相关论文: Deep Modulation (Deepmod): A Self-Taught PHY Layer…
A self-stabilizing protocol has the capacity to recover a legitimate behavior whatever is its initial state. The majority of works in self-stabilization assume a shared memory model or a communication using reliable and FIFO channels. In…
This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY,…
Physical adversarial attacks on deep learning systems is concerning due to the ease of deploying such attacks, usually by placing an adversarial patch in a scene to manipulate the outcomes of a deep learning model. Training such patches…
The design of robust wireless communication systems for industrial applications such as closed loop control processes has been considered manifold recently. Additionally, the ongoing advances in the area of connected mobility have similar…
The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance…
The next generation wireless communication networks are required to support high-mobility scenarios, such as reliable data transmission for high-speed railways. Nevertheless, widely utilized multi-carrier modulation, the orthogonal…
This paper provides a methodology to study the PHY layer vulnerability of wireless protocols in hostile radio environments. Our approach is based on testing the vulnerabilities of a system by analyzing the individual subsystems. By…
We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral…
In the age of information explosion, the conventional optical communication protocols are rapidly reaching the limits of their capacity, as almost all available degrees of freedom (e.g., wavelength, polarization) for division multiplexing…
Deep learning based on artificial neural networks is a powerful machine learning method that, in the last few years, has been successfully used to realize tasks, e.g., image classification, speech recognition, translation of languages,…
Space-division multiplexing is a promising technology in optical fibre communication to improve the transmission capacity of a single optical fibre. However, the number of channels that can be multiplexed is limited by the crosstalks…
As a subfield of network coding, physical-layer network coding (PNC) can effectively enhance the throughput of wireless networks by mapping superimposed signals at receiver to other forms of user messages. Over the past twenty years, PNC…
This letter proposes a new physical layer authentication mechanism operating at the physical layer of a communication system where the receiver has partial control of the channel conditions (e.g., using an intelligent reflecting surface).…
In diffusion-based molecular communication, information transport is governed by diffusion through a fluid medium. The achievable data rates for these channels are very low compared to the radio-based communication system, since diffusion…
From the initial 1997 specification to the undergoing IEEE 802.11ac standardization, a leap in throughput has been observed with every new generation. The expectations for next generations on issues like throughput, range, reliability, and…
Networking protocols are designed through long-time and hard-work human efforts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual efforts to tune individual protocol parameters.…
Covert channel networks are a well-known method for circumventing the security measures organizations put in place to protect their networks from adversarial attacks. This paper introduces a novel method based on bit-rate modulation for…
Physical layer authentication relies on detecting unique imperfections in signals transmitted by radio devices to isolate their fingerprint. Recently, deep learning-based authenticators have increasingly been proposed to classify devices…
Mode-division multiplexing using multimode optical fibers has been intensively studied in recent years, in order to alleviate the transmission capacity crunch. Moreover, the need for secure information transmission based on quantum…
The autonomous evolution of networked AI systems relies heavily on robust environmental perception. However, physical understanding remains brittle in current models because key physical signals are visually ambiguous and sparsely…