arXiv subjects

Taisir Elgorashi

Publications and source records attributed to Taisir Elgorashi.

13 recordsLinked to original sources

arXiv2026

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, imperfect channel state information (CSI) and residual decoding errors deteriorate NOMA performance, especially in realistic dense-user indoor scenarios. In this work, we model an OWC system that integrates light detection and localization (LiDAL) and random linear network coding (RLNC) within a NOMA framework. LiDAL exploits spatio-temporal information to improve user CSI, while RLNC enhances data resilience in the successive decoding process, resulting in a LiDAL-assisted RLNC-NOMA OWC system. Power allocation (PA) is crucial in this system due to complex interactions between multiple users and the coding and detection processes, but optimizing continuous PA dynamically can be computationally prohibitive. To address this, we adopt a deep reinforcement learning (DRL) framework to efficiently learn near-optimal PA strategies. In particular, a DRL-based normalized advantage function (NAF) algorithm is proposed to maximize the average sum rate, and its performance is compared to deep deterministic policy gradient (DDPG), gain ratio PA (GRPA), and exhaustive search. The results indicate that NAF closely matches exhaustive search, is 39% faster than DDPG, and improves the average sum rate by 4.6% over GRPA, while accounting for user location estimation errors.

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arXiv2025

Energy-efficient Deep Reinforcement Learning-based Network Function Disaggregation in Hybrid Non-terrestrial Open Radio Access Networks

This paper explores the integration of Open Radio Access Network (O-RAN) principles with non-terrestrial networks (NTN) and investigates the optimization of the functional split between Centralized Units (CU) and Distributed Units (DU) to improve energy efficiency in dynamic network environments. Given the inherent constraints of NTN platforms, such as Low Earth Orbit (LEO) satellites and high-altitude platform stations (HAPS), we propose a reinforcement learning-based framework utilizing Deep Q-Network (DQN) to intelligently determine the optimal RAN functional split. The proposed approach dynamically adapts to real-time fluctuations in traffic demand, network conditions, and power limitations, ensuring efficient resource allocation and enhanced system performance.The numerical results demonstrate that the proposed policy effectively adapts to network traffic flow by selecting an efficient network disaggregation strategy and corresponding functional split option based on data rate and latency requirements.

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arXiv2025

LiDAL-Assisted RLNC-NOMA in OWC Systems

Optical wireless communication (OWC) is envisioned as a key enabler for immersive indoor data transmission in future wireless communication networks. However, multi-user interference management arises as a challenge in dense indoor OWC systems composed of multiple optical access points (APs) serving multiple users. In this paper, we propose a novel dual-function OWC system for communication and localization. Non-orthogonal multiple access (NOMA) with random linear network coding (RLNC) is designed for data transmission, where NOMA allows the serving of multiple users simultaneously through controlling the power domain, and RLNC helps minimize errors that might occur during signal processing phase. This setup is assisted with a light detection and localization system (LiDAL) that can passively obtain spatio-temporal indoor information of user presence and location for dynamic-user grouping. The designed LiDAL system helps to improve the estimation of channel state information (CSI) in realistic indoor network scenarios, where the CSI of indoor users might be noisy and/or highly correlated. We evaluate the performance of NOMA combined with RLNC by analyzing the probability of successful decoding compared to conventional NOMA and orthogonal schemes. In addition, we derive the Cramer-Rao Lower Bound (CRLB) to evaluate the accuracy of location estimation. The results show that the proposed RLNC-NOMA improves the probability of successful decoding and the overall system performance. The results also show the high accuracy of the unbiased location estimator and its assistant in reducing the imperfection of CSI, leading to high overall system performance.

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arXiv2024

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.

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arXiv2023

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.

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arXiv2021

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.

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arXiv2020

Optimized Distributed Processing in a Vehicular Cloud Architecture

The introduction of cloud data centres has opened new possibilities for the storage and processing of data, augmenting the limited capabilities of peripheral devices. Large data centres tend to be located away from the end users which increases latency and power consumption in the interconnecting networks. These limitations led to the introduction of edge processing where small distributed data centres or fog units are located at the edge of the network close to the end user. Vehicles can have substantial processing capabilities, often un-used, in their on-board-units (OBUs). These can be used to augment the network edge processing capabilities. In this paper we extend our previous work and develop a mixed integer linear programming (MILP) formulation that optimizes the allocation of networking and processing resources to minimize power consumption. Our edge processing architecture includes vehicular processing nodes, edge processing and cloud infrastructure. Furthermore, in this paper our optimization formulation includes delay. Compared to power minimization, our new formulation reduces delay significantly, while resulting in a very limited increase in power consumption.

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arXiv2020

Energy Efficient Distributed Processing for IoT

In this paper, the entire IoT-fog-cloud architecture is modelled, the service placement problem is optimized through Mixed Integer Linear Programming (MILP) and the total power consumption is jointly minimized for processing and networking. Four aspects of IoT service placements are examined: 1) non-splittable services, 2) splittable services, 3) inter-service processing overhead for sub-service synchronization and 4) deployment of special-purpose cloud data centers (SP-DCs). The results showed that for a capacitated problem, service splitting introduces power consumption savings of up to 86% compared to 46% with non-splittable services in relation to processing in general-purpose data centers (GP-DCs). Moreover, it is observed that the inter sub-service processing overhead has a great influence on the total number of service splits. However much insignificant the ratio of the processing overhead, the results showed that this is not a trivial matter and hence much attention needs to paid to this area in order to make the best use of the resources that are available in the edge of the network. Moreover, the optimization results showed that, for very high demands, power savings of up to 50% could be achieved with SP-DCs compared to 30% with GP-DCs.

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arXiv2019

Energy Efficient Routing and Network Coding in Core Networks

We propose network coding as an energy efficient data transmission technique in core networks with non-bypass and bypass routing approaches. The improvement in energy efficiency is achieved through reduction in the traffic flows passing through intermediate nodes. A mixed integer linear program (MILP) is developed to optimize the use of network resources, and the results show that our proposed network coding approach introduces up to 33% power savings for the non-bypass case compared with the conventional architectures. For the bypass case, 28% power savings are obtained considering futuristic network components power consumption. A heuristic based on the minimum hop count routing shows power savings comparable to the MILP results. Furthermore, we study how the change in network topology affects the savings produced by network coding. The results show that the savings are proportional to the average hop count of the network topology. We also derive power consumption analytic bounds and closed form expressions for networks that implement network coding and thus also verify the results obtained by the MILP model.

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arXiv2019

Impact of Distributed Processing on Power Consumption for IoT Based Surveillance Applications

With the rapid proliferation of connected devices in the Internet of Things (IoT), the centralized cloud solution faces several challenges, out of which, there is an overwhelming consensus to put energy efficiency at the top of the research agenda. In this paper, we evaluate the impact of demand splitting over heterogeneous processing resources in an IoT platform, supported by Fog and Cloud infrastructure. We develop a Mixed Integer Linear Programming (MILP) model to study the gains of splitting resource intensive demands among IoT nodes, Fog devices and Cloud servers. A surveillance application is considered, which consists of multiple smart cameras capable of capturing and analyzing real-time video streams. The PON access network aggregates IoT layer demands for processing in the Fog, or the Cloud which is accessed through the IP/WDM network. For typical video analysis workloads, the results show that splitting medium demand sizes among IoT and Fog resources yields a total power consumption saving of up to 32%, even if they can host only 10% of the total workload and this can reach 93% for lower number of demands, compared to the centralized cloud solution. However, the gains in power savings from splitting decreases as the number of splits increases. Keywords: IoT surveillance, PON, energy efficiency, fog, distributed processing.

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arXiv2019

Energy-Efficient Distributed Processing in Vehicular Cloud Architecture

Facilitating the revolution for smarter cities, vehicles are getting smarter and equipped with more resources to go beyond transportation functionality. On-Board Units (OBU) are efficient computers inside vehicles that serve safety and non-safety based applications. However, much of these resources are underutilised. On the other hand, more users are relying now on cloud computing which is becoming costly and energy consuming. In this paper, we develop a Mixed Integer linear Programming (MILP) model that optimizes the allocation of processing demands in an architecture that encompasses the vehicles, edge and cloud computing with the objective of minimizing power consumption. The results show power savings of 70%-90% compared to conventional clouds for small demands. For medium and large demand sizes, the results show 20%-30% power saving as the cloud was used partially due to capacity limitations on the vehicular and edge nodes.

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arXiv2018

Energy Efficient Service Distribution in Internet of Things

The Internet of Things (IoT) networks are expected to involve myriad of devices, ranging from simple sensors to powerful single board computers and smart phones. The great advancement in computational power of embedded technologies have enabled the integration of these devices into the IoT network, allowing for cloud functionalities to be extended near to the source of data. In this paper we study a multi-layer distributed IoT architecture supported by fog and cloud. We optimize the placement of the IoT services in this architecture so that the total power consumption is minimized. Our results show that, introducing local computation at the IoT layer can bring up to 90% power savings compared with general purpose servers in a central cloud.

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arXiv2018

Bounds for Energy-Efficient Survivable IP Over WDM Networks With Network Coding

In this work, we establish analytic bounds for the energy efficiency of 1 + 1 survivable IP over WDM networks using network coding. The analytic bounds are shown to be in close agreement with our previously reported results. They provide verification of the MILP and heuristics proposed previously, in addition to an efficient, compact means to evaluate network results and allow the performance of large networks to be determined easily.

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