analyzed in different settings the effect of sample length anti-aliasing decimation demonstrated. The embedding module serves as a dimensionality reduction unit that maps high dimensional 3D input tensor 1D feature vector further processing by ATN module....

The design and implementation of a novel distributed deadline-based routing spectrum allocation algorithm for tactical ad-hoc networks is reported in this article. Different traffic classes including text, voice, surveillance video, threat alert among others need to be handled by these networks. Each have different quality service (QoS) based deadline requirements. Additionally, are characterized dynamic channel conditions that vary with time location. Even under conditions, it critical...

In this paper, we consider the problem of automatic modulation classification (AMC) with multiple sensors. A distributed hybrid maximum likelihood (HML) based algorithm in presence unknown time offset, phase offset and channel gain is presented. The proposed that employs generalized expectation maximization (GEM) robust to initialization parameters, computationally efficient require much less communication overhead compared performing GEM a centralized setting. Simulation experimental...

Automatic modulation classification (AMC) is a key component of intelligent communication systems used in various military and cognitive radio applications. In AMC, it desired to increase the number different formats that can be classified, reduce computational complexity classification, improve robustness accuracy classifier. Generally, AMC techniques are classified into feature based (FB) likelihood (LB) classifiers. this paper, we propose novel hierarchical hybrid automatic classifier...

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless have only focused on single task which many cases insufficient information system to act on. In this work, first time communication domain, we exploit potential deep neural...

This paper provides hardware implementation considerations for previously developed algorithms designed to improve the classification of modulation weak radio signals utilizing multiple sensors. The case study presented focuses on a likelihood-based approach in centralized data fusion framework. Data sets from sensors are fused obtain more accurate as demonstrated simulations. implemented test bed that consists laboratory grade software defined platforms. performance is examined realistic...

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless have only focused on single task which many cases insufficient information system to act on. In this work, first time communication domain, we exploit potential deep neural...

A novel cross-domain attentional multi-task architecture - xDom for robust real-world wireless radio frequency (RF) fingerprinting is presented in this work. To the best of our knowledge, first time such comprehensive attention mechanism applied to solve RF problem. In paper, we resort IoT WiFi and Bluetooth (BT) emissions (instead synthetic waveform generation) a rich multipath unavoidable interference environment an indoor experimental testbed. We show impact time-frame capture by...

The objective of this work is to design and implement a novel framework for automatic signal classification techniques (FACT) software defined radios (SDR) capable classifying multiple signals simultaneously. focus create modular facilitate the testing implementation new methods. divided into three parts: (i) Sensor resource manager (SRM), which performs initial detection, preprocessing delegation secondary receivers corresponding interest (SOIs); (ii) Modulation classifier block (MCB),...

We propose a novel optimal time slot allocation scheme for clustered underwater acoustic sensor networks that leverages physical (PHY) layer information to minimize the energy consumption due unnecessary retransmissions thereby improving network lifetime and throughput. To reduce overhead computational complexity, we employ two-phase approach where: (i) each member node takes selfish decision on number of slots it needs during next intra-cluster cycle by solving Markov process (MDP), (ii)...

In this paper, we propose a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks. The proposed will enable nodes to adapt various deadline requirements unique each traffic classes. A network needs handle variety of data flowing through the including voice, surveillance video, threat alert among others. Each these classes may have different quality service (QoS) based requirements. It is critical receive packets before expires make crucial...