New algorithms for maximizing cellular wireless network energy efficiency

In this paper, we aim to maximize the energy efficiency of cellular wireless networks. Specifically, we address the power allocation problem in multi-cell multi-carrier systems. Considering realistic base station power consumption models, we formulate a network-wide energy efficiency maximization problem. Using tools from fractional programming, we cast this problem in the framework of bi-criterion optimization where rate maximization and power minimization are weighted accordingly. Interference pricing mechanism is applied to reduce the inter-cell interference and to achieve a higher network performance. We decompose the main problem into subproblems via dual decomposition. These subproblems are independently solved per sector using limited information exchange between base stations. We first derive our expressions and present algorithms for the single-tier networks. Then, we extend our analysis to two-tier networks where picocell base stations are deployed to improve the network performance and reduce the link distances. Lastly, we extend our framework and include the quality-of-service constraints. We obtain closed-form expressions for the power level updates which are determined by the multi-level water-filling algorithm, or, as it is sometimes called as, the modified water-filling algorithm. Based on our simulation results, we demonstrate that the proposed algorithms can outperform the benchmark approaches in terms of energy efficiency by a factor of 2.7.

[1]  Olav Tirkkonen,et al.  A distributed algorithm for network power minimization in multicarrier systems , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[2]  Jie Tang,et al.  Resource Allocation for Energy Efficiency Optimization in Heterogeneous Networks , 2015, IEEE Journal on Selected Areas in Communications.

[3]  Sergio Barbarossa,et al.  Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory—Part I: Nash Equilibria , 2007, IEEE Transactions on Signal Processing.

[4]  Francisco Facchinei,et al.  Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems , 2013, IEEE Transactions on Signal Processing.

[5]  Wei Yu Multiuser Water-filling in the Presence of Crosstalk , 2007, 2007 Information Theory and Applications Workshop.

[6]  Yang Yang,et al.  Parallel stochastic decomposition algorithms for multi-agent systems , 2013, 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[7]  Michael L. Honig,et al.  Monotonic convergence of distributed interference pricing in wireless networks , 2009, 2009 IEEE International Symposium on Information Theory.

[8]  Kemal Davaslioglu,et al.  Energy-Efficient Resource Allocation for Fractional Frequency Reuse in Heterogeneous Networks , 2014, IEEE Transactions on Wireless Communications.

[9]  Luca Venturino,et al.  Energy-Efficient Scheduling and Power Allocation in Downlink OFDMA Networks With Base Station Coordination , 2014, IEEE Transactions on Wireless Communications.

[10]  Jack Yurkiewicz,et al.  Constrained optimization and Lagrange multiplier methods, by D. P. Bertsekas, Academic Press, New York, 1982, 395 pp. Price: $65.00 , 1985, Networks.

[11]  Antti Toskala,et al.  LTE for UMTS - OFDMA and SC-FDMA Based Radio Access , 2009 .

[12]  Wei Yu,et al.  Optimal multiuser spectrum balancing for digital subscriber lines , 2006, IEEE Transactions on Communications.

[13]  Jamie S. Evans,et al.  SCALE: A Low-Complexity Distributed Protocol for Spectrum Balancing in Multiuser DSL Networks , 2009, IEEE Transactions on Information Theory.

[14]  3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (e-utra); Further Advancements for E-utra Physical Layer Aspects (release 9) , 2022 .

[15]  Gerhard Fettweis,et al.  Framework for Link-Level Energy Efficiency Optimization with Informed Transmitter , 2011, IEEE Transactions on Wireless Communications.

[16]  I. Stancu-Minasian Nonlinear Fractional Programming , 1997 .

[17]  Zhu Han,et al.  Game Theory in Wireless and Communication Networks: Theory, Models, and Applications , 2011 .

[18]  W. Utschick,et al.  Distributed resource allocation schemes , 2009, IEEE Signal Processing Magazine.

[19]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[20]  Michael L. Honig,et al.  Distributed interference compensation for wireless networks , 2006, IEEE Journal on Selected Areas in Communications.

[21]  Geoffrey Ye Li,et al.  Distributed Interference-Aware Energy-Efficient Power Optimization , 2011, IEEE Transactions on Wireless Communications.

[22]  Geoffrey Ye Li,et al.  A survey of energy-efficient wireless communications , 2013, IEEE Communications Surveys & Tutorials.

[23]  Boris Goldengorin,et al.  Handbook of combinatorial optimization , 2013 .

[24]  Kemal Davaslioglu,et al.  Efficiency and Fairness Trade-Offs in SC-FDMA Schedulers , 2014, IEEE Transactions on Wireless Communications.

[25]  Kemal Davaslioglu,et al.  Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks , 2014, IEEE Communications Surveys & Tutorials.

[26]  Geoffrey Ye Li,et al.  Energy-efficient link adaptation in frequency-selective channels , 2010, IEEE Transactions on Communications.

[27]  Muhammad Ali Imran,et al.  How much energy is needed to run a wireless network? , 2011, IEEE Wireless Communications.