FASoft: A system for discrete channel frequency assignment

Abstract

Abstract. 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. IntroductionThe radio spectrum is an important natural resource. Many services, both civil and military, require access to the spectrum in order to function, and the demand for frequencies is increasing year by year. Consequently, efficient management of the spectrum requires that frequencies be assigned, for a particular service, in an optimum or near-optimum manner. In practice, this assignment of frequencies is often done using greedy heuristics, which mimic the way the process may be done manually. These methods produce assignments quickly (even for large problems) but inevitably use more spectrum than is necessary. Exact methods based on graph theory give the optimum solution but are computationally unrealistic for problems involving •>50 transmitters. The purpose of this paper is to show how the so-called metaheuristic techniques (simulated annealing, genetic algorithms, and tabu search) can be used to produce optimum solutions to frequency assignment problems. This paper is concerned with the assignment of discrete channels to the transmitters of a system of compatible equipment. In some applications, such as cellular radio networks, the coverage area is divided into a number of cells, typically hexagonal, with each cell assigned a set of frequencies. We refer to such A software system FASoft will be described which produces a frequency assignment for any problem described by a constraint matrix, not necessarily a cellular system. FASoft can handle constraints with transmitters external to the system under consideration. However, it does not handle issues such as intermodulation products or required bandwidth for different modulation schemes such as digital code division multiple access (CDMA), nor does it address problems such as spread spectrum systems and discrete narrowband systems operating in the same 1921

Models and solution techniques for frequency assignment problems

“…Many variants of these ideas are, of course, possible. An overview of many ordering ideas can be found in Hurley et al (1997).…”

Path relinking for the fixed spectrum frequency assignment problem

“…The goal of FS-FAP is then to determine a frequency assignment of minimum cost. Note that in the literature, there exist other similar FS-FAP models which differ mainly in the way the cost function is defined (Aardal et al, 2007;Hale, 1980;Hurley et al, 1997;Kendall and Mohamad, 2004;Mabed et al, 2011;Park et al, 2002).…”

“…Frequency assignment represents a key issue in wireless communications systems and has attracted a lot of attentions (Audhya et al, 2013;Castelino et al, 1996;Fischettti et al, 2000;Flood et al, 2013;Hale, 1980;Hao et al, 1998;Hurley et al, 1997;Kim et al, 2007;Král, 2005;Lai and Coghill, 1996;Maniezzo and Carbonaro, 2000;San Jose-Revuelta, 2007;Smith et al, 1998;Subramanian et al, 2008;Tiourine et al, 2000;Wang and Rushforth, 1996;Wu et al, 2013). The fixed spectrum frequency assignment problem (FS-FAP) studied in this study is among the most typical frequency assignment problems (Aardal et al, 2007;Eisenblätter and Koster, 2010;Montemanni and Smith, 2010).…”

“…These include single solution based approaches like tabu search (Castelino et al, 1996;Hao et al, 1998;Montemanni et al, 2003), adaptive local search (Kendall and Mohamad, 2004), heuristic manipulation technique (Montemanni and Smith, 2010). Populationbased approaches are also very popular: ANTS algorithm (Maniezzo and Carbonaro, 2000), genetic algorithms (Dorne and Hao, 1996;Park et al, 2002;San Jose-Revuelta, 2007), hybrid evolutionary algorithms (Dorne and Hao, 1995;Jin et al, 2001;Kim et al, 2007;Lai and Coghill, 1996), multiple strategy method (Hurley et al, 1997). In addition to heuristic methods, approaches using integer programming were also proposed in the literature to determine lower bounds for the FS-FAP problem, such as those in (Montemanni et al, 2001(Montemanni et al, , 2004.…”

Planning effective cellular mobile radio networks

“…For example, if the network design is used to generate channel separation constraints between pairs of transceivers, then the required separations could have higher values on a poorly designed network relative to a well-designed network. Consequently, frequency assignment algorithms, e.g., [18] and [19], will find assignments that either use a larger range of frequencies than may be necessary (for minimum span assignment) or have a higher number of constraint violations (more interference) in fixed spectrum problems. The cellular network optimization problem involves designing a network using an optimization algorithm that takes into account competing factors.…”