ﻻ يوجد ملخص باللغة العربية
Driven by growing spectrum shortage, Long-term Evolution in unlicensed spectrum (LTE-U) has recently been proposed as a new paradigm to deliver better performance and experience for mobile users by extending the LTE protocol to unlicensed spectrum. In the paper, we first present a comprehensive overview of the LTE-U technology, and discuss the practical challenges it faces. We summarize the existing LTE-U operation modes and analyze several means for LTE-U coexistence with Wi-Fi medium access control protocols. We further propose a novel hyper access-point (HAP) that integrates the functionalities of LTE small cell base station and commercial Wi-Fi AP for deployment by cellular network operators. Our proposed LTE-U access embedding within the Wi-Fi protocol is non-disruptive to unlicensed Wi-Fi nodes and demonstrates performance benefits as a seamless and novel LTE and Wi-Fi coexistence technology in unlicensed band. We provide results to demonstrate the performances advantage of this novel LTE-U proposal.
The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial opera
According to the LTE-U Forum specification, a LTE-U base-station (BS) reduces its duty cycle from 50% to 33% when it senses an increase in the number of co-channel Wi-Fi basic service sets (BSSs) from one to two. The detection of the number of Wi-Fi
Wi-Fi technology is continuously innovating to cater to the growing customer demands, driven by the digitalisation of everything, both in the home as well as the enterprise and hotspot spaces. In this article, we introduce to the wireless community t
For stegoschemes arising from error correcting codes, embedding depends on a decoding map for the corresponding code. As decoding maps are usually not complete, embedding can fail. We propose a method to ensure or increase the probability of embeddin
This paper explores the feasibility of leveraging concepts from deep reinforcement learning (DRL) to enable dynamic resource management in Wi-Fi networks implementing distributed multi-user MIMO (D-MIMO). D-MIMO is a technique by which a set of wirel