The inherent limitations of the network keep on going to be revealed with the continuous deployment of cellular networks. The next generation 6G is motivated by these drawbacks to properly integrate important rate‐hungry applications such as extended reality, wireless brain‐computer interactions, autonomous vehicles, and so on. Also, to support significant applications, 6G will handle large amounts of data transmission in smart cities with much lower latency. It combines many state‐of‐the‐art trends and technology to provide higher data rates for ultra‐reliable and low latency communications. By outlining the system requirements, potential trends, technologies, services, applications, and research progress, this article comprehensively conceptualized the 6G cellular system. Open research issues and current research groups in their field of research are summarized to provide readers with the technology road‐map and the potential challenges to consider in their 6G research.
Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.
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