Optimization Technique In Cellular Network

Project and Seminar Material For Electrical Electronics Engineering EEE

Project and Seminar Material For Electrical Electronics Engineering EEE


Abstract


With multiple air-interface support capabilities and higher cell densities. Future cellular network will offer a diverse spectrum of user services. The resulting dynamic in traffic load and resource demand will challenge present control loop algorithms. In addition, frequent upgrades in the network infrastructure will substantially increase the network operation costs if done using current optimization methodology.

This motivates the development of dynamic control algorithms that can automatically adjust the network to changes in both traffic and network conditions and autonomously adapt when new cells are added to the system. Bell labs is pursuing efforts to realize such algorithms with research on near-term approaches that benefit present third-generation (3G) systems and the development of control features for future networks that performs dynamic parameters adjustment across protocol layers.

In this paper, I describe the development of conceptual approaches, algorithms, modeling, foundation, and real-time measurement that provide the foundation for future dynamic network optimization techniques.


Table Of Content


Preliminary Page(s)

  • Title Page
  • Approval Page
  • Dedication
  • Acknowledgement
  • Abstract
  • Table Of Content

Chapter One

  • 1.0 Introduction
  • 1.1 Aims and objectives
  • 1.2 Scope of work
  • 1.3 Definition of Abbreviation, Acronyms and terms

Chapter Two

  • 2.0 Literature review
  • 2.1 High-level Architecture and implementation detail
  • 2.2 Open-loop Dynamic optimization
  • 2.3 Closed-loop Dynamic optimization

Chapter Three

  • 3.0 Research methodology
  • 3.1 Real-time measurements
  • 3.2 Control algorithms for future networks
  • 3.3 A methodology for Algorithm development
  • 3.4 Formulation of optimization task. Global Downlink power control
  • 3.5 Development of distributed implementation

Chapter Four

  • 4.0 Conclusion
  • 4.1 Recommendation
  • References

Chapter One


1.0 Introduction

Cellular network optimization has traditionally been associated with a process that aims to adjust the network air interference to market-specific traffic and propagation conditions (Lee et al, 1993). The tuning operations in this process focus on a small set of hardware parameters such as cell-site locations and antenna configurations.

The cells themselves are grouped more densely around traffic nucleation points to provide capacity, and the antennas are pointed in compliance with the local terrain and clutter to reduce signal shadows and interference.
Occasionally, software parameters such as handoff thresfolds and cell-power budgets are also adjusted while hardware parameters are easily set during network installation, they are hard to change afterward.

As a result, the optimization with respect to hardware parameters occurs during network planning and development, and it is only repeated in areas where performance problems or infrastructure upgrade are required. Since it is perform as a singular event, the optimization process is fundamentally based upon time-average worst-case traffic and propagation conditions originally, these optimizations were performed through a manual, it erative process relying on network planning tools and drive testing.

Bell labs subsequently introduced the concepts of predictive optimization in the ocelots optimization tool, which computes optimum network parameters directly according to well-defined performance metrics (Drabeck et al,2005). Its introduction has translated into faster network rellouts, improved network performances, and higher capacity.

Notwithstanding its widespread use, the static optimization approach is increasingly approaching its limits. The growth of wireless customer base and the introduction of various new data services mandate the consideration of new objectives such as throughput, delay, latency, and quality of service (Qos). Indeed, the migration to IP multimedia subsystem (IMS) will promote the continuous development of the new services with different resource requirement Qos demand, and traffic characteristics.

Furthermore, data services introduce demand fluctuations that are intrinsically larger than they are for voice services. The multidimensional nature of demand, its temporal dependency, and its increased dynamic range render optimization strategies based on the peak (albelt composite) loading progressively less effective at efficiently allocating and managing network resource.

Additionally, the demand for increasing data rates and the falling costs for networks hardware will drive network architectures towards micro-cellular structures. This development will create frequent infrastructure upgrades with the demand for fast, autonomous, and inexpensive cell integration.


1.1 Aims And Objective

This work objective is to identify the dynamic optimization in future cellular network.

As a consequently we see a growing need for additional dynamic optimization mechanisms with the following capabilities:

  1. State and Time-dependent control parameters to help the network adapt the coverage and capacity tradeoff for multiple services in response to spatio -temporal demand variation.
  2. Coordinated (and potentially additional autonomous) load-balancing mechanism that can address demand and traffic fluctuation by optimally “smoothing out” uncorrelated demand peaks between neighboring cells and even between differing wireless technologies; And
  3. Active measures to address rare but undesirable events, such as reducing dropped and blocked calls.

1.2 Scope Of Work

The work covers the dynamics optimization of future cellular networks in Nigeria, the development of conceptual approaches, algorithms, modeling, simulation, and real-time measurements that provide the foundation for future dynamic optimization techniques.


Chapter Four


4.0 Conclusion

Several facets of the bell labs research program on dynamic network optimization that provides cellular networks with the capabilities to respond to fluctuations in traffic and resource demand been presented.

I have also presented applications to current network traffic patterns, where network trials have demonstrated improved network traffic patterns, as well as longer terms efforts that focus on the development of faster coordinated response mechanisms that can successfully adapt to load fluctuations across cells.


4.1 Recommendation

I have further demonstrated that real-time measurements are a fundamental ingredient to the development of dynamic control mechanism since they reveal the inefficiencies incellular networks and provide information on actual traffic characteristics and user behavior. In addition, I identified impotant interrelations among network properties such as per-call Qos, resource demand, and traffic load, which in turn drice the development of future control algorithms information on actual behavior is already being applied in current dynamic network trials for optimization of recurring traffic patterns.

Finally, real=time measurements become an integral part of future dynamic optimization features for diagnostic and monitoring purposes.

The interplay of these facets is critical to the dynamic optimization roadmap. Features developed for present network validate my dynamic optimization control mechanisms.

I am convinced that dynamic optimization algorithms will continuously drive improvements in key performance metrics, such as throughput, dropped-call rate, and resource utilization.


Optimization Technique In Cellular Network


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