Online Learning Schemes for Power Allocation in Energy Harvesting Communications

Sakulkar, Pranav; Krishnamachari, Bhaskar

doi:10.48550/arxiv.1607.02552

Cited by 2 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Referring to the proof of Lemma 2 in [10], it can be proved that the optimal transmission power, which is denoted by p * (t), is a piecewise linear function. Therefore, let p i be the EHD's transmission power at epoch i, then the residual energy in (7) can be rewritten as:…”

Section: Optimal Offline Transmission Schedulingmentioning

confidence: 99%

“…In recent years, many transmission scheduling strategies have been proposed for the radio frequency (RF) based energy harvesting system [4][5][6][7], where both the intermittency and the randomness of energy arrivals are taken into account for the power management. In those designs, the energy grabbed by an EHD has been widely modeled as a random process [8][9][10][11][12], which may depend on the activity of RF energy sources (e.g., a TV tower or a cellular base station), but is rarely affected by the transmission scheduling on EHD.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting Transmission Scheduling in RF Energy Harvesting Wireless Communications

Luo

Zhao

et al. 2018

Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

View full text Add to dashboard Cite

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies in an RF-based energy harvesting system is mainly based on a classic model, in which the data transmission is scheduled in a fixed feasible energy tunnel. In this paper, we re-examine the classic energy harvesting model and show through the theoretical analysis and experimental results that the bounds of feasible energy tunnel are dynamic, which can be affected by the transmission scheduling due to the impact of residual energy on the harvested one. To describe a practical energy harvesting process more accurately, a new model is proposed by adding a feedback loop that reflects the interplay between the energy harvest and the data transmission. Furthermore, to improve network performance, we revisit the design of an optimal transmission scheduling strategy based on the new model. To handle the challenge of the endless feedback loop in the new model, a recursive algorithm is developed. The simulation results reveal that the new transmission scheduling strategy can balance the efficiency of energy reception and energy utilization regardless of the length of energy packets, achieving improved throughput performance for wireless communications.KEYWORDS RF energy harvesting model, optimal transmission scheduling, residual energy, nonlinear charge.Unfortunately, the above assumption may not be true in real energy harvesting system. Since residual energy is affected by the data transmission, the harvested energy heavily depends on a data

show abstract

Section: Optimal Offline Transmission Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Revisiting Transmission Scheduling in RF Energy Harvesting Wireless Communications

Luo

Zhao

et al. 2018

Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

View full text Add to dashboard Cite

show abstract

“…From constraints ( 6)- (10), we know that all achievable distortion pairs must appear in the shaded area of Fig. 2.…”

Section: B Rate-distortion Modelmentioning

confidence: 99%

“…For energy harvesting powered WSNs, however, formidable challenges still remain since the energy arrivals of each node are often sporadic and irregular. To address this issue, many energy scheduling schemes optimizing the information transmission of energy harvesting communication systems have been suggested in recent years [1]- [8], [10]- [21].…”

Section: Introductionmentioning

confidence: 99%

“…Second, if the energy harvesting process cannot be well predicted, online energy scheduling is required, in which each node adjusts its transmit power based on previous and current energy states in real-time [6]- [11]. This online energy scheduling has been often modeled as Markov Decision Processes (MDP) and solved by Dynamic Programming (DP) [7], [8], [10]. There have also been some works considering both offline and online policies, e.g., [5], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Power Control for Transmitting Correlated Sources With Energy Harvesting Constraints

Dong

Chen

Wang

et al. 2018

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Abstract-We investigate the weighted-sum distortion minimization problem in transmitting two correlated Gaussian sources over Gaussian channels using two energy harvesting nodes. To this end, we develop offline and online power control policies to optimize the transmit power of the two nodes. In the offline case, we cast the problem as a convex optimization and investigate the structure of the optimal solution. We also develop a generalized water-filling based power allocation algorithm to obtain the optimal solution efficiently. For the online case, we quantify the distortion of the system using a cost function and show that the expected cost equals the expected weightedsum distortion. Based on Banach's fixed point theorem, we further propose a geometrically converging algorithm to find the minimum cost via simple iterations. Simulation results show that our online power control outperforms the greedy power control where each node uses all the available energy in each slot and performs close to that of the proposed offline power control. Moreover, the performance of our offline power control almost coincides with the performance limit of the system.

show abstract

Online Learning Schemes for Power Allocation in Energy Harvesting Communications

Cited by 2 publications

References 0 publications

Revisiting Transmission Scheduling in RF Energy Harvesting Wireless Communications

Revisiting Transmission Scheduling in RF Energy Harvesting Wireless Communications

Optimal Power Control for Transmitting Correlated Sources With Energy Harvesting Constraints

Contact Info

Product

Resources

About