Annals of Telecommunications

, Volume 75, Issue 1–2, pp 59–66 | Cite as

Space-time code selection via particle swarm optimization

  • Dimas Mavares T.Email author
  • Miguel Oropeza
  • Reinaldo Velásquez


In this paper, the space-time code selection technique for multiple-inputs single-output systems is optimized using particle swarm optimization. We considered both variable-rate and constant-rate strategies. For a variable-rate technique, we address the problems of minimizing the bit-error rate for a given throughput objective and maximizing the throughput for a given bit-error rate objective. For a constant-rate technique, we address the problem of minimizing the bit-error rate. Results show that it is possible to find BER and throughput values close to those required when using a variable-rate technique with optimized threshold levels. For the constant-rate technique, we obtain considerable energy to noise gains when using optimized threshold levels.


Space-time code selection Transmit diversity MIMO Particle swarm optimization PSO 



  1. 1.
    Gourdin E, Medhi D, Pattavina A (2018) Ann Telecommun 73(1):1. CrossRefGoogle Scholar
  2. 2.
    Zhang P, Yang X, Chen J, Huang Y (2019) China Communications 16:69Google Scholar
  3. 3.
    Cheng X, Zhang R, Yang L (2018) IEEE Internet Things J 6:188CrossRefGoogle Scholar
  4. 4.
    Sternad M, Svensson T, Ottosson T, Ahlen A, Svensson A, Brunstrom A (2007) Proc IEEE 95(12):2432. CrossRefGoogle Scholar
  5. 5.
    Drajic D, Ivanis P (2013) In: 2013 11th international conference on telecommunication in modern satellite, cable and broadcasting services (TELSIKS), vol 01, pp 209–216.
  6. 6.
    Bedeer E, Dobre O, Ahmed M, Baddour K (2014) IEEE Trans Wirel Commun 13(4):2339. CrossRefGoogle Scholar
  7. 7.
    Dong Z, Fan P, Panayirci E, Lei X (2014) IEEE Trans Veh Technol PP(99):1. CrossRefGoogle Scholar
  8. 8.
    Karray MK, Jovanovic M, Błaszczyszyn B (2015) Ann Telecommun 70(11):479. CrossRefGoogle Scholar
  9. 9.
    Alamouti S (1998) IEEE J Sel Areas Commun 16(8):1451CrossRefGoogle Scholar
  10. 10.
    Tarokh V, Jafarkhani H, Calderbank A (1999) IEEE J Sel Areas Commun 17:451CrossRefGoogle Scholar
  11. 11.
    Molisch AF, Win MZ, Winters JH (2003) IEEE Trans Signal Process 51(11):2729MathSciNetCrossRefGoogle Scholar
  12. 12.
    Thoen S, der Perre LV, Gyselinckx B, Engels M (2001) IEEE Trans Commun 49:5CrossRefGoogle Scholar
  13. 13.
    Chen Z, Yuan J, Vucetic B (2005) IEEE Trans Veh Technol 54(4):1312CrossRefGoogle Scholar
  14. 14.
    Mavares D, Torres RP (2008) IEEE Trans Veh Technol 57(1):620CrossRefGoogle Scholar
  15. 15.
    Mavares D, Torres RP (2009) Elsevier Computer Communications 32(3):477CrossRefGoogle Scholar
  16. 16.
    Mavares D, Velasquez R, Candotti K, Huerta M (2015) In: Asia-Pacific conference on computer aided system engineering, pp 63–67Google Scholar
  17. 17.
    Shin H, Lee JH (2002) In: GLOBECOM 2002 - IEEE global telecommunications conference, vol 21, pp 1206–1210Google Scholar
  18. 18.
    Mavares D, Torres RP, Uzcátegui RA (2011) In: The sixth international conference on digital telecommunications (ICDT 2011)Google Scholar
  19. 19.
    Mavares D (2011) Estimación de Canal y Transmisión Adaptativa en Sistemas MISO (Editorial Académica Española, Heinrich-Böcking-Str., Saarbrücken)Google Scholar
  20. 20.
    Kennedy J, Eberhart RC (1995) In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948Google Scholar
  21. 21.
    Clerc M, Kennedy J (2002) Trans Evol Comp 6(1), 58, CrossRefGoogle Scholar

Copyright information

© Institut Mines-Télécom and Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departamento de Electrónica y Ciencias de la ComputaciónPontificia Universidad JaverianaCaliColombia
  2. 2.ABB Stonefield WorksStoneUK
  3. 3.Universidad de CaraboboValenciaVenezuela

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