Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing

Multi-user edge computing (MEC) is a network architecture that enables cloud computing capabilities at the edge of a network, reducing latency and user equipment’s energy consumption. An MEC system that can efficiently supports both the ultra-Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB) services is crucial in providing a diverse and efficient communication for various Internet of Things (IoT) applications. The current MEC models in literature are either deterministic or based on average-based metric hence, not suitable in a practical scenario where task offloading and computation activities are stochastic processes and, the wireless channel is often not interference-free. In this paper, we study the joint task offloading and computation in a mixed traffic of two 5G-based MEC. We consider user equipment (UE) that are cognitive radio enabled and, whose performance are energy constrained. In view of this, energy efficient MEC is formulated as a stochastic optimization with long-term objective while, taking into consideration the tail distribution of the eMBB queue length. The target is to minimize energy consumption and, maximize the achievable data rate subject to probabilistic and statistical constraint on the eMBB task length based on Extreme Value Theorem (EVT), uRLLC reliability and, system capacity. The performance of the proposed MEC model is studied in terms of the latency, energy consumption, user density, and reliability. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed model over the existing model.