Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks
Reads0
Chats0
TLDR
A distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed that enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states.Abstract:
In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links. This problem is formulated as an optimization problem, which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users’ content request distribution while having only limited information on the network’s and users’ states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states. Based on the users’ association and content request distributions, the optimal contents that need to be cached at UAVs and the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 17.8% and 57.1% gains, respectively, in terms of the number of users that have stable queues compared with two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that the LSM significantly improves the convergence time of up to 20% compared with conventional learning algorithms such as Q-learning.read more
Citations
More filters
Journal ArticleDOI
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
TL;DR: This paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks and overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems.
Journal ArticleDOI
Federated Learning for Internet of Things: A Comprehensive Survey
TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing and IoT privacy and security.
Posted Content
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
TL;DR: In this article, the authors provide a comprehensive tutorial on the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications.
Journal ArticleDOI
Federated Learning for Internet of Things: A Comprehensive Survey
TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing and IoT privacy and security.
Journal ArticleDOI
UAV-Relaying-Assisted Secure Transmission With Caching
TL;DR: A novel scheme to guarantee the security of UAV-relayed wireless networks with caching via jointly optimizing the UAV trajectory and time scheduling and a benchmark scheme in which the minimum average secrecy rate among all users is maximized and no user has the caching ability.
References
More filters
Journal ArticleDOI
Performance analysis of the IEEE 802.11 distributed coordination function
TL;DR: In this paper, a simple but nevertheless extremely accurate, analytical model to compute the 802.11 DCF throughput, in the assumption of finite number of terminals and ideal channel conditions, is presented.
Journal ArticleDOI
Wireless communications with unmanned aerial vehicles: opportunities and challenges
TL;DR: An overview of UAV-aided wireless communications is provided, by introducing the basic networking architecture and main channel characteristics, highlighting the key design considerations as well as the new opportunities to be exploited.
Book
Stochastic Network Optimization with Application to Communication and Queueing Systems
TL;DR: In this article, the authors present a modern theory of analysis, control, and optimization for dynamic networks, including wireless networks with time-varying channels, mobility, and randomly arriving traffic.
Journal ArticleDOI
Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks
Qingqing Wu,Yong Zeng,Rui Zhang +2 more
TL;DR: In this paper, the minimum throughput over all ground users in the downlink communication was maximized by optimizing the multiuser communication scheduling and association jointly with the UAV's trajectory and power control.
Journal ArticleDOI
Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks
TL;DR: In this article, a proactive caching mechanism is proposed to reduce peak traffic demands by proactively serving predictable user demands via caching at base stations and users' devices, and the results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users.