DRL-based Power Allocation in LiDAL-Assisted RLNC-NOMA OWC Systems

Non-orthogonal multiple access (NOMA) is a promising technique for optical wireless communication (OWC), enabling multiple users to share the optical spectrum simultaneously through the power domain. However, the imperfection of channel state information (CSI) and residual errors in decoding process deteriorate the performance of NOMA, especially when multi-parameteric and realistic dense-user indoor scenarios are considered. In this work, we model a LiDAL-assisted RLNC-NOMA OWC system, where the light detection and localization (LiDAL) technique exploits spatio-temporal information to improve user CSI, while random linear network coding (RLNC) enhances data resilience in the NOMA successive decoding process. Power allocation (PA) is a crucial issue in communication systems, particularly in the modeled system, due to the complex interactions between multiple users and the coding and detection processes. However, optimizing continuous PA dynamically requires advanced techniques to avoid excessive computational complexity. Therefore, we adopt a deep reinforcement learning (DRL) framework to efficiently learn near-optimal power allocation strategies, enabling enhanced system performance. In particular, a DRL-based normalized advantage function (NAF) algorithm is proposed to maximize the average sum rate of the system, and its performance is analyzed and compared to other widely used DRL-based and conventional PA schemes, such as deep deterministic policy gradient (DDPG), gain ratio PA (GRPA), and exhaustive search.

Energy-efficient Functional Split in Non-terrestrial Open Radio Access Networks

This paper investigates the integration of Open Radio Access Network (O-RAN) within non-terrestrial networks (NTN), and optimizing the dynamic functional split between Centralized Units (CU) and Distributed Units (DU) for enhanced energy efficiency in the network. We introduce a novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms. This approach supports capability of adapting to various NTN-based RANs across different platforms such as LEO satellites and high-altitude platform stations (HAPS), enabling adaptive network reconfiguration to ensure optimal service quality and energy utilization. Simulation results validate the effectiveness of our method, offering significant improvements in energy efficiency and sustainability under diverse NTN scenarios.

Random Linear Network Coding for Non-Orthogonal Multiple Access in Multicast Optical Wireless Systems

Optical Wireless Communication networks (OWC) has emerged as a promising technology that enables high-speed and reliable communication bandwidth for a variety of applications. In this work, we investigated applying Random Linear Network Coding (RLNC) over NOMA-based OWC networks to improve the performance of the proposed high density indoor optical wireless network where users are divided into multicast groups, and each group contains users that slightly differ in their channel gains. Moreover, a fixed power allocation strategy is considered to manage interference among these groups and to avoid complexity. The performance of the proposed RLNC-NOMA scheme is evaluated in terms of average bit error rate and ergodic sum rate versus the power allocation ratio factor. The results show that the proposed scheme is more suitable for the considered network compared to the traditional NOMA and orthogonal transmission schemes.

Power Minimization in Vehicular Cloud Architecture

Modern vehicles equipped with on-board units (OBU) are playing an essential role in the smart city revolution. The vehicular processing resources, however, are not used to their fullest potential. The concept of vehicular clouds is proposed to exploit the underutilized vehicular resources to supplement cloud computing services to relieve the burden on cloud data centers and improve quality of service. In this paper we introduce a vehicular cloud architecture supported by fixed edge computing nodes and the central cloud. A mixed integer linear programming (MLP) model is developed to optimize the allocation of the computing demands in the distributed architecture while minimizing power consumption. The results show power savings as high as 84% over processing in the conventional cloud. A heuristic with performance approaching that of the MILP model is developed to allocate computing demands in real time.