Achieving the Maximum Sum Rate Using D.C. Programming in Cellular Networks
Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancellation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach...
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| Veröffentlicht in: | IEEE transactions on signal processing Jg. 60; H. 3; S. 1331 - 1341 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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New York, NY
IEEE
01.03.2012
Institute of Electrical and Electronics Engineers |
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| ISSN: | 1053-587X, 1941-0476 |
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| Abstract | Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancellation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation. |
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| AbstractList | Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancellation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation. |
| Author | Weber, T. Al-Shatri, H. |
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| Cites_doi | 10.1137/S1052623499359828 10.1109/90.993307 10.1109/SSP.2007.4301310 10.1007/BF00119991 10.1109/TWC.2007.060675 10.1109/TWC.2009.080649 10.1007/978-1-4615-0015-5 10.1007/978-3-662-03199-5 10.1145/1271.1276 10.1109/TCOMM.2006.877962 10.1109/TSP.2008.917378 10.1109/TSP.2008.924796 10.1109/JSAC.2002.1007390 10.1017/CBO9780511804441 10.1109/TCOMM.2010.07.090068 10.1109/TIT.2008.2011433 10.1023/A:1021765131316 10.1109/TWC.2007.05960 10.1109/GLOCOM.2010.5683826 10.1109/JSAC.2011.110214 |
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| Keywords | Performance evaluation Mobile radiocommunication Lower bound Noise reduction Branch and bound method Wireless telecommunication interference channel Cell network Algorithm Power allocation Convex programming Optimization Joint detection Interference suppression D.C. programming Upper bound Simulation Telecommunication network Recursive method Signal processing Objective function Branch-and-bound algorithm Intercell interference |
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| References | ref13 ref12 ref15 ref11 ref2 koskie (ref25) 2006; 13 ref17 ref16 ref19 ref18 al-shatri (ref21) 2011 vucic (ref10) 2010 ref24 ref23 ref26 tan (ref14) 2009 ref20 ref22 al-shatri (ref3) 2009 ref8 ref7 ref9 cover (ref1) 2006 ref4 ref6 ref5 |
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| SubjectTerms | Applied sciences Approximation algorithms Branch-and-bound algorithm D.C. programming Detection, estimation, filtering, equalization, prediction Exact sciences and technology Information, signal and communications theory interference channel Interference channels Optimization power allocation Programming Resource management Signal and communications theory Signal to noise ratio Signal, noise Telecommunications and information theory |
| Title | Achieving the Maximum Sum Rate Using D.C. Programming in Cellular Networks |
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