Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

Study on QoS and Communications Capacity in Heterogeneous Network Convergence

To provide any subscriber from anywhere at anytime with services that have both secured Quality of Service(QoS) and simultaneous expansion of network coverage and communications capacity is a key problem that has to be considered and solved in heterogeneous network convergence.Key technologies for a secured QoS and communications capacity analysis under heterogeneous environment are important subjects for research.Key technologies for a secured QoS are mainly on radio resource management algorithms covering Call Admission Control(CAC) algorithm,vertical handover algorithm,heterogeneous resource allocation algorithm and network selection algorithm.The applications of a novel multi-hop in heterogeneous convergence system serve the purposes of network coverage expansion,transmission power reduction,system communication capacity and throughput increase.

Nonlinear Masking and Iterative Learning Decryption for Secure Communications

Typical masking techniques adopted in the conventional secure communication schemes are the additive masking and modulation by multiplication. In order to enhance security, this paper presents a nonlinear masking methodology, applicable to the conventional schemes. In the proposed cryptographic scheme, the plaintext spans over a pre-specified finite-time interval, which is modulated through parameter modulation, and masked chaotically by a nonlinear mechanism. An efficient iterative learning algorithm is exploited for decryption, and the sufficient condition for convergence is derived, by which the learning gain can be chosen. Case studies are conducted to demonstrate the effectiveness of the proposed masking method.

When Communications Converge

A three-network initiative in China will bring customers novelties and unleash investment opportunities for crossover network applications Since becoming widely used across the country,telecommunications,broadcasting

Photonics 60 GBaud PDM-16QAM fiber-wireless 2×2 MIMO delivery at THz-band

The terahertz photonics technique has bright application prospects in future sixth-generation(6G)broadband communication.In this study,we have experimentally demonstrated a photonics-assisted record-breaking net bit rate of 417 Gbit/s per wavelength signals delivery in a fiber-wireless converged communication system supported by advanced digital-signalprocessing(DSP)algorithms and a polarization multiplexing-based multiple-input multiple-output(MIMO)scheme.In the experiment,up to 60 GBaud(480 Gbit/s)polarization-division-multiplexing 16-ary quadrature-amplitude-modulation(PDM16QAM)signals are transmitted over 20 km fibers and 3 m wireless 2×2 MIMO links at 318 GHz with the bit error rate(BER)under 1.56×10^(−2).It is the first demonstration to our knowledge of signals delivery exceeding 400 Gbit/s per wavelength in a photonics-assisted fiber-wireless converged 2×2 MIMO communication system.