Tradeoff between User Quality-Of-Experience and Service Provider Profit in 5G Cloud Radio Access Network

Abstract

In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.

Review of the Complexity of Managing Big Data of the Internet of Things

“…Sensing and Actuation as a Service Delivery Model [44] A novel system model for Sensing and Actuation as a Service (SAaaS). User Quality-Of-Experience and Service Provider Profit in 5G [51] The Quality-of-Experience (QoE) and the Profit-aware Resource Allocation problems are analysed.…”

“…These tasks seek to simplify business and make services easier for stakeholders. In this way, the new 5G protocol will facilitate access for services and applications in the cloud improving the Quality-of-Experience [51].…”

5G Mobile Services and Scenarios: Challenges and Solutions

“…Afrin et al [17] explored the challenge of mapping the heterogeneous requests from Remote Radio Heads (RRHs) to Baseband Processing Units (BBUs) while considering the both aspects, i.e., end-user QoE and service providers' profit. In the proposed work, a multi-objective non-linear programming solution is developed, with focus on the maximization of QoE and profit-aware resource allocation.…”

Sum Utilization of Spectrum with Spectrum Handoff and Imperfect Sensing in Interweave Multi-Channel Cognitive Radio Networks

“…The scope of 5G services is not limited to personal wireless communications but extends to the services associated with mobile gadgets, wearable devices, sensors, actuators, machines, robots, vehicles, and other applications [2]. 5G technology is expected to be a combination of cooperative heterogeneous networks of multi-tier communication systems and different radio access technologies [3,4]. The heterogeneous feature in 5G technology will provide orders-of-magnitude improvement, including 1000 times higher data volume per area, 10-100 times more connected devices, 10-100 times higher user data rates, one-tenth the energy consumption, and sub-millisecond end-to-end latency.…”

“…The heterogeneous feature in 5G technology will provide orders-of-magnitude improvement, including 1000 times higher data volume per area, 10-100 times more connected devices, 10-100 times higher user data rates, one-tenth the energy consumption, and sub-millisecond end-to-end latency. However, the challenges faced by 5G systems are manifold because of the heterogeneity in terms of services, classification of devices, deployment scenarios, environments, and mobility [4,5]. In addition to its heterogeneous nature, another major challenge surrounding 5G is the random and diverse high-volume user data [5,6].…”