Heterogeneous Resource Allocation for Ensuring End-to-End Quality of Service in Multi-hop Integrated Access and Backhaul Network
- URL: http://arxiv.org/abs/2504.03576v1
- Date: Fri, 04 Apr 2025 16:29:08 GMT
- Title: Heterogeneous Resource Allocation for Ensuring End-to-End Quality of Service in Multi-hop Integrated Access and Backhaul Network
- Authors: Shuaifeng Zhang,
- Abstract summary: Multi-hop integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification.<n>dynamic time division duplex (D-TDD) is a promising solution to adapt to highly dynamic scenarios with asymmetric uplink and downlink traffic.<n>We decompose the integrated optimization problem (IOP) into sub-problems to reduce the solution space.<n>To achieve the system-wide solution, we propose a single-leader heterogeneous multi-follower Stackelberg-game-based resource allocation scheme.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Faced with increasing network traffic demands, cell dense deployment is one of significant means to utilize spectrum resources efficiently to improve network capacity. Multi-hop integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Meanwhile, dynamic time division duplex (D-TDD) is a promising solution to adapt to highly dynamic scenarios with asymmetric uplink and downlink traffic. Thus, dynamic resource allocation between backhaul and access links and high spectral efficiency under ensuring reliable transmission are two key objectives of IAB research. However, due to huge solution space, there are some challenges in multi-hop IAB with D-TDD if only an integrated optimization problem (IOP) is considered. To handle these challenges, we decompose the IOP into sub-problems to reduce the solution space. To tackle these sub-problems, we formulate them separately as the non-cooperative games and design the corresponding utility functions to guarantee the existence of Nash equilibrium solutions. Also, to achieve the system-wide solution, we propose a single-leader heterogeneous multi-follower Stackelberg-game-based resource allocation scheme, which can combine the solving results of all the sub-problems to get the IOP approximate solution. Simulation results show that the proposed scheme can improve throughput performance while meeting spectrum energy efficiency constraints.
Related papers
- CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks [23.140655547353994]
We present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address generalized assignment problems (GAP) in vehicular networks (VNets)<n>Our approach leverages a hybrid quantum-classical structure, integrating a tailored cost function that balances both objective and constraint-specific penalties to improve solution quality and stability.<n>We apply this framework to a user-association problem in VNets, where our method achieves 23.5% improvement compared to the deep neural network (DNN) approach.
arXiv Detail & Related papers (2025-01-14T20:21:06Z) - Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
Low-altitude economy holds significant potential for development in areas such as communication and sensing.<n>We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
arXiv Detail & Related papers (2024-12-14T06:17:33Z) - DiffSG: A Generative Solver for Network Optimization with Diffusion Model [75.27274046562806]
Generative diffusion models are popular in various cross-domain applications.<n>These models hold promise in tackling complex network optimization problems.<n>We propose a new framework for generative diffusion models called Diffusion Model-based Solution Generation.
arXiv Detail & Related papers (2024-08-13T07:56:21Z) - DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing.
Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time.
We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
arXiv Detail & Related papers (2024-06-11T06:31:03Z) - ILP-based Resource Optimization Realized by Quantum Annealing for Optical Wide-area Communication Networks -- A Framework for Solving Combinatorial Problems of a Real-world Application by Quantum Annealing [5.924780594614675]
In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave AdvantageTM quantum annealer system.
Here we report on our investigations for optimizing system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions.
We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave.
arXiv Detail & Related papers (2024-01-01T17:52:58Z) - Double Deep Q-Learning-based Path Selection and Service Placement for
Latency-Sensitive Beyond 5G Applications [11.864695986880347]
This paper studies the joint problem of communication and computing resource allocation, dubbed CCRA, 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.
Numerical simulations show that B&B-CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time.
arXiv Detail & Related papers (2023-09-18T22:17:23Z) - Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [55.08287089554127]
Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability.<n>We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments.<n>We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
arXiv Detail & Related papers (2023-09-04T17:30:21Z) - Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB
networks [0.0]
Integrated Access and Backhauling (IRL) is a viable approach for meeting the unprecedented need for higher data rates of future generations.
In this paper, we show how we can use Deep Q-Learning Network to handle problems with huge action spaces associated with fractional nodes.
arXiv Detail & Related papers (2023-08-31T21:30:25Z) - Multi-Resource Allocation for On-Device Distributed Federated Learning
Systems [79.02994855744848]
This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system.
Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively.
arXiv Detail & Related papers (2022-11-01T14:16:05Z) - Resource Allocation via Model-Free Deep Learning in Free Space Optical
Communications [119.81868223344173]
The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications.
Under this framework, we propose two algorithms that solve FSO resource allocation problems.
arXiv Detail & Related papers (2020-07-27T17:38:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.