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Optimizing Energy-Harvesting Hybrid VLC/RF Networks with Random Receiver Orientation

Amir Hossein Fahim Raouf, Chethan Kumar Anjinappa, Ismail Guvenc

TL;DR

Joint optimization of DC bias and time allocation significantly enhances the data rate compared to optimizing DC bias alone, demonstrating that joint optimization of DC bias and time allocation significantly enhances the data rate.

Abstract

This paper investigates an indoor hybrid visible light communication (VLC) and radio frequency (RF) scenario with two-hop downlink transmission. A light emitting diode (LED) transmits both data and energy via VLC to an energy-harvesting relay node, which then uses the harvested energy to retransmit the decoded information to an RF user in the second phase. The design parameters include the direct current (DC) bias and the time allocation for VLC transmission. We formulate an optimization problem to maximize the data rate under decode-and-forward relaying with fixed receiver orientation. The non-convex problem is decomposed into two sub-problems, solved iteratively by fixing one parameter while optimizing the other. Additionally, we analyze the impact of random receiver orientation on the data rate, deriving closed-form expressions for both VLC and RF rates. An exhaustive search approach is employed to solve the optimization, demonstrating that joint optimization of DC bias and time allocation significantly enhances the data rate compared to optimizing DC bias alone.

Optimizing Energy-Harvesting Hybrid VLC/RF Networks with Random Receiver Orientation

TL;DR

Joint optimization of DC bias and time allocation significantly enhances the data rate compared to optimizing DC bias alone, demonstrating that joint optimization of DC bias and time allocation significantly enhances the data rate.

Abstract

This paper investigates an indoor hybrid visible light communication (VLC) and radio frequency (RF) scenario with two-hop downlink transmission. A light emitting diode (LED) transmits both data and energy via VLC to an energy-harvesting relay node, which then uses the harvested energy to retransmit the decoded information to an RF user in the second phase. The design parameters include the direct current (DC) bias and the time allocation for VLC transmission. We formulate an optimization problem to maximize the data rate under decode-and-forward relaying with fixed receiver orientation. The non-convex problem is decomposed into two sub-problems, solved iteratively by fixing one parameter while optimizing the other. Additionally, we analyze the impact of random receiver orientation on the data rate, deriving closed-form expressions for both VLC and RF rates. An exhaustive search approach is employed to solve the optimization, demonstrating that joint optimization of DC bias and time allocation significantly enhances the data rate compared to optimizing DC bias alone.
Paper Structure (18 sections, 42 equations, 12 figures, 2 tables)

This paper contains 18 sections, 42 equations, 12 figures, 2 tables.

Figures (12)

  • Figure 1: The system model for the considered VLC-RF transmission scenario. The VLC link carries both data and energy to the relay node. The harvested energy is then used at the relay node to forward the data to the far RF user.
  • Figure 2: The transmission block under consideration with consecutive time periods dedicated for VLC (relay) and RF (access) links. The VLC link is used both as a backhaul to relay the data and for energy harvesting.
  • Figure 3: The VLC and RF information rate when $d_{\textrm{r}} = 0$ m, $d_{\textrm{u}} = 4$ m, and $f_{\textrm{c}} = 2.4$ GHz for (a) fixed DC bias and (b) equal time allocation.
  • Figure 4: Summary of the optimization problem that involves LED transmitter, hybrid RF/VLC relay with energy harvesting, and the far RF user that receives the data through the relay node through an RF link. Here, we consider $I_{\textrm{b}, i}$, $T_{\textrm{VLC}, i}$, and $T_{\textrm{RF}, i}$ as the optimization variables to maximize the end-to-end system data rate.
  • Figure 5: The performance of our optimization framework versus the iteration count when $d_r = 0$ m and $d_u = 4$ m.
  • ...and 7 more figures