| L. Couch, Digital and Analog Communication Systems, 6th ed. Englewood Cliffs, NJ: Prentice-Hall, 2000. |
....research has illuminated a few fundamental problems that researchers face in developing chaos based communication schemes. One such problem is developing a chaos based communication scheme that would achieve the so called processing gain. The idea behind spread spectrum communication schemes [9] [10] is that a relatively narrow band information signal is modulated upon a rather wide band carrier. Common spread spectrum technologies utilize averaging or correlation techniques that match the received signal with a certain a priori known pattern. In these methods, the useful signal is ....
B. Sklar, Digital Communications: Fundamentals and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1988.
....which applies the Boolean theorem . After each pass through the product list, products subsumed (covered) by other products are removed. The iterative consensus algorithm generates a complete sum for the input expression and then removes nonessential products to generate a minimal covering sum [19]. In the wc example, the expression for is reduced by iterative consensus. Fig. 7(c) shows the expressions to which the essential predicates of the wc example are reduced in the logic optimization phase. These expressions are both less complex and more parallel than the original functions. ....
J. F. Wakerly, Digital Design: Principles and Practices. Englewood Cliffs, NJ: Prentice Hall, 1994.
....in general, the encoding becomes more reliable as the block size increases. Of the four, the frequency masking algorithm provided the lowest error rate for all block sizes considered. Note that, in general, the error rate approximates Q(x) the complimentary error function given by the expression [12]: Q(x) 1 u 2 expI ldu where x is a function of the block size. For a block size of at least M =2048 samples, corresponding to a bit rate of approximately 20 bits per second, the BER was below one percent for each of the algorithms. Although in practice a much lower error rate may be ....
B. Sklar, Digital Communications: Fundamentals and Applications. Englewood Cliffs, NJ: prentice Hall, 1988.
....method to do some simple fits to time series from maps and flows. In this work, I refine the techniques for construction of the UPO sequences, and I characterize the UPO fits in ways appropriate for communication applications. The work presented here is similar to direct sequence spread spectrum [24, 25], and there has been some other work in that area. Mazzini et al. 7, 9] studied systems based on the chaotic shift map, and found that a chaotic spread spectrum system could achieve performance as good as that as a system using Gold codes. Yang and Chua [11] studied a similar system. In both ....
....bit (E b ) divided by noise power spectral density (N 0 ) The result of this calculation is shown in Fig. 2 (the UPO sequence length was 256 points) Achieving a particular probability of bit error required 3 5 dB more energy per bit than some common digital modulation techniques listed in Sklar [25], but no synchronization is required, so the UPO receiver may be less sensitive to changing channel conditions. V Interference from Other Chaotic Signals A greater problem in a multiuser communication system is separating the desired carrier signal from carrier signals generated by other users. ....
B. Sklar, Digital Communications, Fundamentals and Applications. Englewood Cliffs, NJ: Prentice Hall, 1988.
....predicates are defined. This is referred to as the complete back substituted form since it is the result of fully expanding predicate definition semantics using previously generated predicate results. 44 Optimization of these expressions is performed in PDLO by an iterative consensus technique [36]. This optimization, however, treats the condition variables as being independent of each other. Thus, a separate mechanism must be provided to optimize the resulting expressions with respect to condition relationships. This is accomplished in two phases. First, each conjunctive term of the ....
J. F. Wakerly, Digital Design: Principles and Practices. Englewood Cliffs, NJ: Prentice Hall, 1994.
....on the detection problem, leaving the estimation of parameters needed in most of these detectors, such as signal amplitude, carrier phase and relative symbol delay, to others. While the signal amplitudes are not always required, and carrier phase recovery may be achieved using standard techniques [5], symbol or bit synchronization is not quite as straightforward, and the maintaining of such synchronism in a mobile environment in which propagation delays are timevarying is even more tricky. In this paper, we introduce an algorithm for tracking the time delay of, and simultaneously detecting ....
B. Sklar, Digital communications: fundamentals and applications. Englewood Cliffs, NJ: Prentice Hall, 1988.
No context found.
K. Feher, Digital Communications. Englewood Cliffs, NJ: Prentice -Hall, 1981.
No context found.
S. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
....quadrature am plitude modulation (QAM) generally require estimation and tracking of the fading channel. In general, this means a more complex receiver than for differential modulation schemes, such as differential phase shift keying (DPSK) where the receivers operate without a channel estimate [1]. In orthogonal frequency division multiplexing (OFDM) systems, DPSK is appropriate for relatively low data rates, such as in the European digital audio broadcast (DAB) system [2] However, for more spectrally efficient OFDM systems, coherent modulation is more appropriate. The structure of OFDM ....
....signaling points, h is a channel attenuation vector, and n is a vector of independent identically distributed (i.i.d. complex zero mean Gaussian noise with 2 The noise n is assumed to be uncorrelated with variance the channel h. B. Channel Model We consider a fading multipath channel model [1], consisting of M impulses = Z (2) k=0 where a are zero mean complex Gaussian random variables with a power delay profile O(r) In this paper we use M 5 impulses, an exponentially decaying power delay profile O(r) Ce k rms and delays r that are uniformly and independently distributed ....
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J.G. Proakis, Digital Communications, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1995.
....in any one of the three representation schemes will be exactly equivalent. However, different controller realizations have different degrees of robustness to FWL errors. This property can be utilized to select optimal realizations that optimize some given criteria, and several works [3] [5] have studied many aspects of FWL digital controller design, particularly the critical issue of FWL closed loop stability robustness. The fixed point arithmetic has advantages of computational simplicity and efficiency over the floatingpoint arithmetic, but has much limited dynamic range ....
D. Williamson, Digital Control and Implementation. Englewood-Cliffs, NJ: Prentice-Hall, 1991.
....and , and then over and , respectively, we obtain the following: 37) 38) As has been discussed in the convergence analysis section, the error terms in (37) and (38) above can be shown a.s. to lie in the subspaces and , respectively. As is well known in the least mean square adaptive literature [19], the rate of convergence in the mean of the two adaptations GS BAND and SS BAND is dictated by the disparity of the nonzero eigenvalues of and , respectively. We will show, therefore, that the eigenspread (ratio of the maximum and the minimum nonzero eigenvalues) of is, indeed, smaller than that ....
S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
.... uncorrelated (with an AWGN channel and using all the tones) both the left hand and the right hand side of (11) are random variables [19] This probability is calculated in Appendix A and is (12) which has the same form as the probability of error for a Rayleigh fading channel with path diversity [20]. So given an OFDM symbol using a full set of tones in an AWGN channel, it is less probable that as increases. However, when the data is correlated, there will be less diversity in the data, and it will be more probable that for larger . In this case, the probability is no longer . However, we ....
J. G. Proakis, Digital Communications, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1995.
No context found.
D. Williamson, Digital Control and Implementation. Englewood-Clis, NJ: Prentice-Hall, 1991.
....that the coefficients can be determined from the experimental data. We can use a finite number of terms to approximate the distribution function. In this case, the resulting distribution approximates the hyper Erlang distribution. In fact, we can intuitively illustrate from the Sampling Theorem [34] why the distribution provides the universal approximation to general distribution models. Fig. 1 shows the density function by varying the shape parameter [see (26) We observe that as the shape parameter becomes sufficiently large, the density function approaches the Dirac function. This can ....
J. G. Proakis, Digital Communications, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1995.
No context found.
L. Couch, Digital and Analog Communication Systems, 6th ed. Englewood Cliffs, NJ: Prentice-Hall, 2000.
No context found.
S. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
B. Sklar, Digital Communications. Fundamentals and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1988.
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, Digital Communications---Satellite/Earth Station Engineering. Englewood Cliffs, NJ: Prentice Hall, 1993.
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S. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
M. Morris Mano, Digital Logic and Computer Design. Englewood Cliffs, NJ: Prentice-Hall, 1979.
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S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
B. Sklar, Digital Communications. Englewood Cliffs, NJ: Prentice -Hall, 1988.
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B. Sklar, Digital Communications: Fundamentals and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1988.
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B. Sklar, Digital Communications: Fundamentals and Aplications. Englewood Cliffs, NJ: Prentice Hall, 1988.
No context found.
S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
No context found.
S. G. Wilson, Digital Modulation and Coding. Englewood Cliffs, NJ: Prentice-Hall, 1996.
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