Advertisement

Multi-Objective and Financial Portfolio Optimization of p-Persistent Carrier Sense Multiple Access Protocols with Multi-Packet Reception

  • Ramiro Sámano-RoblesEmail author
  • Atílio Gameiro
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 499)

Abstract

This paper revisits the study of wireless carrier-sense multiple access (CSMA) protocols enabled with multi-packet reception (MPR) capabilities. This study employs a new paradigm in the literature of random access based on multi-objective and financial portfolio optimization tools. Under this new optimization framework, each packet transmission is regarded not only as a network resource, but also as a financial asset with different values of return and risk (or variance of the return). The objective of this network-financial optimization is to find the transmission policy that simultaneously optimizes network metrics (such as throughput and efficient power consumption), as well as economic metrics (such as fairness, return and risk). Two transmission models are considered for performance evaluation: a Bernoulli transmission model that facilitates analytic derivations, and a Markov model that considers the backlog states of the network and that facilitates dynamic stability analysis. This work is focused on the characterization of the boundary (envelope) or the Pareto optimal frontier of different types of trade-off performance region. These regions include the conventional throughput and stability regions, as well as new trade-off regions such as sum-throughput vs. fairness, sum-throughput vs. power consumption, and return vs. risk. Fairness is evaluated by means of the Gini-index, which is used in the field of economics to measure population income inequality. Transmit power is directly linked to the global transmission attempt rate. In scenarios with weak MPR capabilities, the system has problems in achieving simultaneously good values of fairness and high values of sum-throughput. This is because of an underlying non-convex throughput region which is typical of protocols dominated by unresolvable collisions. On the contrary, in scenarios with strong MPR capabilities, good fairness, higher energy consumption efficiency, and high sum-throughput performances can be simultaneously achieved. Carrier-sensing is shown to improve the convexity of the throughput region in scenarios with weak MPR, thereby achieving a better trade-off between metrics, including return and risk. However, the effects of carrier-sensing are shown to disappear in scenarios with strong MPR capabilities or with underlying convex throughput regions. The combination of MPR with carrier-sensing tools helps in reducing risk in the network and to fight issues of wireless random access such as the hidden/exposed terminal problems.

Keywords

S-ALOHA Random access Multi-objective portfolio optimization Pareto optimal trade-off curve 

Notes

Acknowledgments

The research leading to these results has received funding from the ARTEMIS Joint Undertaking under grant agreement no. 621353, the Portuguese National Science Foundation FCT, and by the North Portugal Regional Operational Programme (ON.2 O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by FCT, within project ref. NORTE-07-0124-FEDER-000063 (BEST-CASE, New Frontiers).

References

  1. 1.
    Deliverable D6.5.1: Specification of cognitive and opportunistic functions of the spectrum management framework, FP7 QoSMOS: Quality of Service and MObility driven cognitive radio Systems. http://www.ict-qosmos.eu
  2. 2.
    Karla, I.: Resolving SON interactions via self-learning prediction in cellular wireless networks. In: WICOM, Shangai (2012)Google Scholar
  3. 3.
    Abramson N.: The ALOHA system - another alternative for computer communications. In: Proceedings of the 1970 Fall Joint Computer Conference. AFIPS Press (1970)Google Scholar
  4. 4.
    Naware, V., Mergen, G., Tong, L.: Stability and delay of finite-user slotted ALOHA with multipacket reception. IEEE Trans. Inf. Theory 51(7), 2636–2656 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    Samano-Robles, R., Gameiro, A.: A Slotted-ALOHA protocol with cooperative diversity. In: 4th Annual Wireless Internet Conference WICON 2008, Maui, Hawai, 21 (2008)Google Scholar
  6. 6.
    Baccelli, F.: Stochastic analysis of spatial and opportunistic aloha. IEEE J. Sel. Areas Commun. 27(7), 1105–1119 (2009)CrossRefGoogle Scholar
  7. 7.
    Tobagi, F.A., Kleinrock, L.: Packet switching in radio channels: part IV-stability considerations and dynamic control in carrier sense multiple access. IEEE Trans. Commun. 25(10), 1103–1119 (1977)zbMATHCrossRefGoogle Scholar
  8. 8.
    Samano-Robles, R., Gameiro, A.: The throughput region of wireless random access protocols with multipacket reception. In: Proceedings of the International Workshop on Telecommunications, Sao Paulo, Brazil, vol. 1, pp. 207–212 (2009)Google Scholar
  9. 9.
    Samano-Robles, R., Ghogho, M., McLernon, D.C.: Wireless networks with retransmission diversity and carrier sense multiple access. IEEE Trans. Sig. Proc. 57(9), 3722–3726 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kleinrock, L., Tobagi, F.A.: Packet switching in radio channels: Part I : carrier sense multiple access modes and their throughput-delay characteristics. IEEE Trans. Commun. 23(12), 1400–1416 (1975)zbMATHCrossRefGoogle Scholar
  11. 11.
    Bruno, R., Conti, M., Gregori, E.: Optimization of efficiency and energy consumption in p-persistent CSMA-based wireless LANs. IEEE Trans. Mob. Comput. 1(1), 10–31 (2002)CrossRefGoogle Scholar
  12. 12.
    Marshall, A.W., Olkin, I.: Inequalities: Theory of Majorization and Its Applications. Academic Press, New York (1979)zbMATHGoogle Scholar
  13. 13.
    Sen, A.: On Economic Inequality. Clarendon Press, Oxford (1973)CrossRefGoogle Scholar
  14. 14.
    Elton, E.J., Gruber, M.J., Brown, S.J., Goetzmann, W.N.: Modern Portfolio Theory and Investment Analysis. Wiley, Hoboken (2004)Google Scholar
  15. 15.
    Roberts, L.G.: ALOHA packet system with and without slots and capture. Comput. Commun. Rev. 5(2), 28–42 (1975)CrossRefGoogle Scholar
  16. 16.
    Rao, R., Ephremides, A.: On the stability of interacting queues in a multiple-access system. IEEE Trans. Inf. Theory 4(5), 918–930 (1988)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zorzi, M., Rao, R.: Capture and retransmission control in mobile radio. IEEE J. Sel. Areas Commun. 12(8), 1289–1298 (1994)CrossRefGoogle Scholar
  18. 18.
    Yu, Y., Cai, X., Giannakis, G.B.: On the stability of slotted ALOHA with capture. IEEE Trans. Wirel. Comm. 5(2), 257–261 (2006)CrossRefGoogle Scholar
  19. 19.
    Ghez, S., Verdu, S., Schwartz, S.: Stability properties of slotted Aloha with multipacket reception capability. IEEE Trans. Autom. Control 33(7), 640–649 (1988)zbMATHMathSciNetCrossRefGoogle Scholar
  20. 20.
    Ghez, S., Verdu, S., Schwartz, S.: Optimal decentralized control in the random access multipacket channel. IEEE Trans. Autom. Control 34(11), 1153–1163 (1989)zbMATHMathSciNetCrossRefGoogle Scholar
  21. 21.
    Zhao, Q., Tong, L.: A dynamic queue protocol for multiaccess wireless networks with multipacket reception. IEEE Trans. Wirel. Commun. 3(6), 2221–2231 (2004)CrossRefGoogle Scholar
  22. 22.
    Zhao, Q., Tong, L.: A multiqueue service room MAC protocol for wireless networks with multipacket reception. IEEE Trans. Network. 11(1), 125–137 (2003)CrossRefGoogle Scholar
  23. 23.
    Ngo, M.H., Krishnamurthy, V., Tong, L.: Optimal channel-aware ALOHA protocol for Random Access in WLANs with multipacket reception and decentralized channel state information. IEEE Trans. Sig. Proc. 56(6), 2575–2588 (2008)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Adireddy, S., Tong, L.: Exploiting decentralized channel state information for random access. IEEE Trans. Inform. Theory 51(2), 537–561 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  25. 25.
    Ngo, M.H., Krishnamurty, V.: Game theoretic cross-layer transmission policies in multipacket reception wireless networks. IEEE Trans. Sig. Proc. 55(5), 1911–1926 (2007)CrossRefGoogle Scholar
  26. 26.
    Luo, J., Ephremides, A.: On the throughput, capacity, and stability regions of random multiple access. IEEE Trans. Info. Theory 52(6), 2593–2607 (2006)zbMATHMathSciNetCrossRefGoogle Scholar
  27. 27.
    Luo, W., Ephremides, A.: Stability of N interacting queues in random-access systems. IEEE Trans. Inf. Theory 45(5), 1579–1587 (1999)zbMATHMathSciNetCrossRefGoogle Scholar
  28. 28.
    Gai, Y., Ganesan, S., Krishnamachari, B.: The saturation throughput region of p-persistent CSMA. In: Information Theory and Applications Workshop (ITA), pp. 1–4, February 2011Google Scholar
  29. 29.
    Smura T.: Techno-economic modelling of wireless network and industry architectures, Doctoral dissertation, Aalto University (2012)Google Scholar
  30. 30.
    Niyato, D., Hossain, E.: Spectrum trading in cognitive radio networks: a market-equilibrium-based approach. IEEE Wirel. Commun. 15(6), 71–80 (2008)CrossRefGoogle Scholar
  31. 31.
    Southwell, R., Chen, X., Huang, J.: Quality of service satisfaction games for spectrum sharing. In: IEEE INFOCOM Mini Conference, Turin, Italy, pp. 570–574, April 2013Google Scholar
  32. 32.
    Chen, X., Huang, J.: Spatial spectrum access game: nash equilibria and distributed learning. In: ACM Mobihoc Hilton Head Island, South Carolina, pp. 205–214 (2012)Google Scholar
  33. 33.
    Duan, L., Huang, J., Shou, B.: Duopoly competition in dynamic spectrum leasing and pricing. IEEE Trans. Mob. Comput. 11(11), 1706–1719 (2012)CrossRefGoogle Scholar
  34. 34.
    Tekin, C., et al.: Atomic congestion games on graphs and their applications in networking. IEEE Trans. Network. 20(5), 1541–1552 (2012)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Duan, L., Huang, J., Shou, B.: Investment and pricing with spectrum uncertainty: a cognitive operators perspective. IEEE Trans. Mob. Comput. 10(11), 1590–1604 (2011)CrossRefGoogle Scholar
  36. 36.
    Zhang, Y., Niyato, D., Wang, P., Hossain, E.: Auction-based resource allocation in cognitive radio systems. IEEE Commun. Mag. 50(11), 108–120 (2012)CrossRefGoogle Scholar
  37. 37.
    Huang, J., Berry, R., Honig, M.L.: Auction-based spectrum sharing. Springer J. Mob. Netw. Appl. 11(3), 405–408 (2006)CrossRefGoogle Scholar
  38. 38.
    Wysocki, T.A., Jamalipour, A.: An economic welfare preserving framework for spot pricing and hedging of spectrum rights for cognitive radio. IEEE Trans. Netw. Serv. Manage. 9(1), 87–99 (2012)CrossRefGoogle Scholar
  39. 39.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CISTER/INESC-TEC, ISEPPolytechnic Institute of PortoPortoPortugal
  2. 2.Instituto de TelecomunicaçõesCampus UniversitárioAveiroPortugal

Personalised recommendations