Advanced Spectrum Management in Multicell OFDMA Networks enabling Cognitive Radio Usage F. Bernardo, J. Pérez-Romero, O. Sallent, R. Agustí Radio Communications Group Dept. of Signal Theory and Communications Universitat Politècnica de Catalunya (Spain)

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 2/24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 3/24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 4/24 Introduction (1/3)  Future wireless networks will demand flexible spectrum allocation policies that cope with the detected spectrum scarcity and its underutilization in current networks.  Advanced Spectrum Management (ASM) techniques optimize the use of the spectrum in time and space.  Fixed Spectrum Allocation (FSA) (the classical policies) eases spectrum management and controls the interference between RATs limits the flexibility of spectrum and leads to large pieces of the spectrum wasted due to the time and space varying traffic distribution.  Dynamic Spectrum Allocation (DSA) improves the spectral efficiency while maintaining user’s QoS. permits to pool frequency resources and enable opportunistic secondary use of the spectrum. Better usage of spectrum

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 5/24 Introduction (2/3)  OFDMA (Orthogonal Frequency Division Multiple Access) is the candidate technology to have a flexible radio interface.  Resource Block (RB) is the minimum resource that could be allocated to a user.  In frequency, the whole available bandwidth is divided into groups of adjacent subcarriers or chunks  Can be exploited by an ASM strategy to obtain high spectral efficiency.

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 6/24 Introduction (3/3)  Objectives of the paper:  Present a framework for ASM in a multicell OFDMA system.  Propose a DSA algorithm to decide a proper chunk-to-cell assignment.  Show that ASM may improve system performance and enables opportunistic secondary usage of the spectrum.

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 7/24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 8/24 Proposed ASM Framework (1/3)  Inhomogeneous spatial traffic distribution  There are spatial and temporal variations of network conditions  Additionally, a spectrum broker manages the spectrum transactions between primary and secondary markets

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/2008 9/24 Proposed ASM Framework (2/3)  The proposed ASM framework for a single operator is divided into two decision blocks:  Short-Term Scheduler (STS)  ASM Scheduler  Short-Term Scheduler  Located at the base station  low latencies and high speed channels  Each RB of the time-frequency grid is given fairly to each user. Proportional Fair Scheduler: R m,n (t) represents the instantaneous achievable rate that user m can get at chunk n. W m,n (t) is the window-averaged version of R m,n (t) as follows:

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Proposed ASM Framework (3/3)  ASM scheduler  Decides the chunks to allocated to each cell by executing the DSA algorithm.  Located in a network node with the ability to control a set of cells.  Adapts the system to traffic variations in time and space in the medium- long term  Allows efficient spectrum usage

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Proposed Dynamic Spectrum Allocation Algorithm (1/2)  It is run by the ASM scheduler  It is an heuristic algorithm divided into two steps 1.Computes the number of chunks to assign to a given cell (N j ) 2.Allocates the chunks to each cell taking into account the potential intercell interference and cell load (costs matrix A)

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Proposed Dynamic Spectrum Allocation algorithm (2/2)  The second step, calculates per each base station and chunk the potential intercell interference and assign those chunks with lower cost  The DSA algorithm assigns only the necessary chunks per cell reducing intercell interference.

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Simulation Model  19 Cells and 12 chunks  SINR per chunk taking into account fast fading  Adaptive Coding and Modulation per chunk  Heterogeneous spatial traffic distribution Spatial distribution of the users in the scenario.

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Results (1/3)  Two classic fixed Frequency Reuse Factor (FRF) schemes are compared with the DSA algorithm.  FRF1 that assigns all available chunks to all cells  FRF3 where the bandwidth is divided in 3 subbands and each subband is assigned to a cell within a cluster of three cells.  Results presented in terms of spectral efficiency, users’ dissatisfaction probability and spectrum usage per cell.  Spectral efficiency  Dissatisfaction Probability  Regional Spectrum Usage (RSU)

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Results (2/3)  The DSA algorithm  adapts the number of chunks to system load  Improves spectral efficiency  Maintains user’s requirements Average cell spectral efficiency Dissatisfaction probability Average number of chunks per cell

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Results (3/3) Regional Spectrum Usage per cell for chunks 9 to 12 and for FRF1 (a), FRF3 (b), and DSA (c,d,e,f) System average RSU FRF11 FRF30.67 DSA 30 users0.17 DSA 100 users0.19 DSA 200 users0.28 DSA 300 users0.45 DSA 400 users0.55  Note that RSU(B)  [0,1].  RSU(B)=1  band B is completely used in a cell and/or in neighboring cells.  RSU(B)=0  the band is completely free.  Thus, the lower the RSU(B), the easier that band B can be released in a region around a given cell.

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Conclusions  An approach to an Advanced Spectrum Management (ASM) framework in a multicell OFDMA network has been given.  The proposed DSA algorithm improves overall system’s spectral efficiency while maintains users’ satisfaction.  Also it has been shown that DSA could release spectrum bands in large geographical areas so that this spectrum will not be wasted and could be exploited by secondary cognitive users.  Dynamic reuse is very suitable for future wireless networks because spectrum is a scarce and expensive resource that will be used in a more efficient way, satisfying primary users’ needs and making room for opportunistic secondary users.

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Outline  Introduction  Proposed ASM Framework  Proposed DSA Algorithm  Simulation Model  Results  Conclusions  Future work

Francisco Bernardo Álvarez WCNC 2008 Las Vegas, USA, 03/April/ /24 Future Work  Proposed algorithm nearly approximates the solution for the long-term. Improve the algorithm to adapt also to small variations in the medium-term.  Develop heuristic algorithms that take into account users positions in order to deploy different strategies for the users located at the center and edge part of the cell respectively  Theoretical formulation of the optimal allocation algorithm.  Practical implications of implementing DSA in a a real network and complexity.  Users with heterogeneous preferences to rates.

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