access icon free Evaluation of average bit error rate for wireless networks with alpha-stable interference

The performance of a digital wireless network that operates in a Rayleigh fading environment in the presence of Gaussian noise and heavy-tailed impulsive interference that can be modelled by an α-stable process is investigated. Such wireless networks include ad hoc or cognitive radio networks that operate in an unbounded fading/shadowing environment with a Poisson interference field. For the system under consideration, a novel closed-form expression for the average bit error probability of digital modulation schemes, valid for any arbitrary real values of the characteristic exponent, α, is derived. The validity of the proposed formulation is attested through Monte Carlo simulation results.

Inspec keywords: cognitive radio; Rayleigh channels; error statistics; modulation; ad hoc networks; Monte Carlo methods; radiofrequency interference

Other keywords: alpha-stable interference; Poisson interference field; digital wireless network; Gaussian noise; average bit error rate evaluation; Rayleigh fading environment; unbounded fading-shadowing environment; α-stable process; ad hoc networks; closed-form expression; cognitive radio networks; heavy-tailed impulsive interference; digital modulation schemes; Monte Carlo simulation; average bit error probability

Subjects: Electromagnetic compatibility and interference; Monte Carlo methods; Radio links and equipment; Modulation and coding methods

References

    1. 1)
      • 10. Prudnikov, A.P., Brychkov, Y.A., Marichev, O.I.: ‘Integrals and series Volume 3: More special functions’ (Taylor and Francis, Oxford, UK, 2003, 1st edn).
    2. 2)
      • 9. Yilmaz, F., Alouini, M.-S.: ‘Product of the powers of generalized Nakagami-m variates and performance of cascaded fading channels’. Proc. 2009 IEEE Global Telecommunications Conf., Honolulu, HI, USA, November 2009, pp. 18.
    3. 3)
      • 4. Renzo, M.D., Merola, C., Guidotti, A., Santucci, F., Corazza, G.E.: ‘Error performance of multi-antenna receivers in a Poisson field of interferers: a stochastic geometry approach’, IEEE Trans. Commun., 2013, 61, (5), pp. 20252047 (doi: 10.1109/TCOMM.2013.021913.120424).
    4. 4)
      • 6. Shobowale, Y.M., Hamdi, K.A.: ‘A unified model for interference analysis in unlicensed frequency bands’, IEEE Trans. Wirel. Commun., 2009, 8, (8), pp. 40044013 (doi: 10.1109/TWC.2009.070276).
    5. 5)
      • 1. Ilow, J., Hatzinakos, D.: ‘Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers’, IEEE Trans. Signal Process., 1998, 46, (6), pp. 16011611 (doi: 10.1109/78.678475).
    6. 6)
      • 2. Win, M.Z., Pinto, P.C., Shepp, L.A.: ‘A mathematical theory of network interference and its applications’, Proc. IEEE, 2009, 97, (2), pp. 205230 (doi: 10.1109/JPROC.2008.2008764).
    7. 7)
      • 8. Gradshteyn, I., Ryzhik, I.M.: ‘Tables of integrals, series, and products’ (Academic Press, London, UK, 2000, 6th edn).
    8. 8)
      • 3. Ghannudi, H.E., Clavier, L., Azzaoui, N., Septier, F., Rolland, P.A.: ‘α-stable interference modeling and Cauchy receiver for an IR-UWB ad hoc network’, IEEE Trans. Commun., 2010, 58, (6), pp. 17481757 (doi: 10.1109/TCOMM.2010.06.090074).
    9. 9)
      • 5. Cardieri, P.: ‘Modeling interference in wireless ad hoc networks’, IEEE Commun. Surv. Tutor., 2010, 12, (4), pp. 551572 (doi: 10.1109/SURV.2010.032710.00096).
    10. 10)
      • 10. Prudnikov, A.P., Brychkov, Y.A., Marichev, O.I.: ‘Integrals and series Volume 3: More special functions’ (Taylor and Francis, Oxford, UK, 2003, 1st edn).
    11. 11)
      • 7. Kusaladharma, S., Tellambura, C.: ‘Aggregate interference analysis for underlay cognitive radio networks’, IEEE Wirel. Commun., 2012, 1, (6), pp. 641644 (doi: 10.1109/WCL.2012.091312.120600).
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2013.3231
Loading

Related content

content/journals/10.1049/el.2013.3231
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading