1,720,971 research outputs found
Molecular communication data augmentation and deep learning based detection
No full textThis manuscript presents a novel model for generating synthetic data for a biological molecular communication (MC) system to train a Neural Network (NN) for the purpose of discriminating transmitted bits. To achieve this, a deep learning algorithm was trained using the synthetic data and tested against experimentally measured data. The polynomial curve fitting coefficients are chosen as features. The featurization stage is followed by a NN that captures different aspects of the temporal correlation of the received signals. The real data was collected from an MC testbed that employed transfected Escherichia coli (E. coli) bacteria expressing the light-driven proton pump gloeorhodopsin from Gloeobacter violaceus. By stimulating the bacteria with externally controlled light, protons were secreted, which changed the pH level of the environment. A pH detector was then used to measure the pH of the environment. We propose the use of a deep convolutional neural network to detect the transmitted bits. This paper discusses the data augmentation, processing, and NNs that are pertinent to practical MC problems. The trained algorithm demonstrated an accuracy of over 99.9% in detecting transmitted bits from received signals at a bit rate of 1 bit/min, without requiring any specific knowledge of the underlying channel
Smart Skins in SREs: Improving Communication and Localization Performance
Get PDFThe definition of a Smart Radio Environment (SRE) and its use in improving the performance of a communication system, as well as designing a passive localization system at mm-wave using multiple Smart Electromagnetic Skins (SESs), is investigated here. This dual goal is achieved by properly locating and designing the SESs in such a way that each of them covers a different angular sector and their interaction is minimized. Preliminary numerical results confirms the feasibility of the proposed SRE
Hybrid deep learning-based feature-augmented detection for molecular communication systems
No full textThis manuscript presents a hybrid Deep learning algorithm to detect transmitted symbols from experimental measured data in a biological molecular communication (MC) testbed. The algorithm consists of two deep neural networks capturing different aspects of the time signal, which are combined based on their relative confidence. The MC testbed uses a transfected Escherichia coli (E. coli) bacteria that express the light-driven proton pump gloeorhodopsin from Gloeobacter violaceus. Given an external controllable light stimulus, driven from a light-emitting diode (Transmitter), the bacteria secrete protons that change the pH level of the environment. A pH detector (Receiver) measures the pH of the environment. Modelling such a biological real system accurately is not feasible. Thus, in order to detect the transmitted bits we use both a convolutional and a recurrent neural network in tandem. This paper discusses the data augmentation, processing, and neural networks pertinent to a practical MC problem. The trained algorithm detects the transmitted bits with an accuracy above 99.9%
ProSe Direct Discovery: Experimental Characterization and Context-Aware Heuristic Approach to Extend Public Safety Networks Lifetime
Get PDFDevice-to-device communication, as provided by the third generation partnership project standardization, can play a vital role in designing a reliable pervasive public safety network, which allows the user equipment (UEs) to communicate directly with each other in emergency situations. In this paper, we analyze the performance of direct discovery, one of the features introduced by proximity services. This is examined in heterogeneous environments using the OpenAirInterface open-source software and USRP hardware platform. The experimental results highlight the suitable values for different gains and frequencies of the UEs for performing reliable baseline direct discovery in out-of-coverage scenarios. We evaluate the performance of direct discovery in terms of reliability and maximum range in outdoor and indoor scenarios. Furthermore, we propose a context-aware energy-efficient heuristic algorithm for direct discovery with the aim of extending the network lifetime in emergency scenarios. This heuristic yields significant improvements in UE lifetime (20-52%) and reduces redundant transmissions of discovery messages compared to the baseline approach
A Configurable Radio Jamming Prototype for Physical Layer Attacks against Malicious Unmanned Aerial Vehicles
Get PDFThe goal of this paper is to design and prototype a radio jamming system that is able to interfere the communication drone-remote control, in particular, disabling the motion control system. The drone adopted in the experimental session is the AEE Toruk AP10 Pro, characterized by a digital wireless control system centered at 868MHz. We have created a configurable jamming prototype for limit as much as possible the interference with other radio systems and study the effect of the signal band on the motion control system. We will present our system with both simulation and experimental validation
Experimental Comparison of UAV-Based RSSI and AoA Localization
No full textLocalization of ground transmitters using unmanned aerial vehicles (UAVs) is a research field teeming with activity. While most experimental UAV-based localization systems in the literature use measures of signal strength, we take a novel approach, proposing an angle of arrival (AoA)-based system. We present a prototype that uses a 2 × 2 antenna array connected to a set of four coherent receivers based on software-defined radios. Furthermore, we provide a comparison of a classical RSSI-based ranging approach to a pure AoA-based approach using the 2-D distance ranging error as a common metric
Rooftop Relay Nodes to Enhance URLLC in UAV-Assisted Cellular Networks
No full textRecently, communication in cellular networks assisted by Unmanned Aerial Vehicles (UAVs) has attracted considerable attention, as it provides wireless connectivity to devices in areas with poor coverage. With a single UAV deployed, it is difficult to achieve Line-of-Sight (LoS) probability and network availability targets for critical Ultra-Reliable Low-Latency Communication (URLLC) applications while meeting cost and system complexity requirements. To harness the advantages of UAVs in these situations, an alternative solution is to deploy a multi-UAV system, exploiting inter-connectivity to maintain uninterrupted communication with a ground transmitter. The idea is to deploy a fixed UAV on the side of a building rooftop, which acts as a relay between the ground transmitter and the flying UAV base station, thus increasing the LoS probability. Notably, a two-hop amplify-and-forward relay can provide significant improvements in the channel capacity, channel gain, and thus overall quality of service. In our study, simulations were carried out in four general environments as specified by ITU-R, namely Suburban, Urban, Dense Urban, and High Rise Urban, based on data collected in Los Angeles, USA. Numerical results demonstrate that two-hop communication via a relay UAV increases LoS probability in all environments, thus improving system reliability and feasibility
Real-time Beamforming Testbed and Tracking Relay for mmWave Applications
No full textAs the deployment of fifth generation (5G) mobile wireless networks continues to gain momentum, researchers are already focusing on the challenges and opportunities of the next sixth generation (6G). To meet the ever-increasing demand for higher data rates and support the development of new services, 6G is expected to exploit millimeter wave (mmWave) frequencies. However, the complex propagation characteristics at mmWave require beamforming technology, which introduces significant complexity in the communication system. Herein, we propose a real-time testbed platform to evaluate beamforming and other communication solutions designed for multiple-input multiple-output (MIMO) mmWave-based 6G networks. This platform serves as an enabler for 6G technologies evaluation under realistic propagation conditions, accelerating the development and deployment of robust and efficient 6G networks. To demonstrate the capabilities of our platform, we have implemented a smart relay with real-time beam control and tracking. The platform is able to perform an exhaustive search of 64 reception beams in less than 256 μs. Additionally, the platform can maintain the optimal beam even in mobility scenarios using a gradient-based tracking system that achieves a low overhead of less than 5%, with an update rate of 100 Hz
AI-Empowered UAV Trajectory Optimization in 6G Aerial Networks
No full textRecently, Unmanned Aerial Vehicles (UAVs) have been deployed in various logistics and surveillance applications. Sixth-Generation (6G) cellular networks can further enhance communications to provide ubiquitous coverage, low-latency control, and seamless connectivity among the UAVs. However, achieving constant and end-to-end 3D coverage for user devices is demanding. UAV s have limited battery capacity; thus, energy consumption should be efficiently managed. Optimizing the UAV trajectories improves network performance by diminishing Base Station (BS) load or covering areas with limited radio access. Hence, we propose a Swarm Clustering and Double-Deep-Q-Network (SC-DDQN) framework for efficient communication in aerial networks. The framework constitutes a novel SC- Particle Swarm Optimization (SC-PSO) to improve intra-UAV communication and an Intelligent Trajectory Optimization (ITO) sub-component to optimize Air-to-Ground (A2G) trajectories. The results show that the proposed SC-DDQN framework achieves 40 % faster clustering and a 1.2 % failure probability of reaching a destination compared to the conventional systems, thus providing optimal clustering and trajectory for UAV communications
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