Joint User Association and Power Allocation for Millimeter-Wave Ultra-Dense Networks

Nguyen, Huy; Murakami, Homare; Nguyen, Kien; Ishizu, Koji; Kojima, Fumihide; Kim, Jong-Deok; Chung, Sang-Hwa; Hwang, Won-Joo

doi:10.1007/s11036-019-01286-8

Cited by 16 publications

(10 citation statements)

References 37 publications

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“…13~Fig. 15, we analyze the performance metrics achieved by the schemes over the time horizon with 6000 time slices, in which the number of selected beam-power pairs per time slice is 6 and the number of vehicles in the simulation area is set to 65. Fig.…”

Section: Analysis Of Simulation Resultsmentioning

confidence: 99%

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Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

Gui

Liu

2021

Preprint

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Millimeter-Wave (mmWave) technology is deemed as a feasible approach for future vehicular communications. However, mmWave signals are characterized by high path loss and penetration loss, which can be alleviated by directional communication. Directional transmission performance depends on beam alignment between transmitter and receiver, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this type of works does not take energy efficiency into account. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall, where the power consumption can be reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results show that the EFML scheme improves both the network energy efficiency and the amount data of cellular-assisted vehicular networks at the cost of more beam performance update overhead. However, there is no difference in the cost of updating beam performance after adequate online learning.

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Section: Analysis Of Simulation Resultsmentioning

confidence: 99%

“…The authors in [15] leveraged the advantage of the mmWave characteristics in ultra-dense networks and proposed a method for joint optimization and resource allocation between base stations and users. Specifically, they aimed at maximizing user throughput in the system while also considering fairness.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

Gui

Liu

2021

Preprint

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“…Khalili et al 33 optimizes the problems of power control jointly considering the UA, carrier allocation, and antenna selection. An alternate descent method is used to jointly optimize the UA and power allocation to maximize the system spectral efficiency on the premise of guaranteeing the constraint of QoS in Nguyen et al 34 The model of UA and spectrum allocation are established by the hypergraph theory in Zhuang et al, 35 where the algorithm of graph coloring is adopted for the global spectrum allocation. Authors in Zhuang et al 17 employ the suboptimal solution of convex relaxation to jointly optimize the UA and spectrum allocation.…”

Section: Related Workmentioning

confidence: 99%

Low complexity closed‐loop strategy for mmWave communication in industrial intelligent systems

Chen

Lin

Zhao

et al. 2022

Int J of Intelligent Sys

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Modern communication and computing technology is the basic support of the industrial intelligent systems (IIS). As a key component of IIS, the smart port is essential to be offered low‐complexity and high‐reliability communication service, especially for driverless engineering vehicles. However, it is combined and nonconvex to find the optimal association between vehicles and the road side units (RSUs). Besides, due to the mobility of vehicles and the severe path loss of mmWave links, beam switching and reassociation between vehicles and RSUs are required frequently, which brings a great challenge to the communication for the IIS. A low complexity closed‐loop strategy based on distributed cooperation for mmWave communication in IIS is proposed in this study, in which user association and beam tracking with the assistance of beam pools is proposed. Many‐to‐many user association is established based on distributed multiagent reinforcement learning, where the vehicle can independently select the set of serving RSUs based on the local observation without information exchange with others, reducing the signaling overhead and computational complexity while improving system throughput. Furthermore, multipoint‐cooperation soft switching of beams based on beam tracking improves the reliability of mmWave communication with the smaller training cost. Extensive analysis and simulation results demonstrate that the proposed solution significantly reduces the complexity of the mmWave communication while improving the throughput and stability in IIS.

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“…For instance, in [45], the NP-hard problem of the joint uplink resource allocation of small cells, spectrum resources, and transmission power is decomposed into a potential game for small cell selection and a non-cooperative game for power allocation. The EE maximization problem is formulated in [77] in terms of number of bits delivered per unit of Joule subject to the QoS rate threshold for each user, and an alternating descent algorithm is applied to separate the energy efficiency optimization problem into two sub-problems of EE maximization problem and user throughput fairness. In [78], the EE maximization problem is modeled as a class of optimization problems called fractional programming to minimize the total power consumption of the entire system.…”

Section: Selected Papersmentioning

confidence: 99%

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

2021

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Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

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Joint User Association and Power Allocation for Millimeter-Wave Ultra-Dense Networks

Cited by 16 publications

References 37 publications

Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

Low complexity closed‐loop strategy for mmWave communication in industrial intelligent systems

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

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