Optimization of Soft Frequency Reuse for Irregular LTE Macrocellular Networks

Interference management has been recognized by the industry as a key enabler for 4G systems. Emerging technologies include multicarrier systems such as LTE and WiMAX for which effective management of intercell interference is of utmost importance in order to improve the Quality of Service (QoS) at cell edges. Static Intercell Interference Coordination (ICIC) techniques such as Soft Frequency Reuse (SFR) are aimed at alleviating this problem; however the usage of baseline SFR designs (schemes without optimization) only offers tradeoffs between cell edge performance and spectral efficiency and performance is indeed far from optimal as results herein confirm. Thus, this paper presents a novel multiobjective algorithm in order to address this problem and achieve effective optimization of SFR implementations. Results show that the proposed algorithm succeeds in finding good-quality SFR configurations enhancing simultaneously network capacity and cell edge performance while reducing energy consumption with respect to baseline designs and previous proposals.

[1]  M. Fleischer,et al.  The Measure of Pareto Optima , 2003, EMO.

[2]  Lei Chen,et al.  Generalizing FFR by Flexible Sub-Band Allocation in OFDMA Networks with Irregular Cell Layout , 2010, 2010 IEEE Wireless Communication and Networking Conference Workshops.

[3]  Kalyanmoy Deb,et al.  On self-adaptive features in real-parameter evolutionary algorithms , 2001, IEEE Trans. Evol. Comput..

[4]  Joan J. Olmos,et al.  On the Performance of Static Inter-cell Interference Coordination in Realistic Cellular Layouts , 2010, MONAMI.

[5]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[6]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[7]  Qing Yang,et al.  Novel Algorithm to Calculate Hypervolume Indicator of Pareto Approximation Set , 2007, ICIC.

[8]  Joan J. Olmos,et al.  On the need for dynamic downlink intercell interference coordination for realistic Long Term Evolution deployments , 2014, Wirel. Commun. Mob. Comput..

[9]  James C. Spall,et al.  Introduction to stochastic search and optimization - estimation, simulation, and control , 2003, Wiley-Interscience series in discrete mathematics and optimization.

[10]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[11]  Thomas Weise,et al.  Global Optimization Algorithms -- Theory and Application , 2009 .

[12]  Mark Fleischer,et al.  The measure of pareto optima: Applications to multi-objective metaheuristics , 2003 .

[13]  András Rácz,et al.  On the Impact of Inter-Cell Interference in LTE , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[14]  Joan J. Olmos,et al.  Exponential Effective SIR metric for LTE downlink , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[15]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[16]  Stavros A. Kotsopoulos,et al.  A Decentralized Subchannel Allocation Scheme with Inter-Cell Interference Coordination (ICIC) for Multi-Cell OFDMA Systems , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[17]  M. Raghuwanshi,et al.  Survey on multiobjective evolutionary and real coded genetic algorithms , 2004 .

[18]  Lei Chen,et al.  Soft frequency reuse in large networks with irregular cell pattern: How much gain to expect? , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[19]  Hirotaka Nakayama,et al.  Theory of Multiobjective Optimization , 1985 .

[20]  Sina Zhang,et al.  Inter-cell Interference Coordination in Indoor LTE Systems , 2011 .

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[23]  Yikang Xiang,et al.  Inter-cell Interference Mitigation through Flexible Resource Reuse in OFDMA based Communication Networks , 2007 .

[24]  Jing Xu,et al.  Inter-cell interference coordination based on softer frequency reuse in OFDMA cellular systems , 2008, 2008 International Conference on Neural Networks and Signal Processing.

[25]  Liesbet Van der Perre,et al.  Challenges and enabling technologies for energy aware mobile radio networks , 2010, IEEE Communications Magazine.

[26]  Lei Chen,et al.  Generalized Frequency Reuse Schemes for OFDMA Networks: Optimization and Comparison , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[27]  Catherine Rosenberg,et al.  On fractional frequency reuse in imperfect cellular grids , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[28]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[29]  Thomas Bonald,et al.  Capacity Gains of Some Frequency Reuse Schemes in OFDMA Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[30]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[31]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[32]  Jeffrey G. Andrews,et al.  Analytical Evaluation of Fractional Frequency Reuse for OFDMA Cellular Networks , 2011, IEEE Transactions on Wireless Communications.

[33]  H. Lilliefors.  On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[34]  G. Loftus,et al.  Why Figures with Error Bars Should Replace p Values Some Conceptual Arguments and Empirical Demonstrations , 2015 .

[35]  Joan J. Olmos,et al.  An analytical view of static intercell interference coordination techniques in OFDMA networks , 2012, 2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[36]  P. Gill,et al.  Sequential Quadratic Programming Methods , 2012 .