Network Information Theory

Lessons

1. Network Information Theory (Lecture- 01)
2. How to quantify information?
3. Hartleys measure and shannons measure
4. What is entropy and what is IT Inequality?
5. Chain rules of entropy and mutual information
6. Properties of entropy conditional entropy and relative entropy
7. Network Information Theory (Lecture- 02)
8. Jensens Inequality
9. Fanos Lemma
10. Shannon mcmillan breiman theorem
11. Coding a single random variable
12. Prefix free code
13. Krafts inequality
14. Log sum inequality
15. Data Processing Lemma
16. Network Information Theory (Lecture- 03)
17. Weak law of large numbers strong law of large numbers
18. Network Information Theory (Lecture- 04)
19. Rooted tree with probabilities
20. Shannon fano coding
21. Network Information Theory (Lecture- 05)
22. Huffman code
23. Network Information Theory (Lecture- 06)
24. Coding an information source
25. Lempel ziv 1978
26. Discussion on equipartition property
27. Lempel ziv 1977
28. Network Information Theory (Lecture- 08)
29. Coding of positive integers
30. Discrete stationary source dss
31. Block to variable length coding of dss
32. Network Information Theory (Lecture- 07)
33. Joint typical sequences
34. Network Information Theory (Lecture- 09)
35. Noisy channel coding theorem
36. Elias willems source coding
37. Fanos inequality
38. Network Information Theory (Lecture- 10)
39. Weak converse
40. Discrete memoryless channel
41. Strongly symmetric channel
42. Network Information Theory (Lecture- 12)
43. Uniformly dispersive channel and uniformly focusing channel
44. Questionn
45. Network Information Theory (Lecture- 11)
46. Recap and outline
47. Modeling the medium
48. Mutual information differential entropy capacity
49. Rate distortion function
50. Network Information Theory (Lecture- 14)
51. Achievability of the rate distortion function
52. Converse of rate distortion theorem
53. Network Information Theory (Lecture- 15)
54. Introduction
55. Network Information Theory (Lecture- 13)
56. Introduction to Network IT
57. Classic 1 Hop Example
58. Distributed Source Coding Slepian Wolf Problem
59. Wyner-Ziv problem (Lecture- 17)
60. Slepian-Wolf problem (Lecture- 16)
61. Network Information Theory (Lecture- 18)
62. Wyner ziv problem continued
63. Generalizations
64. Recap Of Distributed Source Coding
65. Network Information Theory (Lecture- 19)
66. Broadcast Channels
67. 2nd Moment Method
68. Some Special Cases (Lecture- 20)
69. Network Information Theory (Lecture- 21)
70. Network Information Theory (Lecture- 24)
71. Network Information Theory (Lecture- 25)
72. Network Information Theory (Lecture- 23)
73. Network Information Theory (Lecture- 27)
74. Network Information Theory (Lecture- 26)
75. Network Information Theory (Lecture- 22)
76. Network Information Theory (Lecture- 28)
77. Superposition Coding
78. Network Information Theory (Lecture- 30)
79. Interference Channels (Lecture- 31)
80. Relay Channels (continued) (Lecture- 29)
81. Degraded Broadcast Channels
82. AWGN Broadcast Channel
83. 1. Binary Symmetric Broadcast Channel
84. Multiple-Access Channels (MACs)
85. Binary Adder Channel
86. 1. Broadcast Channel (Recap)
87. 3. (Scalar) AWGN Channel
88. Multiple-Access Channels (MACs) (continued)
89. Successive Interference Cancellation (SIC)
90. Multiple-Access Channels (MACs) (continued)
91. Rate Splitting
92. Duality - Scalar Case
93. Vector AWGN Channel
94. Interference Channels
95. Multiple-Access Channels (MACs) (continued)
96. Coding Strategies
97. Han Kobayashi Region
98. AWGN ICs
99. Han Kobayashi Region (continued)
100. Relay Channels
101. Physically Degraded RCs
102. Relay Channels (continued)
103. Symbol Relaying (Amplify-Forward)
104. Partial Decode Forward (PDF)
105. 1. Superposition Coding
106. 2. Degraded Broadcast Channels