# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 1 Introduction  
In the era of increasingly constrained spectrum resources, the utilization of visible light as a sustainable energy source emerges as a potent solution, enabling the realization of \"lighting as communication\" through the mechanism of direct vision communication, characterized by its low power consumption. This innovation bears the potential to substantially alleviate the energy demand and spectrum requirements of 6G networks [1]. Visible Light Communication (VLC) harnesses Light Emitting Diodes (LEDs) as emissive sources for light-based signal transmission, concurrently fulfilling roles in illumination and high-speed wireless communication. Its attributes encompass resistance to electromagnetic interference, robust security features, anti-eavesdropping capabilities, environmental sustainability, minimal carbon footprint, cost-effectiveness, and unhindered access to spectrum resources. This renders VLC an auspicious contender for indoor coverage within the realm of 6G networks [2; 3; 4]. Notably, Orthogonal Frequency Division Multiplexing (OFDM), widely proven in radio-frequency (RF) systems due to its formidable resistance against inter-code interference and its optimal utilization of ultra-high frequency bands, has found its application extended to Optical Orthogonal Frequency Division Multiplexing (O-OFDM). This extension garners growing attention due to its heightened spectral efficiency and augmented system capacity in comparison to single-carrier modulationstrategies. O-OFDM's incorporation is especially conspicuous within VLC frameworks [5]. The distinctive characteristic of VLC systems is their reliance on intensity modulation (IM) to impart modulation onto the instantaneous luminous intensity emitted by LEDs, coupled with direct detection (DD) via photodiodes (PDs). This mandates that all signals within the VLC channel exhibit strict non-negativity and real-value. For this purpose, Direct Current (DC)-biased optical OFDM (DCO-OFDM) emerges, augmenting a DC bias and Hermitian symmetry to guarantee non-negative real-valued signals. This integration of IM and DD into a cost-effective architecture has gained wide adoption within VLC systems [6]. Despite the potential of DCO-OFDM-based indoor VLC systems to enable high-speed data transfer and serve as conduits for both lighting and location data, their efficacy hinges on the accuracy of light transmission, rendering them susceptible to environmental variables within indoor spaces, such as fluctuations in light intensity and signal attenuation. The modulated signal invariably undergoes attenuation due to nonlinearities within LEDs and the dispersion of light propagation. This attenuation impairs the orthogonality between subcarriers, collectively undermining system performance and challenging extant paradigms of transceiver design [7]. Consequently, the accurate modeling of channel state information and a comprehensive understanding of channel characteristics assume paramount importance for optimizing transmission efficiency within VLC links. A multitude of investigations have delved into VLC link performance under fading channels, profering diverse equalization techniques to mitigate fading and multipath interference [8]. Channel estimation (CE), a cornerstone of channel equalization at reception, traditionally hinges upon methods like Least Squares (LS) and Minimum Mean Square Error (MMSE). However, LS exhibits susceptibility to noise amplification, and MMSE's implementation complexity is a hurdle [9]. Recent endeavors have directed attention toward leveraging deep learning (DL) to harness its robust data learning, recognition, and prediction capabilities in channel state information acquisition. DL emerges as a potent tool for addressing intricate communication challenges presented by novel or demanding channels [10; 11]. In the realm of VLC, DL has been harnessed in OFDM-based VLC receivers for the channel equalization of received data [12; 13; 14; 15; 16; 17].