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Top1. Introduction
Growth in mobile and Web traffic in new application requirements have attached requirements with higher levels in the service capability of 5th Generation (5G) mobile communication network (Sami, et al., 2021; Islambouli, et al., 2020, June; Mansour, et al., 2022). With the rise and development of the Metaverse, the emergence of computing- intensive and delay- sensitive applications with big amount makes users' requirements for service quality increase exponentially (Sami., & Mourad.., 2020; Inan., & Dikenelli., 2021). The final form of the Metaverse must be decentralized, and the current network ecology cannot fully meet the needs of Metaverse decentralization. Some people believe that the coming Web3.0 era is highly coincident with the network ecology required by the Metaverse. Web3.0 is expanding the data center to the edge (Chen, et al., 2022; Zhang, T. 2022). Compared with the current amount of Internet data, the amount of data generated and consumed in the Metaverse will be hundreds of times higher than the current amount. Relying on high-speed, low latency, and multimodal characteristics, 5G networks have greatly changed the possibilities of Web 3.0 applications. 5G networks can provide faster and more stable network services, support more new technologies, achieve the interconnection of everything, and greatly contribute to the transformation of Web 3.0. In the era of Web 3.0, the increase in the amount of task calculation results in local devices being unable to handle the corresponding computing tasks, while the cloud computing acts as solution of insufficiency in computing power (Tiwari, A., & Garg, R., 2022; Hussain., & Sayed., 2021). However, cloud computing also generates problems like costs of data transmission, cloud storage cost, Internet access management and security (Al-Qerem, et al., 2020; Stergiou, et al., 2021). Therefore, finding a reasonable network resource allocation method is crucial to support 5G networks to provide high-quality user services.
As Mobile Edge Computing (MEC) emerges, the device-cloud architecture is transformed with device-edge-cloud, thus reducing latency accordingly. Additionally, computing task throughput is improved by the strategy of allocating 3 computing reasonably, thus better meeting to the users’ experience quality requirements can be better met and maximize economic benefits (Mychael, et al., 2022). In MEC environments, if the number of concurrent users is large, edge base stations may be overloaded. MEC reduces server load through swarm intelligence collaboration technology. Group intelligence collaboration technology uses a large number of base stations to complete tasks that cannot be completed by a single base station. Edge servers can also collaborate to perform tasks to balance network load. However, swarm intelligence collaboration technology requires the use of a large number of devices, which is suitable for the case of a large number of user devices and a small storage capacity of a single device. In PSO, each bird is considered a particle, and the bird swarm is considered a particle swarm, and each particle is encoded as a task resource scheduler. The main goal of PSO is to find the optimal particle from the population after multiple iterations of updates, that is, the optimal task resource scheduling program.
Relying on the model of edge computing, a method of communication network based on network resource allocation is proposed with major innovations as below: