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整理人: camelsu(2001-01-30 01:24:15), 站内信件
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Altera Viterbi Compiler MegaCore & PCI5.01 Core downloadable now .
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Viterbi Compiler MegaCore Function
Features
High-performance, area-optimized, soft-decision Viterbi decoders for e
rror correction
High-speed parallel architecture with:
Pure logic implementation
Performance of over 100 Mbps
Fully parallel operation
Integral puncturing rate support from 2/3 to 7/8
Medium-speed, hybrid architecture
Mixed serial/parallel implementation
Typical performance of 1 to 7 Mbps
Medium logic area
Low-speed serial architecture with:
Memory based architecture
Typical performance of 0.5 to 3 Mbps
Fully parameterized Viterbi function, including:
Number of coded bits
Constraint length
Number of soft bits
Precision of branch metric accumulation
Traceback depth or maximum block length
Polynomial for each coded bit
Two utilities included to generate test cases and analyze the results,
enabling user to verify the decoder
Optimized for Altera® APEX® 20K, ACEX® 1K, FLEX® 6000,
FLEX 8000, FLEX 10K, and FLEX 10KE devices
Flexible licensing?buy only the features you require
Easy-to-use MegaWizard® Plug-In Manager
Literature
Viterbi Compiler MegaCore Function User Guide, version 1.0 (566 KB)
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General Description
In its basic form, Viterbi decoding (or forward dynamic programming) i
s an efficient, recursive algorithm that performs an optimal exhaustiv
e search.
A convolutional encoder and Viterbi decoder can be used together to pr
ovide error correction over a noisy channel e.g., communications chann
el. A convolutional encoder adds redundancy (i.e., extra bits) to a da
ta stream before transmission. The 憆ate?and the 慻enerating polynomia
ls?describe the encoder. The rate is the number of extra bits per inpu
t bit, e.g. a rate 1/2 encodes 1 bit and produces 2 bits for transmiss
ion. Similarly, a rate 2/3 encodes 2 bits and produces 3 bits for tran
smission. A code can be 憄unctured?to reduce its rate, by deleting som
e of the encoded bits according to a deterministic pattern.
The generating polynomials are bit patterns that denote the bits of th
e input data stream, which are mathematically combined to produce an e
ncoded bit. There is one generating polynomial per encoded bit. The le
ngth in bits of the generating polynomial is called the 庆onstraint le
ngth? systems with higher constraint lengths are generally more robust
. However, the complexity of the Viterbi decoder increases exponential
ly with the constraint length, so it is unusual to find constraint len
gths greater than nine.
Bit errors can occur at the receiver, if the transmission channel is n
oisy. The Viterbi algorithm is efficient at decoding the data stream a
nd correcting errors. The Viterbi decoder uses the redundancy, which t
he convolutional encoder imparted, to decode the bit stream and remove
the errors.
When encoded with the generating polynomials, the Viterbi algorithm fi
nds the most likely sequence of bits that is closest to the actual rec
eived sequence. The decoding is highly mathematical, and uses a state
machine to describe how the received sequence could have resulted from
all possible sequences of bits. While processings bits the decoder re
tains information on the likelihood of the possible sequences, which a
re stored as 悭ccumulated branch metrics?
The receiver can deliver either hard or soft symbols to the Viterbi de
coder. A hard symbol is equivalent to a binary ?. A soft symbol is mul
ti-leveled to represent the confidence in the bit being positive or ne
gative. For instance, if the channel is non-fading and Gaussian, the o
utput of a matched filter quantized to a given number of bits is a sui
table soft input. In both cases 0 is used to represent a punctured bit
. The Viterbi algorithm has better performance with soft input symbols
.
The Viterbi decoder can work on blocks of data, or continuous streams.
It takes in N symbols at a time for processing, where N is the number
of encoded symbols. The traceback length is the delay before the deco
der makes a decision on a bit. For blocks of data, the best performanc
e is achieved if decoding decisions are delayed until all input symbol
s have been processed. For continuous streams this is not possible, an
d for practical purposes the traceback length should be limited to sev
eral times the constraint length.
You can parameterize the Altera high-performance, soft-decision Viterb
i compiler MegaCore® function to implement any number of standard
Viterbi decoders, or to realize a custom application. The function is
capable of a throughput (decoded bits out) of over 100 Mbps, even for
relatively large constraint lengths, such as 7. The function can suppo
rt many different puncturing rates, based on a mother code of 1/2.
The function also supports hard decision decoding, when the number of
soft bits is set to two. Several utilities are included, which generat
e test cases and analyze the results, allowing you to v
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