Optimal Packet Scheduling in an Energy Harvesting Communication System

By Jing Yang, Sennur Ulukus. IEEE Transactions on Communications, 2010

825 citations recorded by the source corpus.

We consider the optimal packet scheduling problem in a single-user energy harvesting wireless communication system. In this system, both the data packets and the harvested energy are modeled to arrive at the source node randomly. Our goal is to adaptively change the transmission rate according to the traffic load and available energy, such that the time by which all packets are delivered is minimized. Under a deterministic system setting, we assume that the energy harvesting times and harvested energy amounts are known before the transmission starts. For the data traffic arrivals, we consider two different scenarios. In the first scenario, we assume that all bits have arrived and are ready at the transmitter before the transmission starts. In the second scenario, we consider the case where packets arrive during the transmissions, with known arrival times and sizes. We develop optimal off-line scheduling policies which minimize the time by which all packets are delivered to the destination, under causality constraints on both data and energy arrivals.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/989d8b036bfd056117ee6078cb25415cd5eb9568

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Citations in the corpus

Throughput Optimal Policies for Energy Harvesting Wireless Transmitters with Non-Ideal Circuit Power (2012, methodology): “The optimal solution for the above problem has been shown in [12], [13] to follow a non-decreasing piecewiseconstant (staircase) function, which is given in (16).”
Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies (2011, methodology): “In [5], [6], minimization of the transmission comp letion time is considered in an energy harvesting system and the optimal solution is obtained usin g a geometric framework similar to the calculus approach presented in [8].”
Throughput Maximization for the Gaussian Relay Channel with Energy Harvesting Constraints (2011, methodology): “It is worth noting that for the case of h0 = 0, our relay channel model degrades to a cascade of two AWGN point-to-point channels that were studied in [3] with individual EH constraints.”
Optimum Transmission Policies for Battery Limited Energy Harvesting Nodes (2010, background): “Using a power-rate function of this form is fairly common [5], [20], and is clearly valid for the additive white Gaussian noise (AWGN) channel.”
A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead (2016, background): “In [116] an ofﬂine power allocation algorithm termed directional waterﬁlling is proposed, while [117] addresses a similar problem but assuming a system in which the data to be transmitted is available at random times.”
Energy Harvesting Wireless Communications: A Review of Recent Advances (2015, background): “Due to the concavity of the rate–power relationship, power must be kept constant between energy harvests [12].”
Lazy is timely: Status updates by an energy harvesting source (2015, background): “When these sensors are powered by energy harvesting, the communication rate is constrained by the stochastic energy variation of the harvesting process; see, for example, [3]–[10].”
Broadcasting with an Energy Harvesting Rechargeable Transmitter (2010, methodology): “We extend [1], [2] in [11] to a fading channel, and develop optimal off-line and on-line scheduling policies under stochastic fading and energy arrival processes.”
Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source (2017, background): “The notion of acquiring energy from nature to power wireless transmitters is intriguing since careful transmission scheduling can render extended or even perpetual operation of the network [30]–[32].”
Designing intelligent energy harvesting communication systems (2014, background): “Accordingly, in the following sections, we present two fundamental approaches: in the offline optimization framework, it is assumed that the exact values of H(t) and I(t) are known in advance at the µP for the whole duration 4 of operation.”
Advances in Energy Harvesting Communications: Past, Present, and Future Challenges (2016, background): “The processing cost means the power consumption spent in the data transmission circuitry, and the circuit power consumption is non-negligible in short range communications, as compared with the data transmission power.”
Mobile Cooperative Computing: Energy-Efficient Peer-to-Peer Computation Offloading (2017, background): “Given the above facts, a control policy for energy-efficient or throughput-optimal transmission is reflected as a “pulled string” across the region/tunnel [24], [27], [28].”