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Cell Selection in 4G Cellular Networks David Amzallag, BT Design Reuven Bar-Yehuda, Technion Danny Raz, Technion Gabriel Scalosub, Tel Aviv University.

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Presentation on theme: "Cell Selection in 4G Cellular Networks David Amzallag, BT Design Reuven Bar-Yehuda, Technion Danny Raz, Technion Gabriel Scalosub, Tel Aviv University."— Presentation transcript:

1 Cell Selection in 4G Cellular Networks David Amzallag, BT Design Reuven Bar-Yehuda, Technion Danny Raz, Technion Gabriel Scalosub, Tel Aviv University

2 May 2008 Israeli Networking Seminar 2008 2 Cell Selection and Current 3G Cellular Networks Cell Selection: Which BS covers an MS MSs demands << BSs capacities Mostly voice Data < 15Mb/s Local SNR-based protocols are pretty good Generally, one station servicing every client South Harrow area, NW London (image courtesy of Schema) Cover-by-One (CBO)

3 May 2008 Israeli Networking Seminar 2008 3 Future 4G Cellular Networks High MS demand Video, data, … x10-x100 higher (100Mb/s-1Gb/s) Capacities will be an issue < x20 higher reduced costs missing good planning solutions Technology enables having several stations cover a client 802.16e MIMO South Harrow area, NW London (image courtesy of Schema) Research Goal: Explore the potential of Cover-by-Many (CBM)

4 May 2008 Israeli Networking Seminar 2008 4 Model Bipartite graph (Base) Stations For every, capacity. (Mobile) Clients For every, demand and profit. Coverage Area For every, Notation extended to sets, e.g.,

5 May 2008 Israeli Networking Seminar 2008 5 Model (cont.) Goal: Find a set, and a cover plan (CP) is maximized All-or-Nothing (AoN) Constraint Capacity Constraint All-or-Nothing Demand Maximization (AoNDM) Deceptively “simple” resource allocation problem The same as previously well studied problems?

6 May 2008 Israeli Networking Seminar 2008 6 Previous Work Cell Selection Minimize MSs transmission power [Hanly 95] Maximize throughput (via load balancing) [Sang et al. 08] General Assign. (GAP) 1/2-approx. vs. APX hard [Shmoys-Tardos 93, Chekuri-Khanna 00] Multiple Knapsack PTAS [Chekuri-Khanna 00] Budgeted Cell-planning NP-hard to approximate Sufficient capacities: -approx. [Amzallag et al. 05]

7 May 2008 Israeli Networking Seminar 2008 7 Our Results AoNDM: Hard to approximate to within -AoNDM: Bad News: Still NP-hard Good News: A -approx. CBM algorithm Based on a simpler and faster -approx. CBO algorithm Simulation: CBM is up to 20% better than SNR-based -AoNDM:

8 May 2008 Israeli Networking Seminar 2008 8 A (1-r)/(2-r)-Approx. - Intuition A local-ratio algorithm Based on decomposing the profit function Greedy approach A CP x for S is maximal if it cannot be extended: WLOG, is saturated

9 May 2008 Israeli Networking Seminar 2008 9 If p(j)=d(j) Maximality Suffices! No edge to. -saturated Maximal Solution Algorithm sketch: Decompose profit function: Demand-proportional chunks Recurse! Greedily maximize How?

10 May 2008 Israeli Networking Seminar 2008 10 A (1-r)-Approx. – The Extra Mile Previous algorithm might be wasteful: Solution: Maximize usage of A flow-based algorithm. Slightly increased complexity  Cover-by-Many

11 May 2008 Israeli Networking Seminar 2008 11 Experimental Study - Settings -grid A client in every node

12 May 2008 Israeli Networking Seminar 2008 12 Experimental Study - Settings -grid A client in every node Data Clients: Large demand Few

13 May 2008 Israeli Networking Seminar 2008 13 Experimental Study - Settings -grid A client in every node Picocells: Small capacity Small radius many Microcells: Large capacity Large radius few Data Clients: Large demand Few Voice Clients: Small demand Many High-load: Profit:

14 May 2008 Israeli Networking Seminar 2008 14 Experimental Study - Results

15 May 2008 Israeli Networking Seminar 2008 15 Summary 4G technology will support cover-by-many. Good approximation algorithms for realistic scenarios. CBM is 10%-20% better than SNR-based methods. Future Work: Practical: Online & local CBM policies Theoretical: Approximation independent of r ?

16 Thank You!

17 May 2008 Israeli Networking Seminar 2008 17 Hardness of Approximation Reduction from Maximum Independent Set Theorem: AoNDM Cannot be approximated better than unless 1 2 3 4 5 6 (1,2) (2,3) (3,4) (4,5) (5,6) (3,6) (5,1) 1 2 3 4 5 6

18 May 2008 Israeli Networking Seminar 2008 18 A (1-r)/(2-r)-Approximation Algorithm If return If return Set For every j s.t. try adding j to the cover Return x Cleanup… How?

19 May 2008 Israeli Networking Seminar 2008 19 A (1-r)-Approximation

20 May 2008 Israeli Networking Seminar 2008 20 A (1-r)-Approximation Ignore!!

21 May 2008 Israeli Networking Seminar 2008 21 A (1-r)-Approximation Ignore!!

22 May 2008 Israeli Networking Seminar 2008 22 A (1-r)-Approximation Ignore!!


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