7 research outputs found
A Comprehensive Review of D2D Communication in 5G and B5G Networks
The evolution of Device-to-device (D2D) communication represents a significant breakthrough within the realm of mobile technology, particularly in the context of 5G and beyond 5G (B5G) networks. This innovation streamlines the process of data transfer between devices that are in close physical proximity to each other. D2D communication capitalizes on the capabilities of nearby devices to communicate directly with one another, thereby optimizing the efficient utilization of available network resources, reducing latency, enhancing data transmission speed, and increasing the overall network capacity. In essence, it empowers more effective and rapid data sharing among neighboring devices, which is especially advantageous within the advanced landscape of mobile networks such as 5G and B5G. The development of D2D communication is largely driven by mobile operators who gather and leverage short-range communications data to propel this technology forward. This data is vital for maintaining proximity-based services and enhancing network performance. The primary objective of this research is to provide a comprehensive overview of recent progress in different aspects of D2D communication, including the discovery process, mode selection methods, interference management, power allocation, and how D2D is employed in 5G technologies. Furthermore, the study also underscores the unresolved issues and identifies the challenges associated with D2D communication, shedding light on areas that need further exploration and developmen
Resource Allocation for D2D Communications Based on Matching Theory
PhDDevice-to-device (D2D) communications underlaying a cellular infrastructure takes advantage
of the physical proximity of communicating devices and increasing resource utilisation.
However, adopting D2D communications in complex scenarios poses substantial
challenges for the resource allocation design. Meanwhile, matching theory has emerged
as a promising framework for wireless resource allocation which can overcome some limitations
of game theory and optimisation. This thesis focuses on the resource allocation
optimisation for D2D communications based on matching theory.
First, resource allocation policy is designed for D2D communications underlaying cellular
networks. A novel spectrum allocation algorithm based on many-to-many matching
is proposed to improve system sum rate. Additionally, considering the quality-of-service
(QoS) requirements and priorities of di erent applications, a context-aware resource allocation
algorithm based on many-to-one matching is proposed, which is capable of providing
remarkable performance enhancement in terms of improved data rate, decreased
packet error rate (PER) and reduced delay.
Second, to improve resource utilisation, joint subchannel and power allocation problem
for D2D communications with non-orthogonal multiple access (NOMA) is studied. For
the subchannel allocation, a novel algorithm based on the many-to-one matching is
proposed for obtaining a suboptimal solution. Since the power allocation problem is
non-convex, sequential convex programming is adopted to transform the original power
allocation problem to a convex one. The proposed algorithm is shown to enhance the
network sum rate and number of accessed users.
Third, driven by the trend of heterogeneity of cells, the resource allocation problem for
NOMA-enhanced D2D communications in heterogeneous networks (HetNets) is investigated. In such a scenario, the proposed resource allocation algorithm is able to closely
approach the optimal solution within a limited number of iterations and achieves higher
sum rate compared to traditional HetNets schemes.
Thorough theoretical analysis is conducted in the development of all proposed algorithms,
and performance of proposed algorithm is evaluated via comprehensive simulations.
This thesis concludes that matching theory based resource allocation for D2D communications
achieves near-optimal performance with acceptable complexity. In addition,
the application of D2D communications in NOMA and HetNets can improve system
performance in terms of sum rate and users connectivity
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Artificial Intelligence-Generated Content (AIGC) is an automated method for
generating, manipulating, and modifying valuable and diverse data using AI
algorithms creatively. This survey paper focuses on the deployment of AIGC
applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile
AIGC networks, that provide personalized and customized AIGC services in real
time while maintaining user privacy. We begin by introducing the background and
fundamentals of generative models and the lifecycle of AIGC services at mobile
AIGC networks, which includes data collection, training, finetuning, inference,
and product management. We then discuss the collaborative cloud-edge-mobile
infrastructure and technologies required to support AIGC services and enable
users to access AIGC at mobile edge networks. Furthermore, we explore
AIGCdriven creative applications and use cases for mobile AIGC networks.
Additionally, we discuss the implementation, security, and privacy challenges
of deploying mobile AIGC networks. Finally, we highlight some future research
directions and open issues for the full realization of mobile AIGC networks