# DC-Bias and Power Allocation in Cooperative VLC Networks for Joint Information and Energy Transfer

## TL;DR

This paper proposes an optimization framework for joint power and bias control in cooperative VLC networks to balance data transmission and energy harvesting, employing iterative convex approximation methods.

## Contribution

It introduces a novel iterative optimization approach for joint DC-bias and power allocation in VLC networks, addressing the non-convex problem of balancing data rate and energy harvesting.

## Key findings

- Significant performance improvements with proposed algorithms.
- Effective trade-off management between sum-rate and harvested energy.
- Insights into the impact of network parameters on performance trade-offs.

## Abstract

Visible light communications (VLC) have emerged as strong candidates for meeting the escalating demand for high data rates. Consider a VLC network, where multiple access-points (APs) serve both energy-harvesting users (EHUs), i.e., users which harvest energy from light intensity, and information-users (IUs), i.e., users which gather data information. In order to jointly balance the achievable sum-rate at the IUs and the energy harvested by the EHUs, the paper considers maximizing a network-wide utility, which consists of a weighted-sum of the IUs sum-rate and the EHUs harvested energy, subject to individual IU rate constraint, individual EHU harvested-energy constraint, and AP power constraints, so as to jointly determine the direct current (DC)-bias value at each AP, and the users powers. The paper solves such a difficult non-convex optimization problem using an iterative approach which relies on inner convex approximations, and compensates for the used approximations using proper outer-loop updates. The paper further considers solving the special cases of the problem, i.e., maximizing the sum-rate, and maximizing the total harvested-energy, both subject to the same constraints. Numerical results highlight the significant performance improvement of the proposed algorithms, and illustrate the impacts of the network parameters on the performance trade-off between the sum-rate and harvested-energy.