  # Performance Oriented Association in Cellular Networks with - PowerPoint PPT Presentation

## Performance Oriented Association in Cellular Networks with Technology Diversity Abishek Sankararaman, Jeong-woo Cho, Franois Baccelli. Outline Motivation and Background. Main mathematical model. Summary of Results. Conclusions

1. Performance Oriented Association in Cellular Networks with Technology Diversity Abishek Sankararaman, Jeong-woo Cho, François Baccelli.

2. Outline • Motivation and Background. • Main mathematical model. • Summary of Results. • Conclusions

3. Background MVNOs (Mobile Virtual Network Operators) These operators pool together various wireless technologies (for eg. 2G, 3G, 4G LTE) to create a service. A UE can dynamically chose between the various technologies depending on whichever yields higher instantaneous benefit.

4. Background A new model of cellular service by

5. What the Paper is About ? Technology Diversity Leveraging the presence and control of multiple wireless technologies operating on non-overlapping bandwidth . Google Fi - The different cellular operators and WiFi operate on separate bands. MVNOs - Multiple orthogonal technologies (For ex. 3G and 4G LTE).

6. What the Paper is About ? Technology Diversity Leveraging the presence and control of multiple wireless technologies operating on non-overlapping bandwidth . Google Fi - The different cellular operators and WiFi operate on separate bands. MVNOs - Multiple orthogonal technologies (For ex. 2G, 3G and 4G LTE). Technology Diversity - A framework to leverage and evaluate the benefits from the diversity of wireless technologies.

7. The Problem we study - Base Station Association Which Base-Station/Access Point must a UE associate with ? A principled way to exploit the diversity in the network.

8. The “optimal” BS Association is not an obvious choice First Guess - Connect to the nearest BS irrespective of the type of BS it is.

9. The “optimal” BS Association is not an obvious choice First Guess - Connect to the nearest BS irrespective of the type of BS it is. Signal Nearest BS SINR = Interference + Noise > SNR SNR ( Basis for nearest BS association. )

10. The “optimal” BS Association is not an obvious choice First Guess - Connect to the nearest BS irrespective of the type of BS it is. Signal Nearest BS SINR = Interference + Noise > SNR SNR ( Basis for nearest BS association. ) But bandwidths are not overlapping and thus interference only from one type of Base Stations.

11. The “optimal” BS Association is not an obvious choice First Guess - Connect to the nearest BS irrespective of the type of BS it is. Signal Nearest BS SINR = Interference + Noise > SNR SNR ( Basis for nearest BS association. ) But bandwidths are not overlapping and thus interference only from one type of Base Stations. SINR < SINR Thus, in this example

12. Mathematical Framework - Network Model Consider a network comprised of different technologies. T The BS/APs of technology is distributed as a independent i φ i ⊂ R 2 Poisson Point Process with intensity λ i

13. Mathematical Framework - Network Model Consider a network comprised of different technologies. T The BS/APs of technology is distributed as a independent i φ i ⊂ R 2 Poisson Point Process with intensity λ i j th Denote by to be the closest point to the origin and X i j ∈ φ i by r i j = || X i j ||

14. Mathematical Framework - Network Model Consider a network comprised of different technologies. T The BS/APs of technology is distributed as a independent i φ i ⊂ R 2 Poisson Point Process with intensity λ i j th Denote by to be the closest point to the origin and X i j ∈ φ i by r i j = || X i j || There is a typical user at the origin of the Euclidean plane who wishes to associate to a BS. Palm theory connects the viewpoint of a single user to the average performance experienced by the users in the network.

15. Mathematical Framework - Network Model T = 2 φ 1 = with intensity λ 1 φ 2 = λ 2 with intensity X 1 X 2 1 1

16. Mathematical Framework - Signal Model BS of technology transmits at power i P i Signal from BS of technology i attenuated with distance as given by the function l i ( · ) : R + → R + Independent fading from the jth nearest BS of technology i to H i the typical user - j

17. Mathematical Framework - Signal Model BS of technology transmits at power i P i Signal from BS of technology i attenuated with distance as given by the function l i ( · ) : R + → R + Independent fading from the jth nearest BS of technology i to H i the typical user - j r 1 j r 2 j P 1 H 1 j l 2 ( r 1 j ) P 2 H 2 j l 2 ( r 2 j ) (Signal Power from the BS) (Signal Power from the BS)

18. Performance Metrics (Non-overlapping bandwidths = ⇒ Interference from only one technology) SINR i,j SINR of the typical UE when it associates to the 0 jth nearest BS of technology i. • For each technology , denote by bounded non-increasing i p i ( · ) : R + → R + denoting the reward function. functions

19. Performance Metrics (Non-overlapping bandwidths = ⇒ Interference from only one technology) SINR i,j SINR of the typical UE when it associates to the 0 jth nearest BS of technology i. • For each technology , denote by bounded non-increasing i p i ( · ) : R + → R + denoting the reward function. functions • If the typical UE connects to the jth nearest BS of technology p i ( SINR i,j i, then it receives a reward of 0 )

20. Performance Metrics T = 2 φ 1 = with intensity λ 1 φ 2 = λ 2 with intensity X 2 2 The reward received by the UE in this example is p 2 ( SINR 2 , 2 0 )

21. Performance Metrics T = 2 φ 1 = with intensity λ 1 φ 2 = λ 2 with intensity X 2 2 The reward received by the UE in this example is p 2 ( SINR 2 , 2 0 ) Examples of common reward functions • Coverage p i ( x ) = 1 ( x ≥ β i ) p i ( x ) = B i log 2 (1 + x ) • Average Achievable Rate

22. Information at the UE Goal- Design association schemes exploiting available network “information” at the UE, that maximize expected reward of a typical UE.

23. Information at the UE Goal- Design association schemes exploiting available network “information” at the UE, that maximize expected reward of a typical UE. Examples of Information that a UE can know - • Nearest BS of all technologies. • Nearest BS of all technologies. k • Instant fading and the distance to the nearest BS k • Noisy estimate of the instant fading from the nearest BS k of each technology.

24. How Information affects Optimal Association SINR < SINR When averaged across fading

25. How Information affects Optimal Association SINR < SINR When averaged across fading

26. How Information affects Optimal Association SINR < SINR When averaged across fading Sudden very good signal which the UE can sense.

27. How Information affects Optimal Association SINR < SINR When averaged across fading Sudden very good signal which the UE can sense. In this case, UE should associate to

28. Information at the UE Notion of Information at the typical UE formalized through the notion of filtrations of a sigma algebra.

29. Information at the UE - Formalization Notion of Information at the typical UE formalized through the notion of filtrations of a sigma algebra. The Probability space containing the independent ( Ω , F , P ) - { H i j } i ∈ [1 ,T ] ,j ∈ N { φ i } T RVs and 1=1

30. Information at the UE - Formalization Notion of Information at the typical UE formalized through the notion of filtrations of a sigma algebra. The Probability space containing the independent ( Ω , F , P ) - { H i j } i ∈ [1 ,T ] ,j ∈ N { φ i } T RVs and 1=1 F I ⊆ F - The information sigma-algebra.

31. Information at the UE - Formalization Notion of Information at the typical UE formalized through the notion of filtrations of a sigma algebra. The Probability space containing the independent ( Ω , F , P ) - { H i j } i ∈ [1 ,T ] ,j ∈ N { φ i } T RVs and 1=1 F I ⊆ F - The information sigma-algebra. π : Ω → [1 , T ] × N F I - This is measurable function denoting the association scheme.

32. Information at the UE - Examples Examples of Information that a UE can know - • Nearest BS of all technologies. • Nearest BS of all technologies. k • Instant fading and the distance to the nearest BS k • Noisy estimate of the instant fading from the nearest BS k of each technology.

33. Information at the UE - Examples Examples of Information that a UE can know - { r i 1 } T � � • Nearest BS of all technologies. F I = σ i =1 • Nearest BS of all technologies. k • Instant fading and the distance to the nearest BS k • Noisy estimate of the instant fading from the nearest BS k of each technology.

34. Information at the UE - Examples Examples of Information that a UE can know - { r i 1 } T � � • Nearest BS of all technologies. F I = σ i =1 { r i k } T � � • Nearest BS of all technologies. F I = σ k i =1 • Instant fading and the distance to the nearest BS k • Noisy estimate of the instant fading from the nearest BS k of each technology.

More recommend