Resource Management in Multicloud IoT Radio Access Network

Awais, Muhammad; Ahmed, Ashfaq; Ali, Syed Azhar; Naeem, Muhammad; Ejaz, Waleed; Anpalagan, Alagan

doi:10.1109/jiot.2018.2878511

Cited by 17 publications

(10 citation statements)

References 25 publications

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“…The requested rate of randomly generated users in the interval [1,3], unit: Mpbs; 6) In the interval [10,50], the distance between user location and AP is randomly generated, the order of magnitude is 10, and the unit is m. The the simulation results of the two bandwidth allocation distribution algorithms is shown in Fig. 6 and Fig.…”

Section: A Single System Model Simulation and Results Analysismentioning

confidence: 99%

“…3) The number of initial users in the area a is 5 in a single WiFi area, 10 in area B, and 10 in VLC; 4) The signal-to-noise ratio of the transmitted signal is 10dB; 5) At the end of each scheduling cycle, the change range of the number of people in the two networks is [-5,5]; 6) The rate of randomly generated user requests in the interval [1,3], in Mbps; 7) In the interval [10,50], it generates the distance between WiFi user and AP randomly in area a, the unit is m, and the order of magnitude is 10. The distance between WiFi user location and WiFi hotspot in area B is 20m, and the distance between VLC user location and VLC hotspot is 10m; 8) The simulation assumes that the threshold of decisionmaking between the two systems after information interaction is the average user rate and whether it reaches 60% of the system resources.…”

Section: B Joint System Model Simulation and Results Analysismentioning

confidence: 99%

“…To solve the above problems, we propose a hybrid VLC-WiFi network, which uses a WiFi network to solve the limited VLC coverage problem. Considering the VLC network alone, due to the lack of effective coordination between VLC and other existing networks, as well as its own small signal coverage range and unstable optical link, it reduces the user experience of mobile terminals (MT) [9][10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Resource Allocation for Hybrid Visible Light Communications (VLC)-WiFi Networks

Yang

Zhang

et al. 2020

IEEE Access

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Visible Light Communication (VLC) is becoming a promising technology of wireless communication, due to high data rate, low cost, and immunity to Radio Frequency (RF) interference. However, the VLC systems often suffer from service disruptions because of the limited coverage of light. Therefore, the heterogeneous networks of VLC and WiFi are proposed to achieve high-speed indoor communication and seamless coverage. This paper investigates an efficient heterogeneous network resource management algorithm, which is not only significant to integrate the current network resources but also has practical significance to improve the performance of heterogeneous networks. We establish a distributed joint resource management system model and propose two kinds of resource management allocation strategies to allocate bandwidth among the users with maximum fairness. The simulation results show the feasibility of the heterogeneous network and the effectiveness of the algorithm.

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Section: A Single System Model Simulation and Results Analysismentioning

confidence: 99%

Section: B Joint System Model Simulation and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resource Allocation for Hybrid Visible Light Communications (VLC)-WiFi Networks

Yang

Zhang

et al. 2020

IEEE Access

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“…It maximizes the network utility by formulating and solving the conflict graphs. In [135], overall network utilization of multicloud CRAN has been maximized by solving the constraint resource allocation problem using the heuristic algorithm. Additionally, resource allocation for delay sensitive applications in energy harvesting CRAN has been addressed in [78], where the presented solution maximizes the utility of user equipment using a Lyapunov optimization technique.…”

Section: A Objectivesmentioning

confidence: 99%

A comprehensive survey on resource allocation for CRAN in 5G and beyond networks

Ejaz

Sharma

Saadat

et al. 2020

Journal of Network and Computer Applications

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The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non-Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions.

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“…Similar performance can be achieved for uplink by applying the ideas in [ 21 ]. However, due to complexity, latency, connectivity, scalability, and synchronization problems, the deployment of multi-cloud radio-access-networks (M-CRAN) is recently considered in a few works such as [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. For example, Reference [ 27 ] studies the problem of optimization of precoding and joint compression of baseband signals across multiple clusters of BSs in downlink.…”

Section: Introductionmentioning

confidence: 99%

Degrees-Of-Freedom in Multi-Cloud Based Sectored Cellular Networks

Gelincik

Othman

2020

Entropy

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This paper investigates the achievable per-user degrees-of-freedom (DoF) in multi-cloud based sectored hexagonal cellular networks (M-CRAN) at uplink. The network consists of N base stations (BS) and K ≤ N base band unit pools (BBUP), which function as independent cloud centers. The communication between BSs and BBUPs occurs by means of finite-capacity fronthaul links of capacities C F = μ F · 1 2 log ( 1 + P ) with P denoting transmit power. In the system model, BBUPs have limited processing capacity C BBU = μ BBU · 1 2 log ( 1 + P ) . We propose two different achievability schemes based on dividing the network into non-interfering parallelogram and hexagonal clusters, respectively. The minimum number of users in a cluster is determined by the ratio of BBUPs to BSs, r = K / N . Both of the parallelogram and hexagonal schemes are based on practically implementable beamforming and adapt the way of forming clusters to the sectorization of the cells. Proposed coding schemes improve the sum-rate over naive approaches that ignore cell sectorization, both at finite signal-to-noise ratio (SNR) and in the high-SNR limit. We derive a lower bound on per-user DoF which is a function of μ BBU , μ F , and r. We show that cut-set bound are attained for several cases, the achievability gap between lower and cut-set bounds decreases with the inverse of BBUP-BS ratio 1 r for μ F ≤ 2 M irrespective of μ BBU , and that per-user DoF achieved through hexagonal clustering can not exceed the per-user DoF of parallelogram clustering for any value of μ BBU and r as long as μ F ≤ 2 M . Since the achievability gap decreases with inverse of the BBUP-BS ratio for small and moderate fronthaul capacities, the cut-set bound is almost achieved even for small cluster sizes for this range of fronthaul capacities. For higher fronthaul capacities, the achievability gap is not always tight but decreases with processing capacity. However, the cut-set bound, e.g., at 5 M 6 , can be achieved with a moderate clustering size.

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Resource Management in Multicloud IoT Radio Access Network

Cited by 17 publications

References 25 publications

Resource Allocation for Hybrid Visible Light Communications (VLC)-WiFi Networks

Resource Allocation for Hybrid Visible Light Communications (VLC)-WiFi Networks

A comprehensive survey on resource allocation for CRAN in 5G and beyond networks

Degrees-Of-Freedom in Multi-Cloud Based Sectored Cellular Networks

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