On the Cell Coverage Area of Overlap Area-Based Cellular Model in Wireless Cellular Systems: Base Station Locations

2009 ◽  
Author(s):  
B. Homnan
Keyword(s):  
Base Station ◽  
Cellular Systems ◽  
Cellular Model ◽  
Coverage Area ◽  
Cell Coverage ◽  
Wireless Cellular Systems ◽  
Overlap Area

scholarly journals Analisis Penataan Sel Untuk Layanan Sistem WCDMA Di Area Jalan Tengah I Kerobokan

2017 ◽  
Vol 16 (2) ◽  
pp. 95
Author(s):  
Alit Winaya ◽  
Gede Sukadarmika ◽  
Linawati Linawati
Keyword(s):  
Performance Indicator ◽  
Telecommunication Network ◽  
Base Station ◽  
Propagation Model ◽  
Base Stations ◽  
Quality Service ◽  
Coverage Area ◽  
Cell Coverage ◽  
Antenna Tilt ◽  
Tilt Change

In WCDMA system, signal service coverage determines quality service of telecommunication network . To give maximum network service at Jl. Tengah I Kerobokan area, thus requiring the study of the arrangement of the cell coverage on BTS Protpeliatan Sector ? and BTS AnyarKaja Sektor ?. The method used is calculation the distance of base station with proper propagation model which suits the condition of both base stations, also antenna azimuth and tilting were given, and simuation using Atoll software to see the predicted coverage area of BTS. As the result, suitable propagation for BTS Protpeliatan Sector ? is Okumurra Hatta model and BTS AnyarKaja Sektor ? is Cost 231 Hatta model, maximum coverage signal service of BTS Protpeliatan Sector ? is 1,17 km and for BTS AnyarKaja Sektor ? is 1,11 km. Requirement of antenna tilt change from 0º to 0,95º for BTS Protpeliatan Sektor ? and for pada BTS AnyarKaja Sektor ?, antenna tilt change from 0º to 1º and change of 30º antenna sector direction from 180º to 210º. From simulation using Atoll software with above parameters, acknowledge that area of Jl. Tengah I Kerobokan already get maximum service with received signal level pass the KPI (Key Performance Indicator) standard.  Dalam sistem WCDMA, cakupan layanan sinyal merupakan penentu kualitas layanan jaringan telekomunikasi. Untuk memberikan layanan jaringan yang  maksimal di area Jl. Tengah I Kerobokan maka dilakukan analisis penataan cakupan layanan sel pada BTS Protpeliatan Sektor ? dan BTS AnyarKaja Sektor ?. Metode yang digunakan yaitu perhitungan jarak cakupan BTS dengan model propagasi yang paling sesuai dengan kondisi kedua BTS, penentuan arah azimuth dan tilting antena, serta simulasi menggunakan Atoll untuk melihat prediksi cakupan layanan BTS. Didapatkan bahwa model propagasi yang sesuai dengan BTS Protpeliatan Sektor ? adalah  model Okumurra Hatta dan BTS AnyarKaja Sektor ? adalah model Cost 231 Hatta, jarak cakupan layanan maksimum BTS Protpeliatan Sektor ? adalah sejauh 1,17 km dan pada BTS AnyarKaja Sektor ? sejauh 1, 11 km. Diperlukan perubahan tilt antena dari 0º menjadi 0,95º  pada BTS Protpeliatan Sektor ? dan perubahan sudut tilt dari 0º menjadi 1º serta perubahan arah sekorisasi antena sebesar 30º dari 180º menjadi 210 º pada BTS AnyarKaja Sektor ?. Dari hasil simulasi yang dilakukan menggunakan software Atoll dengan parameter tersebut, diketahui bahwa area Jl. Tengah I Kerobokan telah mendapatkan layanan yang maksimal dengan level penerimaan sinyal yang telah memenuhi standar KPI (Key Performance Indictator).  

scholarly journals Deep Learning applied to the handoff of cellular systems: a survey

2019 ◽  
Author(s):  
Federico Aguirre
Keyword(s):  
Deep Learning ◽  
Cell Phone ◽  
State Of The Art ◽  
Base Station ◽  
Cellular Systems ◽  
Coverage Area ◽  
Learning Techniques ◽  
A Cell ◽  
Spatio Temporal ◽  
Phone User

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Times New Roman'; min-height: 15.0px} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px 'Times New Roman'} span.s1 {font: 12.0px 'Times New Roman'} <p><br></p> <p> <b>Mobility is a key aspect in current cellular networks, allowing users to access the provided services almost anywhere. When a user transitions from a base station’s coverage area to another cell being serviced by another station, a handoff process takes place, where resources are released in the first base station, and allocated in the second for the purpose of servicing the user. Predicting the future location of a cell phone user allows the handoff process to be optimized. This optimization allows for a better utilization of the available resources, regarding bot the transmitted power and the frequency allocation, resulting in less amount of wasted power in unwanted directions and the possibility of reusing frequencies in a single base station. To achieve this goal, Deep Learning techniques are proposed, which have proven to be efficient tools for predicting and detecting patterns. The purpose of this paper is to give an overview of the state of the art in Deep Learning techniques for making spatio-temporal predictions, which could be used to optimize the handoff process in cellular systems. </b></p>

scholarly journals Deep Learning applied to the handoff of cellular systems: a survey

2019 ◽  
Author(s):  
Federico Aguirre
Keyword(s):  
Deep Learning ◽  
Cell Phone ◽  
State Of The Art ◽  
Base Station ◽  
Cellular Systems ◽  
Coverage Area ◽  
Learning Techniques ◽  
A Cell ◽  
Spatio Temporal ◽  
Phone User

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Times New Roman'; min-height: 15.0px} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px 'Times New Roman'} span.s1 {font: 12.0px 'Times New Roman'} <p><br></p> <p> <b>Mobility is a key aspect in current cellular networks, allowing users to access the provided services almost anywhere. When a user transitions from a base station’s coverage area to another cell being serviced by another station, a handoff process takes place, where resources are released in the first base station, and allocated in the second for the purpose of servicing the user. Predicting the future location of a cell phone user allows the handoff process to be optimized. This optimization allows for a better utilization of the available resources, regarding bot the transmitted power and the frequency allocation, resulting in less amount of wasted power in unwanted directions and the possibility of reusing frequencies in a single base station. To achieve this goal, Deep Learning techniques are proposed, which have proven to be efficient tools for predicting and detecting patterns. The purpose of this paper is to give an overview of the state of the art in Deep Learning techniques for making spatio-temporal predictions, which could be used to optimize the handoff process in cellular systems. </b></p>

A Review on 3GPP 5G Security Aspects

2019 ◽  
Author(s):  
Rajavelsamy R ◽  
Debabrata Das
Keyword(s):  
Network Architecture ◽  
Base Station ◽  
Cellular Systems ◽  
Core Network ◽  
User Privacy ◽  
Wireless Backhaul ◽  
Level Of Use ◽  
Unified Authentication ◽  
3Rd Generation Partnership Project ◽  
5G Security

5G promises to support new level of use cases that will deliver a better user experience. The 3rd Generation Partnership Project (3GPP) [1] defined 5G system introduced fundamental changes on top of its former cellular systems in several design areas, including security. Unlike in the legacy systems, the 5G architecture design considers Home control enhancements for roaming customer, tight collaboration with the 3rd Party Application servers, Unified Authentication framework to accommodate various category of devices and services, enhanced user privacy, and secured the new service based core network architecture. Further, 3GPP is investigating the enhancements to the 5G security aspects to support longer security key lengths, False Base station detection and wireless backhaul in the Phase-2 of 5G standardization [2]. This paper provides the key enhancements specified by the 3GPP for 5G system, particularly the differences to the 4G system and the rationale behind the decisions.

Analysis of the Effect of Tilting Antenna on the Coverage Area of ??the 4G LTE Network (Case Study of Trenggalek District)

2019 ◽  
Vol 9 (4) ◽  
pp. 43-48
Author(s):  
Rizal Aji Istantowi
Keyword(s):  
Base Station ◽  
Base Stations ◽  
Optimal Conditions ◽  
Coverage Area ◽  
Lte Networks ◽  
Lte Network ◽  
Big Cities ◽  
4G Lte ◽  
4G Lte Networks

4G LTE networks in big cities are already well available. Meanwhile, on small to medium-sized cities, the 4G LTE network is not evenly distributed and maximized. This study chooses the variable tilting antenna to the coverage area, because in sending information from a base station using an antenna. The average RSRP value (dBm) of the existing base station in the calculation with a distance of 200 m is -122.90 dBm, a distance of 500 m is -136.79 dBm, and a distance of 1000 m -147.30 dBm. Meanwhile, in the simulation with a distance of 200 m of -108.22 dBm, a distance of 500 m of -121.81 dBm, and a distance of 1000 m of -132.69 dBm. The coverage area value of the existing base station in the calculation is 5.29%, while in the simulation it is 11.18%. The average RSRP value (dBm) at optimal conditions for calculations at a distance of 200 m is -80.13 dBm, at a distance of 500 m is -94.03 dBm and at a distance of 1000 m is -104.56 dBm. Meanwhile, the simulation at a distance of 200 m is -98.09 dBm, at a distance of 500 m is -112.79 dBm and at a distance of 1000 m is -123.31 dBm. The value of the coverage area for the calculation is 20.32%, while for the simulation it is 15.01%. The current need for base stations in Trenggalek District that has been met is 68%.

Determination of Cell Coverage Area and its Applications in High-Speed Railway Environments

2017 ◽  
Vol 66 (5) ◽  
pp. 3515-3525 ◽  
Author(s):  
Bo Ai ◽  
Ruisi He ◽  
Guangkai Li ◽  
Ke Guan ◽  
Danping He ◽  
...  
Keyword(s):  
High Speed ◽  
Coverage Area ◽  
High Speed Railway ◽  
Cell Coverage

An Efficient IR-HARQ Power Allocation for Relay-Aided Cellular Networks

2018 ◽  
Vol 27 (12) ◽  
pp. 1850195
Author(s):  
P. Mangayarkarasi ◽  
J. Raja
Keyword(s):  
Power Allocation ◽  
Cellular Networks ◽  
Channel Capacity ◽  
Energy Efficient ◽  
Base Station ◽  
Relay Station ◽  
Decode And Forward ◽  
Relay Placement ◽  
Cell Coverage ◽  
The Impact

Energy-efficient and reliable data transmission is a challenging task in wireless relay networks (WRNs). Energy efficiency in cellular networks has received significant attention because of the present need for reduced energy consumption, thereby maintaining the profitability of networks, which in turn makes these networks “greener”. The urban cell topography needs more energy to cover the total area of the cell. The base station does not cover the entire area in a given topography and adding more number of base stations is a cost prohibitive one. Energy-efficient relay placement model which calculates the maximum cell coverage is proposed in this work that covers all sectors and also an energy-efficient incremental redundancy-hybrid automatic repeat request (IR-HARQ) power allocation scheme to improve the reliability of the network by improving the overall network throughput is proposed. An IR-HARQ power allocation method maximizes the average incremental mutual information at each round, and its throughput quickly converges to the ergodic channel capacity as the number of retransmissions increases. Simulation results show that the proposed IR-HARQ power allocation achieves full channel capacity with average transmission delay and maintains good throughput under less power consumption. Also the impact of relaying performance on node distances between relay station and base station as well as between user and relay station and relay height for line of sight conditions are analyzed using full decode and forward (FDF) and partial decode and forward (PDF) relaying schemes. Compared to FDF scheme, PDF scheme provides better performance and allows more freedom in the relay placement for an increase in cell coverage.

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