Optimal energy allocation and admission control for communications satellites

Fu, A.; Modiano, Eytan; Tsitsiklis, John N.

doi:10.1109/infcom.2002.1019310

Cited by 24 publications

(37 citation statements)

References 12 publications

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“…We emphasize that our system model is continuous rather than slotted. In slotted models, e.g., [3], [4], [9], the energy input-output relationship is written for an entire slot. Such models allow energies larger than E max to enter the battery and be used for transmission in a given single slot.…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…An earlier line of research considered the problem of energy management in communications satellites [9], [10]. In [9], various energy allocation problems in solar powered communication satellites are solved using dynamic programming. In [10], optimal energy allocation to a fixed number of time slots is derived under time-varying channel gains and with offline and online knowledge of the channel state at the transmitter.…”

Section: Introductionmentioning

confidence: 99%

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Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

Özel

Tutuncuoglu

Yang

et al. 2011

IEEE J. Select. Areas Commun.

1,051

1,104

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Abstract-Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

Özel

Tutuncuoglu

Yang

et al. 2011

IEEE J. Select. Areas Commun.

1,051

1,104

View full text Add to dashboard Cite

show abstract

“…The energy allocation and admission control problem of single satellite is discussed in [7], and the authors use the dynamic programming methods to obtain the optimal transmission requests selection strategies for the maximum system profile. In [8], the authors send the multimedia broadcast flow in high bit rate burst instead of the continuous low bit rate mode, and turn off the RF elements to save energy in the spare time.…”

Section: Related Workmentioning

confidence: 99%

“…For the energy limitation of satellites, decreasing the energy consumption would certainly improve the availability of the satellite systems. There are some researches focusing on the satellite energy-saving problems [7] [8], however, most of them aim at the energy and resource allocation of the single satellite, not the whole satellite network or satellite constellation. Compared to the durative solar-coverage of geostationary orbit (GEO) satellite, the low earth orbit (LEO) satellite would move into the shadow behind earth almost in every orbit cycle, and then only the battery can be available, which is the same condition as the wireless sensor networks [2].…”

Section: Introductionmentioning

confidence: 99%

Improving Availability through Energy-Saving Optimization in LEO Satellite Networks

Tang

Zhang

et al. 2014

Information and Communication Technology

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Part 2: The 2014 Asian Conference on Availability, Reliability and Security, AsiaARES 2014International audienceRecently, satellite networks are widely used for communication in areas lack of network infrastructures, and will act as the backbones in the next generation internet. Therefore, the availability of satellite networks is very important. In space, the energy is always limited for satellites, and highly efficient energy utilization would certainly improve the availability of satellite systems. In this paper, we consider the energy-saving optimization for the LEO satellite network instead of a single satellite. We modify and extend the multicommodity flow model [3] to switch off satellite nodes and links as much as possible in LEO satellite networks. Taking advantage of the multi-coverage scheme and traffic distribution patents in satellite networks, we improve the heuristic algorithms in [3] to turn off the unnecessary satellites, up-down links and inter-satellite links respectively up to 59 %, 61 % and 72 % under the constraints of link utilization and routing hops increase ratio, and the total energy saving ratio can be up to 65 %. Finally, the availability of LEO satellite networks has been deeply developed

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A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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Optimal energy allocation and admission control for communications satellites

Cited by 24 publications

References 12 publications

Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

Improving Availability through Energy-Saving Optimization in LEO Satellite Networks

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

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