# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 1 Introduction  
### Motivation  
Wireless communication systems such as Visible Light Communication (VLC) have proven to increase spectral efficiency and provide reliable data connections at higher data rates. However, the channel model in VLC can be free space or underwater. The increase in the channel non-linearities that affect the performance of the system requires receiver designs to be more complex [1]. It becomes more challenging when the communication system involves more variables to be estimated. The traditional equalizers and receiver designs can mitigate these effects to some extent. However, Artificial Intelligence (AI) has been proven to be effective in wireless communication systems for modulation detection, jitter mitigation, nonlinearity mitigation, and phase estimation [1, 2, 3]. Next-generation communication systems are expected to be reliable, robust, spectrally efficient, and fast, which can accommodate the demand of increasing users without sacrificing system performance. Furthermore, the requirements such as environmentally friendly properties,power, and bandwidth efficiency are also top of the chart. Visible Light Communication (VLC) (also known as Li-Fi) is related technology that is a solution to meet these requirements for next-generation communication systems. VLC systems are based on visible light sources, such as Light-Emitting Diodes (LEDs), which are environment friendly and have the spectrum range of 380-780 nm, i.e., a non-interfering range with Radio Frequency (RF)-based communication systems. Despite numerous advantages, VLC systems also face the challenge of handling a large number of users in the context of multiuser communication. It is worth noting, however, that the Orbital Angular Momentum (OAM) of RF waves has been recently the focus of the researchers because of its helical phase wavefront due to successive phase shifts in space [4; 5; 6; 7; 8; 9]. Similar to the RF waves, the photons of the visible light beam can create a helical phase waveform with a phase front of \(\exp(il\phi)\) for the azimuthal angle, \(\phi\), per \(l\) photon [10]. It is important to mention that OAM is not similar to Spin Angular Momentum (SAM). SAM is associated with circular polarization, and OAM is associated with the spatial distribution of photons as shown in Figure 1. The successive phase shifts create orthogonal OAM modes, which are the key feature of OAM waves, and make multiplexing and demultiplexing the OAM easy with less interference. One of the advantages of OAM is the all-time availability of traditional resources, such as frequency and time, which can be used to increase spectral efficiency. The OAM wave has multiple orthogonal topological charges and provides a novel technique to dramatically boost spectrum efficiency, and, thus, it is predicted to be a potential multiplexing technique in future wireless communication networks. However, the disadvantage of OAM is the requirement of a high number of LEDs in order to achieve spatial orthogonality, which leads to an increase in the system's complexity and power consumption. To combat this, the Index Modulation (IM) technique can be integrated with the OAM to increase spectrum efficiency while reducing power consumption at the same time [4; 6; 7]. IM is a type of digital modulation that uses the ON-OFF mechanism to assign some of the transmitted bits as the indices to other transmitting bits. In this research, OAM-IM is used for a multiuser communication system using Power-Domain Non-Orthogonal Multiple Access (PD-NOMA). The performance of OAM-IM is compared with that of the Orthogonal Frequency Division Multiplexing (OFDM)-IM-based multiuser communication system. For the receiver design, we primarily used the maximum likelihood (ML) method.