To apply visible light communication systems in different scenarios, this article utilizes an excellent temperature-adjustable light source mixed with RGB LEDs and applies it in a visible light communication system. It uses color division multiplexing technology to achieve three-channel communication, thereby improving the communication bandwidth of the system. The communication system adopts three constant current driving circuits to control the duty cycle of Pulse Width Modulation (PWM) of each channel, thereby changing the proportion of RGB LEDs, and obtaining different color temperatures to achieve the purpose of color control for mixed-color LEDs. The experimental results show that when adjusting the color temperature, the change in luminous flux is very small, with fluctuations of less than 2.24%. When adjusting the brightness, the color temperature fluctuation is within 40 K, which is less than the 50 K color temperature limit that the human eye can distinguish, and the average color temperature error is 0.609%. Color tolerance less than 5.5 × 10⁻³ indicates good dimming effect, and the communication performance of the system is better in the high color temperature range, which is significantly superior to the low color temperature range. When the error rate is below 3.8 × 10⁻³, the total modulation bandwidth of the three channels reaches 11.7 MHz.

Visible light communication (VLC) is widely regarded as a key enabler for future vehicular networks, thanks to its extremely large unlicensed bandwidth and non-interference with existing radio frequency (RF) communication networks. With the goal of maximizing the benefits of both RF and VLC technologies, aggregated VLC–RF vehicular networks, in which any vehicle can be served by both RF and VLC access points (APs) concurrently, have recently become a more robust and promising approach for enhancing vehicle-to-everything (V2X) applications and improving the quality-of-service (QoS) of vehicular networks. This paper focuses on the joint spectrum reuse and power allocation problem in aggregated VLC–RF vehicular networks with delayed channel state information (CSI) feedback, where vehicle-to-vehicle (V2V) links opportunistically reuse the RF spectrum allocated to vehicle-to-infrastructure (V2I) links. Specifically, we focus on maximizing the total V2I achievable rate to support high-rate content delivery, and guaranteeing the required reliability of V2V links tasked with exchanging safety-critical information. Furthermore, the sum V2I achievable rate maximization problem is decomposed into four subproblems, which are iteratively solved through an efficient block coordinate descent (BCD)-based alternating optimization algorithm. Moreover, simulation results validate the convergence and efficiency of the proposed algorithm while highlighting the impact of critical parameters on system performance, providing valuable insights for resource allocation in aggregated VLC–RF vehicular networks.

With advances in emerging material technologies, intelligent reflecting surface (IRS)-assisted vehicular networks have been gaining growing interest. By adaptively shaping the wireless propagation environment, IRSs can improve vehicular network quality of service (QoS). However, most IRS-assisted vehicular network studies are limited to individual RF or VLC frameworks, while only a few investigate IRS-assisted aggregated VLC-RF vehicular networks that combine wide RF coverage with high VLC data rates. In this paper, aggregated VLC-RF vehicular networks are supported by both optical IRSs (OIRSs) and RF IRSs, and a resource allocation scheme is developed to improve the total achievable rate. First, we establish a system model for IRS-assisted aggregated VLC-RF vehicular networks, and then formulate a problem to maximize the total achievable rate. Furthermore, we decompose the maximization of the total achievable rate into five subproblems and solve them iteratively via an efficient alternating optimization scheme based on block coordinate descent (BCD). Moreover, simulation results validate the convergence and efficiency of our algorithm, while highlighting the effects of crucial parameters on system performance, providing valuable insights for resource allocation in IRS-assisted aggregated VLC–RF vehicular networks.