With the advancements of high-resolution cameras, highly sensitive photodetectors, and energy-efficient light emitting-diodes, optical wireless communication (OWC) has emerged as key-enabling technology for 6G and beyond. Similarly, digital twin (DT) technology has been recently proposed to meet the diverse requirements of emerging applications and provide efficient optimization of the overall system resources by enabling selfsustaining and proactive online learning. In DT, various technology disciplines, including big data, artificial intelligence, intelligent computing, communication, and security-aware technologies, are integrated to create a virtual replica that reflects the physical system. Motivated by this, the presented article discusses DT and its role in the reliable and ubiquitous implementation of OWC networks. We first provide an overview of this emerging research area by shedding light on the key enabling technologies and potential applications in the context of smart-autonomous OWC networks. Subsequently, we provide recent advancements and design aspects related to DT-assisted OWC systems. Finally, future research directions and the impact of different system factors on the overall network performance are discussed. To the best of the authors’ knowledge, this is the first in-depth review in the literature on the integration of DT with OWC networks.

The upcoming sixth generation (6G) is expected to support a wide range of applications that require efficient sensing, accurate localization, and reliable communication capabilities. Furthermore, 6G is expected to catalyze the development of new use cases that will require working in extreme environmental and hazardous conditions and have ultra-small size and low-cost wireless devices. Thus, developing sustainable multi-functional wireless networks that are capable of incorporating billions of low-power devices and supporting their sensing and communication requirements on top of energy harvesting capability is of paramount importance. Motivated by this, we consider in this work a rate-splitting multiple access (RSMA)-based multifunctional wireless network with sensing, energy harvesting, and communication capabilities. We employ trust region policy optimization (TRPO), a deep  reinforcement learning (DRL) algorithm, to efficiently allocate the available resources and manage the interference between the three functionalities. TRPO/DRL is capable to learn a near-optimal policy for the resource allocation problem in a complex and dynamic environment. This enables us to obtain near-optimal transmit precoders, power splitting ratios, and ratesplitting among the common and private rates in a multiple access setting. Simulation results demonstrate the effectiveness of RSMA in mitigating the interference in such multi-functional networks and its capability to accommodate the rate and energy harvesting requirements of the devices while still capable of sensing multiple targets.

Spatial Modulation (SM) has been proposed as a multiple-input-multiple-output (MIMO)-based technique to overcome the inter-channel interference experienced in conventional MIMO systems. It has been further shown that SM enhances energy efficiency and reduces the receiver’s complexity. Nevertheless, under high antenna correlation scenarios, the detection performance of the antenna indices degrades significantly. To address this critical concern, in this paper, we propose three autoencoder-based frameworks for spatial modulation. The first scenario, similar to conventional spatial modulation, trains the encoder for data modulation and the decoder for data demodulation as well as antenna index detection. The performance of this framework deteriorates in high antenna correlation scenarios. Therefore, two novel solutions are presented to embed the antenna index into the transmitted signal in order to reduce the receiver’s reliance on the channel conditions. The first framework adds a  phase-shift keying-based antenna signature, while the other trains the encoder to learn an appropriate antenna index embedding. Simulation results show that the two enhanced frameworks result in a significantly enhanced performance, compared to conventional spatial modulation, in terms of block error rate and power efficiency under a high correlation setup (about 18 dB and 24 dB gain, respectively, at a Rician factor of 20 dB).