79,235 research outputs found
Resource Allocation in Moving Small Cell Network using Deep Learning based Interference Determination
© 2019 IEEE. Mobile cellular users traveling in city buses are experiencing poor quality of signals due to the interference and the large number of mobile devices. To enhance the Quality-of-Service (QoS), deployment of small cell networks in city buses is a promising solution. The deployment of small cells in vehicular environment makes the resource allocation more challenging because of the dynamic interference relationships experienced by them. Therefore, resource allocation in vehicular environment within moving small cells (MSCs) needs to be handled carefully. In this study, we investigate the problem of resource allocation in city bus transit system with multiple routes. Then, we propose a Percentage Threshold Interference Graph (PTIG) based allocation of resources to MSCs in a network. City buses of multiple routes travel with variable speed and may share some of the same road segments which make it difficult to extract the exact interference patterns between them. Therefore, Long Short Term Memory (LSTM) neural networks are used to predict the city buses locations. The predicted locations of city buses are then used to generate PTIG by finding the dynamic interference relationship between MSCs. Graph coloring algorithm is used to allocate the resources to PTIG. Numerical results are presented to show the comparison of resource allocation using PTIG and Time Interval based Interference Graph (TIIG) in terms of resource block utilization and time complexity
이동 셀이 포함된 이종 셀룰러 네트워크에서 자원할당에 관한 연구
학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이용환.Due to the popularity of smart phones and wireless services, demand for high traffic has become a heavy burden in wireless cellular communication system. Deployment of small cells has been proposed as one of feasible solutions to support increasing traffic demand. However, it may need to resolve technical issues including the management of frequent handovers, cross-tier and inter-cell interference.
In this thesis, we consider the employment of small moving cells (SMCs) in a heterogeneous cellular network to improve transmission performance of the whole network. An SMC can provide services for a small number of users moving together with a mobility of up to a few hundred Km/hour. For ease of interference management and in consideration of SMC mobility, the macro cell shares the resource with SMCs in an orthogonal manner. To maximally utilize the resource, the macro cell adjusts the amount of resource for the SMCs in response to the change of SMC operational environments and utilizes the rest of the resource for itself. It can also allocate resource to each SMC in an orthogonal manner. Exploiting that the peak-to-average load ratio (PALR) is much larger than one, SMCs can maximally utilize the resource without inter-cell interference. Finally, the proposed resource allocation scheme is verified by computer simulation.Contents
Abstract i
Contents iii
List of Figures iv
List of Tables v
1. Introduction 1
2. System model 3
2.1 Heterogeneous cellular network with small moving cells 3
2.2 Signal-to-interference plus noise ratio (SINR) 4
2.3 FA Size 5
2.4 Traffic load 6
3. Resource allocation for SMCs 9
3.1 Previous works 9
3.2 Proposed resource allocation 12
3.2.1 Resource allocation based on the mean FA size 13
3.2.2 Resource allocation based on the peak FA size 15
3.3 Resource adjustment for SMCs 16
3.4 Overhead 17
4. Performance evaluation 19
5. Conclusions 26
References 27
1. 초 록 30Maste
QoS enhancement with deep learning-based interference prediction in mobile IoT
© 2019 Elsevier B.V. With the acceleration in mobile broadband, wireless infrastructure plays a significant role in Internet-of-Things (IoT) to ensure ubiquitous connectivity in mobile environment, making mobile IoT (mIoT) as center of attraction. Usually intelligent systems are accomplished through mIoT which demands for the increased data traffic. To meet the ever-increasing demands of mobile users, integration of small cells is a promising solution. For mIoT, small cells provide enhanced Quality-of-Service (QoS) with improved data rates. In this paper, mIoT-small cell based network in vehicular environment focusing city bus transit system is presented. However, integrating small cells in vehicles for mIoT makes resource allocation challenging because of the dynamic interference present between small cells which may impact cellular coverage and capacity negatively. This article proposes Threshold Percentage Dependent Interference Graph (TPDIG) using Deep Learning-based resource allocation algorithm for city buses mounted with moving small cells (mSCs). Long–Short Term Memory (LSTM) based neural networks are considered to predict city buses locations for interference determination between mSCs. Comparative analysis of resource allocation using TPDIG, Time Interval Dependent Interference Graph (TIDIG), and Global Positioning System Dependent Interference Graph (GPSDIG) is presented in terms of Resource Block (RB) usage and average achievable data rate of mIoT-mSC network
Mobility-aware QoS assurance in software-defined radio access networks: an analytical study
Software-defined networking (SDN) has gained a tremendous attention in the recent years, both in academia and industry. This revolutionary networking paradigm is an attempt to bring the advances in computer science and software engineering into the information and communications technology (ICT) domain. The aim of these efforts is to pave the way for completely programmable networks and control-data plane separation. Recent studies on feasibility and applicability of SDN concepts in cellular networks show very promising results and this trend will most likely continue in near future. In this work, we study the benefits of SDN on the radio resource management (RRM) of future-generation cellular networks. Our considered cellular network architecture is in line with the recently proposed Long-Term Evolution (LTE) Release 12 concepts, such as user/control plane split, heterogeneous networks (HetNets) environment, and network densification through deployment of small cells. In particular, the aim of our RRM scheme is to enable the macro base station (BS) to efficiently allocate radio resources for small cell BSs in order to assure quality-of-service (QoS) of moving users/vehicles during handovers. We develop an approximate, but very time- and space-efficient algorithm for radio resource allocation within a HetNet. Experiments on commodity hardware show algorithm running times in the order of a few seconds, thus making it suitable even in cases of fast moving users/vehicles. We also confirm a good accuracy of our proposed algorithm by means of computer simulations
Resource Allocation for Next Generation Radio Access Networks
Driven by data hungry applications, the architecture of mobile networks is
moving towards that of densely deployed cells where each cell may use a different
access technology as well as a different frequency band. Next generation
networks (NGNs) are essentially identified by their dramatically increased data
rates and their sustainable deployment. Motivated by these requirements, in
this thesis we focus on (i) capacity maximisation, (ii) energy efficient configuration
of different classes of radio access networks (RANs). To fairly allocate
the available resources, we consider proportional fair rate allocations. We
first consider capacity maximisation in co-channel 4G (LTE) networks, then
we proceed to capacity maximisation in mixed LTE (including licensed LTE
small cells) and 802.11 (WiFi) networks. And finally we study energy efficient
capacity maximisation of dense 3G/4G co-channel small cell networks.
In each chapter we provide a network model and a scalable resource allocation
approach which may be implemented in a centralised or distributed manner
depending on the objective and network constraints
V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks
Benefited from the widely deployed infrastructure, the LTE network has
recently been considered as a promising candidate to support the
vehicle-to-everything (V2X) services. However, with a massive number of devices
accessing the V2X network in the future, the conventional OFDM-based LTE
network faces the congestion issues due to its low efficiency of orthogonal
access, resulting in significant access delay and posing a great challenge
especially to safety-critical applications. The non-orthogonal multiple access
(NOMA) technique has been well recognized as an effective solution for the
future 5G cellular networks to provide broadband communications and massive
connectivity. In this article, we investigate the applicability of NOMA in
supporting cellular V2X services to achieve low latency and high reliability.
Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
to tackle the technical hurdles in designing high spectral efficient scheduling
and resource allocation schemes in the ultra dense topology. We then extend it
to a more general V2X broadcasting system. Other NOMA-based extended V2X
applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin
- …