arXiv subjects

Masoud Shokrnezhad

Publications and source records attributed to Masoud Shokrnezhad.

26 records · Page 2Linked to original sources

arXiv2024

Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions

In the context of emerging 6G services, the realization of everything-to-everything interactions involving a myriad of physical and digital entities presents a crucial challenge. This challenge is exacerbated by resource scarcity in communication infrastructures, necessitating innovative solutions for effective service implementation. Exploring the potential of Semantic Communications (SemCom) to enhance point-to-point physical layer efficiency shows great promise in addressing this challenge. However, achieving efficient SemCom requires overcoming the significant hurdle of knowledge sharing between semantic decoders and encoders, particularly in the dynamic and non-stationary environment with stringent end-to-end quality requirements. To bridge this gap in existing literature, this paper introduces the Knowledge Base Management And Orchestration (KB-MANO) framework. Rooted in the concepts of Computing-Network Convergence (CNC) and lifelong learning, KB-MANO is crafted for the allocation of network and computing resources dedicated to updating and redistributing KBs across the system. The primary objective is to minimize the impact of knowledge management activities on actual service provisioning. A proof-of-concept is proposed to showcase the integration of KB-MANO with resource allocation in radio access networks. Finally, the paper offers insights into future research directions, emphasizing the transformative potential of semantic-oriented communication systems in the realm of 6G technology.

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arXiv2024

ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G

Anticipation for 6G's arrival comes with growing concerns about increased energy consumption in computing and networking. The expected surge in connected devices and resource-demanding applications presents unprecedented challenges for energy resources. While sustainable resource allocation strategies have been discussed in the past, these efforts have primarily focused on single-domain orchestration or ignored the unique requirements posed by 6G. To address this gap, we investigate the joint problem of service instance placement and assignment, path selection, and request prioritization, dubbed PIRA. The objective function is to maximize the system's overall profit as a function of the number of concurrently supported requests while simultaneously minimizing energy consumption over an extended period of time. In addition, end-to-end latency requirements and resource capacity constraints are considered for computing and networking resources, where queuing theory is utilized to estimate the Age of Information (AoI) for requests. After formulating the problem in a non-linear fashion, we prove its NP-hardness and propose a method, denoted ORIENT. This method is based on the Double Dueling Deep Q-Learning (D3QL) mechanism and leverages Graph Neural Networks (GNNs) for state encoding. Extensive numerical simulations demonstrate that ORIENT yields near-optimal solutions for varying system sizes and request counts.

cs.NI↗

arXiv2024

Joint Network Slicing, Routing, and In-Network Computing for Energy-Efficient 6G

To address the evolving landscape of next-generation mobile networks, characterized by an increasing number of connected users, surging traffic demands, and the continuous emergence of new services, a novel communication paradigm is essential. One promising candidate is the integration of network slicing and in-network computing, offering resource isolation, deterministic networking, enhanced resource efficiency, network expansion, and energy conservation. Although prior research has explored resource allocation within network slicing, routing, and in-network computing independently, a comprehensive investigation into their joint approach has been lacking. This paper tackles the joint problem of network slicing, path selection, and the allocation of in-network and cloud computing resources, aiming to maximize the number of accepted users while minimizing energy consumption. First, we introduce a Mixed-Integer Linear Programming (MILP) formulation of the problem and analyze its complexity, proving that the problem is NP-hard. Next, a Water Filling-based Joint Slicing, Routing, and In-Network Computing (WF-JSRIN) heuristic algorithm is proposed to solve it. Finally, a comparative analysis was conducted among WF-JSRIN, a random allocation technique, and two optimal approaches, namely Opt-IN (utilizing in-network computation) and Opt-C (solely relying on cloud node resources). The results emphasize WF-JSRIN's efficiency in delivering highly efficient near-optimal solutions with significantly reduced execution times, solidifying its suitability for practical real-world applications.

cs.NI↗

arXiv2023

A Scalable Communication Model to Realize Integrated Access and Backhaul (IAB) in 5G

Our vision of the future world is one wherein everything, anywhere and at any time, can reliably communicate in real time. 5G, the fifth generation of cellular networks, is anticipated to use heterogeneity to deliver ultra-high data rates to a vastly increased number of devices in ultra-dense areas. Improving the backhaul network capacity is one of the most important open challenges for deploying a 5G network. A promising solution is Integrated Access and Backhaul (IAB), which assigns a portion of radio resources to construct a multi-hop wireless backhaul network. Although 3GPP has acknowledged the cost-effectiveness of the IAB-enabled framework and its orchestration has been extensively studied in the literature, its transmission capacity (i.e., the number of base stations it can support) has not been sufficiently investigated. In this paper, we formulate the problem of maximizing transmission capacity and minimizing transmit powers for IAB-enabled multi-hop networks, taking into account relay selection, channel assignment, and power control constraints. Then, the solution space of the problem is analyzed, two optimality bounds are derived, and a heuristic algorithm is proposed to investigate the bounds. The claims are finally supported by numerical results.

cs.NI↗

arXiv2023

Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications

The Metaverse is a new paradigm that aims to create a virtual environment consisting of numerous worlds, each of which will offer a different set of services. To deal with such a dynamic and complex scenario, considering the stringent quality of service requirements aimed at the 6th generation of communication systems (6G), one potential approach is to adopt self-sustaining strategies, which can be realized by employing Adaptive Artificial Intelligence (Adaptive AI) where models are continually re-trained with new data and conditions. One aspect of self-sustainability is the management of multiple access to the frequency spectrum. Although several innovative methods have been proposed to address this challenge, mostly using Deep Reinforcement Learning (DRL), the problem of adapting agents to a non-stationary environment has not yet been precisely addressed. This paper fills in the gap in the current literature by investigating the problem of multiple access in multi-channel environments to maximize the throughput of the intelligent agent when the number of active User Equipments (UEs) may fluctuate over time. To solve the problem, a Double Deep Q-Learning (DDQL) technique empowered by Continual Learning (CL) is proposed to overcome the non-stationary situation, while the environment is unknown. Numerical simulations demonstrate that, compared to other well-known methods, the CL-DDQL algorithm achieves significantly higher throughputs with a considerably shorter convergence time in highly dynamic scenarios.

cs.NI↗

arXiv2023

QoS-Aware Service Prediction and Orchestration in Cloud-Network Integrated Beyond 5G

Novel applications such as the Metaverse have highlighted the potential of beyond 5G networks, which necessitate ultra-low latency communications and massive broadband connections. Moreover, the burgeoning demand for such services with ever-fluctuating users has engendered a need for heightened service continuity consideration in B5G. To enable these services, the edge-cloud paradigm is a potential solution to harness cloud capacity and effectively manage users in real time as they move across the network. However, edge-cloud networks confront a multitude of limitations, including networking and computing resources that must be collectively managed to unlock their full potential. This paper addresses the joint problem of service placement and resource allocation in a network-cloud integrated environment while considering capacity constraints, dynamic users, and end-to-end delays. We present a non-linear programming model that formulates the optimization problem with the aiming objective of minimizing overall cost while enhancing latency. Next, to address the problem, we introduce a DDQL-based technique using RNNs to predict user behavior, empowered by a water-filling-based algorithm for service placement. The proposed framework adeptly accommodates the dynamic nature of users, the placement of services that mandate ultra-low latency in B5G, and service continuity when users migrate from one location to another. Simulation results show that our solution provides timely responses that optimize the network's potential, offering a scalable and efficient placement.

cs.NI↗

arXiv2023

Near-optimal Cloud-Network Integrated Resource Allocation for Latency-Sensitive B5G

Nowadays, while the demand for capacity continues to expand, the blossoming of Internet of Everything is bringing in a paradigm shift to new perceptions of communication networks, ushering in a plethora of totally unique services. To provide these services, Virtual Network Functions (VNFs) must be established and reachable by end-users, which will generate and consume massive volumes of data that must be processed locally for service responsiveness and scalability. For this to be realized, a solid cloud-network Integrated infrastructure is a necessity, and since cloud and network domains would be diverse in terms of characteristics but limited in terms of capability, communication and computing resources should be jointly controlled to unleash its full potential. Although several innovative methods have been proposed to allocate the resources, most of them either ignored network resources or relaxed the network as a simple graph, which are not applicable to Beyond 5G because of its dynamism and stringent QoS requirements. This paper fills in the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including VNF placement and assignment, traffic prioritization, and path selection considering capacity constraints as well as link and queuing delays, with the goal of minimizing overall cost. We formulate the problem as a non-linear programming model, and propose two approaches, dubbed B\&B-CCRA and WF-CCRA respectively, based on the Branch \& Bound and Water-Filling algorithms. Numerical simulations show that B\&B-CCRA can solve the problem optimally, whereas WF-CCRA can provide near-optimal solutions in significantly less time.

cs.NI↗

arXiv2023

Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications

Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloud-network integrated infrastructure is necessary to supply these services in a real-time responsive, and scalable way. Due to their diverse characteristics and limited capacity, communication and computing resources must be collaboratively managed to unleash their full potential. Although several innovative methods have been proposed to orchestrate the resources, most ignored network resources or relaxed the network as a simple graph, focusing only on cloud resources. This paper fills the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including function placement and assignment, traffic prioritization, and path selection considering capacity constraints and quality requirements, to minimize total cost. We formulate the problem as a non-linear programming model and propose two approaches, dubbed B\&B-CCRA and WF-CCRA, based on the Branch \& Bound and Water-Filling algorithms to solve it when the system is fully known. Then, for partially known systems, a Double Deep Q-Learning (DDQL) architecture is designed. Numerical simulations show that B\&B-CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA obtains near-optimal solutions in the absence of request-specific information.

cs.NI↗