paper-with-me

홈 › Papers

Interference-Constrained Scheduling of a Cognitive Multi-hop Underwater Acoustic Network

2023-12-04 · Chen Peng, Urbashi Mitra

This paper investigates optimal scheduling for a cognitive multi-hop underwater acoustic network with a primary user interference constraint. The network consists of primary and secondary users, with multi-hop transmission adopted for both user types to provide reliable communications. Critical characteristics of underwater acoustic channels, including significant propagation delay, distance-and-frequency dependent attenuation, half-duplex modem, and inter-hop interference, are taken into account in the design and analysis. In particular, time-slot allocation is found to be more effective than frequency-slot allocation due to the underwater channel model. The goal of the network scheduling problem is to maximize the end-to-end throughput of the overall system while limiting the throughput loss of primary users. Both centralized and decentralized approaches are considered. Partially Observable Markov Decision Processes (POMDP) framework is applied to formulate the optimization problem, and an optimal dynamic programming algorithm is derived. However, the optimal dynamic programming solution is computationally intractable. Key properties are shown for the objective function, enabling the design of approximate schemes with significant complexity reduction. Numerical results show that the proposed schemes significantly increase system throughput while maintaining the primary throughput loss constraint. Under certain traffic conditions, the throughput gain over frequency-slot allocation schemes can be as high as 50%.

📄 PDF Abstract BibTeX arXiv:2312.01625

Code (0)

등록된 구현이 없습니다.

Tasks

Scheduling

Similar Papers 제목 키워드 기반

Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

2017-10-23 · Anestis Tsakmalis, Symeon Chatzinotas, Björn Ottersten

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitiv…

Active Learning

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications

2025-06-16 · Minwoo Kim, Youngchol Choi, Yeongjun Kim, Eojin Seo 외

Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the cons…

Deep Reinforcement Learning

Joint Transmission in QoE-Driven Backhaul-Aware MC-NOMA Cognitive Radio Network

2020-08-30 · Hosein Zarini, Ata Khalili, Hina Tabassum, Mehdi Rasti

In this paper, we develop a resource allocation framework to optimize the downlink transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled with multi-carrier non-orthogonal multiple acc…

Scheduling

Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence

2026-03-28 · Kai Tian, Jialun Wang, Chuan Lin, Guangjie Han 외 arxiv

With the rapid advancement of underwater net-working and multi-agent coordination technologies, autonomous underwater vehicle (AUV) ad-hoc networks have emerged as a pivotal framework for executing complex maritime missi…

Autonomous Underwater Cognitive System for Adaptive Navigation: A SLAM-Integrated Cognitive Architecture

2025-11-14 · K. A. I. N Jayarathne, R. M. N. M. Rathnayaka, D. P. S. S. Peiris arxiv

Deep-sea exploration poses significant challenges, including disorientation, communication loss, and navigational failures in dynamic underwater environments. This paper presents an Autonomous Underwater Cognitive System…