Abstract
Multi-access edge computing (MEC) enables mobile users to offload their computation tasks to the server located at the edge of the cellular network. Thereby, MEC prolongs the battery lifespan of mobile devices and significantly enhances their computation capacities. However, a significant challenge is to offload the computation tasks to the MEC server in an energy-efficient manner. Meanwhile, in the fifth-generation (5G) networks, mobile users with different service requirements classified as enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users will coexist in the current cellular network. Therefore, it is important to appropriately multiplex these users in the cellular network. In this work, we address the issues of these two promising technologies together. Firstly, we formulate an energy-efficient task offloading, and scheduling of eMBB and URLLC users as a mixed-integer non-linear problem. Then, we decompose the problem into multiple sub-problems in order to transform into convex form and alternately solve them until converging to the desired solutions by using the block coordinate descent (BCD) algorithm. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed algorithm over classical existing schemes.
Highlights
With the explosive growth of the Internet of Things (IoT) devices, computation-intensive applications (e.g., Augmented Reality (AR), face recognition, Virtual Reality (VR), online gaming, and traffic monitoring) are appearing as an integral part of our daily activities
RELATED WORKS The existing works can be categorized into two groups: i) energy efficient task offloading in the multi-access edge computing, and ii) resource slicing for enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) traffic
We find the critical requirements frequently overlooked in the previous works, and formulate the joint efficient computation task offloading and scheduling of eMBB and URLLC traffic
Summary
With the explosive growth of the Internet of Things (IoT) devices, computation-intensive applications (e.g., Augmented Reality (AR), face recognition, Virtual Reality (VR), online gaming, and traffic monitoring) are appearing as an integral part of our daily activities. A. RESEARCH CONTRIBUTIONS All of the existing works have discussed an efficient task offloading, and computation resource allocation mechanism in the MEC system and further on the scheduling of the eMBB and URLLC users separately. RESEARCH CONTRIBUTIONS All of the existing works have discussed an efficient task offloading, and computation resource allocation mechanism in the MEC system and further on the scheduling of the eMBB and URLLC users separately These two research issues are coupled because eMBB users will need high computation service due to their limited computation capacity (i.e., CPU resources) and battery lifetime. We formulate an efficient joint task offloading, and scheduling of eMBB and URLLC users problem that minimizes energy consumption and maximizes the achievable data rate of the eMBB users subject to the latency of eMBB users, the CPU capacity of the MEC server, the maximum transmit power of eMBB users, the reliability of the URLLC traffic, and the resource block allocation to the eMBB users constraints.
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