Minimization of Transmission Duration of Data Packets over an Energy Harvesting Fading Channel

Ozcelik, F. Mehmet; Uctu, Goksel; Uysal-Biyikoğlu, Elif

doi:10.1109/lcomm.2012.111412.121568

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…Morsi et al [15] calculate an on-off transmission policy either assuming a finite or infinite energy buffer. The same control problem is considered in [16,17] in a deterministic setting: data arrivals and the amount of harvested energy are known in advance. Policies obtained from such model can be a benchmark but given the random nature of energy harvesting, one expects that the results cannot be attained in a random setting.…”

Section: Related Workmentioning

confidence: 99%

A two-queue model for optimising the value of information in energy-harvesting sensor networks

Patil

Turck

Fiems

2018

Performance Evaluation

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We consider a discrete-time queueing model with two coupled queues for studying the optimal transmission policy of an energy-harvesting sensor node. In particular, the sensor node under study operates energy neutral and harvests energy according to a Bernoulli process. Discretising energy into "energy chunks", the battery is modelled as a first queue, whereas a second queue is introduced to hold the information at the sensor node. We consider a hybrid wireless sensor network in which a mobile sink is used to collect data. From the vantage point of the sensor node, this means that the sensor can only send when the sink is sufficiently close. When this is the case, the sensor decides whether to transmit its data or not depending on the amount of available energy and the value of the information (VoI). Focusing on the optimal transmission policy, we formulate the optimal control problem as a Markov Decision Process with a level-dependent block-triangular transition probability matrix. We find the optimal policy which maximises the mean VoI transmitted from the node in the long run. Finally, we investigate the structure of the optimal policy and the mean VoI collected from the node for different system parameters by means of some numerical experiments.

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Section: Related Workmentioning

confidence: 99%

A two-queue model for optimising the value of information in energy-harvesting sensor networks

Patil

Turck

Fiems

2018

Performance Evaluation

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“…The authors of paper [3] solved the optimal policy of TCTM in the fading channel through the directional waterfilling approach. In [4], the fading channel was studied with the energy harvesting constraint, and the problem of minimizing the energy required for packet transmission was considered. In [5], the TCTM for the uplink transmissions with multiple nodes was considered.…”

Section: Introductionmentioning

confidence: 99%

RL-Based Transmission Completion Time Minimization with Energy Harvesting for Time-Varying Channels

Kim

Shin

Yang

et al. 2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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In this paper, we consider the problem of minimizing the transmission completion time in energy harvesting devices on time-varying channels with a reinforcement learning approach. Because of the randomness of energy arrival and fading channel in wireless communications, a reinforcement learning algorithm often converges to suboptimal points with a degraded performance. To solve this problem, we first prove that the expected discounted reward sum in the environment is an increasing function of negative time, amount of data sent, channel gain, harvested energy, and remaining battery. We leverage this proof to construct a partially monotonic network that efficiently approximates the optimal action-value function for learning. Experimental results show that our approach with the exploitation of the partial monotonicity of the desired function achieves better performance than existing power allocation policies. Further experiments show that the performance of our learning-based approach is close to the theoretical upper bound over rapidly time-varying channels. Index Terms-energy harvesting communications, transmission completion time minimization, reinforcement learning.

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“…A point-to-point channel with an EH Tx and arrival data during the transmission process has been considered in [7]. The authors in [8] have continued the work of [7] by considering a fading channel.…”

Section: Introductionmentioning

confidence: 99%

Optimal Transmission Policies for Multi-Hop Energy Harvesting Systems

Rezaee

Mirmohseni

Aggarwal

et al. 2018

IEEE Trans. on Green Commun. Netw.

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In this paper, we consider a multi-hop energy harvesting (EH) communication system in a fullduplex mode, where arrival data and harvested energy curves in the source and the relays are modeled as general functions. This model includes the EH system with discrete arrival processes as a special case. We investigate the throughput maximization problem considering minimum utilized energy in the source and relays and find the optimal offline algorithm. We show that the optimal solution of the twohop transmission problem have three main steps: (i) Solving a point-to-point throughput maximization problem at the source; (ii) Solving a point-to-point throughput maximization problem at the relay (after applying the solution of first step as the input of this second problem); (iii) Minimizing utilized energy in the source. In addition, we show that how the optimal algorithm for the completion time minimization problem can be derived from the proposed algorithm for throughput maximization problem. Also, for the throughput maximization problem, we propose an online algorithm and show that it is more efficient than the benchmark one (which is a direct application of an existing point-to-point online algorithm to the multi-hop system).

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Minimization of Transmission Duration of Data Packets over an Energy Harvesting Fading Channel

Cited by 18 publications

References 13 publications

A two-queue model for optimising the value of information in energy-harvesting sensor networks

A two-queue model for optimising the value of information in energy-harvesting sensor networks

RL-Based Transmission Completion Time Minimization with Energy Harvesting for Time-Varying Channels

Optimal Transmission Policies for Multi-Hop Energy Harvesting Systems

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