On Semi-Static Interference Coordination under Proportional Fair Scheduling in LTE Systems

In this paper we consider the design of semi-static inter-cell interference coordination schemes for LTE networks. In this approach, base stations coordinate the power settings per resource block over long time spans such as seconds. In order to optimize the power settings, one needs to employ models which predict the rate of terminals over the next coordination period under the usage of a given power setting. However, these models are typically quite simple and neglect the impact from fading as well as from dynamic resource allocation performed at the base stations on a millisecond basis. Ignoring such properties of OFDMA networks leads therefore to suboptimal transmit power settings. In this paper, we study the impact from a precise rate prediction model that accurately accounts for fading and dynamic resource allocation. On the down-side, this more precise model leads to a much more involved optimization problem to be solved once per coordination period. We propose two different heuristic methods to deal with this problem. Especially the usage of genetic algorithm results to be promising to counteract the complexity increase. We then study the overall system performance and find precise rate prediction models to be essential for semi-static interference coordination as they provide significant performance improvements in comparison to approaches with simpler models.

[1]  James Gross Admission control based on OFDMA channel transformations , 2009, 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops.

[2]  Farshad Naghibi,et al.  How bad is interference in IEEE 802.16e systems? , 2010, 2010 European Wireless Conference (EW).

[3]  Gábor Fodor,et al.  A Dynamic Resource Allocation Scheme for Guaranteed Bit Rate Services in OFDMA Networks , 2008, 2008 IEEE International Conference on Communications.

[4]  Adam Wolisz,et al.  Dynamic resource allocation in OFDM systems: an overview of cross-layer optimization principles and techniques , 2007, IEEE Network.

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  András Rácz,et al.  Intercell Interference Coordination in OFDMA Networks and in the 3GPP Long Term Evolution System , 2009, J. Commun..

[7]  Eitan Altman,et al.  Self-Organizing Fractional Power Control for Interference Coordination in OFDMA Networks , 2011, 2011 IEEE International Conference on Communications (ICC).

[8]  Victor C. M. Leung,et al.  Dynamic Frequency Allocation in Fractional Frequency Reused OFDMA Networks , 2008 .

[9]  Eitan Altman,et al.  Self-Optimizing Strategies for Interference Coordination in OFDMA Networks , 2011, 2011 IEEE International Conference on Communications Workshops (ICC).

[10]  Adam Wolisz,et al.  Optimal power masking in soft frequency reuse based OFDMA networks , 2009, 2009 European Wireless Conference.

[11]  Markus Rupp,et al.  System Level Simulation of LTE Networks , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[12]  Malcolm Sambridge,et al.  Genetic algorithms: a powerful tool for large-scale nonlinear optimization problems , 1994 .

[13]  James Gross,et al.  Analytical Model of Proportional Fair Scheduling in Interference-Limited OFDMA/LTE Networks , 2013, 2013 IEEE 78th Vehicular Technology Conference (VTC Fall).