2 research outputs found
Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS
Due to the development of communication technology and the rise of user
network demand, a reasonable resource allocation for wireless networks is the
key to guaranteeing regular operation and improving system performance. Various
frequency bands exist in the natural network environment, and heterogeneous
cellular network (HCN) has become a hot topic for current research. Meanwhile,
Reconfigurable Intelligent Surface (RIS) has become a key technology for
developing next-generation wireless networks. By modifying the phase of the
incident signal arriving at the RIS surface, RIS can improve the signal quality
at the receiver and reduce co-channel interference. In this paper, we develop a
RIS-assisted HCN model for a multi-base station (BS) multi-frequency network,
which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and
considers the case of multiple network coverage users, which is more in line
with the realistic network characteristics and the concept of 6G networks. We
propose the optimization objective of maximizing the system sum rate, which is
decomposed into two subproblems, i.e., the user resource allocation and the
phase shift optimization problem of RIS components. Due to the NP-hard and
coupling relationship, we use the block coordinate descent (BCD) method to
alternately optimize the local solutions of the coalition game and the local
discrete phase search algorithm to obtain the global solution. In contrast,
most previous studies have used the coalition game algorithm to solve the
resource allocation problem alone. Simulation results show that the algorithm
performs better than the rest of the algorithms, effectively improves the
system sum rate, and achieves performance close to the optimal solution of the
traversal algorithm with low complexity.Comment: 18 page
Leveraging Machine Learning Techniques towards Intelligent Networking Automation
In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the computational costs of implementing the proposed mechanisms.
Accordingly, this thesis tackles the challenges that four specific research problems present. The first topic addresses the problem of balancing traffic in dense Internet of Things (IoT) network scenarios where the end devices and the Base Stations (BSs) form complex networks. By applying ML techniques to discover patterns in the association between the end devices and the BSs, the proposed scheme can balance the traffic load in a IoT network to increase the packet delivery ratio and reduce the energy cost of data delivery. The second research topic proposes an intelligent congestion control for internet connections at edge network elements. The design includes a congestion predictor based on an Artificial Neural Network (ANN) and an Active Queue Management (AQM) parameter tuner. Similarly, the third research topic includes an intelligent solution to the inter-domain congestion. Different from second topic, this problem considers the preservation of the private network data by means of Federated Learning (FL), since network elements of several organizations participate in the intelligent process. Finally, the fourth research topic refers to a framework to efficiently gathering network telemetry (NT) data. The proposed solution considers a traffic-aware approach so that the NT is intelligently collected and transmitted by the network elements.
All the proposed schemes are evaluated through use cases considering standardized networking mechanisms. Therefore, we envision that the solutions of these specific problems encompass a set of methods that can be utilized in real-world scenarios towards the realization of the INA paradigm