Autonomous Network Technology Innovation in Digital and Intelligent Era

The issues of wireless communication network autonomy,the definition of capability level and the concept of AI-native solution based on the integration of the information communication data technology(ICDT)are first introduced in this paper.A series of innovative technologies proposed by ZTE Corporation,such as an autonomous evolution network and intelligent orchestration network,are then analyzed.These technologies are developed to realize the evolution of wireless networks to Level-4 and Level-5 intelligent networks.It is expected that the future AI-native intelligent network system will be built based on innovative technologies such as digital twins,intent-based networking,and the data plane and intelligent plane.These new technical paradigms will promote the development of intelligent B5G and 6G networks.

Physical generation of random numbers using an asymmetrical Boolean network

Autonomous Boolean networks(ABNs)have been successfully applied to the generation of random number due to their complex nonlinear dynamics and convenient on-chip integration.Most of the ABNs used for random number generators show a symmetric topology,despite their oscillations dependent on the inconsistency of time delays along links.To address this issue,we suggest an asymmetrical autonomous Boolean network(aABN)and show numerically that it provides large amplitude oscillations by using equal time delays along links and the same logical gates.Experimental results show that the chaotic features of aABN are comparable to those of symmetric ABNs despite their being made of fewer nodes.Finally,we put forward a random number generator based on aABN and show that it generates the random numbers passing the NIST test suite at 100 Mbits/s.The unpredictability of the random numbers is analyzed by restarting the random number generator repeatedly.The aABN may replace symmetrical ABNs in many applications using fewer nodes and,in turn,reducing power consumption.

Self-healing and SDN:bridging the gap

Achieving high programmability has become an essential aim of network research due to the ever-increasing internet traffic.Software-Defined Network(SDN)is an emerging architecture aimed to address this need.However,maintaining accurate knowledge of the network after a failure is one of the largest challenges in the SDN.Motivated by this reality,this paper focuses on the use of self-healing properties to boost the SDN robustness.This approach,unlike traditional schemes,is not based on proactively configuring multiple(and memory-intensive)backup paths in each switch or performing a reactive and time-consuming routing computation at the controller level.Instead,the control paths are quickly recovered by local switch actions and subsequently optimized by global controller knowledge.Obtained results show that the proposed approach recovers the control topology effectively in terms of time and message load over a wide range of generated networks.Consequently,scalability issues of traditional fault recovery strategies are avoided.

Navigation of Non-holonomic Mobile Robot Using Neuro-fuzzy Logic with Integrated Safe Boundary Algorithm

In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.