Nonconvex Optimization for Power Control in Wireless CDMA Networks

In this paper, we propose an efficient power control algorithm for the downlink wireless CDMA systems. The goal of our paper is to achieve the optimum and fair resource utilization by maximizing a weighted sum utility with the power constraint. In fact, the objective function in the power optimization problem is always nonconcave, which makes the problem difficult to solve. We make progress in solving this type of optimization problem using PSO (particle swarm optimization). PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. It’s proved that the proposed algorithm converges to the global optimal solutions in this paper. Numerical examples show that our algorithm can guarantee the fast convergence and fairness within a few iterations. It also demonstrates that our algorithm can efficiently solve the nonconvex optimization problems when we study the different utility functions in more realistic settings.

[1]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[2]  Michael L. Honig,et al.  Forward-link CDMA resource allocation based on pricing , 2000, 2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540).

[3]  Mung Chiang,et al.  Nonconcave network utility maximization through sum of squares method , 2005, IEEE Conference on Decision and Control.

[4]  Ness B. Shroff,et al.  Non-convex optimization and rate control for multi-class services in the Internet , 2005, IEEE/ACM Transactions on Networking.

[5]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[6]  H.M. Elkamchouchi,et al.  Power Control in CDMA System using Particle Swarm Optimization , 2007, 2007 National Radio Science Conference.

[7]  Ness B. Shroff,et al.  Downlink power allocation for multi-class CDMA wireless networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[8]  Ness B. Shroff,et al.  Downlink power allocation for multi-class wireless systems , 2005, IEEE/ACM Transactions on Networking.

[9]  Jiang Chuanwen,et al.  A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation , 2005, Math. Comput. Simul..

[10]  David Tse,et al.  Multiaccess Fading Channels-Part I: Polymatroid Structure, Optimal Resource Allocation and Throughput Capacities , 1998, IEEE Trans. Inf. Theory.

[11]  Dimitri P. Bertsekas,et al.  Data Networks , 1986 .

[12]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  Ness B. Shroff,et al.  A utility-based power-control scheme in wireless cellular systems , 2003, TNET.

[14]  Shengyu Zhang,et al.  Distributed rate allocation for inelastic flows , 2005, IEEE/ACM Trans. Netw..

[15]  David J. Goodman,et al.  Power control for wireless data , 1999, 1999 IEEE International Workshop on Mobile Multimedia Communications (MoMuC'99) (Cat. No.99EX384).

[16]  N.B. Shroff,et al.  Joint resource allocation and base-station assignment for the downlink in CDMA networks , 2006, IEEE/ACM Transactions on Networking.

[17]  Daniel Pérez Palomar,et al.  Power Control By Geometric Programming , 2007, IEEE Transactions on Wireless Communications.

[18]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[19]  Andrea Baiocchi,et al.  Radio resource sharing for ad hoc networking with UWB , 2002, IEEE J. Sel. Areas Commun..

[20]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization method for image clustering , 2005, Int. J. Pattern Recognit. Artif. Intell..

[21]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[22]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).