0000000000763095
AUTHOR
Muhammad Zeeshan Asghar
showing 8 related works from this author
Combinatorial Optimization for Artificial Intelligence Enabled Mobile Network Automation
2021
This chapter discusses combinatorial optimization techniques for enabling intelligent automation in mobile networks. A number of discrete optimization problems pertinent to mobile network automation can be solved effectively using artificial intelligence based combinatorial optimization approaches such as heuristics and metaheuristics. Relevant use-cases include both initial parameter assignment during network roll-out, and continuous optimization of configuration management parameters during network operation and maintenance. We discuss mobile network automation use-cases and motivation for using different heuristics and metaheuristics in designing network optimization algorithms. To this …
Performance evaluation of OpenFlow enabled Commodity and Raspberry-pi Wireless Routers
2017
Software defined network (SDN) allows the decoupling of data and control plane for dynamic and scalable network management. SDN is usually associated with OpenFlow protocol which is a standard interface that enables the network controllers to determine the path of network packets across a network of switches. In this paper, we evaluate openflow performance using commodity wireless router and raspberry pi with two different SDN controllers. Our test setup consists of wired and wireless client devices connected to openflow enabled commodity wireless router and raspberry pi. All clients used traffic generator tool to transmits data to a sink server host. The results are promising and paves the…
Dual Connectivity in Non-Stand Alone Deployment mode of 5G in Manhattan Environment
2020
| openaire: EC/H2020/815191/EU//PriMO-5G The main target of this paper is to analyze the performance of an outdoor user in a dense micro cellular Manhattan grid environment using a ray launching simulation tool. The radio propagation simulations are performed using a Shoot and Bouncing Ray (SBR) method. The network performance is analyzed at three different frequencies i.e. 1.8 GHz, 3.5 GHz, and 28 GHz. Additionally, the benefits of combining LTE and potential 5G frequency bands by using feature of Dual Connectivity (DC) in an outdoor scenario has been highlighted. The considered performance metrics are received signal level, SINR, application throughput. The acquired simulation results fro…
Towards proactive context-aware self-healing for 5G networks
2017
In this paper, we suggest a new research direction and a future vision for Self-Healing (SH) in Self-Organizing Networks (SONs). The problem we wish to solve is that traditional SH solutions may not be sufficient for the future needs of cellular network management because of their reactive nature, i.e., they start recovering after detecting already occurred faults instead of preparing for possible future faults in a pre-emptive manner. The detection delays are especially problematic with regard to the zero latency requirements of 5G networks. To address this problem, existing SONs need to be upgraded from reactive to proactive response. One of the dimensions in SH research is to employ more…
Cell state prediction through distributed estimation of transmit power
2019
Determining the state of each cell, for instance, cell outages, in a densely deployed cellular network is a difficult problem. Several prior studies have used minimization of drive test (MDT) reports to detect cell outages. In this paper, we propose a two step process. First, using the MDT reports, we estimate the serving base station’s transmit power for each user. Second, we learn summary statistics of estimated transmit power for various networks states and use these to classify the network state on test data. Our approach is able to achieve an accuracy of 96% on an NS-3 simulation dataset. Decision tree, random forest and SVM classifiers were able to achieve a classification accuracy of…
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
Correlation-Based Cell Degradation Detection for Operational Fault Detection in Cellular Wireless Base-Stations
2013
The management and troubleshooting of faults in mobile radio networks are challenging as the complexity of radio networks is increasing. A proactive approach to system failures is needed to reduce the number of outages and to reduce the duration of outages in the operational network in order to meet operator’s requirements on network availability, robustness, coverage, capacity and service quality. Automation is needed to protect the operational expenses of t he network. Through a good performance of the network element and a low failure probability the network can operate more efficiently reducing the necessity for equipment investments. We present a new method that utilizes the correlatio…
Design and evaluation of self-healing solutions for future wireless networks
2016
This doctoral dissertation is aimed at the creation of comprehensive and innovative Self-Organizing Networks (SON) solutions for the Network Management of future wireless networks. More specifically, the thesis focuses on the Self-Healing (SH) part of SON. Faults can appear at several functional areas of a complex cellular network. However, the most critical domain from a fault management viewpoint is the Radio Access Network (RAN). The fault management of network elements is not only difficult but also imposes high costs both in capital investment (CAPEX) and operational expenditures (OPEX). The SON concept has emerged with the goal to foster automation and to reduce human involvement in man…