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Some hard combinatorial optimisation problems from mobile wireless communications

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

Publication date18/05/2016
Number of pages2
Original languageEnglish
EventFourth International Symposium on Combinatorial Optimization - Lloyd's Baia Hotel, Vietri Sul Mare, Italy
Duration: 16/05/201618/05/2016


ConferenceFourth International Symposium on Combinatorial Optimization
Abbreviated titleISCO 2016
CityVietri Sul Mare
Internet address


In the past decade, a revolution in telecommunications has been taking place.
There has been an inexorable trend towards mobile wireless communications, in which there are a large number of portable devices (such as smartphones) scattered across a geographical region. Each such region is divided into a number of so-called cells. Each cell contains a powerful transmitter called a base station. When they wish to send or receive data, the portable devices have to send requests to one or more nearby base stations.

It turns out that mobile wireless communications are a rich source of new and difficult combinatorial optimisation problems. These include strategic problems, such as where and when to locate new base stations, tactical problems, such as how much power to give to each base station, and operational problems, such as how to assign incoming user requests to the available frequency bands.

In this talk, we focus on operational problems associated with so-called
orthogonal frequency-division multiple access (OFDMA) systems. In these systems, there are a large number of channels available, each of which can be allocated to at most one user. On the other hand, a user can be assigned to more than one channel. The rate at which data is transmitted over a given channel is a nonlinear function of the power allocated to that channel, the bandwidth of the channel, and the noise associated with the channel. So one faces the problem of simultaneously assigning channels to users and allocating the available power to the channels. This leads to several different combinatorial optimization problems, depending on the particular objective in question, the side-constraints imposed, and the time-horizon of interest.

We show that some of these joint channel assignment and power allocation problems can be tackled successfully via mixed-integer linear programming, especially if one uses clever pre-processing tricks, strong cutting planes, and symmetry-breaking techniques. On the other hand, some of the problems still present a formidable challenge.