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DPS 861A Josua Purba Jill O'Sullivan Raul Zevallos Sergio Boniche China Pankey.

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Presentation on theme: "DPS 861A Josua Purba Jill O'Sullivan Raul Zevallos Sergio Boniche China Pankey."— Presentation transcript:

1 DPS 861A Josua Purba Jill O'Sullivan Raul Zevallos Sergio Boniche China Pankey

2  What is Resource Management on Cellular System ?  Current Research on LTE Resource Management  Research Questions  So What?  LTE Technology Overview  Why use Game Theory?  Research Methodology  Future Research  Conclusion

3  What are the resources?  Bandwidth (Spectrum Frequency) The RF Spectrum frequency where the signal information are sent. Limited in size. Could be 5 MHz (WCDMA), 1.4, …,5,10,20 MHz (LTE) Affect the rate and application run on the system Control the capacity of the system to handle the users  Power Transmit Power of Radio signal Can cause interference to other user/sector/cell if to big Different system different requirements

4  Code On Code Division Multiplex technique (CDMA family, WCDMA, HSPA) Limited number of code Could not use too many code – would cause interference, thus reduce performance. This could make receiver more complex.

5  Resource Management on LTE System [17]  The role of RRM is essentially to : Ensure that radio resources are efficiently utilized Taking advantage of the available adaptation techniques Serve users according to their quality of service (QoS) attributes. Usually RRM handles Mobility Management (Handover) from 1 BS to another.

6  The mechanisms include [17]  Bearer admission control  multi-user time and frequency domain packet scheduling  QoS-aware  Hybrid automatic repeat request (ARQ) management  link adaptation with dynamic switching between different transmission modes.  The available transmission modes include single- and dual-codeword transmissions for multi-antenna configurations  Localized and distributed subcarrier transmission.

7  BS User Plane and Control Plane Architecture [17]

8  Spectrum pooling [10],[11]  Licensed Users (LU) share spectrum with Rental Users (RU)  RU get the same Bandwidth size like LU  RU needs to detect LU before use the spectrum  Interference issue from RU to LU and vice versa  Works on FDMA/TDMA and OFDM system  Study the packet delay, throughput and the blocking probability for a spectrum pooling system by using Markov chain. [11]

9  Spectrum pooling [10]

10  Spectrum Pooling + Random Access [13]  Spectrum Pooling use Round Robin – not efficient  Combine it with Random Access to improve utilization radio resources and improve throughput  Use Wifi for the experiment  Heterogeneous system (TV and Wireless) [14]  Share (Sell) TV spectrum to service providers  Use double auction game theory  One between TV station and service providers  One between service providers and users

11  Scheduling [21]  Classical scheduling goals in a communication system are to maximize utilization (throughput) and to allow communication for all users (fairness).  Study the fairness vs. efficiency on OFDMA scheduling.  Compare various kind of game theory criteria for cooperative bargaining.  Found Kalai-Smorodinsky solutions as alternative to proportional fairness (Nash solution), both offer compromise between efficiency and fairness.

12  Adaptive [15]  Exploits the time diversity, frequency diversity as well as multiuser diversity in the time, frequency and user domain, respectively.  Adopt a two-step allocation method to reduce the scheduling complexity and meanwhile improve the scheduling performance.  Allocate users into 2 dimension frequency and time domain like grids.

13  Adaptive [15]

14  Optimal Solution [9]  Investigate the issue of power control and subcarrier assignment in a sectorized two-cell downlink OFDMA (WIMAX) system impaired by multicell interference.  Usually with practical problem, this would not have simple closed form solution.  Some of available bandwidth would be reused by different base station, subject to multi cell interference.  The rest of the available bandwidth would be shared in an orthogonal way between the different base stations, no multi cell interference  The paper provide simpler form of general solution.

15  Cognitive Radio [8]  Propose and validate a Cognitive RRM scheme in the context of LTE network segments.  Use cognitive features that provide the system with knowledge which observed from past interactions with the environment.  The system will be able to apply already known solutions in timely manner when identifying a problem that has been already addressed in the past.  Assume: all sub carrier use the same modulation type and power level (comment: not practical)  Proposed scheme can result in significant efficiency improvement in terms of performance and network adaptation.

16  Game Theory – Auction Theory [20]  Develop theory on allocate wireless channel with auction theorem.  Consider fair competition over independent wireless fading channel.  each user submits a bid according to the channel condition (assume known in the beginning time slot)  Use centralized scheduler that assign time slots according to the Nash equilibrium strategy based on users’ average money amount.

17  What is the optimum way to allocate bandwidth dynamically on OFDMA LTE system with auction theory, scheduling and cognitive radio? Is it possible to find general optimum solution?  What is the complexity of the dynamic bandwidth allocation with auction theorem compare to results without game theory?  How to apply time notion as multiple step decision of auction theory on allocation the bandwidth dynamically?

18  RIM CEO mention the need to conserve bandwidth (http://www.mobilecrunch.com/2010/02/16/rim-ceo-pulls-an-att-we-need-to-conserve- bandwidth/)http://www.mobilecrunch.com/2010/02/16/rim-ceo-pulls-an-att-we-need-to-conserve- bandwidth/)  At the end 2009, AT&T ask its customer to reduce to use their smart phone by giving incentive.  Operator can increase the capacity and efficiency of the network. Thus increase the revenue … bottom line … make money and customer satisfaction  Why LTE ?  People/customer use the technology not only research but also commercial (real implementation)  Majority market use LTE compare to Wimax and Ultra Mobile Broadband (UMB)

19  Support for, and mobility between, Multiple heterogeneous systems:  legacy system (GSM, GPRS, EDGE, WCDMA, HSPA)  Non-3GPP system (Wifi, Wimax, EV-DO, satellite)  All IP Network  Enhanced Air Interface allow increased data rate  With Mobility: 100 MBps (DL) and 50 MBps(UL)  Stationary: 1GBps (DL) and 500 MBps (UL)

20  Support for higher throughput and lower latency  User Plane Latency: < 5ms  Control Plane Latency (Transition Time to Active State): < 100ms (from idle to active)  Increase Control Plane Capacity: > 200 users per cell (for 5MHz Spectrum)  Mobility Support:  Up to 500 Kmph  Optimized for low speed from 0 to 15 Kmph

21  Spectrum Flexibility to achieve higher spectrum efficiency [18]: where RB: Resource Block

22  Channel Bandwidth Definition [18]:

23  High Level Overview BS Architecture [19]

24  LTE scheduler on protocol stack [16]

25  Channel quality variations in time and freq [16]

26  Down Link (DL)  OFDM (Orthogonal Frequency Division Multiplexing) use a large number of narrowband sub- carrier for multi carrier transmission.  OFDM avoids the problem with multipath reflections by sending message bits slow enough so it has high tolerance for multipath delay spread.  OFDMA: assigning different sub channel to different user.  Use the same principle as HSPA for scheduling of share channel data and fast link adaptation.

27  Down Link (DL)  OFDM symbols are grouped into resource block which has 180KHz in frequency domain and 0.5 ms in time domain.  Each user is allocated a number of resource block in time-frequency grid.  The more resource block the higher the rate.  The scheduling mechanism control the number of resource block at any given time.

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29  Up Link (UL)  Use SC-FDMA(Single Carrier Frequency Division Multiple Access).  It adds DFT/IDFT to OFDMA architecture.  It groups the resource block in away reduce PAPR (Peak Average Power Ratio).

30  Multiple Antenna [16]  Use Multiple Input Multiple Output (MIMO) to increase data rate, diversity, increase capacity and beam forming.  It use 2x2 or 4x4 MIMO system.

31  The DL PHY resource space for one TTI. Pilot symbols for channel estimation purposes are not illustrated [17].

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34  What is Game theory? [7,12]  Mathematical models of interaction between two or more rational decision makers  Study and analysis of situations where conflict of interests are present.  Game theory concepts apply whenever the actions of several agents are interdependent.  These agents may be individuals, groups, firms, or any combination of these.  The concepts of game theory provide a model to formulate, structure, analyze, and understand strategic scenarios.

35  Advantages of Game theory  Simplicity  Compare to typical math derivation  Dynamic  Decision made based on its condition at the time  Different decision for different condition  Distributed  Users involved in making decision

36  Limitations of Game theory  Real world conflicts are complex  Model at best can capture important aspect  No unified solution to general conflict resolution  Players are (usually) considered rational  determine what is best for them given that others are doing the same (not cooperative)  But it can provide intuitions, suggestions and partial prescriptions

37  Mathematical derivation and optimization  Start from system model (still evolve)  Assumption and important parameter  Apply Game theory to system  Find optimization  Use software to help optimization  Formulize the algorithm  If time permit, simulate with software package

38  System Model: combination cognitive radio and game theory. INPUT ( Context, Profiles, Policies) Optimization & Decision ( Game Theory) LTE Network Element (eNB, segment, cell ) Management Infrastructure “Learning” Infrastructure Abstraction Environment Sensing Configuration capabilities Decision efficiency User preferences

39  Assumption:  One Sector, One Cell, One BS  Multiple Users (N)  With Interference and Power Control  Multiple or repeated step Auction Theory that include notion of time  Parameter or Variable:  Bandwidth size and frequency  Time  Number of User  Type of Service  Number of Resource Block  Bidding strategy  Interference (SINR)  Slot number  Rate or throughput  Number of sub-carriers

40  Apply Game theory to system  Auction Theorem  Part of Game Theory  Definition: A public sale of property or merchandise to the highest bidder.  Auctions have rules and bidders.  Auctioneer decides what rules to use but takes bidders as given.  Auction mechanism tries to maximize the seller’s revenue through the bidding of each player.  Show the supply (limited) and demand (a lot).  BS has limited resources and many users wants them.

41  Consider the following scenarios:  Non-Cooperative (Competitive) Games: Realistic  Cooperative Games: User willing to compromise  Repeated and Evolutionary Games: dynamic scenario  Auction model:  N: number of users (i=1..N)  B: Bidding strategy (Bi= bidding strategy of user i)  P: Pay off function (Pi = Pay off function of user i)  R: Rate or throughput (Ri = Rate of user i)

42  Auction model (continued):  K: number of sub-carrier (Ki= sub-carrier of user i)  SIR: Signal-to-Interference Ratio (SIRi of user i)  Bidding function model :  The goal is to maximize the revenue by extracting each user’s willingness to pay about an object  I plan to use Sealed bid: bidders tell auctioneer their bids without interacting with each other.  Sealed bid has the following rules:  First-price. Winner pays its own bid. Losers pay nothing.

43  Sealed bid has the following rules (continued):  Second-price. Winner pays highest losing bid. Losers pay nothing.  All-pay. Each bidder (including losers) pays its own bid.  Have not decided what strategy to use, but my candidate might be Second-price.

44  Current auction model for throughput and fairness analysis [22]:  Sum rate maximization  Does not consider fairness.  Assign sub carrier to user that has best channel condition.  Max-Min fairness  Most strict fairness criterion since every users data rate are equal.  Maximize user who has lowest data rate.  Proportional fairness  Trade off between Sum rate maximization and Max-Min fairness.  Maximize sum of logarithmic utility function.

45  Proposed the new method and utility function:  Find utility function F = [N, K, {Bi}, Pi{.}, SIRi]  Include scheduling to equation:  Proportional fairness: Nash Solution S = argmax Σ Ri = argmax Π Ri  Kalai – Smorodinsky fairness algorithm S = argmax {min (Ri / Ri max)}  Need to work the detail more in LTE context.

46  Key Issues in analysis  Steady state characterization  Steady state optimality  Convergence  Stability  Scalability

47  Optimization  Find Cost function  Cooperative, non-cooperative and repetition.  Heuristic: Case by case Case by case for few cases Find common case or case that is used many times Shorter time frame to develop  General Solution The goal : find Global optimum and unique solution Is it possible to find it on multiple step? General case answer all possibilities Longer time frame to develop

48  Use software to help optimization  Use Matlab to plot the function  Find Optimum point  Formulize the algorithm in terms of steps to LTE protocol stack procedures.  If time permit, simulate with software package

49  Design and implement the algorithm using network simulation software such as: OPNET or OMNET  Add the fading and multipath on the analysis.  Add power control restriction on the analysis  Add case with 2 sectors, 2 cell, 2 BS and handover as part of the analysis  Add MIMO to BS only.  Add MIMO to BS and terminals (users)

50  Dynamic resource allocation research is very important as the demand for bandwidth increase rapidly.  Different kind of methodology can be applied to find optimum solution on dynamic bandwidth allocation.  Many researchers use game theory for dynamic resource allocation since it has dynamic, less complexity and distributed characteristic.

51 1. 3GPP Standard and Specification (http://www.3gpp.org/)http://www.3gpp.org/ 2. UMTS Forum (http://www.umts-forum.org/) 3. 3GPP Long Term Evolution on Wiki (http://en.wikipedia.org/wiki/3GPP_Long_Term_Evolution) 4. LTE Tutorial from Radio Electronics (http://www.radio- electronics.com/info/cellulartelecomms/lte-long-term-evolution/lte-ofdm-ofdma- scfdma.php). 5. Ericsson, “LTE Overview”, Uen Rev B, June 2009.http://www.umts-forum.org/)http://en.wikipedia.org/wiki/3GPP_Long_Term_Evolutionhttp://www.radio- electronics.com/info/cellulartelecomms/lte-long-term-evolution/lte-ofdm-ofdma- scfdma.php) 6. The Mobile Broadband Evolution: 3GPP Release 8 and Beyond, 3G Americas, February Martin Shubik, “Game theory, complexity and simplicity part 1: a tutorial”, Publisher John Wiley & Sons, Inc. New York, NY, USA, Pages: , Volume 3, Issue 2 (Nov./Dec. 1997), Pages: , ISSN: , Saatsakis, A., Tsagkaris, K., von-Hugo, D., Siebert, M., Rosenberger, M., Demestichas, P,”Cognitive Radio Resource Management for Improving the Efficiency of LTE Network Segments in the Wireless B3G World”, 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN Ksairi, N.; Bianchi, P.; Ciblat, P.; Hachem, W., “Resource Allocation for Downlink Cellular OFDMA Systems: Part 1- Optimal Allocation”, Signal Processing, IEEE Transactions on : Accepted for future publication Volume PP, Forthcoming, 2009.

52 10. T.A. Weiss and F.K. Jondral,”Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency”, IEEE Radio Communication, March Fatih Capar, Friedrich Jondral,” Resource Allocation in a Spectrum Pooling System for Packet Radio Networks Using OFDM/TDMA”, IST Mobile & Wireless Telecommunications Summit June 16-19, Thessaloniki, Greece Game Theory on wiki (http://en.wikipedia.org/wiki/Game_theory)http://en.wikipedia.org/wiki/Game_theory 13. Shimizu,Yoshitaka; Nuno, Fusao,”Performance Evaluation of Novel DSA Scheme that Combines Polling Method with Random Access Method”,The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'06),Helsinki, Finland, Dusit Niyato, Ekram Hossain, Zhu Han, “Dynamic Spectrum Access in IEEE Based Cognitive Wireless Networks: A Game Theoretic Model for Competitive Spectrum Bidding and Pricing”, IEEE Wireless Communications, April Xing Zhang, En Zhou, Renshui Zhu, Shiming Liu, Wenbo Wang, “Adaptive multiuser radio resource allocation for OFDMA systems”, IEEE Global Telecommunications Conference, GLOBECOM '05, St. Louis, MO, 23 January David Astély, Erik Dahlman, Anders Furuskär, Ylva Jading, Magnus Lindström, Stefan Parkvall, "LTE: The Evolution of Mobile Broadband", IEEE Communications Magazine, Vol. 47, no. 4, April 2009

53 17. Klaus I. Pedersen, Troels E. Kolding, Frank Frederiksen, István Z. Kovács, Daniela Laselva, and Preben E. Mogensen, " An Overview of Downlink Radio Resource Management for UTRAN Long-Term Evolution ", IEEE Communications Magazine, Vol. 47, no. 7, July GPP TS : "Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception“, V9.2.0 ( ) GPP TS : "Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; “, V9.2.0 ( ). 20. Jun Sun, Eytan Modiano, Lizhong Zheng,” Wireless Channel Allocation Using an Auction Algorithm”, IEEE Journal on Selected Areas in Communications, Vol. 24, No. 5, May Ibing, A.; Boche, H.,” Fairness vs. Efficiency: Comparison of Game Theoretic Criteria for OFDMA Scheduling”, Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers (ACSSC), 4-7 Nov. 2007, Pages: 275 – 279, Pacific Grove, CA. 22. Sang-Wook Han, Youngnam Han, “A Competitive Fair Subchannel Allocation for OFDMA System Using an Auction Algorithm”, IEEE 66 th Vehicular Technology Conference (VTC), pp , Sept Oct Baltimore, MD.

54 23. Reshef, Ehud,” LTE & WIMAX Evolution to 4G”, Comsys, 29 October 2008.


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