The increasing demand for gigabit-per-second speeds and higher wireless node density is driving the need for spatial reuse and the utilization of higher frequencies above the legacy sub-6 GHz bands. Since these super-...
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Traditionally, conical ridge horn antennas are used for feeding large reflectors, but they can cause grating lobes in arrays. This paper introduces a compact Vivaldi antenna for monopulse radar, featuring a planar fee...
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Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)***,several security vulnerabilities and att...
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Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)***,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G *** this regard,UAVs have appeared as a winning candidate to mitigate security *** this paper,we leverage UAVs to propose two *** first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver ***,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,*** secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF ***,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation ***,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.
In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and multiple users. In particular, we propose a novel de...
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Optical wireless communications in space are degraded by atmospheric turbulence, light attenuation, and detector noise. In this paper, we develop a neural network (NN) channel estimator that is optimized across a wide...
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Optical wireless communications in space are degraded by atmospheric turbulence, light attenuation, and detector noise. In this paper, we develop a neural network (NN) channel estimator that is optimized across a wide range of signal-to-noise ratio levels during the training stage. In addition, we propose a novel autoencoder (AE) model to develop a complete physical layer communication system in space optical communications (SOC). The AE is designed to work with both perfect and imperfect channel state information (CSI), providing a flexible and versatile solution for SOC. Batch normalization and multiple-decoders are incorporated into the proposed AE, which improves receiver learning capabilities by allowing the use of more than one path to update encoder and decoder weights. This novel approach can reduce the error in detection relative to state-of-the-art models. Using the system tool kit simulator, we examine our system’s performance in a downlink SOC channel that connects a geostationary satellite to a ground station in Log-normal fading channel. Furthermore, we evaluate the performance of our system in a downlink channel that establishes a connection between a Low Earth Orbit satellite and a ground station, operating in Gamma-Gamma fading channel. The numerical results show that the proposed channel estimator NN is superior to state-of-the-art learning-based frameworks and achieves the same level of performance as the minimum mean square error estimator. Additionally, with no fading and for both perfect and imperfect CSI with different code rates and fading channels, the proposed AE-based detection outperforms both benchmark learning frameworks and most popular convolutional codes. 2023 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://***/licenses/by/4.0/
This article presents a frequency-domain analysis that models the behavior of Constant-on-Time (COT)-controlled buck converters in wide-input-output-range scenarios. Based on this analysis, a design strategy is propos...
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Wireless signals are vulnerable to various security threats, like eavesdropping and jamming, due to the inherent broadcast nature of the wireless channel. Encryption may ensure the confidentiality of the data but does...
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Wireless signals are vulnerable to various security threats, like eavesdropping and jamming, due to the inherent broadcast nature of the wireless channel. Encryption may ensure the confidentiality of the data but does not guarantee successful communication among legitimate users in the presence of strong adversaries, like wideband jammers. In this scenario, hiding a secret signal in presence of another mundane ongoing communication is one of the ways to minimize its chances of getting intercepted. Wireless Steganography is a process of embedding a secret signal inside another signal that acts as a cover to hide the signal of interest. In this paper, we propose to encode secret bits into covert signals that are statistically indistinguishable from a hardware noise generated by a low-cost transmitter. As the covert signal resembles hardware noise, it can be transmitted over any waveform, making it adaptable and portable to any communication link. Each generated complex signal sample is merged with a cover signal sample, yielding a 50% embedding rate. We create the encoding and decoding models by creating a pair of complex-valued neural networks (NNs), which is trained in presence of another NN model, critic. The critic model differentiates between true hardware noise and encoder-generated covert signal, thus providing essential feedback to the NN pair to improve the encoding technique. The decoder undergoes a transfer learning process to adapt to the residual channel effects in over-the-air experiments. In an indoor testbed, we successfully decoded the covert communication that mimics a range of hardware noises and is transmitted using different modulation orders of cover OFDM waveform. Our steganalysis indicates that the covert signal can be generated to mimic specific hardware, which remains indistinguishable in different statistical tests. Our method performs an order of magnitude better in statistical steganalysis compared to the state-of-the-art method in this fi
Due to the increase in demand for electricity, the lack of fossil fuels, and the use of renewable energy sources, the use of energy storage systems becomes necessary. The use of storage systems in different parts of m...
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This work aims to develop a planar microwave sensor fabricated on a flexible polyimide substrate to monitor the water content of fruits nondestructively. The sensor is based on a planar loop resonator tuned with a con...
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Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long *** such,several Battery Management Systems(BMS)ha...
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Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long *** such,several Battery Management Systems(BMS)have been developed to maintain system reliability and extend the battery’s operative *** estimation of the battery’s State of Charge(SOC)is a key challenge in the BMS due to its non-linear *** paper presents a comprehensive review on the most recent classifications and mathematical models for SOC *** trends for SOC estimation methods are also presented.
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