scholarly journals Self-Organizing Network (SON) Functionalities for Mobility Management in WiFi Networks

2021 ◽  
Author(s):  
◽  
Lina Hao
Keyword(s):  
Cellular Networks ◽  
Mobility Management ◽  
Network Performance ◽  
Data Transfer ◽  
Critical Issue ◽  
Base Stations ◽  
Machine Learning Techniques ◽  
Self Healing ◽  
Wifi Networks ◽  
Self Organizing

<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks.  In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management.  Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

scholarly journals Self-Organizing Network (SON) Functionalities for Mobility Management in WiFi Networks

2021 ◽  
Author(s):  
◽  
Lina Hao
Keyword(s):  
Cellular Networks ◽  
Mobility Management ◽  
Network Performance ◽  
Data Transfer ◽  
Critical Issue ◽  
Base Stations ◽  
Machine Learning Techniques ◽  
Self Healing ◽  
Wifi Networks ◽  
Self Organizing

<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks.  In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management.  Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

scholarly journals A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

2017 ◽  
Vol 19 (4) ◽  
pp. 2392-2431 ◽  
Author(s):  
Paulo Valente Klaine ◽  
Muhammad Ali Imran ◽  
Oluwakayode Onireti ◽  
Richard Demo Souza
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Self Organizing

scholarly journals New framework for interference and energy analysis of soft frequency reuse in 5G networks

2020 ◽  
Vol 9 (5) ◽  
pp. 1941-1949
Author(s):  
Achonu Adejo ◽  
Osbert Asaka ◽  
Habeeb Bello- Salau ◽  
Caroline Alenoghena
Keyword(s):  
Cellular Networks ◽  
Spectral Efficiency ◽  
Network Performance ◽  
Base Station ◽  
Frequency Reuse ◽  
Base Stations ◽  
System Capacity ◽  
Management Technique ◽  
5G Networks ◽  
Soft Frequency Reuse

Cellular networks are expanding massively due to high data requirements from mobile devices. This has motivated base station densification as an essential requirement for the 5G network. The implication is obvious benefits in enhanced system capacity, but also increased challenges in terms of interference. One important interference management technique which has been widely adopted in cellular networks is frequency reuse. In this article, an analysis is presented based on network interference and energy expended by base stations in downlink communication when Soft frequency reuse (SFR) is deployed. A framework is presented that captures the bandwidth overlaps in SFR across base station assignments, computes the interference probabilities arising and derives new performance equations which are verified using simulations. Results show an improvement of over previous SFR implementations that do not consider the interference probabilities. Thus, a more in-depth and accurate modelling of SFR in 5G networks is achieved. Furthermore, the downlink power allocation is investigated as against other parameters like the center ratio and edge bandwidth. The result shows that signal-to-interference-noise ratio (SINR) and spectral efficiency give different performance under energy consideration. A framework is developed on how to tune a base station to achieve desired network performance in user SINR or cell spectral efficiency depending on the operator’s preference.

scholarly journals Benefits of Self-Organizing Networks (SON) for Mobile Operators

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Olav Østerbø ◽  
Ole Grøndalen
Keyword(s):  
Cellular Networks ◽  
Mobile Networks ◽  
Cost Savings ◽  
Base Stations ◽  
Parallel Operation ◽  
Configuration Optimization ◽  
Network Parameters ◽  
And Performance ◽  
Self Organizing Networks ◽  
Self Organizing

Self-Organizing Networks (SON) is a collection of functions for automatic configuration, optimization, diagnostisation and healing of cellular networks. It is considered to be a necessity in future mobile networks and operations due to the increased cost pressure. The main drivers are essentially to reduce CAPEX and OPEX, which would otherwise increase dramatically due to increased number of network parameters that has to be monitored and set, the rapidly increasing numbers of base stations in the network and parallel operation of 2G, 3G and Evolved Packet Core (EPC) infrastructures. This paper presents evaluations on the use of some of the most important SON components. Mobile networks are getting more complex to configure, optimize and maintain. Many SON functions will give cost savings and performance benefits from the very beginning of a network deployment and these should be prioritized now. But even if many functions are already available and can give large benefits, the field is still in its infancy and more advanced functions are either not yet implemented or have immature implementations. It is therefore necessary to have a strategy for how and when different SON functions should be introduced in mobile networks.

scholarly journals Femtocells Underlaid Macro-cellular Networks

2021 ◽  
Author(s):  
Joydev Ghosh
Keyword(s):  
Cellular Networks ◽  
Coverage Probability ◽  
Network Performance ◽  
Point Processes ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Base Stations ◽  
Good Growth ◽  
Density Region ◽  
Single Antenna

<div>This work presents the evaluation of the downlink (DL) performance of a dual-layer cellular networks by using energy efficiency (EE) metric, where femto base stations (FBSs), macro base stations (MBSs) and users (FUs) form independent spatial Poisson point processes (PPPs). The proposed network model is developed by considering number of antennas at each BS alongside a single antenna at each user with the use of the conventional spectrum re-utilization approach. Then, Coverage probability and EE expressions for the duallayer cellular networks are exclusively derived analytically. It is also demonstrated that simulation results are almost in-line with the analytical one in the PPP-based model. While coverage probability deteriorates with less margin in the lower FBS density region compared to the scheme presented in [10] signalled not much turnaround of the network performance, EE in the lower and the upper FBS density regions are likely to remain between 6x10^-3 to 9.2 x10^-3 Bits/Joule and 4.6 x10^-3 to 7.1x10^-3 Bits/Joule, respectively. Proposed scheme tells us that it is firmly on course to match up with Vehicular Ad-hoc NETworks (VANET) applications without incurring high cost as EE, low latency, coverage probability and low power adaptability are back on good growth path. </div>

scholarly journals Intelligent Interference Management in UAV-Based HetNets

Telecom ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 472-488
Author(s):  
Simran Singh ◽  
Abhaykumar Kumbhar ◽  
İsmail Güvenç ◽  
Mihail L. Sichitiu
Keyword(s):  
Cellular Networks ◽  
Hot Spots ◽  
Network Performance ◽  
Interference Management ◽  
Base Stations ◽  
Computationally Efficient ◽  
Q Learning ◽  
Conventional Optimization ◽  
Coverage Holes ◽  
Mobile Base

Unmanned aerial vehicles (UAVs) can play a key role in meeting certain demands of cellular networks. UAVs can be used not only as user equipment (UE) in cellular networks but also as mobile base stations (BSs) wherein they can either augment conventional BSs by adapting their position to serve the changing traffic and connectivity demands or temporarily replace BSs that are damaged due to natural disasters. The flexibility of UAVs allows them to provide coverage to UEs in hot-spots, at cell-edges, in coverage holes, or regions with scarce cellular infrastructure. In this work, we study how UAV locations and other cellular parameters may be optimized in such scenarios to maximize the spectral efficiency (SE) of the network. We compare the performance of machine learning (ML) techniques with conventional optimization approaches. We found that, on an average, a double deep Q learning approach can achieve 93.46% of the optimal median SE and 95.83% of the optimal mean SE. A simple greedy approach, which tunes the parameters of each BS and UAV independently, performed very well in all the cases that we tested. These computationally efficient approaches can be utilized to enhance the network performance in existing cellular networks.

scholarly journals Femtocells Underlaid Macro-cellular Networks

2021 ◽  
Author(s):  
Joydev Ghosh
Keyword(s):  
Cellular Networks ◽  
Coverage Probability ◽  
Network Performance ◽  
Point Processes ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Base Stations ◽  
Good Growth ◽  
Density Region ◽  
Single Antenna

<div>This work presents the evaluation of the downlink (DL) performance of a dual-layer cellular networks by using energy efficiency (EE) metric, where femto base stations (FBSs), macro base stations (MBSs) and users (FUs) form independent spatial Poisson point processes (PPPs). The proposed network model is developed by considering number of antennas at each BS alongside a single antenna at each user with the use of the conventional spectrum re-utilization approach. Then, Coverage probability and EE expressions for the duallayer cellular networks are exclusively derived analytically. It is also demonstrated that simulation results are almost in-line with the analytical one in the PPP-based model. While coverage probability deteriorates with less margin in the lower FBS density region compared to the scheme presented in [10] signalled not much turnaround of the network performance, EE in the lower and the upper FBS density regions are likely to remain between 6x10^-3 to 9.2 x10^-3 Bits/Joule and 4.6 x10^-3 to 7.1x10^-3 Bits/Joule, respectively. Proposed scheme tells us that it is firmly on course to match up with Vehicular Ad-hoc NETworks (VANET) applications without incurring high cost as EE, low latency, coverage probability and low power adaptability are back on good growth path. </div>

scholarly journals Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 408 ◽  
Author(s):  
Mohammed Alsharif ◽  
Anabi Kelechi ◽  
Jeong Kim ◽  
Jin Kim
Keyword(s):  
Energy Efficiency ◽  
Wireless Network ◽  
Cellular Networks ◽  
High Speed ◽  
Network Performance ◽  
Energy Savings ◽  
Load Condition ◽  
Base Stations ◽  
Entire Area ◽  
Trade Off

Recently, cellular networks’ energy efficiency has garnered research interest from academia and industry because of its considerable economic and ecological effects in the near future. This study proposes an approach to cooperation between the Long-Term Evolution (LTE) and next-generation wireless networks. The fifth-generation (5G) wireless network aims to negotiate a trade-off between wireless network performance (sustaining the demand for high speed packet rates during busy traffic periods) and energy efficiency (EE) by alternating 5G base stations’ (BSs) switching off/on based on the traffic instantaneous load condition and, at the same time, guaranteeing network coverage for mobile subscribers by the remaining active LTE BSs. The particle swarm optimization (PSO) algorithm was used to determine the optimum criteria of the active LTE BSs (transmission power, total antenna gain, spectrum/channel bandwidth, and signal-to-interference-noise ratio) that achieves maximum coverage for the entire area during the switch-off session of 5G BSs. Simulation results indicate that the energy savings can reach 3.52 kW per day, with a maximum data rate of up to 22.4 Gbps at peak traffic hours and 80.64 Mbps during a 5G BS switched-off session along with guaranteed full coverage over the entire region by the remaining active LTE BSs.

scholarly journals A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6546
Author(s):  
Eva Masero ◽  
Luis A. Fletscher ◽  
José M. Maestre
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Cellular Networks ◽  
Predictive Control ◽  
Large Scale ◽  
Network Performance ◽  
Renewable Energy Sources ◽  
High Energy ◽  
Base Stations ◽  
Next Generation

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates).

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