mohatta2015.pdf
signal processing from power amplifier operation control point of view
signal processing from power amplifier operation control point of view
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THE IDEA 155<br />
detected value. Thus, the signed soft value would be +428. Similarly, the signed<br />
soft value for s 2 would be +640.<br />
The approach just described is optimistic, in that it implicitly assumes the other<br />
detected symbol values are correct. This is why we only change one symbol value.<br />
More accurate soft information can be obtained by defining the second sequence<br />
metric as the best metric corresponding to the set of possible sequences for which<br />
52 is opposite to its detected value.<br />
What we're really doing is approximating the MAPSD approach for soft information<br />
generation with the MLSD metrics. Specifically, we are assuming that for<br />
the set of sequences for which the s 2 is the detected value, the detected sequence<br />
dominates. Similarly, for the set of sequences for which s^ is not the detected<br />
value, one sequence dominates. This notion of considering only dominant terms<br />
when forming soft information is referred to as dual maxima or simply dual-max.<br />
7.1.2.3 Coding In general, some form of forward error correction (FEC) encoding<br />
is used to protect the message symbols. One way to achieve this is to send additional<br />
symbols that are functions of the message symbols. This adds extra information<br />
or redundancy to the packet, which can be used to correct symbol errors during<br />
the decoding process. Coding is also used for forward error detection (FED). In<br />
general, some codes are designed for FEC and some for FED. However, they need<br />
not be used the way they were intended to be used.<br />
A simple example of a code for either FEC or FED is a parity check code. In<br />
the Alice and Bob example, we sent two symbols, si and s 2 . We could send a third<br />
symbol, S3 = S1S2· First, suppose the coding is used for FED. At the receiver, we<br />
would check whether S3 is equal to S1I2· If not, an error would be declared. (One<br />
way to handle such an error is to have the packet sent a second time.) Second,<br />
suppose the coding is used for FEC. At the receiver, we would use r\, r 2 and<br />
r.-j to form soft information. For example, suppose the MMSE linear equalization<br />
decision variables have values z\ — —0.18914, 22 = 0.26488, and Z3 = 0.16822.<br />
In the decoding process, we would form message metrics for each possible metric.<br />
For example, message 1 has si = +1 and s 2 = —1· The parity symbol would be<br />
53 = (+1)(—1) = —1. The message metric would be (+l)«i + (—1)22 + ( — 1)^3,<br />
which equals —0.622246. For messages 2 and 3, the message metrics would be<br />
0.092477 and 0.243956. We would declare the third message the detected message<br />
as it has the largest message metric.<br />
7.1.3 Joint demodulation and decoding<br />
With MLSD and MAPSD, we take advantage of the fact that the symbols can only<br />
take on the values +1 and —1. There is further signal structure that we can use<br />
to our advantage. We can use the fact that there are a finite number of possible<br />
messages or packet values. All symbol sequences are not possible.<br />
The best way to use this information would be to perform maximum likelihood<br />
packet detection (MLPD). Like MLSD, we consider all possible packet values (a<br />
subset of all possible symbol sequences) and form a metric for each one. The<br />
packet with the best metric is the detected packet value or message.