Rate Splitting in VCSEL-based Optical Wireless Networks

Optical wireless communication is an effective potential solution for enabling high speed next generation cellular networks. In this paper, laser sources, in particular, vertical-cavity surface-emitting (VCSEL) lasers are used for data transmission due to their high modulation speed compared with light emitting diode (LED) sources. To manage multi-user interference, rate splitting (RS) is implemented where the message of a user is split into common and private parts. However, the performance of RS is limited in high density networks. Therefore, hierarchical rate splitting (HRS) particularly suited in high density networks is considered. The results demonstrate the high data rate achieved by using VCSELs. Moreover, HRS is more suitable for achieving high performance in optical networks compared with RS

Q-learning algorithm for resource allocation in WDMA-based optical wireless communication networks

Visible Light Communication (VLC) has been widely investigated during the last decade due to its ability to provide high data rates with low power consumption. In general, resource management is an important issue in cellular networks that can highly effect their performance. In this paper, an optimisation problem is formulated to assign each user to an optimal access point and a wavelength at a given time. This problem can be solved using mixed integer linear programming (MILP). However, using MILP is not considered a practical solution due to its complexity and memory requirements. In addition, accurate information must be provided to perform the resource allocation. Therefore, the optimisation problem is reformulated using reinforcement learning (RL), which has recently received tremendous interest due to its ability to interact with any environment without prior knowledge. In this paper, the resource allocation optimisation problem in VLC systems is investigated using the basic Q-learning algorithm. Two scenarios are simulated to compare the results with the previously proposed MILP model. The results demonstrate the ability of the Q-learning algorithm to provide optimal solutions close to the MILP model without prior knowledge of the system

Optimizing Rate Splitting in Laser-based Optical Wireless Networks

Optical wireless communication (OWC) is a promising technology that has the potential to provide Tb/s aggregate rates. In this paper, interference management is studied in a Laser-based optical wireless network where vertical-cavity surface-emitting (VCSEL) lasers are used for data transmission. In particular, rate splitting (RS) and hierarchical rate splitting (HRS) are proposed to align multi-user interference, while maximizing the multiplexing gain of the network. Basically, RS serves multiple users simultaneously by splitting a message of a user into common and private messages, each message with a certain level of power, while on the other side users decode their messages following a specific methodology. The performance of the conventional RS scheme is limited in high density wireless networks. Therefore, the HRS scheme is developed aiming to achieve high rates where users are divided into multiple groups, and a new message called outer common message is used for managing inter-group interference. We formulate an optimization problem that addresses power allocation among the messages of the HRS scheme to further enhance the performance of the network. The results show that the proposed approach provides high achievable rates compared with the conventional RS and HRS schemes in different scenarios

Reinforcement Learning for Resource Allocation in Steerable Laser-Based Optical Wireless Systems

Vertical Cavity Surface Emitting Lasers (VCSELs) have demonstrated suitability for data transmission in indoor optical wireless communication (OWC) systems due to the high modulation bandwidth and low manufacturing cost of these sources. Specifically, resource allocation is one of the major challenges that can affect the performance of multi-user optical wireless systems. In this paper, an optimisation problem is formulated to optimally assign each user to an optical access point (AP) composed of multiple VCSELs within a VCSEL array at a certain time to maximise the signal to interference plus noise ratio (SINR). In this context, a mixed-integer linear programming (MILP) model is introduced to solve this optimisation problem. Despite the optimality of the MILP model, it is considered impractical due to its high complexity, high memory and full system information requirements. Therefore, reinforcement Learning (RL) is considered, which recently has been widely investigated as a practical solution for various optimisation problems in cellular networks due to its ability to interact with environments with no previous experience. In particular, a Q-learning (QL) algorithm is investigated to perform resource management in a steerable VCSEL-based OWC systems. The results demonstrate the ability of the QL algorithm to achieve optimal solutions close to the MILP model. Moreover, the adoption of beam steering, using holograms implemented by exploiting liquid crystal devices, results in further enhancement in the performance of the network considered

Resource Allocation in Laser-based Optical Wireless Cellular Networks

Optical wireless communication provides data transmission at high speeds which can satisfy the increasing demands for connecting a massive number of devices to the Internet. In this paper, vertical-cavity surface-emitting(VCSEL) lasers are used as transmitters due to their high modulation speed and energy efficiency. However, a high number of VCSEL lasers is required to ensure coverage where each laser source illuminates a confined area. Therefore, multiple users are classified into different sets according to their connectivity. Given this point, a transmission scheme that uses blind interference alignment (BIA) is implemented to manage the interference in the laser-based network. In addition, an optimization problem is formulated to maximize the utility sum rate taking into consideration the classification of the users. To solve this problem, a decentralized algorithm is proposed where the main problem is divided into sub-problems, each can be solved independently avoiding complexity. The results demonstrate the optimality of the decentralized algorithm where a sub-optimal solution is provided. Finally, it is shown that BIA can provide high performance in laser-based networks compared with zero forcing (ZF) transmit precoding scheme