Communications and Radar Signal Processing by Nicholas D. Sidiropoulos, Fulvio Gini, Rama Chellappa and

By Nicholas D. Sidiropoulos, Fulvio Gini, Rama Chellappa and Sergios Theodoridis (Eds.)

This moment quantity of a 5 quantity set, edited and authored by way of global top specialists, offers a assessment of the foundations, tools and methods of vital and rising learn themes and applied sciences in communications and radar engineering.

With this reference resource you will:

  • Quickly take hold of a brand new region of research 
  • Understand the underlying ideas of an issue and its application
  • Ascertain how a subject pertains to different components and study of the study matters but to be resolved

    • Quick instructional stories of vital and rising issues of study in communications and radar engineering
    • Presents center ideas in sign processing idea and indicates their application
    • Reference content material on center ideas, applied sciences, algorithms and functions
    • Comprehensive references to magazine articles and different literature on which to construct extra, extra particular and distinctive wisdom
    • Edited through leading people in the sphere who, via their attractiveness, were in a position to fee specialists to jot down on a specific topic

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    More precisely, sn is a realization of pseudo-noise stationary process. In practice, the training sequence is generally generated through a shift register. Let us consider rs (m) = E[sn+m sn∗ ] the autocorrelation function and the associated spectrum Ss (e2iπ f ) = m rs (m)e−2iπ m f . s. E[sn sn∗ ]e2iπ(n −n) f Ts → Ss (e−2iπ f Ts ), when N → ∞. n,n =0 As a consequence, we have that CRB(τ ) = 4π 2 N R N0 . , with flat spectrum) is optimal [28,29]. 4 Further, the best training sequence correlation property may be different for different synchronization parameters.

    This problem can be treated similarly to the frequency estimation issue in the flat channel context [24,25] and so will be omitted here. 2, and the channel can also be estimated by following the approaches given in the “Channel Estimation” E-reference. The communication protocol permits the transmitter to explicitly adjust its signaling so as to facilitate receiver side synchronization. Therefore, there are two main classes of estimation problems: • Data-Aided (DA)2 : the receiver knows a sequence of transmitted symbols sn .

    Therefore, we will only focus on the single-carrier case in this Section. The transmitted signal (in baseband) [12,13] is given by N −1 xa (t) = sn h a (t − kTs ), n=0 where • • • {s0 , . . , s N −1 } are the transmitted symbols belonging to ASK, PSK or QAM constellations or any linearly precoded scheme, with N being the sequence length used for estimating the synchronization (sync) parameters; Ts is the symbol period which is assumed to be known throughout this chapter. For more information about the blind estimation of Ts , the reader is referred to [17]; h a (t) is the shaping filter that classically is a square-root Nyquist filter.

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