723 research outputs found
Enabling RAN Slicing Through Carrier Aggregation in mmWave Cellular Networks
The ever increasing number of connected devices and of new and heterogeneous
mobile use cases implies that 5G cellular systems will face demanding technical
challenges. For example, Ultra-Reliable Low-Latency Communication (URLLC) and
enhanced Mobile Broadband (eMBB) scenarios present orthogonal Quality of
Service (QoS) requirements that 5G aims to satisfy with a unified Radio Access
Network (RAN) design. Network slicing and mmWave communications have been
identified as possible enablers for 5G. They provide, respectively, the
necessary scalability and flexibility to adapt the network to each specific use
case environment, and low latency and multi-gigabit-per-second wireless links,
which tap into a vast, currently unused portion of the spectrum. The
optimization and integration of these technologies is still an open research
challenge, which requires innovations at different layers of the protocol
stack. This paper proposes to combine them in a RAN slicing framework for
mmWaves, based on carrier aggregation. Notably, we introduce MilliSlice, a
cross-carrier scheduling policy that exploits the diversity of the carriers and
maximizes their utilization, thus simultaneously guaranteeing high throughput
for the eMBB slices and low latency and high reliability for the URLLC flows.Comment: 8 pages, 8 figures. Proc. of the 18th Mediterranean Communication and
Computer Networking Conference (MedComNet 2020), Arona, Italy, 202
Packet Scheduling Algorithms in LTE/LTE-A cellular Networks: Multi-agent Q-learning Approach
Spectrum utilization is vital for mobile operators. It ensures an efficient use of spectrum bands, especially when obtaining their license is highly expensive. Long Term Evolution (LTE), and LTE-Advanced (LTE-A) spectrum bands license were auctioned by the Federal Communication Commission (FCC) to mobile operators with hundreds of millions of dollars. In the first part of this dissertation, we study, analyze, and compare the QoS performance of QoS-aware/Channel-aware packet scheduling algorithms while using CA over LTE, and LTE-A heterogeneous cellular networks. This included a detailed study of the LTE/LTE-A cellular network and its features, and the modification of an open source LTE simulator in order to perform these QoS performance tests. In the second part of this dissertation, we aim to solve spectrum underutilization by proposing, implementing, and testing two novel multi-agent Q-learning-based packet scheduling algorithms for LTE cellular network. The Collaborative Competitive scheduling algorithm, and the Competitive Competitive scheduling algorithm. These algorithms schedule licensed users over the available radio resources and un-licensed users over spectrum holes. In conclusion, our results show that the spectrum band could be utilized by deploying efficient packet scheduling algorithms for licensed users, and can be further utilized by allowing unlicensed users to be scheduled on spectrum holes whenever they occur
Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory
Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
The ever-increasing number of resource-constrained Machine-Type Communication
(MTC) devices is leading to the critical challenge of fulfilling diverse
communication requirements in dynamic and ultra-dense wireless environments.
Among different application scenarios that the upcoming 5G and beyond cellular
networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include QoS provisioning,
handling highly dynamic and sporadic MTC traffic, huge signalling overhead and
Radio Access Network (RAN) congestion. In this regard, this paper aims to
identify and analyze the involved technical issues, to review recent advances,
to highlight potential solutions and to propose new research directions. First,
starting with an overview of mMTC features and QoS provisioning issues, we
present the key enablers for mMTC in cellular networks. Along with the
highlights on the inefficiency of the legacy Random Access (RA) procedure in
the mMTC scenario, we then present the key features and channel access
mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT.
Subsequently, we present a framework for the performance analysis of
transmission scheduling with the QoS support along with the issues involved in
short data packet transmission. Next, we provide a detailed overview of the
existing and emerging solutions towards addressing RAN congestion problem, and
then identify potential advantages, challenges and use cases for the
applications of emerging Machine Learning (ML) techniques in ultra-dense
cellular networks. Out of several ML techniques, we focus on the application of
low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss
some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future
publication in IEEE Communications Surveys and Tutorial
- …