Vector Space Concept Signal Space Concept Any set

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Vector Space Concept

Vector Space Concept

Signal Space Concept • Any set of M energy signals {si(t)} as linear combinations

Signal Space Concept • Any set of M energy signals {si(t)} as linear combinations of N orthogonal basis functions, where N ≤ M • Real value energy signals : s 1(t), s 2(t), . . s. M(t), each of duration T sec

• The set of coefficients si can be viewed as a N-dimensional vector.

• The set of coefficients si can be viewed as a N-dimensional vector. • Bears a one-to-one relationship with the transmitted signal si(t)

Synthesizer for generating the signal si(t) Analyzer for generating the set of signal vectors

Synthesizer for generating the signal si(t) Analyzer for generating the set of signal vectors si

Each signal in the set si(t) is completely determined by the vector of its

Each signal in the set si(t) is completely determined by the vector of its coefficients

• The signal vector si can be extended to 2 D, 3 D

• The signal vector si can be extended to 2 D, 3 D etc. Ndimensional Euclidian space • Provides mathematical basis for the geometric representation of energy signals that is used in noise analysis • Allows definition of – Length of vectors (absolute value) – Angles between vectors – Squared value (inner product of si with itself)

geometric representation of signals for the case when N 2 and M 3. (two

geometric representation of signals for the case when N 2 and M 3. (two dimensional space, three signals)

average energy in a signal:

average energy in a signal:

Gram-Schmidt Orthogonalization Procedure first basis function starting with s 1 : or using s

Gram-Schmidt Orthogonalization Procedure first basis function starting with s 1 : or using s 2 define the coefficient s 21 : we introduce the intermediate function g 2 as:

the second basis function φ2(t) as: • In general : • the coefficients :

the second basis function φ2(t) as: • In general : • the coefficients : • Given a function gi(t) we can define a set of basis functions, which form an orthogonal set, as: • For the special case of i = 1; gi(t) = si(t)

NUMERICLAS …

NUMERICLAS …

Maximum likelihood decoding

Maximum likelihood decoding

OPTIMUM RECEIVER USING COHERENT DETECTION The received vector:

OPTIMUM RECEIVER USING COHERENT DETECTION The received vector:

CORRELATION RECEIVER (a) Detector or demodulator. (b) Signal transmission decoder.

CORRELATION RECEIVER (a) Detector or demodulator. (b) Signal transmission decoder.

MATCHED FILTER RECEIVER

MATCHED FILTER RECEIVER

PROBABILITY OF ERROR

PROBABILITY OF ERROR

• The probability of error is invariant to rotation and translation of the

• The probability of error is invariant to rotation and translation of the signal constellation. – In maximum likelihood detection the probability of symbol error Pe depends solely on the Euclidean distances between the message points in the constellation – The additive Gaussian noise is spherically symmetric in all directions in the signal space.

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