Optimal Energy Allocation for Wireless Communications With Energy Harvesting Constraints
By Chin Keong Ho, Rui Zhang. IEEE Transactions on Signal Processing, 2011
699 citations recorded by the source corpus.
We consider the use of energy harvesters, in place of conventional batteries with fixed energy storage, for point-to-point wireless communications. In addition to the challenge of transmitting in a channel with time selective fading, energy harvesters provide a perpetual but unreliable energy source. In this paper, we consider the problem of energy allocation over a finite horizon, taking into account channel conditions and energy sources that are time varying, so as to maximize the throughput. Two types of side information (SI) on the channel conditions and harvested energy are assumed to be available: causal SI (of the past and present slots) or full SI (of the past, present and future slots). We obtain structural results for the optimal energy allocation, via the use of dynamic programming and convex optimization techniques. In particular, if unlimited energy can be stored in the battery with harvested energy and the full SI is available, we prove the optimality of a water-filling energy allocation solution where the so-called water levels follow a staircase function.
Subfields: Aerospace Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/620a12113f0ae1f687dfcf6da5abbe6d3491fa85
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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).”
- Throughput Maximization for the Gaussian Relay Channel with Energy Harvesting Constraints (2011, methodology): “With the block Markov random EH model, the authors in [4] studied the throughput maximization problem over the fading AWGN channel and derived the optimal power allocation polices via dynamic programming and convex optimization techniques.”
- Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach (2015, background): “Please refer to [25] for the detail of this algorithm.”
- Throughput Maximization for UAV-Enabled Mobile Relaying Systems (2016, background): “Furthermore, (26) and (27) show that the optimal power allocations across the different slots are given by the “staircase” WF solution [27], with non-increasing and non-decreasing water levels at S and R, respectively.”
- Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source (2017, background): “Optimal transmission scheduling for throughput and delay optimization has been studied under both infinite battery setting [30], [32], [33] and finite battery setting [31], [34]–[37].”
- A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead (2016, methodology): “Tools from stochastic optimization are used to develop design protocols assuming that the statistics of the energy process are known [125]–[127].”
- Optimal Resource Allocation in Full-Duplex Wireless-Powered Communication Network (2014, background): “, [1], [2] and the references therein) has recently received a great deal of attention since it provides more cost-effective and”
- Energy Harvesting Wireless Communications: A Review of Recent Advances (2015, methodology): “See also [15] for a staircase water-filling algorithm.”
- Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks (2013, methodology): “For point-to-point wireless systems powered by energy harvesting, the optimal power-allocation algorithms have been designed and shown to follow modified water-filling by Ho and Zhang [7] and Ozel et al. [8].”
- Advances in Energy Harvesting Communications: Past, Present, and Future Challenges (2016, background): “The data throughput maximization problem is discussed in [36], [76], [77], [80].”
- Relaying Protocols for Wireless Energy Harvesting and Information Processing (2012, methodology): “We have assumed perfect channel knowledge at the destination and assumed negligible overhead for pilot transmission, which is inline with the previous work in this research field [2], [3], [6], [11], [12], [15].”