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for n suf ciently large Therefore, by an appropriate choice of and n, we can make Pe as small as we like So, for any choice of > 0, there exists an and n such that over all randomly chosen rate R codes of block length n, the expected distortion is less than D + Hence, there must exist at least one code C with this rate and block length with average distortion less than D + Since was arbitrary, we have shown that (R, D) is achievable if R > R(D) We have proved the existence of a rate distortion code with an expected distortion close to D and a rate close to R(D) The similarities between the random coding proof of the rate distortion theorem and the random coding proof of the channel coding theorem are now evident.

Dr. T is a single psychologist looking for a mate. Dr. T conducts group psychotherapy and one of the group members mentions after a session that Dr. T should meet her niece. The patient feels that they would make a great couple. Dr. T agrees to go on a blind date with his patient s niece. They hit it off immediately and soon agree to marry. Dr. T continues seeing the patient in group psychotherapy but now also sees the patient at family events with his new wife. Dr. T developed a dual relationship with his patient by involving himself in the patient s family as well as dating and eventually marrying the patient s niece.

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The human ear is a lter, as is the telephone receiver (earpiece) The two are in tandem, as we would expect For the telephone listener, noise is an annoyance Interestingly we nd that noise annoys a listener more near the reference frequencies of a voice channel than at other frequencies When using the North American 500-type telephone set with average listeners, a simple 0-dBm tone at 1000 Hz causes a certain level of annoyance To cause the same level of annoyance, a 300-Hz tone would have to be at a level of about +17 dBm; a 400-Hz tone at about +115 dBm; a 600-Hz tone at about +4 dBm, and a 3000-Hz tone also at about +4 dBm for equal annoyance levels for a population of average listeners.

the region of interest and have wireless connectivity They are typically battery powered with limited communication and computation abilities Also each node in a wireless sensor network is equipped with a variety of sensing modalities, such as acoustic, seismic, and infrared Having location information can be very useful and has so many applications, it can answer questions like: Are we almost to the campsite What lab bench was I standing by when I prepared these tissue samples How should our search-andrescue team move to quickly locate all the avalanche victims Can I automatically display this stock devaluation chart on the large screen I am standing next to Where is the nearest cardiac de brillation unit and so on Service providers can also use location information to provide some novel location-aware services The navigation system based on a global positioning system (GPS) is an example.

We will explore the parallels further by considering the Gaussian example, which provides some geometric insight into the problem It turns out that channel coding is sphere packing and rate distortion coding is sphere covering Channel coding for the Gaussian channel Consider a Gaussian channel, Yi = Xi + Zi , where the Zi are iid N(0, N) and there is a power constraint P on the power per symbol of the transmitted codeword Consider a sequence of n transmissions The power constraint implies that the transmitted sequence lies within a sphere of radius nP in Rn The coding problem is equivalent to nding a set of 2nR sequences within this sphere such that the probability of any of them being mistaken for any other is small the spheres of radius nN around each them are of almost disjoint.

The question arose of why should the transmission engineer be penalized in design of a system for noise of equal level across the voice channel We therefore have shaped the voice channel as a function of frequency and annoyance This shaping is called a weighting curve For the voice channel we will be dealing with two types of weighting: (1) C-message, used in North America, and (2) psophometric weighting as recommended by CCITT Figure C1 shows these weighting curves Weighting networks have been developed to simulate the corresponding response of C-message and psophometric weighting Now we want to distinguish between at response and weighted response Of course, the curves in Figure C1 show weighted response Flat response, regarding a voice channel, has a low-pass response down 3 dB at 3 kHz and rolls off at 12 dB per octave.

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