Derek H. Smith

FASoft: A system for discrete channel frequency assignment

This paper describes a system, FASoft, for discrete channel frequency assignment. In practice, the assignment of frequencies in a network of compatible equipment is often done manually or by the use of a single computational technique. FASoft incorporates state-of-the-art heuristics, sequential assignment algorithms, and a maximal clique algorithm to aid in the assignment process. Lower bounding procedures are included into the system to assess the performance of the assignment techniques and to provide an assessment of how close a particular assignment is to the optimal. The results show that FASoft produces optimal solutions to several practical examples.

Improving heuristics for the frequency assignment problem

Abstract Lower bounds for the frequency assignment problem can be found from maximal cliques and subgraphs related to cliques. In this paper we show that for many types of problem optimal assignments can be found by a process of assigning these subgraphs first, fixing the assignment and then extending the assignment to the full problem. We demonstrate the advantages of the method for some typical examples. In particular we give the first optimal assignments of several variants of the “Philadelphia” problems. These problems have been used by several authors to assess assignment methods and lower bounds.

An improved tabu search algorithm for the fixed-spectrum frequency-assignment problem

A tabu search algorithm with a dynamic tabu list for the fixed-spectrum frequency-assignment problem is presented. For cellular problems, the algorithm can be combined with an efficient cell reoptimization step. The algorithm is tested on several sets of test problems and compared with existing algorithms of established performance. In particular, it is used to improve some of the best existing assignments for COST 259 benchmarks. These results add support to the claim that the algorithm is the most effective available, at least when solution quality is a more important criterion than solution speed. The algorithm is robust and easy to tune.

Bounds for the frequency assignment problem

Abstract The problem of assigning radio frequencies to a set of transmitters in a region is related to the theory of vertex colourings of graphs. Real frequency assignment problems often deal with a large number of transmitters. Exact methods of solution may be impracticable and heuristic methods must be used. Lower bounds for the frequency assignment problem can be used to assess the performance of these heuristic methods. In this paper, we present and assess a number of lower bounds. Methods of reducing the size of the problem before application of the heuristic methods are also described.

A Permutation Based Genetic Algorithm for Minimum Span Frequency Assignment

We describe a Genetic Algorithm (GA) for solving the minimum span frequency assignment problem (MSFAP).The MSFAP involves assigning frequencies to each transmitter in a region, subject to a number of constraints being satisfied, such that the span, i.e. the range of frequencies used, is minimized. The technique involves finding an ordering of the transmitters for use in a sequential (greedy) assignment process. Results are given which show that our GA produces optimal solutions to several practical problem instances, and compares favourably to simulated annealing and tabu search algorithms.

An ANTS algorithm for the minimum-span frequency-assignment problem with multiple interference

The frequency-assignment problem is concerned with the assignment of discrete channels to the transmitters of a radio network. Separation of the frequencies assigned to transmitters is necessary to avoid interference. However, unnecessary separation causes an excess requirement for spectrum, the cost of which may be very high. The aim of the work is to minimize the frequency spectrum required for a given level of reception quality over the network. In contrast to the majority of the work presented in the literature, the model chosen to formalize the problem takes multiple interference into consideration. This should provide a better approximation of reality. The method proposed to solve this problem is an approximate nondeterministic tree search (ANTS) algorithm, which is a metaheuristic algorithm based on a computational paradigm inspired by the way real ant colonies function.

Advances in the data compression of digital elevation models

Abstract The maintenance and dissemination of spatial databases requires efficient strategies for handling the large volumes of data that are now publicly available. In particular, satellite and aerial imagery, radar, LiDAR, and digital elevation models (DEMs) are being utilised by a sizeable user-base, for predominantly environmental applications. The efficient dissemination of such datasets has become a key issue in the development of web-based and distributed computing environments. However, the physical size of these datasets is a major bottleneck in their storage and transmission. The problem is often exaggerated when the data is supplied in less efficient, proprietary or national data formats. This paper presents a methodology for the lossless compression of DEMs, based on the statistical correlation of terrain data in local neighbourhoods. Most data and image compression algorithms fail to capitalise fully on the inherent redundancy in spatial data. At the same time, users often prefer a uniform solution to all their data compression requirements, but these solutions may be far from optimal. The approach presented here can be thought of as a simple pre-processing of the elevation data before the use of traditional data compression software frequently applied to spatial data sets, such as GZIP. Identification and removal of the spatial redundancy in terrain data, with the use of optimal predictors for DEMs and optimal statistical encoders such as Arithmetic Coding, gives even higher compression ratios. Both GZIP and our earlier approach of combining a simple linear prediction algorithm with Huffman Coding are shown to be far from optimal in identifying and removing the spatial redundancy in DEMs. The new approaches presented here typically halve the file sizes of our earlier approach, and give a 40–62% improvement on GZIP-compressed DEMs.

Lower bounding techniques for frequency assignment

Abstract The frequency assignment problem is an NP complete problem of great importance to the radiocommunications industry. Most current solution techniques for real frequency assignment problems use heuristic algorithms to obtain suboptimal solutions in an acceptable time. By formulating the problems in terms of graph colourings, lower bounds can be obtained to assess the quality of these heuristic solutions. Bounds based on the travelling salesman problem have proved to be successful, in some cases giving tight bounds when applied to a suitable subproblem. However, for general problems these bounds may be difficult to calculate or are far from optimal. The choice of subproblem is critical in evaluating these bounds and can also be of use in the application of the heuristic algorithms. In this paper we present a number of new and improved techniques for determining lower bounds.

A new lower bound for the channel assignment problem

The strength of lower bounds for the span in channel assignment problems is discussed with reference to standard benchmark problems. It is shown that in some circumstances current bounds are capable of very significant improvement. The use of methods from mathematical programming is outlined and a new lower bound applicable to cellular problems is presented.

Assignment of frequency lists in frequency hopping networks

Metaheuristic algorithms for frequency assignment problems have received considerable attention in the literature, but few papers have addressed the question of list assignment in frequency hopping networks. The work of Bjo/spl uml/rklund, Va/spl uml/rbrand, and Yuan is a notable exception, which will be extended here in a number of directions. The current paper sets out to determine the relative merits of pregeneration of the lists before assignment and allowing the assignment algorithm to modify the lists as it proceeds. The relative merits of synchronization within individual cells and synchronization at a single site are considered. Some other options, such as splitting the spectrum into a part for control carriers and a part for traffic carriers, are also evaluated. The COST 259 benchmarks for frequency assignment are modified to create some benchmarks for frequency hopping problems. This will allow other authors to compare the results of new metaheuristic algorithms with the effective but relatively straightforward simulated annealing (SA) algorithm used here.