In cellular networks, e.g., as specified by 3GPP (3rd Generation Partnership Project), increasing traffic demand results in a need for more radio spectrum bandwidth. One way to provide more radio spectrum bandwidth is expansion into unlicensed frequency spectra, e.g., as typically used by WLAN (Wireless Local Area Network) technologies. For example, in 3GPP meeting contribution RP-140240, 3GPP TSG RAN Meeting #63, Fukuoka, Japan, 3-6 Mar. 2014, it is proposed to study extension of the LTE (Long Term Evolution) radio technology for operation in unlicensed frequency bands.
In an unlicensed frequency band, typically more bandwidth than the maximum standardized LTE carrier bandwidth (currently 20 MHz) is available. Accordingly, the conventional practice of running all LTE base stations of a network on the same frequency may be suboptimal since the larger available bandwidth allows for reducing intra-cell interference by distributing base stations over multiple different frequency channels. Further, channel quality in unlicensed frequency bands may vary depending on time, location and/or frequency, which means that also the optimum selection of the frequency channel may vary for each base station. Therefore, it is generally desirable to perform selection of the frequency channels for the base stations on the basis of a constantly running automated algorithm.
In a general context, the problem of frequency channel selection has been studied intensively for a long time and many different algorithms have been proposed. On a high-level, one may distinguish algorithms that are intended for offline frequency planning of cellular networks like GSM, and real-time algorithms that are intended to be implemented as a Self-optimizing Network (SON) feature in base stations. The main difference between these two categories is the amount if input information they require and the computation time until they deliver results. Offline algorithms are typically allowed to run for a very long time (hours, days) and can afford a significantly higher computational complexity, while real-time algorithms should deliver results in seconds or faster, and may need to cope with limited input information.
Frequency selection algorithms can be implemented in a distributed or a centralized way. Distributed means that independent algorithm instances run, for example, in each base station. The different algorithm instances influence each other for example in terms of how much interference another instance sees on a given channel. In a centralized approach, all information is gathered in a central location, which allows for a more complete assessment of the overall situation and facilitates finding an optimal solution. Distributed algorithms have a higher risk of being trapped in local minima and are typically iterative, i.e., which means that the system typically runs through a number of suboptimal stages before it may reach a steady state. In a centralized algorithm, even if it is based on iterations, system operation can converge in one step. Further, a centralized algorithm is more likely to find a global optimum because information from various parts of the network can be considered. In each case, finding an algorithm which offers a suitable tradeoff between system performance gains, computational complexity, execution time and other aspects is a complex task.
A distributed algorithm for WLAN technologies is for example described in “A Self-Managed Distributed Channel Selection Algorithm for WLANs” by D. J. Leith et al., Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 4th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, pp. 1-9, 3-6 Apr. 2006. This algorithm uses channel selection probabilities based on interference measurements. If measured interference exceeds a threshold, the channel selection probability of this channel is reduced by the same amount. However this algorithm may in some case provide insufficient efficiency in terms of finding the best channel and speed of convergence.
Accordingly, there is a need for techniques which allow for efficiently controlling frequency channel utilization in a cellular network.