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Signal-Space Analysis

Signal-Space Analysis. ENSC 428 – Spring 2 008 Reference: Lecture 10 of Gallager. Digital Communication System. Representation of Bandpass Signal. Bandpass real signal x ( t ) can be written as:. Note that. In-phase. Quadrature-phase. Representation of Bandpass Signal. (1). (2).

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Signal-Space Analysis

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  1. Signal-Space Analysis ENSC 428 – Spring 2008 Reference: Lecture 10 of Gallager

  2. Digital Communication System

  3. Representation of Bandpass Signal Bandpass real signal x(t) can be written as: Note that In-phase Quadrature-phase

  4. Representation of Bandpass Signal (1) (2) Note that

  5. Relation between and x f f f f -fc fc fc

  6. Energy of s(t)

  7. Representation of bandpass LTI System

  8. Key Ideas

  9. Examples (1): BPSK

  10. Examples (2): QPSK

  11. Examples (3): QAM

  12. Geometric Interpretation (I)

  13. Geometric Interpretation (II) • I/Q representation is very convenient for some modulation types. • We will examine an even more general way of looking at modulations, using signal space concept, which facilitates • Designing a modulation scheme with certain desired properties • Constructing optimal receivers for a given modulation • Analyzing the performance of a modulation. • View the set of signals as a vector space!

  14. Basic Algebra: Group • A group is defined as a set of elements G and a binary operation, denoted by · for which the following properties are satisfied • For any element a, b, in the set, a·b is in the set. • The associative law is satisfied; that is for a,b,c in the set (a·b)·c= a·(b·c) • There is an identity element, e, in the set such that a·e= e·a=a for all a in the set. • For each element a in the set, there is an inverse element a-1 in the set satisfying a· a-1 = a-1·a=e.

  15. A set of non-singular n×n matrices of real numbers, with matrix multiplication Note; the operation does not have to be commutative to be a Group. Example of non-group: a set of non-negative integers, with + Group: example

  16. Unique identity? Unique inverse fro each element? • a·x=a. Then, a-1·a·x=a-1·a=e, so x=e. • x·a=a • a·x=e. Then,a-1·a·x=a-1·e=a-1, so x=a-1.

  17. Abelian group • If the operation is commutative, the group is an Abelian group. • The set of m×n real matrices, with + . • The set of integers, with + .

  18. Application? • Later in channel coding (for error correction or error detection).

  19. Algebra: field • A field is a set of two or more elements F={a,b,..} closed under two operations, + (addition) and * (multiplication) with the following properties • F is an Abelian group under addition • The set F−{0}is anAbelian group under multiplication, where 0 denotes the identity under addition. • The distributive law is satisfied: (a+b)*g = a*g+b*g

  20. Immediately following properties • a*b=0 impliesa=0or b=0 • For any non-zero ,*0= ? • *0 +  = *0 + *1= *(0 +1)= *1=; therefore *0 =0 • 0*0 =? For a non-zero , its additive inverse is non-zero.0*0=(+(- ) )*0 = *0+(- )*0 =0+0=0

  21. Examples: • the set of real numbers • The set of complex numbers • Later, finite fields (Galois fields) will be studied for channel coding • E.g., {0,1} with + (exclusive OR), * (AND)

  22. Vector space • A vector space V over a given field F is a set of elements (called vectors) closed under and operation + called vector addition. There is also an operation * called scalar multiplication, which operates on an element of F (called scalar) and an element of V to produce an element of V. The following properties are satisfied: • V is an Abelian group under +. Let 0 denote the additive identity. • For every v,w in V and every a,b in F, we have • (a*b)*v= a*(b*v) • (a+b)*v= a*v+b*v • a*( v+w)=a*v+ a *w • 1*v=v

  23. Examples of vector space • Rn over R • Cn over C • L2 over

  24. Subspace.

  25. Linear independence of vectors

  26. Basis

  27. Finite dimensional vector space

  28. Finite dimensional vector space • A vector space V is finite dimensional if there is a finite set of vectors u1, u2, …, un that span V.

  29. Finite dimensional vector space

  30. Example: Rn and its Basis Vectors

  31. Inner product space: for length and angle

  32. Example: Rn

  33. Orthonormal set and projection theorem

  34. Projection onto a finite dimensional subspace Gallager Thm 5.1 Corollary: norm bound Corollary: Bessel’s inequality

  35. Gram –Schmidt orthonormalization

  36. Gram-Schmidt Orthog. Procedure

  37. Step 1 : Starting with s1(t)

  38. Step 2 :

  39. Step k :

  40. Key Facts

  41. Examples (1)

  42. cont … (step 1)

  43. cont … (step 2)

  44. cont … (step 3)

  45. cont … (step 4)

  46. Example application of projection theorem Linear estimation

  47. L2([0,T])(is an inner product space.)

  48. Significance? IQ-modulation and received signal in L2

  49. On Hilbert space over C. For special folks (e.g., mathematicians) only L2 is a separable Hilbert space. We have very useful results on 1) isomorphism 2)countable complete orthonormal set Thm If H is separable and infinite dimensional, then it is isomorphic to l2(the set ofsquare summable sequence of complex numbers) If H is n-dimensional, then it is isomorphic to Cn. The same story with Hilbert space over R. In some sense there is only one real and one complex infinite dimensional separable Hilbert space. L. Debnath and P. Mikusinski, Hilbert Spaces with Applications, 3rd Ed., Elsevier, 2005.

  50. Hilbert space Def) A complete inner product space. Def) A space is complete if every Cauchy sequence converges to a point in the space. Example: L2

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