Revisiting Transmission Scheduling in RF Energy Harvesting Wireless Communications

Abstract

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

RF Energy Harvesting Wireless Communications: RF Environment, Device Hardware and Practical Issues

“…The amount of energy harvested from an energy packet not only depends on the power density and the duration of energy packets but also on the residual energy in the battery. As verified in the experiment reported in [60], when the power and length of energy packets are fixed, the amount of harvested energy in each energy replenishment is a concave and non-monotonic function of the residual energy in the battery, as demonstrated in Figure 16. The nonlinear relation between the harvested energy and the residual energy in the battery is raised in [61] and formulated in [60].…”

“…To accurately describe the energy harvesting process, a new model is proposed in [60], as illustrated in Figure 17b. In the new model, an energy feedback loop from the data transmission strategy to the harvested energy is formed, which describes the impact of nonlinear charging on the energy harvesting process.…”

“…The KKT approach has been used in solving nonlinear programming problems in wide areas [62,63]. ) As studied in [60], assume a total number of N energy packets arrive at an RF-EHWC device, and let Eir be the residual energy at the moment of energy packet i arrival. By leveraging the inherent relationship among the residual energy, the transmission power, and the harvested energy, Eir can be represented by E1r through the energy harvesting function, i.e., Eir=Qi(E1r), where i=2,…,N.…”

“…( a ) Classic energy harvesting model without considering the nonlinear charging characteristic. ( b ) New feedback model with the nonlinear battery charging considered [60]. …”

Impact of Varying Radio Power Density on Wireless Communications of RF Energy Harvesting Systems

“…This property converts continuous power management into a piecewise-linear optimization problem. The proof of Property 2 can refer to Section 5.2 of our previous work [32]. This property arises from the nonlinear charging characteristic of the energy buffer.…”