Exploring the Road to 6G: ABC-Foundation for Intelligent Mobile Networks

The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applications.Therefore the research and development on 6 G have been put on the agenda.Regarding demands and characteristics of future 6 G,artificial intelligence(A),big data(B)and cloud computing(C)will play indispensable roles in achieving the highest efficiency and the largest benefits.Interestingly,the initials of these three aspects remind us the significance of vitamin ABC to human body.In this article we specifically expound on the three elements of ABC and relationships in between.We analyze the basic characteristics of wireless big data(WBD)and the corresponding technical action in A and C,which are the high dimensional feature and spatial separation,the predictive ability,and the characteristics of knowledge.Based on the abilities of WBD,a new learning approach for wireless AI called knowledge+data-driven deep learning(KD-DL)method,and a layered computing architecture of mobile network integrating cloud/edge/terminal computing,is proposed,and their achievable efficiency is discussed.These progress will be conducive to the development of future 6 G.

Benefits Analysis of Beam Hopping in Satellite Mobile System with Unevenly Distributed Traffic

Satellite mobile system and space-airground integrated network have a prominent superiority in global coverage which plays a critical role in remote and non-land regions, as well as emergency communications. However, due to the gradual angle attenuations of the satellite antennas, it is difficult to achieve full frequency multiplex among different beams as terrestrial 5G network. Multi-color frequency reuse is widely adopted in both academic and industry. Beam hopping scheme has attracted the attention of researchers recently due to the allocation flexibility. In this paper, we focus on analyzing the performance benefits of beam hopping compared with multi-color frequency reuse scheme in non-uniform user and traffic distributions in satellite system. Aerial networks are also introduced to form a space-airground integrated network for coverage enhancement,and the capacity improvement is analyzed. Besides,additional improved techniques are provided to make comprehensive analysis and comparisons. Theoretical analysis and simulation results indicate that the beam hopping scheme has a prominent superiority in the system capacity compared with the traditional multicolor frequency reuse scheme in both satellite mobile system and future space-air-ground integrated network.

Effects of air bubbles on underwater optical wireless communication [Invited]

The received signal intensity fluctuation and communication performance of an underwater optical wireless communication(UOWC) system under the air bubble effects are experimentally investigated. For different bubble density and size, lognormal, gamma, Weibull, and generalized extreme value distributions are tested to fit the fluctuation of the signal intensity at the receiving end. The best fitting distribution is found to vary with bubble parameters. The communication system performance with on–off keying and pulse position modulation is further studied.

Sample-Efficient Deep Reinforcement Learning with Directed Associative Graph

Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.However,most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes.In this paper,in order to utilize more sample information,we propose another learning system based on directed associative graph(DAG).The DAG is built on all trajectories in real time,which includes the whole connection relation of all samples among all episodes.Through planning with directed edges on DAG,we offer another perspective to estimate stateaction pair,especially for the unknowns to deep neural network(DNN)as well as episodic memory(EM).Mixed loss function is generated by the three learning systems(DNN,EM and DAG)to improve the efficiency of the parameter update in the proposed algorithm.We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments.Furthermore,the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.

Design and Implementation of a Time Synchronization Method for UOWC Networks

Underwater optical wireless communication (UOWC) technology facilitates high-speed data transmission among multiple nodes in underwater networks. Nevertheless, the absence of a common clock poses a challenge to achieving systematic and reliable access for multiple nodes within these networks. This paper presents a time synchronization method for UOWC networks to ensure the successful execution of the media access control (MAC) protocol. In this method, the node obtains timestamps by exchanging messages with the optical access point (OAP). Subsequently, the node calculates the clock drift relative to the OAP and the propagation time,ensuring that transmitted data packets can arrive approximately at the time specified by the OAP. To validate the effect of the proposed method, an experimental UOWC prototype, including the OAP and nodes, is implemented using field programmable gate array (FPGA). The experimental results demonstrate that the maximum difference between the actual arrival times of two data packets that are expected to reach the OAP simultaneously according to the MAC protocol meets the requirements of the quasi-synchronous code division multiple access (QS-CDMA) system, thereby substantiating the effectiveness of this synchronization method.

Compensation of turbulence-induced wavefront aberration with convolutional neural networks for FSO systems

To reduce the atmospheric turbulence-induced power loss, an Alex Net-based convolutional neural network(CNN) for wave-front aberration compensation is experimentally investigated for free-space optical(FSO) communication systems with standard single mode fiber-pigtailed photodiodes. The wavefront aberration is statistically constructed to mimic the received light beams with the Zernike mode-based theory for the Kolmogorov turbulence. By analyzing impacts of CNN structures, quantization resolution/noise, and mode count on the power penalty, the Alex Net-based CNN with 8 bit resolution is identified for experimental study. Experimental results indicate that the average power penalty decreases to 1.8 d B from 12.4 d B in the strong turbulence.