Heuristic Approaches to Solve the Frequency Assignment Problem

The frequency assignment problem is a computationally hard problem with many applications including the mobile telephone industry and tactical communications. The problem may be modelled mathematically as a T-colouring problem for an undirected weighted graph; it is required to assign to each vertex a value from a given set such that for each edge the difference in absolute value between the values at the corresponding vertices is greater than or equal to the weight of the edge. This problem was solved using novel and existing metaheuristic algorithms and their relative successes were compared. Early work of this thesis used greedy, steepest descent and backtracking algorithms as a means of investigating the factors which influence the performance of an algorithm (selection of frequency, ordering of variables, provision of an incremental objective function). Later simulated annealing, tabu search and divide and conquer techniques were used and the results compared. A novel divide and conquer technique incorporating metaheuristics is described and results using test data based on real problems is presented. The divide and conquer technique (with either tabu search or simulated annealing) was found to improve significantly upon the corresponding metaheuristic when implemented alone and acting on non-trivial scenarios. The results were significant and consistent. The divide and conquer (with simulated annealing) algorithm in particular was shown to be robust and efficient in its solution of the frequency assignment problems presented. The results presented in this thesis consistently out-perform those obtained by the Defence, Evaluation and Research Agency, Malvern. In addition this method lends itself to parallelisation since the problem is broken into smaller independent parts. The divide and conquer algorithm does not exploit knowledge of the constraint network and should be applicable to a number of different problem domains. Algorithms capable of solving the frequency assignment problem most effectively will become valuable as demand for the electromagnetic spectrum continues to grow

Optimising BFWA networks

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimising BFWA networks

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimising BFWA networks

Broadband Fixed Wireless Access (BFWA) networks are an attractive alternative to cable-based technologies, in offering low-cost, high-speed data services, telephony and video-on-demand to residential and business users. However, in order to compete successfully with available alternative telecommunications solutions, the planning and design of efficient networks is crucial. This thesis presents two tools that enable the planning and evaluation of BFWA networks. AgentOpt is a network design and optimisation tool. A detailed account of the novel scheme, using the principles of emergent, selforganising systems, which AgentOpt employs for finding profit-optimal networks is given. The use of two distinct types of agent entity allows the multi-objective profit/coverage nature of the network planning problem to be satisfied. AgentOpt networks are compared with designs produced by other methods to establish to what extent this decentralised agent approach can optimise BFWA networks. The Network Validation Tool (NVT) analyses the network designs produced by AgentOpt and other automatic cell planning tools (ACPs). This is achieved through simulating the subscription take-up of the potential users in the network. By repetition of this process, statistical data about the various design configurations of the network is produced. This allows a planning engineer to compare and contrast network solutions that may differ in design but perform similarly in terms of expected profit. In this work the NVT is used to formulate some general guidelines about the best-practice use of ACPs

A decomposition approach for the Frequency Assignment Problem

The Frequency Assignment Problem (FAP) is an important optimization problem that arises in operational cellular wireless networks. Solution techniques based on meta-heuristic algorithms have been shown to be successful for some test problems but they have not been usually demonstrated on large scale problems that occur in practice. This thesis applies a problem decomposition approach in order to solve FAP in stances with standard meta-heuristics. Three different formulations of the problem are considered in order of difficulty: Minimum Span (MS-FAP), Fixed Spectrum (MS-FAP), and Minimum Interference FAP (MI-FAP). We propose a decomposed assignment technique which aims to divide the initial problem into a number of subproblems and then solves them either independently or in sequence respecting the constraints between them. Finally, partial subproblem solutions are recomposed into a solution of the original problem. Standard implementations of meta-heuristics may require considerable run times to produce good quality results whenever a problem is very large or complex. Our results, obtained by applying the decomposed approach to a Simulated Annealing and a Genetic Algorithm with two different assignment representations (direct and order-based), show that the decomposed assignment approach proposed can improve their outcomes, both in terms of solution quality and runtime. A number of partitioning methods are presented and compared for each FAP, such as clique detection partitioning based on sequential orderings and novel applications of existing graph partitioning and clustering methods adapted for this problem