Open AccessExact bit‐error‐rate analysis of underlay decode‐and‐forward multi‐hop cognitive networks with estimation errors
Author(s) -
HoVan Khuong,
Sofotasios Paschalis C.
Publication year - 2013
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2013.0254
Subject(s) - underlay , computer science , rayleigh fading , cognitive radio , hop (telecommunications) , bit error rate , multipath propagation , fading , cognitive network , algorithm , interference (communication) , transmitter power output , channel (broadcasting) , topology (electrical circuits) , telecommunications , mathematics , signal to noise ratio (imaging) , wireless , transmitter , combinatorics
This work is devoted to the error rate analysis of underlay multi‐hop cognitive networks with arbitrary number of hops in the presence of multipath fading. Novel analytic expressions are derived in closed‐form for the case of Rayleigh fading, which are validated extensively through extensive comparisons with results from computer simulations. In addition, the corresponding asymptotic performance for large maximum transmit power or large maximum interference power is investigated in detail. The derived expressions provide useful insights on the behaviour of the network performance under different operation parameters and include several previous works as special cases. Furthermore, their algebraic representation is relatively simple which renders them convenient to handle both analytically and numerically. The offered results also demonstrate that underlay multi‐hop cognitive networks suffer significantly from the error floor phenomenon, the channel estimation error and the order of locating unlicensed users of different maximum transmit power levels, whereas for the linear network model their performance is highly dependant on the number of hops. Moreover, it is shown that optimum positioning of helpers in underlay multi‐hop cognitive networks depends on numerous factors and differs substantially from those in traditional multi‐hop networks.