# Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators
## 1 Introduction
New industries are becoming digital and being fully connected on an unprecedented scale, increasing the need of cyber resilience and security. In this way, FSO affords the integration along with spread spectrum coding techniques [49; 50; 51] to support higher security levels. Such combination can be useful against eavesdroppers. Optical code-division multiple-access (OCDMA) is a promising technique to asynchronously transmit multiple users' signals data over a network in a robust way [52; 53; 54; 55; 56; 57; 58; 59]. Data of individual users are encoded before transmission by multiplexing many information channels simultaneously with the use of spread spectrum codes in order to increase user's security and privacy. Furthermore, OCDMA has several desirable networking features such as symmetric bandwidth support for up- and down-links, soft-capacity on-demand, and burst transmission support. The use of spread spectrum codes can offer a higher security to network users the similar fashion as the wireless CDMA, which was commercially deployed in the 1990's. FSO based on OCDMA with optical spread codes are regarded as a potent alternative for the next generation mobile networks. As has already been mentioned, the energy consumption poses real challenges in existing cellular networks. The fronthaul energy consumption is responsible for the large portion of the network operational expenditure (OPEX) [60]. Since the current energy production is still mainly based on fossil sources it directly contributes to a high carbon footprint. Continuous improvements of the mobile fronthaul energy efficiency and reduction in carbon dioxide emissions by means of 'greener' technologies have become of major importance to many industries [61]. Therefore the energy efficiency is becoming one of the most important metrics in beyond 5G systems and green technologies will play a pivotal role in the next generation mobile solutions. Reinforcement learning methods also have been used to lower the energy consumption of base stations [62], whereas an evaluation of cellular traffic and communication parameters based on machine learning tools was addressed in Ref. [63]. The power consumption was considered as an optimization problem in Ref. [64], whereas a game-theoretic model is used in Ref. [65]. Lastly, many solutions were proposed in Ref. [66] to cope with energy consumption reduction, but without addressing the hardware solution counterpart. Energy-efficient data modulators (regardless of the network architecture) will be the key factor in lowering the overall system energy consumption. Optical modulators can be implemented on a combination of different technologies, such as photonics [67], III-V compound [68], barium titanate [69], or graphene [70; 71; 72]. Nonetheless, existing technologies still face some challenges to meet not only energy-per-bit efficiency, but also bandwidth and footprint needs [73]. Graphene, is a CMOS compatible technology, provides a fast and scalable fabrication environment, with a single layer of carbon atoms, and has many unique physical and chemical properties not yet fully exploited in optical modulators design. The first graphene-based electro-absorption modulator was demonstrated in 2011 [74]. Since then, several graphene integrated modulators have been proposed [70; 71; 72; 74; 75; 76; 77; 78]. Graphene-based modulators have already demonstrate their ability to outperform traditional modulators, but there is still plenty of room for optimization [78; 79; 80; 81; 68]. It has been shown that judicious design and optimization of modulators can enable their unprecedented performance [82; 83; 84; 68; 72; 84]. Within this broad context and having in mind the network security and energy efficiency, in this paper, the next-generation fronthaul network architecture based on FSO, spread spectrum encoding techniques, and the use of graphene modulators is proposed and evaluated. The new mobile fronthaul network is fully described.