Related papers: Traffic model of LTE using maximum flow algorithm …
Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods been proposed.…
Increasing data rate in wireless networks can be accomplished through a two-pronged approach, which are 1) increasing the network flow rate through parallel independent routes and 2) increasing the user's link rate through beamforming…
Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or…
Increasing demand for massive device connectivity in underserved regions drives the development of advanced low Earth orbit (LEO) satellite communication systems. Beam-hopping LEO systems without connection establishment provide a promising…
This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data.…
Recent endeavors aimed at forecasting future traffic flow states through deep learning encounter various challenges and yield diverse outcomes. A notable obstacle arises from the substantial data requirements of deep learning models, a…
In this paper, we consider a resource allocation with carrier aggregation optimization problem in long term evolution (LTE) cellular networks. In our proposed model, users are running elastic or inelastic traffic. Each user equipment (UE)…
License-assisted access LTE (LAA-LTE) has been proposed to deal with the intense contradiction between tremendous mobile traffic demands and crowded licensed spectrums. In this paper, we investigate the coexistence mechanism for LAA-LTE…
During the past few decades wireless technology has seen a tremendous growth. The recent introduction of high-end mobile devices has further increased subscriber's demand for high bandwidth. Current cellular systems require manual…
Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has…
This paper investigates uplink multiple access for the coexistence of enhanced mobile broadband+ (eMBB+) and massive machine-type communications+ (mMTC+) in terminal-centric cell-free massive MIMO (CF-mMIMO) systems. We propose a…
Frequency reuse in cells is one of the strategies that LTE uses to maximize the spectrum efficiency. However, it leads to an interference among the cells, especially at the cell edges where the probability for a cell-edge user to be…
The Least Loaded (LL) routing algorithm has been in recent decades the routing method of choice in circuit switched networks and therefore it provides a benchmark against which new methods can be compared. This paper improves the…
Long Term Evolution (LTE), which has its root on commercial mobile communications, recently becomes an influential solution to future public safety communications. To verify the feasibility of LTE for public safety, it is essential to…
Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for…
We study the routing problem for vehicles with limited energy through a network of inhomogeneous charging nodes. This is substantially more complicated than the homogeneous node case studied in [1]. We seek to minimize the total elapsed…
Massive Machine-Type Communications (MTC) over cellular networks is expected to be an integral part of wireless "Smart City" applications. The Long Term Evolution (LTE)/LTE-Advanced (LTE-A) technology is a major candidate for provisioning…
LTEs uplink (UL) efficiency critically depends on how the interference across different cells is controlled. The unique characteristics of LTEs modulation and UL resource assignment poses considerable challenges in achieving this goal…
The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with…
With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements.…