Kin Fai Poon

Self-Optimization of Pilot Power in Enterprise Femtocells Using Multi objective Heuristic

Deployment of a large number of femtocells to jointly provide coverage in an enterprise environment raises critical challenges especially in future self-organizing networks which rely on plug-and-play techniques for configuration. This paper proposes a multi-objective heuristic based on a genetic algorithm for a centralized self-optimizing network containing a group of UMTS femtocells. In order to optimize the network coverage in terms of handled load, coverage gaps, and overlaps, the algorithm provides a dynamic update of the downlink pilot powers of the deployed femtocells. The results demonstrate that the algorithm can effectively optimize the coverage based on the current statistics of the global traffic distribution and the levels of interference between neighboring femtocells. The algorithm was also compared with the fixed pilot power scheme. The results show over fifty percent reduction in pilot power pollution and a significant enhancement in network performance. Finally, for a given traffic distribution, the solution quality and the efficiency of the described algorithm were evaluated by comparing the results generated by an exhaustive search with the same pilot power configuration.

Deployment challenges of femtocells in future indoor wireless networks

Femtocell technology is expected to improve both coverage and capacity, especially indoors. Providers of cellular services are increasingly implementing this technology in their networks. However, the decentralized manner of femtocells operation introduces several challenges. This paper presents an overview of the femtocells. It highlights the network requirements for deploying femtocells and discusses the major issues that affect their operation and performance. The paper also describes the key challenges of deploying femtocells and some of the proposed solutions. It then presents the future direction on applying optimization to enhance femtocell performance.

An enhanced Ant Colony Optimization for FTTH access network design

Fiber deployment for the last mile of access networks is critical to provide bandwidth-intensive services such as high speed internet access, video on demand, online gaming, etc. However, to design a cost-effective FTTH access network, it requires the consideration of many factors, such as the number, positions of network components and routing of cables. Usually, the difficulty of producing consistent and cost-effective solutions is proportional to the size and complexity of the network. In the past, many approaches based on metaheuristics and mixed integer linear programming were applied to solve this kind of problem with different outcomes. In this paper, we focus on how Ant Colony Optimization (ACO) method can be employed to solve this problem effectively. Different network examples will be provided to illustrate optimal or near optimal solutions.

Performance evaluation of heuristic techniques for coverage optimization in femtocells

Self-optimization of coverage is an essential element for successful deployment of enterprise femtocells. This paper evaluates the performance of genetic algorithm, particle swarm and simulated annealing heuristic techniques to solve a multi-objective coverage optimization problem when a number of femtocells are deployed to jointly provide indoor coverage. This paper demonstrates the different behaviors of the proposed algorithms. The results show that genetic algorithm and particle swarm have a higher potential of solving the problem compared to simulated annealing. This is due to their faster convergence time which is an important parameter for dynamic update of femtocells.

Using Ant Colony Optimization to design GPON-FTTH networks with aggregating equipment

With the huge demands for the provision of inexpensive and fast broadband services, Gigabit Passive Optical Network (GPON) has been considered to be the most attractive solution for providing broadband access network. However, due to the consideration of many design factors such as the number, types, positions of network elements and routing information, the optical network planning process often exhibits several challenges from the optimization point of view. This problem is generally NP-hard and cannot be solved in polynomial time by any currently known algorithms. In this paper, we present an algorithm based on the Ant Colony Optimization (ACO) method with dedicated post-processing. Given a geographical location of a Greenfield area, our proposed solution minimizes the overall GPON network deployment cost by selecting the optimum type of aggregating equipment, routing information and cost effective locations of network elements. In the result section, different network examples will be provided to illustrate the effectiveness of the ACO approach for this type of problem.

Application of AI Methods to Practical GPON FTTH Network Design and Planning

The optimal design of telecommunication network infrastructure demands consideration of many complex factors such as type, number and position of components and cable paths. The difficulty of producing a consistent and cost-effective solution increases with network size and complexity. A network planning optimisation tool developed at EBTIC (Etisalat British Telecom Innovation Centre) can generate different network topologies and evaluate them to arrive rapidly at an optimal or near-optimal solution. It has been trialled in Greenfield areas for FTTH (Fibre To The Home) deployment. This case study reviews the operation of EBTIC optimisation tool, how the design problem has been formulated and summarises the key steps in the practical application of the algorithms. In addition, the business benefits provided by the tool in the working environment are provided.

Towards achieving practical GPON FTTH designs

Nowadays, fiber-based access networks are considered to be a major solution to provide the triple-play (voice, video and data) services. However, telecom companies are forced to provide a cost effective solution due to the rapid technological evolution and fierce competition. Return on investment for the deployment of Greenfield networks or transformation of existing legacy networks need to be maximized. In addition, the network should be designed in such a way that it is easy to deploy and to maintain. To achieve these objectives, an intelligent network planning system is required to minimize the cost of network design with the practical considerations to alleviate the future maintenance. In this paper, we provide an approach that uses road information in order to tackle the FTTH access network planning problem with the focus on handling practical scenarios.

Solar powered air cooling for idle parked cars: Architecture and implementation

This paper presents the design and implementation of a novel, low-cost solar powered car cooling system for idle parked cars. This system is targeted towards Gulf countries such as the United Arab Emirates as high ambient temperature changes may cause interior damage to the car and pose a significant burn threat for young, disabled or elderly passengers. The main objective of this system is to significantly reduce the temperature inside the parked vehicle without starting the engine and using vehicles own A/C system. The proposed cooling prototype utilizes thermoelectric element to cool the air inside the car and mirco fan system to accelerate the heat exchange. The system is powered by the external battery and recharged by the top-mounted solar panels. The systems connectivity is leveraged by Wi-Fi technology operated via mobile phones that enable remote monitoring and control of the temperature inside the car. The initial tests carried out on the car model indicate modest cooling effects in the range of 4° C, but can be significantly boosted by the increased power and improved heat exchange.

Physical layer design of optical networks with practical considerations

Nowadays, many telecom operators consider fiber-based access networks as a major solution to provide bandwidth hungry services such as High Definition TV over IPTV, online gaming, video streaming, high speed Internet, etc. However, todays telecom market is facing tough competitions with very tight budget and limited human resources. Return on investment for the deployment of new networks or transformation of legacy networks to fiber-based networks need to be maximized. One way to minimize the capital expenditure (CAPEX) costs is through automation of the network design. Not only can it reduce the time and the cost of manual design but it can also optimize the design with a minimum cost. In this paper, our network planning system based on the mixed integer linear programming (MILP) approach is proposed with the main focus on solving practical scenarios which are related to design constraints with clear boundaries of planning areas. Advantages of having clear boundaries from the practical point of view are discussed in detail and the model to solve this problem is provided.

Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms

Femtocells are small base stations used to enhance cellular coverage in an indoor environment. However, dense femtocell deployments can lead to severe performance degradation. This paper adopts a new strategy to self-optimize the pilot power of femtocells by creating disjoint femtocell clusters which are managed by the chosen cluster heads (CHs). Each CH optimizes the coverage of its connected members by applying a multi-objective heuristic based on genetic algorithm. The simulation results show that the proposed approach can significantly reduce both the computational time and the data overhead compared with the centralized power optimization.