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Journal ArticleDOI

Base Station switching and edge caching optimisation in high energy-efficiency wireless access network

19 Jun 2021-Computer Networks (Elsevier)-Vol. 192, pp 108100
TL;DR: The caching feature of the MEC paradigm is considered in an heterogeneous RAN, powered by a renewable energy generator system, energy batteries and the power grid, where micro cell BSs are deactivated in case of renewable energy shortage.
About: This article is published in Computer Networks.The article was published on 2021-06-19. It has received 10 citations till now. The article focuses on the topics: Energy consumption & Efficient energy use.
Citations
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Journal ArticleDOI
15 Aug 2021-Sensors
TL;DR: In this paper, the authors proposed an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture.
Abstract: The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user's quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

30 citations

Journal ArticleDOI
TL;DR: A fuzzy multi-objective mathematical model in which each traffic is a fuzzy variable and a decision-making model based on possibility theory is presented, which results not only in a reduction of CO 2 emissions but also guarantees good network coverage.
Abstract: The development of future wireless access networks often results in very high energy consumption. To reduce this consumption, decision-makers (DM) minimize the number of base stations ( $$\hbox {BS}_s$$ ) installed while using a dynamic BS on/off strategy. However, reducing the number of base stations leads to insufficient network coverage. Indeed, for better coverage, the decision-maker (DM) should install enough base stations. We can therefore see that we have two contradictory objectives. On the other hand, we can easily notice that the information of the network traffic evolves over time. Therefore and in order to make a realistic study, we will consider the traffic information as an imprecise and uncertain value instead of a constant value. For the reasons aforementioned, we introduce in this paper, a fuzzy multi-objective mathematical model in which each traffic is a fuzzy variable, and then, we present a decision-making model based on possibility theory. To solve this problem, we used two meta-heuristic algorithms. The obtained results proved the efficiency of our model compared to previous studies. Indeed, the proposed methodology results not only in a reduction of $$\hbox {CO}_2$$ emissions (between 18.15 and 24.18%) but also guarantees good network coverage.

3 citations

Journal ArticleDOI
01 Jan 2023-Sensors
TL;DR: In this article , a cluster-based multi-user multi-server (CMUMS) caching algorithm is proposed to optimize the MEC content caching mechanism and control the distribution of high-popular tasks.
Abstract: The work on perfecting the rapid proliferation of wireless technologies resulted in the development of wireless modeling standards, protocols, and control of wireless manipulators. Several mobile communication technology applications in different fields are dramatically revolutionized to deliver more value at less cost. Multiple-access Edge Computing (MEC) offers excellent advantages for Beyond 5G (B5G) and Sixth-Generation (6G) networks, reducing latency and bandwidth usage while increasing the capability of the edge to deliver multiple services to end users in real time. We propose a Cluster-based Multi-User Multi-Server (CMUMS) caching algorithm to optimize the MEC content caching mechanism and control the distribution of high-popular tasks. As part of our work, we address the problem of integer optimization of the content that will be cached and the list of hosting servers. Therefore, a higher direct hit rate will be achieved, a lower indirect hit rate will be achieved, and the overall time delay will be reduced. As a result of the implementation of this system model, maximum utilization of resources and development of a completely new level of services and innovative approaches will be possible.

2 citations

Journal ArticleDOI
Chenyang Wang1, Ruibin Li1, Zheng Di1, Chao Qiu1, Xiaofei Wang1 
TL;DR: A network representation model is proposed to regard the multidimensional relations as a probability in a third-order (3-D) tensor space, and an anchored user selection algorithm is developed to maintain the stability of multiple D2D social communities by choosing and retaining critical users adaptively under the limited network resources.
Abstract: Recently, integrated with the advanced communication technologies (e.g., 5G) and artificial intelligence (AI), mobile-edge intelligence (MEI) is regarded as the promising method to deal with the emerging challenges. Specifically, Device-to-Device (D2D) communications have been put forward to reduce the traffic pressure while extending cellular network capacity. However, the stability of the social network is important for the design of efficient and reliable traffic offloading strategy, which is often absent from the related work. Besides, most existing studies merely model the relation between a node pair as a binary or continuous value, neglecting the rich information between users. Moreover, many traditional models are conducted based on small-scale data sets or online Internet services, severely confining their applications in the D2D scenario. Thus, it is necessary to understand the network structure and select the key users to address the aforementioned challenges. In this article, we first propose a network representation model, named MPPT, to regard the multidimensional relations as a probability in a third-order (3-D) tensor space. Then, a mobile D2D social community is derived by integrating an edge base station (BS) and the nearby D2D users, and develop an anchored user selection algorithm to maintain the stability of multiple D2D social communities by choosing and retaining critical users adaptively under the limited network resources. Finally, we devise a probability-based onion layers anchored $(k,r)$ -core (P-OLAK) algorithm to identify the anchor users. The large-scale data sets-based experimental results show the superiorities of the proposed methods.

2 citations

Proceedings Article
01 Jan 2019
TL;DR: In this article, the authors use a Markov reward process to investigate the possibility of combining small area solar panels with a connection to the power grid to run a macro BS, and show that solar panels of the order of 1-2 kW peak, with a surface of about 5-10 m^2, combined with limited capacity energy storage, and a smart energy management policy, can lead to an effective exploitation of renewable energy.
Abstract: The limited power requirements of new generations of base stations (BSs) make the use of renewable energy sources, solar in particular, extremely attractive for mobile network operators. Exploiting solar energy implies a reduction of the network operation cost as well as of the carbon footprint of radio access networks, but previous research works indicate that the area of the solar panels that are necessary to power a standard macro BS is large, so large to make the solar panel deployment problematic, especially within urban areas. In this paper we use a modeling approach based on Markov reward processes to investigate the possibility of combining small area solar panels with a connection to the power grid to run a macro BS. By so doing, it is possible to increase the amount of renewable energy used to run a radio access network, while also reducing the cost incurred by the network operator to power its base stations. We assume that energy is drawn from the power grid only when needed to keep the BS operational, or during the night, that corresponds to the period with lowest electricity price. This has advantages in terms of both cost and carbon footprint. We show that solar panels of the order of 1-2 kW peak, i.e., with a surface of about 5-10 m^2, combined with limited capacity energy storage (of the order of 10-15 kWh, corresponding to about 3-5 car batteries), and a smart energy management policy, can lead to an effective exploitation of renewable energy.

2 citations

References
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Journal ArticleDOI
TL;DR: This article describes the scenarios identified for the purpose of driving the 5G research direction and gives initial directions for the technology components that will allow the fulfillment of the requirements of the identified 5G scenarios.
Abstract: METIS is the EU flagship 5G project with the objective of laying the foundation for 5G systems and building consensus prior to standardization. The METIS overall approach toward 5G builds on the evolution of existing technologies complemented by new radio concepts that are designed to meet the new and challenging requirements of use cases today?s radio access networks cannot support. The integration of these new radio concepts, such as massive MIMO, ultra dense networks, moving networks, and device-to-device, ultra reliable, and massive machine communications, will allow 5G to support the expected increase in mobile data volume while broadening the range of application domains that mobile communications can support beyond 2020. In this article, we describe the scenarios identified for the purpose of driving the 5G research direction. Furthermore, we give initial directions for the technology components (e.g., link level components, multinode/multiantenna, multi-RAT, and multi-layer networks and spectrum handling) that will allow the fulfillment of the requirements of the identified 5G scenarios.

1,934 citations

Journal ArticleDOI
TL;DR: This work shows that the uncoded optimum file assignment is NP-hard, and develops a greedy strategy that is provably within a factor 2 of the optimum, and provides an efficient algorithm achieving a provably better approximation ratio of 1-1/d d, where d is the maximum number of helpers a user can be connected to.
Abstract: Video on-demand streaming from Internet-based servers is becoming one of the most important services offered by wireless networks today. In order to improve the area spectral efficiency of video transmission in cellular systems, small cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are being proposed, such that video traffic to nomadic users can be handled by short-range links to the nearest small cell access points (referred to as “helpers”). As the helper deployment density increases, the backhaul capacity becomes the system bottleneck. In order to alleviate such bottleneck we propose a system where helpers with low-rate backhaul but high storage capacity cache popular video files. Files not available from helpers are transmitted by the cellular base station. We analyze the optimum way of assigning files to the helpers, in order to minimize the expected downloading time for files. We distinguish between the uncoded case (where only complete files are stored) and the coded case, where segments of Fountain-encoded versions of the video files are stored at helpers. We show that the uncoded optimum file assignment is NP-hard, and develop a greedy strategy that is provably within a factor 2 of the optimum. Further, for a special case we provide an efficient algorithm achieving a provably better approximation ratio of 1-(1-1/d )d, where d is the maximum number of helpers a user can be connected to. We also show that the coded optimum cache assignment problem is convex that can be further reduced to a linear program. We present numerical results comparing the proposed schemes.

1,331 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully.
Abstract: For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

1,129 citations

Posted Content
TL;DR: A low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee is provided, and it is observed that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully.
Abstract: For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rates for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor A_j>=1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide optimal association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values A_j are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a max received power association.

1,003 citations

Journal ArticleDOI
TL;DR: A real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at theedge is envisions.
Abstract: MEC is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile RAN. MEC servers are deployed on a generic computing platform within the RAN, and allow for delay-sensitive and context-aware applications to be executed in close proximity to end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisions a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use cases ranging from mobile edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open research issues that need to be addressed in order to efficiently integrate MEC into the 5G ecosystem.

700 citations