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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PROBLEMS 169<br />
parity-check (LDPC) codes [Gal62]. Error detection is usually performed with a<br />
Cyclic Redundancy Code (CRC).<br />
For MLSD, a soft output Viterbi algorithm (SOVA) provides soft information.<br />
The two main approaches are given in [Bat87] and [Hag89]. These two can be<br />
made equivalent by adding a certain term to the latter [Fos98]. Performance of<br />
various soft information generation approaches are compared in [Han96j. With<br />
approximate MLSD approaches, paths corresponding to all possible bit values may<br />
not be present. One solution is to set the soft value to some maximum value [Nas96].<br />
The term dual maxima is introduced in [Vit98].<br />
Kaiman filtering can be used as a form of equalization for estimating soft bit<br />
values [Thi97].<br />
7.6.3 Joint demodulation and decoding<br />
The classic reference for turbo equalization is [Dou95]. Turbo equalization for<br />
differential modulation can be found in [Hoe99, Nar99]. Channel estimation can<br />
also be included in the turbo equalization process [Ger97].<br />
Linear turbo equalization is described in [LaoOl, Tüc02a, Tüc02b]. There is some<br />
evidence that using unadjusted feedback from the decoder can improve performance<br />
[Vog05].<br />
So far we have considered structure provided by encoding. There may be further<br />
structure in the information bits as well. Using this information at the receiver is<br />
referred to as joint source/channel decoding [Hag95], and interesting performance<br />
gains are possible [Fin02].<br />
Joint demodulation and decoding of CDMA signals is discussed in [Gia96]. Turbo<br />
equalization has been extended to joint detection of cochannel signals (multiuser<br />
detection) [Moh98, Ree98, Wan99].<br />
PROBLEMS<br />
The idea<br />
7.1 Consider the Alice and Bob example and Table 6.1.<br />
a) Find the two symbol metrics for si<br />
b) What is the MAP symbol estimate for si?<br />
c) Does it agree with the MLSD estimate?<br />
7.2 Consider the Alice and Bob example and Table 6.1.<br />
a) Suppose the noise power is 0.01 instead of 100. Recompute the symbol<br />
metrics.<br />
b) If you observed a numerical issue, what was it? If not, try using a simple<br />
calculator.<br />
7.3 Consider the Alice and Bob example. Suppose instead that z\ = —4 and<br />
zi = 3.<br />
a) What are the detected symbol values?<br />
b) Do the detected symbol values correspond to a valid message?
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