Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
With the emerging applications of the Internet of things, artificial intelligence, and satellite communications, the future network will be featured as the Internet of everything around the globe. The network heterogeneity, applications cloudification, and personalized user services demand a revolutionary change in the network architecture. With the rapid development of cloud native technology,the new network should support heterogeneous networks and personalized quality of services for users. In this paper,we propose a Cybertwinbased cloud native network (CCNN) that merges the radio access network (RAN), the IP bearer network, and the data center network and is based on the cloud native data center network using Kubernetes as a network operating system for unified virtualization of computing, storage, and network resources, unified scheduling and allocation,and unified operation and management. Then, we propose a fully decoupled RAN architecture that can flexibly and efficiently utilize the resource for personlized user services. We also propose a Cybertwin-based management framework built on Kubernetes for integrated networking, computing and storage resource scheduling. Finally, we design an immunology-inspired intrinsic security architecture with zero trust security system and adaptive defense system. The proposed CCNN is a new network architecture expected to address future generation communications and networks challenges.
Radio map is an advanced technology that mitigates the reliance of multiple-input multiple-output (MIMO) beamforming on channel state information (CSI). In this paper, we introduce the concept of deep learning-based radio map,which is designed to be generated directly from the raw CSI data. In accordance with the conventional CSI acquisition mechanism of MIMO,we first introduce two baseline schemes of radio map,i.e.,CSI prediction-based radio map and throughput predictionbased radio map.To fully leverage the powerful inference capability of deep neural networks, we further propose the end-to-end structure that outputs the beamforming vector directly from the location information. The rationale behind the proposed end-to-end structure is to design the neural network using a task-oriented approach,which is achieved by customizing the loss function that quantifies the communication quality. Numerical results show the superiority of the task-oriented design and confirm the potential of deep learning-based radio map in replacing CSI with location information.
Penetration testing(PT)is an active method of evaluating the security of a network by simulating various types of cyber attacks in order to identify and exploit vulnerabilities. Traditional PT involves a time-consuming and labor-intensive process that is prone to errors and cannot be easily formulated. Researchers have been investigating the potential of deep reinforcement learning (DRL) to develop automated PT (APT) tools. However, using DRL in APT is challenged by partial observability of the environment and the intractability problem of the huge action space.This paper introduces RLAPT,a novel DRL approach that directly overcomes these challenges and enables intelligent automation of the PT process with precise control. The proposed method exhibits superior efficiency, stability, and scalability in finding the optimal attacking policy on the simulated experiment scenario.
In recent years,with the rapid development of Internet and hardware technologies, the number of Internet of things(IoT)devices has grown exponentially. However,IoT devices are constrained by power consumption,making the security of IoT vulnerable.Malware such as Botnets and Worms poses significant security threats to users and enterprises alike. Deep learning models have demonstrated strong performance in various tasks across different domains, leading to their application in malicious software detection. Nevertheless, due to the power constraints of IoT devices, the well-performanced large models are not suitable for IoT malware detection. In this paper we propose a malware detection method based on Markov images and MobileNet, offering a cost-effective, efficient, and high-performing solution for malware detection. Additionally, this paper innovatively analyzes the robustness of opcode sequences.
We analyzed the performance of a freespace optical(FSO)system in this study, considering the combined effects of atmospheric turbulence, fog absorption, and pointing errors. The impacts of atmospheric turbulence and foggy absorption were modeled using the Fisher-Snedecor F distribution and the Gamma distribution,respectively.Next,we derived the probability density function (PDF) and cumulative probability density function of the optical system under these combined effects. Based on these statistical findings, closed-form expressions for various system metrics, such as outage probability, average bit error rate (BER), and ergodic capacity, were derived. Furthermore, we used a deep neural network(DNN)to predict the ergodic capacity of the system,achieving reduced running time and improved accuracy. Finally, the accuracy of the prediction results was validated by comparing them with the analytical results.
Telecommunication has undergone significant transformations due to the continuous advancements in internet technology, mobile devices, competitive pricing,and changing customer preferences. Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer (ChatGPT) has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays, the exploration of network resource management,control, and operation is still in the initial stage. In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic (NetLM), a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics. The continual convergence of knowledge space and artificial intelligence (AI) technologies constitutes the core of intelligent network management and control. Multi-modal representation learning is used to unify the multi-modal information of network indicator data, traffic data, and text data into the same feature space. Furthermore, a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels. Finally, some potential cases are provided that NetLM can benefit the telecom industry.
Channel knowledge map(CKM)has recently emerged as a viable new solution to facilitate the placement and trajectory optimization for unmanned aerial vehicle (UAV) communications, by exploiting the siteand location-specific radio propagation information. This paper investigates a CKM-assisted multi-UAV wireless network, by focusing on the construction and utilization of CKMs for multi-UAV placement optimization. First, we consider the CKM construction problem when data measurements for only a limited number of points are available. Towards this end, we exploit a data-driven interpolation technique, namely the Kriging method, to construct CKMs to characterize the signal propagation environments. Next, we study the multi-UAV placement optimization problem by utilizing the constructed CKMs,in which the multiple UAVs aim to optimize their placement locations to maximize the weighted sum rate with their respectively associated ground base stations(GBSs). However, the weighted sum rate function based on the CKMs is generally non-differentiable, which renders the conventional optimization techniques relying on function derivatives inapplicable. To tackle this issue,we propose a novel iterative algorithm based on derivative-free optimization, in which a series of quadratic functions are iteratively constructed to approximate the objective function under a set of interpolation conditions, and accordingly, the UAVs’ placement locations are updated by maximizing the approximate function subject to a trust region constraint. Finally, numerical results are presented to validate the performance of the proposed designs.It is shown that the Kriging method can construct accurate CKMs for UAVs. Furthermore, the proposed derivative-free placement optimization design based on the Kriging-constructed CKMs achieves a weighted sum rate that is close to the optimal exhaustive search design based on ground-truth CKMs, but with much lower implementation complexity.In addition,the proposed design is shown to significantly outperform other benchmark schemes.
Edge computation offloading has made some progress in the fifth generation mobile network (5G). However, load balancing in edge computation offloading is still a challenging problem. Meanwhile, with the continuous pursuit of low execution latency in 5G multi-scenario, the functional requirements of edge computation offloading are further exacerbated. Given the above challenges,we raise a unique edge computation offloading method in 5G multi-scenario, and consider user satisfaction. The method consists of three functional parts: offloading strategy generation,offloading strategy update, and offloading strategy optimization. First, the offloading strategy is generated by means of a deep neural network (DNN), then update the offloading strategy by updating the DNN parameters. Finally, we optimize the offloading strategy based on changes in user satisfaction. In summary,compared to existing optimization methods, our proposal can achieve performance close to the optimum. Massive simulation results indicate the latency of the execution of our method on the CPU is under 0.1 seconds while improving the average computation rate by about 10%.
Most of the existing automatic modulation recognition (AMR) studies focus on optimizing the network structure to improve performance, without fully considering cooperation among the basic networks to play their respective advantages. In this paper, we propose a robust and efficient collaboration framework based on the combination scheme(CFCS).This scheme effectively explores the spatial and temporal characteristics of complex signals by associating the advantages of convolutional neural network (CNN) and long and short-term memory(LSTM)network. In addition,the robustness of the CFCS is verified by transfer learning. Experiments demonstrate that the recognition rate of CFCS for highorder modulation signals such as 64QAM,128QAM,and 256QAM is more than 90% at high signal-to-noise ratios (SNRs),and 24 modulation types are effectively identified. Moreover, CFCS was transferred from RML2018.01a to RML2016.10b using transfer learning, which can still be deployed efficiently while reducing the training time by 20%. The CFCS has strong generalization ability and excellent recognition performance.
This paper explores the ergodic channel capacity of multiple-input single-output(MISO)free-space optical (FSO) communication systems, assisted by (optical) re-configurable intelligent surfaces[(O)RIS], made of concave reflectors. On the one hand, RIS technology mitigates dead zones in communication systems. Additionally, it increases the data rate and communication range, enhances the communication channel by making it intelligent,and improves the system’s capacity. Finally, the RIS technology improves the spectrum and energy efficiencies of the considered systems. On the other hand, transmitting diversity mitigates deep fade and helps to achieve beamforming to regulate the beam sent in a specific direction. Finally, multiple light sources help to send different versions of the same information at other time slots. Furthermore, compared to flat reflectors, concave mirrors provide economic advantages enabled by their natural shape, which helps converge the impinging light beams into the same focal point. In this paper, we harness the full potential of ORIS and MISO technologies in an FSO system by exploiting the hollow of concave reflectors to focus the reflected beams on a single user.We derive an approximated closed-form expression, provide results of the proposed ORIS-aided FSO systems’ergodic channel capacity,and discuss the suitable type of concave reflector. These results show that all types of concave mirrors provide similar results except when the thickness of the reflector is large enough to impact the reflected light.
