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

By Omur Ozel, Kaya Tutuncuoglu, Jing Yang, Sennur Ulukus, Aylin Yener. IEEE Journal on Selected Areas in Communications, 2011

1,089 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/782df3f50e6ad88e53945baa0135b4e4ac2a2fe9

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Citations in the corpus

Mobile Cooperative Computing: Energy-Efficient Peer-to-Peer Computation Offloading (2017, background): “The data causality constraints are analogous with the energy causality constraints for energy-harvesting transmissions [24], [30].”
Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications (2019, methodology): “[5] optimized the average throughput with finite epochs of an energy harvesting communication system with the KKT condition.”
Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices (2016, background): “With noncausal side information (SI) 2, including the channel side information (CSI) and energy side i nformation (ESI), the maximum throughput of point-to-point EH fading channels can be achi eved by the directional waterfilling algorithm [21].”
Designing intelligent energy harvesting communication systems (2014, background): “It can be shown that the optimal policy has a threshold structure, i.e., the packet is transmitted if V (i) is greater than a given value vth, which depends on both S(i) and H(i) [7], [8], and the optimal thresholds can be found numerically with the PIA.”
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].”
Energy Harvesting Wireless Communications: A Review of Recent Advances (2015, background): “An alternative approach to the feasible tunnel approach is the directional water-filling algorithm presented in [14].”
Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (2015, background): “Among others, the algorithmic design of local computing and offloading becomes more complex under the energy harvesting constraint due to MPT, which prevents energy consumption from exceeding the amount of harvested energy at every time instant [19].”
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.”
Throughput Maximization for UAV-Enabled Mobile Relaying Systems (2016, background): “It is interesting to note that such a result is analogous to the optimal power allocation in energy harvesting communications [26]–[28], though they are owing to two different causality constraints, i.”
Optimal Packet Scheduling in an Energy Harvesting Communication System (2010, background): “Under a deterministic system setting, we assume that the energy harvesting times and harvested energy amounts are known before the transmission starts.”
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.”
Advances in Energy Harvesting Communications: Past, Present, and Future Challenges (2016, background): “The considered problem is a special case of a frequency selective fading channel, and the authors point out a non-trivial tradeoff between the information and power transfer.”
Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks (2013, background): “From a network perspective, Huang investigated the throughput of a mobile ad-hoc network (MANET) powered by energy harvesting where the network spatial throughput is maximized by optimizing the transmit power level under an outage constraint [9].”