Moazzam Islam Tiwana

Airborne internet access through submarine optical fiber cables

Internet access for passengers travelling in aircrafts is thought to be one of the unresolved major challenges for ubiquitous Internet provision. Vast oceanic remote regions along the busy air routes of the world require low-cost, reliable, and high-speed Internet for the aircraft. Satellite links can provide Internet coverage in such remote areas; however, their services are still costly with low bandwidth and longer delays. Fortunately, the submarine optical cables deployed across the oceans pass along the same busy air routes. These cables can be utilized as high-speed Internet backbone for wireless Internet access to the aircraft. Dedicated ships stationed along these submarine optical fiber cables can be exploited to provide Internet, security, and navigation services to aircrafts and ships. A novel architecture for such a ground/sea-to-air access network is proposed. A complete solution, design, and analysis of the proposed technique are thoroughly discussed. In contrast to the traditional land mobile radio cellular systems, the high speed of the aircraft results in reduced available handover time margins. To address the challenges related to the high-speed mobility of aircraft, an analysis for the impact of various parameters on the performance of handovers is presented. Using the proposed analytical model, a mathematical relation for the handover margin with the velocity of aircraft, direction of the aircrafts motion, and propagation environment is derived on the basis of path-loss propagation model.

A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility

With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach.

Self-organizing inter-cell interference coordination in 4G and beyond networks using genetic algorithms

ABSTRACT The design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.

Samoorganizirajuće mreže: Podržano učenje za optimizaciju LTE mobilnosti

With the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as co-operative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput.

Enhancemant of the Statistical Learning Automated Healing (SLAH) technique using packet scheduling

Automated healing aims to reduce cost of network operations by automated fault diagnosis and rectification. This paper investigates the use of Packet Scheduling (PS) in automated healing. PS has been integrated into a previously proposed scheme of Statistical Learning Automated Healing (SLAH) for LTE. SLAH locally optimizes the Radio Resource Management (RRM) parameters of the faulty eNodeBs (eNBs). SLAH uses Logistic Regression (LoR) to extract the closed form relationship between the RRM parameters and the network measurements which are in the form of Key Performance Indicators (KPIs). This paper uses PS in SLAH to achieve the required minimum coverage constraint on an eNB by coverage/capacity compromise. Similarly, if this minimum coverage requirement of an eNB is already satisfied, additional capacity gain for an eNB can be achieved. This enhanced SLAH methdology has been used to rectify faults due to excessive interference suffered by an eNB from its first tier neighbours. Simulation results of the case study done prove that this technique converges in few iterations.