Performance Analysis of Sparse Array based Massive MIMO via Joint Convex Optimization

Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.

Micro-Service-Based Radio Access Network

The convergence of information,communication,and data technologies(ICDT)has been identified as one of the developing trends of the sixth generation(6G)network.Service-based architecture(SBA)as one of the promising information technology,has been preliminarily introduced into the fifth generation(5G)core network(CN)and successfully commercialized,which verifies its feasibility and effectiveness.However,SBA mainly focuses on the control plane of CN at present and the SBA-CN user plane is being studied by the industry.In addition to further evolving the SBA-CN,SBA radio access network(RAN)should also be considered to enable an end-toend SBA,so as to meet more comprehensive and extreme requirements of future applications,as well as support fast rollout requirements of RAN devices.

Cloud-Assisted Distributed Edge Brains for Multi-Cell Joint Beamforming Optimization for 6G

In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are expected,and the importance of automatic and accurate beamforming assisted by AI will become more prominent.In existing network,servers are“patched”to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead,large inference latency and potential risks of data security.Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response,but it is inefficient in collecting and sharing global information among base stations.In this paper,we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization.We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation.Compared with the centralized solution,our solution improves prediction accuracy by 24.66%,and reduces storage cost by 83.82%.Compared with the decentralized solution,our solution improves prediction accuracy by 68.26%,and improves coverage performance by 0.4 dB.At last,the future research work is prospected.

Dynamic user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN

The rapid development of communications industry has spawned more new services and applications.The sixth-generation wireless communication system(6G)network is faced with more stringent and diverse requirements.While ensuring performance requirements,such as high data rate and low latency,the problem of high energy consumption in the fifth-generation wireless communication system(5G)network has also become one of the problems to be solved in 6G.The wide-area coverage signaling cell technology conforms to the future development trend of radio access networks,and has the advantages of reducing network energy consumption and improving resource utilization.In wide-area coverage signaling cells,on-demand multi-dimensional resource allocation is an important technical means to ensure the ultimate performance requirements of users,and its effect will affect the efficiency of network resource utilization.This paper constructs a user-centric dynamic allocation model of wireless resources,and proposes a deep Q-network based dynamic resource allocation algorithm.The algorithm can realize dynamic and flexible admission control and multi-dimensional resource allocation in wide-area coverage signaling cells according to the data rate and latency demands of users.According to the simulation results,the proposed algorithm can effectively improve the average user experience on a long time scale,and ensure network users a high data rate and low energy consumption.

Native intelligence for 6G mobile network: technical challenges,architecture and key features

The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertical industries, however, the application models of "patching" and "plug-in" have hindered the effect of AI applications. Meanwhile, the application of AI in all walks of life puts forward requirements for new capabilities of the future network, such as distributed training, real-time collaborative inference, local data processing, etc., which require "native intelligence design" in future networks. This paper discusses the requirements of native intelligence in the 6 th generation mobile communication system(6 G) networks from the perspectives of 5 G intelligent network challenges and the "ubiquitous intelligence" vision of 6 G, and analyzes the technical challenges of the AI workflows in its lifecycle and the AI as a service(AIaaS) in cloud network. The progress and deficiencies of the current research on AI functional architecture in various industry organizations are summarized. The end-to-end functional architecture for native AI for 6 G network and its three key technical characteristics are proposed: quality of AI services(QoAIS) based AI service orchestration for its full lifecycle, deep integration of native AI computing and communication, and integration of native AI and digital twin network. The directions of future research are also prospected.

Performance analysis on reconfigurable intelligent surface and network-controlled repeater in 3GPP release-18

As a candidate technique to achieve sixth-generation wireless communication(6G),reconfigurable intelligent surface(RIS)has become popular in both academia and industry.For better exploration of the advantages of RIS,we compare the performances of RIS and network-controlled repeater(NCR)in 3GPP release-18.We first theoretically analyze the received signal power and signal-to-noise ratio performances for both RIS and NCR.Then,we simulate the reference signal received power and signal-to-interference-and-noise ratio performances at the system level for both RIS and NCR in the frequency range 1 and frequency range 2 bands.Finally,several insights on engineering applications are given based on the comparison between RIS and NCR.