Joint Resource Allocation and Dynamic Activation of Energy Harvesting Small Cells in OFDMA HetNets

Lohani, Sudha; Hossain, Ekram; Bhargava, V.K.

doi:10.1109/twc.2017.2785301

Cited by 20 publications

(5 citation statements)

References 38 publications

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“…Specifically, due to the curse of dimensionality, computational complexity in such non-sequential optimizing algorithms grows exponentially with an increase in the number of cells and timeslots. Moreover, given the long-term accumulation nature of the objective function in P 2 , traditional nonsequential optimizing algorithms necessitate precise availability of statistical channel information on future values at the beginning of the scheduling period [39]. Nevertheless, dynamic changes in satellite-to-terrestrial channel conditions may lead to inaccuracies when obtaining future statistical information in advance.…”

Section: Decentralized Resource Allocation Solutionmentioning

confidence: 99%

Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

Zhao,

Yu,

Pan

et al. 2024

IEEE Access

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The densification of low-Earth orbit (LEO) constellations and the development of inter-satellite links enable the cooperative transmission of multiple satellites. This paper investigates a decentralized resource allocation strategy for multi-satellite cooperation networks under statistical channel state information (CSI). Our formulation aims at minimizing the total matching error between non-uniform traffic requests and achievable throughput by jointly optimizing transmit power and beam illumination pattern. The original problem is a stochastic mixed-integer quadratic programming with the long-term accumulation nature, whose stochastic objective is first addressed by incorporating the outage probability constraint. Then we decompose the deterministic problem into a series of single-timeslot subproblems on the greedy basis, each of which is further decomposed for each satellite following the decentralized decision principle with a limited number of inter-satellite interactions required to obtain the overall resource allocation. Finally, the optimization problem at each satellite is solved by sequential quadratic programming (SQP)-based alternating optimization algorithm that iterates between the transmit power allocation and beam illumination design. Numerical results show the effectiveness of the proposed decentralized algorithm in reducing the total matching error by at least 27% over other baseline approaches.

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Section: Decentralized Resource Allocation Solutionmentioning

confidence: 99%

Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

Zhao,

Yu,

Pan

et al. 2024

IEEE Access

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“…Lohani, et al [67] proposed energy-and interference-aware resource allocation with energy harvesting-enabled SBSs. The allocation was done by considering the channel state, type of activity, and amount of harvested energy.…”

Section: ) Energy Harvestingmentioning

confidence: 99%

“…These options offer a smart way to determine the resource allocation method. The authors in [52,67] used different optimization techniques to determine the resource allocation by the Markov queueing system, mixed MINLP and PSO. In [52], the authors proposed three schemes based on traffic congestion calculation methods and analyzed the schemes' complexity as the number of iterations needed to achieve stable results.…”

Section: ) Computational Complexitymentioning

confidence: 99%

“…This was followed by the bisectionbased scheme that has a simpler searching method than the sequential search and time-ordered schemes, and used the last value of the allocated resource to reduce the number of iterations. Lohani, et al [67] proposed an online approach to determine the resource allocation based on a dynamic programming algorithm. Although this method yielded a promising result in terms of throughput compared to an offline approach, it suffers from high computational complexity as it increases exponentially with users.…”

Section: ) Computational Complexitymentioning

confidence: 99%

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Survey of Radio Resource Management in 5G Heterogeneous Networks

et al. 2020

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Radio resource management (RRM) for future fifth-generation (5G) heterogeneous networks (HetNets) has emerged as a critical area due to the increased density of small-cell networks and radio access technologies. Recent research has mostly concentrated on resource management, including spectrum utilization and interference mitigation, but the complexities of these resources have been given little attention. This paper provides an overview of the issues arising from future 5G systems and highlights their importance. The different approaches used in recently published surveys categorizing RRM schemes are discussed, and the survey method is presented. We report on a survey of HetNet RRM schemes that have been studied recently, with a focus on the joint optimization of radio resource allocation with other mechanisms. These RRM schemes were subcategorized according to their optimization metrics and qualitatively analyzed and compared. An analysis of the complexity of RRM schemes in terms of implementation and computation is presented. Several potential scopes of research for future RRM in 5G HetNets are also identified.

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“…By leveraging the priori distributions of the renewable energy arrival and data arrival, an optimal deterministic offline resource scheduling policy was exhibited in [5] to minimize the energy consumption. Assuming the availability of non-causal information, the authors in [6] devised an offline resource allocation algorithm for the system power costs minimization. While, it is hard to obtain the exact non-causal information during the time-varying energy harvesting process in realworld applications and the capacity of the battery is limited as well.…”

Section: Introductionmentioning

confidence: 99%

ADMM-Based Distributed Online Algorithm for Energy Management in Hybrid Energy Powered Cellular Networks

Zhai

et al. 2019

IEEE Access

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Energy harvesting has been considered as a promising technique to decrease the conventional grid energy expenditure. However, most renewable energy sources are unreliable and random. To overcome these drawbacks, besides the commonly adopted approaches such as purchasing power from the grid and deploying batteries, energy cooperation is an appealing solution. In this paper, we investigate the energy management problem by jointly optimizing the data admission rate, transmit power, energy sharing among base stations (BSs), battery charging and discharging rate, and the energy purchased from the grid in hybrid energy powered cellular networks. First, the long-term average total cost minimization problem under the constraints on limited battery size and users' data rate requirements is formulated as a stochastic optimization problem. Employing the Lyapunov optimization technique and alternating direction method of multipliers (ADMM), we propose an online distributed algorithm, referred to as distributed online energy management algorithm (DOEMA), where the current system states are needed, without requiring the system statistic or future information. Furthermore, the proposed algorithm can be implemented in a parallel and completely distributed fashion, which could provide more engineering guidelines for practical communication protocols compared with the centralized algorithm. The extensive simulation results are conducted to demonstrate the correctness of the theoretical analysis and validate the performance improvement against other algorithms in terms of system total cost reduction.INDEX TERMS Multi-cell networks, renewable energy, energy cooperation, stochastic optimization, ADMM, distributed online algorithm.QI ZENG received the Ph.D. degree in communication and information system from

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Joint Resource Allocation and Dynamic Activation of Energy Harvesting Small Cells in OFDMA HetNets

Cited by 20 publications

References 38 publications

Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

Survey of Radio Resource Management in 5G Heterogeneous Networks

ADMM-Based Distributed Online Algorithm for Energy Management in Hybrid Energy Powered Cellular Networks

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