1,206 research outputs found
The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve
With the introduction of caching capabilities into small cell networks
(SCNs), new backaul management mechanisms need to be developed to prevent the
predicted files that are downloaded by the at the small base stations (SBSs) to
be cached from jeopardizing the urgent requests that need to be served via the
backhaul. Moreover, these mechanisms must account for the heterogeneity of the
backhaul that will be encompassing both wireless backhaul links at various
frequency bands and a wired backhaul component. In this paper, the
heterogeneous backhaul management problem is formulated as a minority game in
which each SBS has to define the number of predicted files to download, without
affecting the required transmission rate of the current requests. For the
formulated game, it is shown that a unique fair proper mixed Nash equilibrium
(PMNE) exists. Self-organizing reinforcement learning algorithm is proposed and
proved to converge to a unique Boltzmann-Gibbs equilibrium which approximates
the desired PMNE. Simulation results show that the performance of the proposed
approach can be close to that of the ideal optimal algorithm while it
outperforms a centralized greedy approach in terms of the amount of data that
is cached without jeopardizing the quality-of-service of current requests.Comment: Accepted for publication at Transactions on Wireless Communication
NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
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