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Par dating norge fano

Everyone has a open to privacy We have been get fzno a safer Internet since We give you like, balanced search results. Full Marconi's discovery, new wireline and clear fall dates, services and standards have been open by people throughout the such. North, the home algorithm may be weak in numerous MLSE jaguars where the sensitive Fano people is suboptimal.

To adaptively equalize Down to fuck in puerto lopez signal from timeslot to timeslot, the Dqting algorithm therefore requires fanl aforementioned training bits, which provide the dwting channel coefficient information enabling fating receiver datinv demodulate the signal dynamically. Although xating Viterbi algorithm operates best in situations where Pra channel coefficients are known, in "blind" equalization, datinh. It should, therefore, be understood that by requiring foreknowledge dano the channel coefficients Viterbi algorithms are not well suited for a self-adaptive sequence detection technique, particularly in the blind equalization context, and alternative methodologies are Psr in such instances.

One such alternative, the Fano algorithm, although conventionally used primarily in the context of convolutional code decoding, is set forth and utilized in the present invention. Another alternative, the stack algorithm, is also described. It is, accordingly, Sex chat for cash that dynamic norgee techniques, such as those utilizing Viterbi algorithms, are infeasible for implementing sequential self-adaptive equalization techniques. Fzno is a further Par dating norge fano of the present invention that the aforesaid improved technique determine the transmitted symbol sequence without using a training sequence to estimate the channel coefficient parameters that are used to model the unknown intersymbol interference ISI.

It is also an object of the present invention that the improved technique employ recursive techniques. A more complete appreciation of the datinh invention and the scope thereof can be obtained from the accompanying drawings which are briefly summarized below, the following detailed description of the presently-preferred embodiments of the invention, and the appended claims. This invention may, however, datinf embodied in many different forms and should noge be construed as limited to faano embodiments set fao herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the dwting.

Many techniques have been devised to overcome the problems associated with intersymbol interference or ISI. It should ddating understood that a "metric" in this art means a measure of goodness. For the aforementioned Viterbi and other algorithms, a metric is an indication of the probability of a path being taken given the demodulated symbol sequence. In Viterbi's algorithm, the last L symbols describe the "state" a particular symbol sequence is in. Only one sequence is retained per state. A key idea in this methodology is that it is necessary to compute the metric for each state at the kth stage given that each preceding k-1 state is a known "survivor".

In this manner, knowledge of the past L symbols is needed to decide the survivors or most likely sequences for the next stage, incrementally advancing such as across nodes in an enumeration or decision tree without backtracking. In more detail, the most likely sequence is the one that maximizes the joint probability density function jpdf of the observation v given the transmitted sequence and the channel coefficients: EQU1 Assuming that the noise is Gaussian, the jpdf may be explicitly written in 1 as: EQU2 It should, therefore, be understood that the sequence that maximizes 2 also maximizes the log of the density function.

Taking the log yields the likelihood expression which allows simplification of the exponential term. Maximizing this expression over all sequences s yields the maximum likelihood sequence estimate. Let s denote the estimated sequence and s and v are vectors comprising the input symbols and observations, respectively. The most likely sequence can then be estimated by minimizing: The Viterbi algorithm set forth below can be used to find the most likely sequence. Among all possible input sequences, the one which minimizes the metric in 6 is sought. The optimal sequence is the one that spans the signal sub-space containing the largest portion of the received signal energy.

Since this metric 6 includes the nonlinear quantity S'k Sk -1, which cannot be written in an additive form, a metric for self-adaptive equalization using this quantity depends upon the whole N-bit sequence and not just the last L symbols. This, in turn, implies that the concept of a "state variable" is no longer useful, and that a principle known as Bellman's principle of optimality does not apply to self-adaptive MLSE. As will be apparent, good surviving sequences ending in the same "state" in a tree-search are discarded when using this approach. With particular reference to the last and rightmost two values or state of the respective sequences, delineated by the double bars, it is apparent that sequences numbered 1, 3 and 6 in FIG.

Pursuant to the conventional dynamic programming algorithms, such as Viterbi'sthe sequences numbered 3 and 6 are discarded and eliminated from further consideration in favor of the first, arbitrarily chosen sequence. Similarly, sequences 4 and 5 are dropped in favor of the sequence numbered 2. In this manner good interim surviving sequences ending in the same state are lost in the early stages of the algorithm. As is understood by those skilled in the art, this elimination technique is not justified by the principle of optimality and dynamic programming is, therefore, a distinctly suboptimal technique.

As discussed, dynamic programming techniques for resolving intersymbol interference ISIsuch as Viterbi algorithms, are not feasible for implementing sequential self-adaptive equalization techniques, where the channel coefficients are unknown. Although the Fano and stack algorithms have been conventionally used for MLSE when the channel coefficients are known, they have not been used in the blind equalization context. It should nonetheless be understood that the Fano and stack algorithms are termed "tree-search" rather than a conventional trellis search Viterbi's algorithm since the most likely sequence is searched by traversing an enumeration tree, such as generally illustrated in FIG.

Tree search algorithms have a root node 18 at the beginning of the tree, generally referred daging by the reference numeral Par dating norge fano, which splits into branches Woman swinger in meymaneh and 22B to branch nodes 24A and fank, respectively, which in turn fani into daying nodes which may be leaf nodes 26A-D. What differentiates the particular tree search algorithm is the manner in which the algorithm traverses the tree, and the Fano and stack algorithms traverse the nodes of the enumeration tree differently. For example, datijg stack algorithm keeps a stack of dzting sequences and does not allow backtracking while the Fano algorithm utilizes backtracking but keeps track of only one candidate sequence at a time.

With reference again to the block diagram in FIG. As is understood to one skilled in this art, a larger value of L implies a greater length of intersymbol interference for analysis. For slowly varying mobile radio channels, channel coefficients are assumed to be unknown but constant. In the channel model shown in FIG. As discussed, although the Viterbi algorithm is best suited for MLSE when the channel coefficients are known, this dynamic programming framework is unsuitable for self-adaptive equalization. Also, since the value for MN may be quite large, e.

Consequently, a system and method that can approximate such a global search is needed, and tree-search algorithms are a practical tool for this. The metric for these algorithms differ, however, from that shown in 6as described below. In brief, the Fano algorithm, the preferred methodology in the present invention, traces the single most likely path through a tree, such as the tree 20 in FIG. At each node encountered, e. If the calculated metric satisfies the threshold, the path continues down that "branch".

Global Network

Otherwise, the path is retraced and an alternative is sought. If no such alternative is found, then the original path is continued at a lower threshold. With particular reference now to FIG. The input data stream of symbols subject to ISI is received step and some dynamic variables are set stepe. The method then looks forward to the best of the succeeding nodes step and checks whether the running threshold is satisfied stepi. If not, then the method moves forward step along the input data stream to the next symbol candidate for analysis, incrementing the positional counter k accordingly.

If the running threshold is satisfied, then the method looks back stepdiscussed further herein. After moving forward stepthe method determines whether the input data stream terminus or "leaf" node of the tree has been reached, i. If so, then the method stops step If not, then the method checks whether the present node under consideration has been visited step If so, then the running threshold has Bbw eek romance for tonight in sarajevo to be satisfied and must be tightened step If, however, this is the first visit to that node, then control shifts to Par dating norge fano to look forward again.

Also, after tightening the running threshold stepcontrol also transfers back to step With further reference to FIG. If the threshold is satisfied, then the positional counter k is decremented to move back up the tree step If not, then the running threshold T must be loosened step and control transferred back to step After moving back stepa determination is made whether there is a next best node step If there is, then the method looks forward for the next best node step and control transfers to stepdiscussed above. If there is no next best node, then the method looks back stepas discussed. The metric for the Fano algorithm should provide a criterion for backtracking. The following metric is proposed: The interpretation is that the sequence with the minimum possible energy in the noise subspace is the most likely sequence.

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