Sequence design for MPG QS-CDMA systems based on heuristic combinatorial optimization

This paper deals with a quasi-synchronous code division multiple access (QS-CDMA) system with a multiple processing gain (MPG) variable data rate scheme in the uplink direction and subject to multipath fading channels. An analytic expression is obtained for the signal-to-noise plus interference ratio (SNIR) at the Rake receiver output (finger). In order to maximize the SNIR, three combinatorial optimization methods applied to sequences selection are compared. Then, sequence selection methodology for this class of system is evaluated, comparing the local search optimization results with unitary Hamming distance (1-opt LS), evolutionary programming with cloning (EP-C), and simulated annealing (SA) ones. The contribution of this work consists of considering the self-interference (SI) effect, besides the multiple access interference (MAI), in the description of the proposed objective function for sequence selection and a compared analysis of three heuristic algorithms maximizing that SNIR. Also, numeric results have demonstrated the effectiveness of the proposed method for the QS-CDMA sequences selection. Copyright © 2010 John Wiley & Sons, Ltd.

[1]  Naoki Suehiro,et al.  Class of binary sequences with zero correlation zone , 1999 .

[2]  Pingzhi Fan.  Spreading Sequence Design and Theoretical Limits for Quasisynchronous CDMA Systems , 2004, EURASIP J. Wirel. Commun. Netw..

[3]  S.P. Singh,et al.  Modified simulated Annealing Algorithm for Poly Phase Code Design , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[4]  T. Abrao,et al.  Spreading sequences comparison for DS-CDMA systems , 2004, Eighth IEEE International Symposium on Spread Spectrum Techniques and Applications - Programme and Book of Abstracts (IEEE Cat. No.04TH8738).

[5]  Gordon L. Stüber.  Principles of mobile communication , 1996 .

[6]  Chang Wook Ahn,et al.  A genetic algorithm for shortest path routing problem and the sizing of populations , 2002, IEEE Trans. Evol. Comput..

[7]  Lane A. Hemaspaandra,et al.  Using simulated annealing to design good codes , 1987, IEEE Trans. Inf. Theory.

[8]  S. S. Rao,et al.  Annealed neural network based multiuser detector in code division multiple access communications , 2000 .

[9]  Paul Jean Etienne Jeszensky,et al.  CDMA systems sequences optimization by simulated annealing , 1998, 1988 IEEE 5th International Symposium on Spread Spectrum Techniques and Applications - Proceedings. Spread Technology to Africa (Cat. No.98TH8333).

[10]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[11]  P. M. Grant,et al.  Digital communications. 3rd ed , 2009 .

[12]  Gordon L. Stuber,et al.  Principles of mobile communication (2nd ed.) , 2001 .

[13]  P.J.E. Jeszensky,et al.  Genetic Algorithm Applied to Multipath Multiuser Channel Estimation in DS/CDMA Systems , 2006, 2006 IEEE Ninth International Symposium on Spread Spectrum Techniques and Applications.

[14]  Franz Rothlauf,et al.  Representations for genetic and evolutionary algorithms , 2002, Studies in Fuzziness and Soft Computing.

[15]  Mohsen Sarraf,et al.  W-CDMA and cdma2000 for 3G Mobile Networks , 2002 .

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

[17]  Pingzhi Fan,et al.  A class of pseudonoise sequences over GF(P) with low correlation zone , 2001, IEEE Trans. Inf. Theory.

[18]  Fumiyuki Adachi,et al.  Tree-structured generation of orthogonal spreading codes with different lengths for forward link of DS-CDMA mobile radio , 1997 .

[19]  Tony Ottosson,et al.  Coding, Modulation and Multiuser Decoding for DS-CDMA Systems , 1997 .

[20]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[21]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[22]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[23]  Mohsen Shiva,et al.  A Very Near Optimum Interference Cancellation Algorithm for CDMA Signals , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[24]  Taufik Abrão,et al.  DS/CDMA Multiuser Detection with Evolutionary Algorithms , 2006, J. Univers. Comput. Sci..

[25]  Yves Crama,et al.  Local Search in Combinatorial Optimization , 2018, Artificial Neural Networks.