This work introduces a new perspective for physical media sharing in multiuser communication systems by proposing a novel scheme that enables the recovery of the content of the transmitted message whenever collisions happen. An alarm monitoring system is taken as a merely illustrative toy-model, but indeed the framework is generally applicable. In this scenario, the alarm system is designed to first decide whether a predetermined event has happened over a certain period; if such an event has been identified, the decision node also needs to correctly classify from which sensor the message comes. Simulations corroborate the effectiveness of the proposed method in terms of the event transmission efficiency, when compared with variations of conventional methods like TDMA and slotted ALOHA.

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system’s perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage.

This paper explores the use of semantic knowledge inherent in the cyber-physical system (CPS) being studied in order to minimize the use of explicit communication, which refers to the use of physical radio resources to transmit potentially informative data. It is assumed that the acquired data have a function in the system, usually related to its state estimation, which may trigger control actions. We propose a semantic-functional approach that can leverage semantic-enabled implicit communication while ensuring the system maintains its required functionality and performance. We illustrate the potential of this proposal through simulations of a swarm of drones jointly performing remote sensing in a given area. Our numerical results demonstrate that the proposed method offers the best design option regarding the ability to accomplish a previously established task—remote sensing in the addressed case—while minimizing the use of explicit communication by controlling the trade-offs that jointly determine the performance and effectiveness of the CPS in terms of resource utilization. In this sense, we establish a fundamental relationship among energy, communication, and functionality, considering a specific end application.

Implementing line-of-sight (LOS) and none-line-of-sight (NLOS) identification in ultra-wideband (UWB) systems is crucial. Convolutional neural network (CNN) based identification methods can extract higher-level features automatically, but they are based on channel impulse response (CIR)-turned image ingested features that impose calculation complexity and do not make use of manual features due to the data inundation risk. In this letter, we propose a novel multilayer perceptron (MLP)-based LOS/NLOS identification algorithm that can utilize both manually extracted features and feature from CNN based on raw CIR inputs with only 7.39% calculation complexity as compared to the traditional image-based CNN. Three experiments, at a teaching building, an office, and an underground mine, were conducted to verify the proposed method’s performance. Our proposed features are conducive to LOS/NLOS identification, especially the proposed raw CIR-based feature from the CNN, achieving 26.9% improvement over existing manual features. Furthermore, the proposed method outperformed the traditional image-based CNN with an improvement of 44.16%.

Accurate altimetry is essential for location-based services in commercial and industrial applications. However, current altimetry methods only provide low-accuracy measurements, particularly in multistorey buildings with irregular structures, such as hollow areas found in various industrial and commercial sites. This paper innovatively proposes a tightly coupled indoor altimetry system that utilizes floor identification to improve height measurement accuracy. The system includes two optimized algorithms that improve floor identification accuracy through activity detection and address the problem of difficult convergence of z-axis coordinates due to indoor coplanarity by applying constraints to iterative least squares (ILS). Two experiments were conducted in a teaching building and a laboratory, including an irregular environment with a hollow area. The results show that our proposed method for identifying floors based on activity detection outperforms other methods. In dynamic experiments, our method effectively eliminates repeated transformations during the up- and downstairs process, and in static experiments, it minimizes the impact of barometric drift. Furthermore, our proposed altimetry method based on constrained ILS achieves significantly improved positioning accuracy compared to ILS, 1D-CNN, and WC. Specifically, in the teaching building, our method achieves improvements of 0.84 m, 0.288 m, and 0.248 m, respectively, while in the laboratory, the improvements are 2.607 m, 0.696 m, and 0.625 m.

In this article, we are proposing a closed-form solution for the capacity of the single quantum channel. The Gaussian distributed input has been considered for the analytical calculation of the capacity. In our previous couple of papers, we invoked models for joint quantum noise and the corresponding received signals; in this current research, we proved that these models are Gaussian mixtures distributions. In this paper, we showed how to deal with both of cases, namely (I)the Gaussian mixtures distribution for scalar variables and (II) the Gaussian mixtures distribution for random vectors. Our target is to calculate the entropy of the joint noise and the entropy of the received signal in order to calculate the capacity expression of the quantum channel. The main challenge is to work with the function type of the Gaussian mixture distribution. The entropy of the Gaussian mixture distributions cannot be calculated in the closed-form solution due to the logarithm of a sum of exponential functions. As a solution, we proposed a lower bound and a upper bound for each of the entropies of joint noise and the received signal, and finally upper inequality and lower inequality lead to the upper bound for the mutual information and hence the maximum achievable data rate as the capacity. In this paper reader will able to visualize an closed-form capacity experssion which make this paper distinct from our previous works. These capacity experssion and coresses ponding bounds are calculated for both the cases: the Gaussian mixtures distribution for scalar variables and the Gaussian mixtures distribution for random vectors as well.

In this paper we are interested to model quantum signal by statistical signal processing methods. The Gaussian distribution has been considered for the input quantum signal as Gaussian state have been proven to a type of important robust state and most of the important experiments of quantum information are done with Gaussian light. Along with that a joint noise model has been invoked, and followed by a received signal model has been formulated by using convolution of transmitted signal and joint quantum noise to realized theoretical achievable capacity of the single quantum link. In joint quantum noise model we consider the quantum Poisson noise with classical Gaussian noise. We compare the capacity of the quantum channel with respect to SNR to detect its overall tendency. In this paper we use the channel equation in terms of random variable to investigate the quantum signals and noise model statistically. These methods are proposed to develop Quantum statistical signal processing and the idea comes from the statistical signal processing.

This research combines complex systems science, geographical information systems, and environmental noise modelling to analyse effects of future air mobility in urban settings and plan efficient routes for vehicles. The research used the environmental noise maps of an urban agglomeration produced under the Environmental Noise Directive (END) as input to inform the UAV operations. These maps reveal potential routes for the UAV operations where the noise impact of the vehicle can be embedded within a high background noise due to the existing sources modelled under the END. When an agent based model is superimposed on a real-world map simple strategies of the diverse agents in interaction with the environment reveal patterns, such as dominant paths, points of congestion, and suggest positioning of terrestrial infrastructure. We investigate how agents can overcome the conflicts and find trade-off solutions by interacting only with their immediate neighbours-therefore enabling autonomy, decentralization, and putting to use emergent self-organising behaviour. The potential impact of increased drone operations on urban and peri urban regions is significant. Route optimisation which does not consider the noise is likely to impact on quite areas within our residential spaces and should be considered as part of noise action planning.

Aside from significant advancements in the development of optical and quantum components, the performance of practical quantum key distribution systems is largely determined by the type and settings of the error key reconciliation procedure. It is realized through public channel and it dominates the communication complexity of the quantum key distribution process. The practical utilization significantly depends on the computational capacities that are of great importance in satellite-oriented quantum communications. Here we present SarDub19 error key estimation and reconciliation protocol that improves performances of practical quantum systems.

We investigate how an IRS (Intelligent Reflective Surface) deployment within a terahertz (THz) band communication system, can detect and classify indoor vapour clouds expelled in violent expiratory events, such as coughing. This detection is of interest for public health systems. Since indoor spaces are sites for deployment of wireless communication infrastructure in any case, combined sensing and communication is an attractive solution. Using 6G networks to accomplish this while not affecting communication performance, is a relevant and useful application of THz signal’s properties. We use machine learning to distinguish between violent expiratory events and other aerosol emissions.

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure.

The evolution of fifth-generation (5G) networks needs to support the latest use cases, which demand robust network connectivity for the collaborative performance of the network agents, such as multirobot systems and vehicle-to-anything (V2X) communication. Unfortunately, the user device’s limited communication range and battery constraint confirm the unfitness of known robustness metrics suggested for fixed networks, when applied to time-switching communication graphs. Furthermore, the calculation of most of the existing robustness metrics involves nondeterministic polynomial (NP)-time complexity, and hence are best fitted only for small networks. Despite a large volume of works, the complete analysis of a low-complexity temporal robustness metric for a communication network is absent in the literature, and the present work aims to fill this gap. More in detail, our work provides a stochastic analysis of network robustness for a massive machine-type communication (mMTC) network. The numerical investigation corroborates the exactness of the proposed analytical framework for the temporal robustness metric. Along with studying the impact on network robustness of various system parameters, such as cluster head (CH) probability, power threshold value, network size, and node failure probability, we justify the observed trend of numerical results probabilistically.

—For a continuous-input-continuous-output arbitrarily distributed quantum channel carrying classical information, the channel capacity can be computed in terms of the distribution of the channel envelope, received signal strength over a quantum propagation field and the noise spectral density. If the channel en-velope is considered to be unity with unit received signal strength, the factor controlling the capacity is the noise . Quantum channel carrying classical information will suffer from the combination of classical and quantum noise. Assuming additive Gaussian-distributed classical noise and Poisson-distributed quantum noise, we formulate a hybrid noise model by deriving a joint Gaussian-Poisson distribution in this letter. For the transmitted signal, we consider the mean of signal sample space instead of considering a particular distribution and study how the maximum mutual in- formation varies over such mean value. Capacity is estimated by maximizing the mutual information over unity channel envelope.