Wireless Networks

, Volume 20, Issue 6, pp 1495–1514 | Cite as

Measurement-adaptive cellular random access protocols

  • Anastasios GiovanidisEmail author
  • Qi Liao
  • Sławomir Stańczak


This work considers a single-cell random access channel (RACH) in cellular wireless networks. Communications over RACH take place when users try to connect to a base station during a handover or when establishing a new connection. Within the framework of Self-Organizing Networks (SONs), the system should self-adapt to dynamically changing environments (channel fading, mobility, etc.) without human intervention. For the performance improvement of the RACH procedure, we aim here at maximizing throughput or alternatively minimizing the user dropping rate. In the context of SON, we propose protocols which exploit information from measurements and user reports in order to estimate current values of the system unknowns and broadcast global action-related values to all users. The protocols suggest an optimal pair of user actions (transmission power and back-off probability) found by minimizing the drift of a certain function. Numerical results illustrate considerable benefits of the dropping rate, at a very low or even zero cost in power expenditure and delay, as well as the fast adaptability of the protocols to environment changes. Although the proposed protocol is designed to minimize primarily the amount of discarded users per cell, our framework allows for other variations (power or delay minimization) as well.


Random access channel Self-Organizing Network (SON) Measurements Collision resolution Drift minimization Power control 


  1. 1.
    3GPP TR 36.902 "Self-configuring and self-optimizing network (SON) use cases and solutions", release 9.Google Scholar
  2. 2.
    3GPP TS 36.300 (v 8.7.0) (May 2009) "Technical specification group radio access network; Evolved universal terrestrial radio access (E-UTRA) and evolved universal terrestial radio access network (E-UTRAN); overall description" Release 8.Google Scholar
  3. 3.
    3GPP TS 36.321 (V 10.4.0) 3rd Generation Partnership Project; Technical specification group radio access network; Evolved universal terrestrial radio access (E-UTRA); Medium Access Control (MAC) protocol specification, Release 10, Dec 2011.Google Scholar
  4. 4.
    Abramson, N. (1970). The ALOHA system: Another alternative for computer communications. In Proceedings of AFIPS fall joint computer conference, Vol. 27.Google Scholar
  5. 5.
    Al Harthi, Y., Borst, S., & Whiting, P. (2011). Distributed adaptive algorithms for optimal opportunistic medium access. Mobile Networks and Applications (Springer), 16(2), 217–230.CrossRefGoogle Scholar
  6. 6.
    Amirijoo, M., Frenger, P., Gunnarsson, F., Moe, J., & Zetterberg, K. (2009). On self-optimization of the random access procedure in 3G long term evolution. In Proceedings of IEEE integrated network management-workshops, 2009, New York, NY, USA, pp. 177–184.Google Scholar
  7. 7.
    Asmussen, S. (2000). Applied probability and queues. New York: Springer.Google Scholar
  8. 8.
    Berman, A., & Plemmons, R. J. (1994). Nonnegative matrices in the mathematical sciences, part 11. Classics in Applied Mathematics. SIAM.Google Scholar
  9. 9.
    Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE JSAC, 18(3), 535–547.Google Scholar
  10. 10.
    Boorstyn, R. R., Kershenbaum, A., Maglaris, B., & Sahin, V. (1987). Throughput analysis in multihop CSMA packet radio networks. IEEE Transactions on Communications, COM-35(3), 267–274.CrossRefGoogle Scholar
  11. 11.
    Cheung, M. H., Mohsenian Rad, A. H., Wong, V. W., & Schober, R. (2010). Random access for elastic and inelastic traffic in WLANs. IEEE Transactions on Wireless Communications, 9(6), 1861–1866.CrossRefGoogle Scholar
  12. 12.
    Chiu, D. M., & Jain, R. (1989). Analysis of the increase and decrease algorithms for congestion avoidance in computer networks. Computer Networks and ISDN Systems 17, North Holland, pp. 1–14.Google Scholar
  13. 13.
    del Angel, G., & Fine, T. L. (2004). Optimal power and retransmission control policies for random access systems. IEEE/ACM Transactions on Networking, 12(6), 1156–1166.CrossRefGoogle Scholar
  14. 14.
    Dimic, G., Sidiropoulos, N. D., & Zhang, R. (2004). Medium access control—Physical cross-layer design. IEEE Signal Processing Magazine, 21(5), 40–50.Google Scholar
  15. 15.
    Ephremides, A., & Hajek, B. (1998). Information theory and communication networks: an unconsummated union. IEEE Transactions on on Information Theory, 44(6), 2416–2434.CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Giovanidis, A., Liao, Q.,& Stanczak, S. (2012). A distributed interference-aware load balancing algorithm for LTE multi-cell networks. In Proceedings international ITG workshop on smart antennas (WSA), Dresden, Germany.Google Scholar
  17. 17.
    Giovanidis, A., Wunder, G., & Boche, H. (2008). A short-term throughput measure for communications using ARQ protocols. In Proceedings of 7th ITG conference on SCC.Google Scholar
  18. 18.
    Gupta, P., Sankarasubramaniam, Y., & Stolyar, A. (2005). Random-access scheduling with service differentiation in wireless networks. INFOCOM, 3, 1815–1825.Google Scholar
  19. 19.
    Hajek, B., & van Loon, T. (1982). Decentralized dynamic control of a multiaccess broadcast channel. IEEE Transactions on Automatic Control, AC-27(3), 559–569.CrossRefGoogle Scholar
  20. 20.
    Heusse, M., Rousseau, F., Guillier, R., & Duda, A. (2005). Idle sense: An optimal access method for high throughput and fairness in rate diverse wireless LANs. In Proceedings of ACM SIGCOMM’05, Philadelphia, Pennsylvania, USA.Google Scholar
  21. 21.
    Kleinrock, L., & Lam, S. S. (1975). Packet switching in a multiaccess broadcast channel: Performance evaluation. IEEE Transactions on Communications, COM-23(4), 410–423.CrossRefGoogle Scholar
  22. 22.
    Lam, S. S., & Kleinrock, L. (1975). Packet switching in a multiaccess broadcast channel: Dynamic control procedures. IEEE Transactions on Communications, COM-23(9), 891–904.CrossRefGoogle Scholar
  23. 23.
    Lee, W., Kim, D., Choi, S., Park, K. J., Choi, S., & Han, K. Y. (2012). Self-optimization of RACH power considering multi-cell outage in 3GPP LTE systems. In Proceedings of the 75th VTC spring.Google Scholar
  24. 24.
    Liao, Q., Kaliszan, M., & Stanczak, S. (2011). A virtual soft handover method based on base station cooperation with fountain codes. In Proceedings of the 17th European wireless conference, Vienna, Austria.Google Scholar
  25. 25.
    Liu, J., Yi, Y., Proutiere, A., Chiang, M., & Poor, H. (2009). Towards utility-optimal random access without message passing. Wireless Communications and Mobile Computing (published online) 00, 1–12.Google Scholar
  26. 26.
    Neely, M., Modiano, E., & Rohrs, C. (2003). Power allocation and routing in multibeam satellites with time-varying channels. IEEE/ACM Transactions on Networking, 11(1) 138–152.Google Scholar
  27. 27.
    Neely, M., Modiano, E., & Rohrs, C. (2005). Dynamic power allocation and routing for time-varying wireless networks. IEEE JSAC, 23(1), 89–130.Google Scholar
  28. 28.
    Osterbo, O., & Grondalen, O. (2012). Benefits of Self-Organizing Networks (SON) for mobile operators. Hindawi Publishing Corporation. Journal of Computer Networks and Communications.Google Scholar
  29. 29.
    Papapanagiotou, I., Vardakas, J., Paschos, G., Logothetis, M., & Kotsopoulos, S. (2007). Performance evaluation of IEEE 802.11e based on on-off traffic model. In Proceedings of the 3rd international conference on Mobile multimedia communications (MobiMedia).Google Scholar
  30. 30.
    Proutiere, A., Yi, Y., & Chiang, M. (2008). Throughput of random access without message passing. In Proceedings of 42nd annual conference on information sciences and systems, (CISS).Google Scholar
  31. 31.
    Puterman, M. L. (2005). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.Google Scholar
  32. 32.
    Sharma, G., Ganesh, A., & Key, P. (2006). Performance analysis of contention based medium access control protocols. Proceedings of the 25th IEEE INFOCOM, Barcelona, Spain, pp. 1–12.Google Scholar
  33. 33.
    Takagi, H., & Kleinrock, L. (1985). Throughput analysis for persistent CDMA systems. IEEE Transactions on Communications, COM-33(7), 627–638.CrossRefMathSciNetGoogle Scholar
  34. 34.
    Tassiulas, L., & Ephremides, A. (1992). Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 37(12), 1936–1948.Google Scholar
  35. 35.
    Tassiulas, L., & Ephremides, A. (1993). Dynamic server allocation to parallel queues with randomly varying connectivity. IEEE Transactions on Information theory, 39(2).Google Scholar
  36. 36.
    Tong, L., Zhao, Q., & Mergen, G. (2001). Multipacket reception in random access wireless networks: From signal processing to optimal medium access control. IEEE Communications Magazine 108–112.Google Scholar
  37. 37.
    Williams, D. (1991). Probability with martingales. Cambridge University Press.Google Scholar
  38. 38.
    Yilmaz, O. N. C., Hamalainen, J., & Hamalainen, S. (2011). Self-optimization of random access channel in 3GPP LTE. In Proceedings of 7th international wireless communications and mobile computing conference (IWCMC) Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Anastasios Giovanidis
    • 1
    Email author
  • Qi Liao
    • 2
  • Sławomir Stańczak
    • 3
  1. 1.INRIA-TRECParis Cedex 13France
  2. 2.Fraunhofer Institute for TelecommunicationsHeinrich Hertz Institute (HHI)BerlinGermany
  3. 3.HHI and Heinrich-Hertz-Lehrstuhl für Informationstheorie und theoretische InformationstechnikTechnische Universität BerlinBerlinGermany

Personalised recommendations