Collaborative Resource Management for Negotiable Multi-Operator Small Cell Networks

Shah, Shashi; Lim, Yuto; Tan, Yasuo

doi:10.3390/s19163550

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The ubiquitous wireless cellular network coverage/connectivity requirement has moved MNO's interest towards dense deployment of small cells with far narrower coverage areas compared to MBS. Today, small multioperator cells belonging to different telecommunications operators are currently making effective use of resources, i.e., uninterrupted coverage and the ability to prevent under-use of network resources [202]. They work together to use caching techniques, store virtualization features, and edge computing to provide highperformance and large-scale computing services on demand [40].…”

Section: Competition or Cooperation Of Multi-operator Networkmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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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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Section: Competition or Cooperation Of Multi-operator Networkmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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“…Hence, such scheme could potentially lead to coverage holes. To the best of our knowledge, the research works in [7]- [8] are the only ones that suggest C-RAN sharing based on coalitional game theory, which could present advantages for heterogeneous scenarios. The scheme is consistent with the C-RANs, where MNOs share their infrastructure (RRH, BBU Pools), aiming to set the rules for profitable collaboration.…”

Section: Introductionmentioning

confidence: 99%

On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

Marzouk

Barraca

Radwan

2020

IEEE Commun. Surv. Tutorials

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An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

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“…One of the noticeable trends in the development of contemporary wireless networks is the continuous densification of wireless networks by an increasing number of network nodes, such as access points, base stations, radio remote heads, or simply transmission points [ 1 , 2 , 3 ]. Moreover, some of the nodes can act only as relays.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Transmit Profile Selection in Dense Wireless Networks

Kułacz

Kliks

Kryszkiewicz

et al. 2020

Sensors

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The development of wireless networks can be characterized by both the increased number of deployed network nodes as well as their greater heterogeneity. As a consequence, the distance between the neighboring nodes decreases significantly, the density of such a wireless network is very high, and it brings to the mind the analogy to the human brain and nervous system, where a highly simplified scheme of information delivery is applied. Motivated by this similarity, in this paper, we study the possibility of the application of various transmission profiles in order to optimize the overall energy consumption in such dense wireless networks. The transmission profile specifies the radio access and energy consumption of the wireless transceiver (network node), and is characterized by the tuple of parameters, e.g., the total transmit power or minimal required signal-to-noise ratio (SNR). In the considered multi-hop network, we assume that each node can be set to the most promising transmission profile to achieve some predefined goals, such as (sensor) network reliability or transmission energy efficiency. We have proposed the new graph-based routing algorithm in such a dense wireless network, where total power consumption of message delivery is minimized by multihop and multimode transmission. The theoretical definition of the prospective transmission schemes is supported by the analysis of the results of the simulation experiments.

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Collaborative Resource Management for Negotiable Multi-Operator Small Cell Networks

Cited by 5 publications

References 24 publications

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

Dynamic Transmit Profile Selection in Dense Wireless Networks

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