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 transcript:

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

April 2008 INFOCOM 2008 (Phoenix, AZ) 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)

April 2008 INFOCOM 2008 (Phoenix, AZ) 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 e MIMO South Harrow area, NW London (image courtesy of Schema) Research Goal: Explore the potential of Cover-by-Many (CBM)

April 2008 INFOCOM 2008 (Phoenix, AZ) 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.,

April 2008 INFOCOM 2008 (Phoenix, AZ) 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?

April 2008 INFOCOM 2008 (Phoenix, AZ) 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]

April 2008 INFOCOM 2008 (Phoenix, AZ) 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:

April 2008 INFOCOM 2008 (Phoenix, AZ) 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

April 2008 INFOCOM 2008 (Phoenix, AZ) 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?

April 2008 INFOCOM 2008 (Phoenix, AZ) 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

April 2008 INFOCOM 2008 (Phoenix, AZ) 11 Experimental Study - Settings -grid A client in every node

April 2008 INFOCOM 2008 (Phoenix, AZ) 12 Experimental Study - Settings -grid A client in every node Data Clients: Large demand Few

April 2008 INFOCOM 2008 (Phoenix, AZ) 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:

April 2008 INFOCOM 2008 (Phoenix, AZ) 14 Experimental Study - Results

April 2008 INFOCOM 2008 (Phoenix, AZ) 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 ?

Thank You!