Resource Allocation for Energy Efficiency in 5G Wireless Networks

Nguyen, Long D.

doi:10.4108/eai.27-6-2018.154832

Cited by 26 publications

(13 citation statements)

References 51 publications

(88 reference statements)

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“…Resource allocation is one significant aspect in any systems. In IIoT applying 5G wireless networks [15], the new systems should be more dynamic and intelligent to simultaneously satisfy various network demands, by adopting new wireless technologies and approaches. To this end, resource allocation is faced with major challenges such as interference alignment, security attacks, or green communication.…”

Section: Energy Efficiency In Iiotmentioning

confidence: 99%

Real-time Optimisation for Industrial Internet of Things (IIoT): Overview, Challenges and Opportunities

Nguyen¹,

Kortun²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Industrial Internet-of-Things (IIoT) with massive data transfers and huge numbers of connected devices, in combination with the high demand for greater quality-of-services, signal processing is no longer producing small data sets but rather, very large ones (measured in gigabytes or terabytes), or even higher. This has posed critical challenges in the context of optimisation. Communication scenarios such as online applications come with the need for real-time optimisation. In such scenarios, often under a dynamic environment, a strict real-time deadline is the most important requirement to be met. To this end, embedded convex optimisation, which can be redesigned and updated within a fast time-scale given sufficient computing power, is a candidate to deal with the challenges in real-time optimisation applications. Real-time optimisation is now becoming a reality in signal processing and wireless networks of IIoT. Research into new technologies to meet future demands is receiving urgent attention on a global scale, especially when 5G networks are expected to be in place in 2020. This work addresses the fundamentals, technologies and practically relevant questions related to the many challenges arising from real-time optimisation communications for industrial IoT.

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Section: Energy Efficiency In Iiotmentioning

confidence: 99%

Real-time Optimisation for Industrial Internet of Things (IIoT): Overview, Challenges and Opportunities

Nguyen¹,

Kortun²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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“…The authors in [11] investigated different resources allocation schemes for EE maximization in various 5G wireless networks. In [12], the energy efficiency of a single cell D2Dcellular converged network is studied and the optimal D2D distance to maximize EE for video delivery is addressed.…”

Section: Notationsmentioning

confidence: 99%

“…In those schemes, the EE is targeted alone or as a joint objective with other performance metrics such as latency and sum-rate maximization. [11]- [15]. However, the EE maximization problem is a nonconvex problem [11], in particular, fractional programming is used to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Resources Allocation in Underlay Device-to-Device Communications Networks: A Reduced-Constraints Approach

et al. 2020

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Device-to-Device (D2D) communications underlaying cellular networks have emerged as a necessity for a substantial increase in the system throughput and the number of active devices for the future cellular networks. In underlay D2D networks, it is conventional to use different interference management (IM) techniques to allow D2D transmitters to reuse the cellular users' subcarriers. Conventionally, those IM techniques pair a specific number (one or more) of D2D transmitters to each subcarrier and/or allow each D2D transmitter to transmit on a specific number of subcarriers simultaneously in order to achieve the target rates. Due to the mixed-integer nature of those IM techniques, convex optimization techniques can not be used, and usually complex heuristic or game-theoretic approaches are exploited. In this paper, we introduce a reduced-constraints approach to seek sub-optimal joint power allocation and channel assignment solutions for two non-convex, mixed-integer, and non-linear programs (MINLP). Specifically, via the reduced-constraints approach and variable transformation techniques, we can exploit primal-dual algorithms to solve system power minimization and energy-efficiency maximization problems. Extensive numerical simulation results show that the proposed approach outperforms state-of-the-art techniques.

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“…There are significant efforts in the network EE optimization under the QoS constraints and a transmit power budget [32,33]. Nguyen [34] tackles the resource allocation for the critical EE in 5G wireless networks, which considers the EE in small cells, massive multiple-input multiple-output (MIMO) [35,36] and cell-free networks [37]. In order to dynamically allocate wireless resources [19] such as cell slices and virtual base stations, Kwak et al [38] study bandwidth slicing and resource allocation problems for supporting the mixture of IoT and video streaming services.…”

Section: Network Slicing and Resource Allocationmentioning

confidence: 99%

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

Feng

et al. 2019

Applied Sciences

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The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.

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Resource Allocation for Energy Efficiency in 5G Wireless Networks

Cited by 26 publications

References 51 publications

Real-time Optimisation for Industrial Internet of Things (IIoT): Overview, Challenges and Opportunities

Real-time Optimisation for Industrial Internet of Things (IIoT): Overview, Challenges and Opportunities

Resources Allocation in Underlay Device-to-Device Communications Networks: A Reduced-Constraints Approach

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

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