Covert Communication in Integrated High Altitude Platform Station Terrestrial Networks

In recent years,Internet of Things(IoT)technology has emerged and gradually sprung up.As the needs of largescale IoT applications cannot be satisfied by the fifth generation(5G)network,wireless communication network needs to be developed into the sixth generation(6G)network.However,with the increasingly prominent security problems of wireless communication networks such as 6G,covert communication has been recognized as one of the most promising solutions.Covert communication can realize the transmission of hidden information between both sides of communication to a certain extent,which makes the transmission content and transmission behavior challenging to be detected by noncooperative eavesdroppers.In addition,the integrated high altitude platform station(HAPS)terrestrial network is considered a promising development direction because of its flexibility and scalability.Based on the above facts,this article investigates the covert communication in an integrated HAPS terrestrial network,where a constant power auxiliary node is utilized to send artificial noise(AN)to realize the covert communication.Specifically,the covert constraint relationship between the transmitting and auxiliary nodes is derived.Moreover,the closed-form expressions of outage probability(OP)and effective covert communication rate are obtained.Finally,numerical results are provided to verify our analysis and reveal the impacts of critical parameters on the system performance.

Analysis of the differential-phase-shift-keying protocol in the quantum-key-distribution system

The analysis is based on the error rate and the secure communication rate as functions of distance for three quantum-key-distribution （QKD） protocols： the Bennett-Brassard 1984, the Bennett Brassard Mermin 1992, and the coherent differential-phase-shift keying （DPSK） protocols. We consider the secure communication rate of the DPSK protocol against an arbitrary individual attack, including the most commonly considered intercept-resend and photonnumber splitting attacks, and concluded that the simple and efficient differential-phase-shift-keying protocol allows for more than 200 km of secure communication distance with high communication rates.

Study of low bit rate speech codec algorithm in underwater acoustic communication

At medium or long distance （〉 10 kin） underwater acoustic speech communication, information transfer rate is constrained by the complicated, time varying channel and limited bandwidth. The bit rate of speech coding is required to be as low as possible. The time delay of underwater acoustic wave propagation can be used for low bit rate speech coding. After investigating the Mixed Excitation Linear Prediction （MELP） standard and taking account of the auditory perceptual features, a variable and adjustable bit rate speech codec algorithm has been proposed, whose average bit rate is about 600 bps. The average Perceptual Evaluation of Speech Quality Mean Opinion Score （PESQ MOS） of synthesized speeches is about 2.8. It has been proved by the computer simulation and sea trial that the performance of the proposed algorithm is well and robust when bit error rate is no more than 10-3. The synthesized speech is vivid and intelligible, and keeps main individual characteristics of speaker.

Intelligent throughput stabilizer for UDP-based rate-control communication system

Michiko Harayama*and Noboru Miyagawa Abstract:In view of the successful application of deep learning,mainly in the field of image recognition,deep learning applications are now being explored in the fields of communication and computer networks.In these fields,systems have been developed by use of proper theoretical calculations and procedures.However,due to the large amount of data to be processed,proper processing takes time and deviations from the theory sometimes occur due to the inclusion of uncertain disturbances.Therefore,deep learning or nonlinear approximation by neural networks may be useful in some cases.We have studied a user datagram protocol(UDP)based rate-control communication system called the simultaneous multipath communication system(SMPC),which measures throughput by a group of packets at the destination node and feeds it back to the source node continuously.By comparing the throughput with the recorded transmission rate,the source node detects congestion on the transmission route and adjusts the packet transmission interval.However,the throughput fluctuates as packets pass through the route,and if it is fed back directly,the transmission rate fluctuates greatly,causing the fluctuation of the throughput to become even larger.In addition,the average throughput becomes even lower.In this study,we tried to stabilize the transmission rate by incorporating prediction and learning performed by a neural network.The prediction is performed using the throughput measured by the destination node,and the result is learned so as to generate a stabilizer.A simple moving average method and a stabilizer using three types of neural networks,namely multilayer perceptrons,recurrent neural networks,and long short-term memory,were built into the transmission controller of the SMPC.The results showed that not only fluctuation reduced but also the average throughput improved.Together,the results demonstrated that deep learning can be used to predict and output stable values from data with complicated time fluctuations that are difficultly analyzed.

Reaching a stochastic consensus in the noisy networks of linear MIMO agents:Dynamic output-feedback and convergence rate

This paper addresses the leader-following consensus problem of linear multi-agent systems(MASs) with communication noise. Each agent's dynamical behavior is described by a linear multi-input and multi-output(MIMO) system, and the agent's full state is assumed to be unavailable. To deal with this challenge, a state observer is constructed to estimate the agent's full state. A dynamic output-feedback based protocol that is based on the estimated state is proposed. To mitigate the effect of communication noise, noise-attenuation gains are also introduced into the proposed protocol. In this study, each agent is allowed to have its own noise-attenuation gain. It is shown that the proposed protocol can solve the mean square leader-following consensus problem of a linear MIMO MAS. Moreover, if all noise-attenuation gains are of Q(t-β), where b∈(0,1), the convergence rate of the MAS can be quantitatively analyzed. It turns out that all followers' states converge to the leader's state in the mean square sense at a rate of O(t-β).