Toward Energy Efficient and Balanced User Associations and Power Allocations in Multiconnectivity-Enabled mmWave Networks

Jin, Kezhong; Cai, Xuebing; Du, Jianing; Park, Hosung; Tang, Zhenzhou

doi:10.1109/tgcn.2022.3172355

Cited by 20 publications

(15 citation statements)

References 51 publications

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“…The efficacy of rank-sum-weight method should also be investigated for other multi-objective formulations in the area of AGC. Moreover, other optimization algorithms [52], [53] should also be investigated for singleobjective and multi-objective formulations [54], [55] of AGC problem.…”

Section: Discussionmentioning

confidence: 99%

Rank-Sum-Weight Method Based Systematic Determination of Weights for Controller Tuning for Automatic Generation Control

et al. 2022

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The design and performance evaluation of a grey wolf optimizer (GWO) aided rank-sumweight method based proportional-integral-derivative regulator with derivative filter (PIDn) for automatic generation control (AGC) of two-area interconnected power systems are presented in this research. The derivative gain filter is used to lessen the impacts of noise in the input signal. Sub-objectives based on integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviation, and areacontrol errors (ACEs) are used to formulate the objective function for adjusting regulator settings. A single overall objective function is formed by combining these sub-objectives. ITSEs of two areas, ITSEs of tieline power deviation, and ITSEs of ACEs of two areas comprise up the overall objective function. In the control design, the weights in the overall objective function are used to evaluate relative significance of each sub-objective. In contrast to previous techniques, where weights are either considered equal by ignoring the relative relevance of sub-objectives or selected randomly, the weights in this article are obtained using the rank-sum-weight method systematically. Using the GWO algorithm, the overall objective function is minimized. For six different circumstances including different load disturbances in interconnected areas, the effectiveness of the proposed GWO aided rank-sum-weight method based controller is examined. The performance of the GWO-tuned controller is also compared to those of other controllers tuned using the differential evolution, elephant herding optimization, Nelder-Mead simplex, membrane computing, and Luus-Jaakola algorithms. Time domain specifications are tabulated for each of the six circumstances. The findings are also plotted to demonstrate the frequency and tie line power fluctuations. A statistical analysis is also performed in order to assess the overall efficacy of the suggested controller.

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Section: Discussionmentioning

confidence: 99%

Rank-Sum-Weight Method Based Systematic Determination of Weights for Controller Tuning for Automatic Generation Control

et al. 2022

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“…To achieve this goal, two main approaches have been considered in the MC literature: the effective management of energy-intensive handovers and optimal management of the user–cell association. The authors of [ 74 ], for example, have shown the value of MC in reducing energy consumption.…”

Section: Taxonomymentioning

confidence: 99%

Multi-Connectivity for 5G Networks and Beyond: A Survey

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et al. 2022

Sensors

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To manage a growing number of users and an ever-increasing demand for bandwidth, future 5th Generation (5G) cellular networks will combine different radio access technologies (cellular, satellite, and WiFi, among others) and different types of equipment (pico-cells, femto-cells, small-cells, macro-cells, etc.). Multi-connectivity is an emerging paradigm aiming to leverage this heterogeneous architecture. To achieve this, multi-connectivity proposes to enable UE to simultaneously use component carriers from different and heterogeneous network nodes: base stations, WiFi access points, etc. This could offer many benefits in terms of quality of service, energy efficiency, fairness, mobility, and spectrum and interference management. Therefore, this survey aims to present an overview of multi-connectivity in 5G networks and beyond. To do so, a comprehensive review of existing standards and enabling technologies is proposed. Then, a taxonomy is defined to classify the different elements characterizing multi-connectivity in 5G and future networks. Thereafter, existing research works using multi-connectivity to improve the quality of service, energy efficiency, fairness, mobility management, and spectrum and interference management are analyzed and compared. In addition, lessons common to these different contexts are presented. Finally, open challenges for multi-connectivity in 5G networks and beyond are discussed.

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“…Numerical results indicate that when the phenomenon of mutual coupling is taken into consideration, the proposed approach outperforms other conventional approaches related to EE optimization. In [22], the authors investigated appropriate user association and power allocation strategies in multi-connectivity enabled mmWave networks. In this context, the goal was to optimize overall system EE and balance user rate among all users and traffic load among all BSs.…”

Section: A Related Workmentioning

confidence: 99%

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

et al. 2022

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The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the MLassisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration.

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Toward Energy Efficient and Balanced User Associations and Power Allocations in Multiconnectivity-Enabled mmWave Networks

Cited by 20 publications

References 51 publications

Rank-Sum-Weight Method Based Systematic Determination of Weights for Controller Tuning for Automatic Generation Control

Rank-Sum-Weight Method Based Systematic Determination of Weights for Controller Tuning for Automatic Generation Control

Multi-Connectivity for 5G Networks and Beyond: A Survey

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

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