QoS Aware Adaptive Subcarrier Allocation in OFDMA Systems Mustafa Ergen & Sinem Coleri {ergen,csinem}@eecs.berkeley.edu University of California Berkeley

Introduction Motivation Orthogonal Frequency Division Multiple Access(OFDMA) OFDMA System Resource Allocation Problem Algorithms Optimal Suboptimal Simulation Conclusion The analysis, Research and consultancy (ARC) group forecasts that the fixed wireless deployments in both homes and business will reach 28 million by 2005. 802.16 comiitee broadband wireless access based on OFDMA.

Motivation Broadband Wireless Access OFDM OFDMA Ex: IEEE 802.16, Wireless MAN OFDM Eliminates InterSymbol Interference OFDMA BWA is alternative to DSL technologies Physical layer should mitigate non LOS environments in indoor. 802.16 10-66GHz 802.16a 2-11GHz

OFDM Diagram

Multiuser OFDM OFDM-TDMA OFDM-FDMA OFDMA OFDM-TDMA OFDM-FDMA OFDMA Subcarrier Time OFDM-TDMA Multiuser OFDM OFDM-TDMA OFDM-FDMA OFDMA OFDM-FDMA Time Subcarrier OFDMA Time … User 1 User 2 User 3 … Subcarrier

Resource Allocation Goals: Dynamic subcarrier selection Improve system performance with adaptive modulation More bits transmitted in large channel gain carriers Provide QoS Rate and BER Instead of assigning a fixed frequency or time slot to each user, the performance will increase. The users will not use the subcarriers that are in deep fade. The performance will increase since it is quite unlikely that this subcarriers will be in deep fade for all the other users.

Resource Allocation Assumptions: Base station knows the channel Base station informs the mobiles for allocation subcarrier Base Station user

System oCoS=Ptotal for downlink oCoS=Pu for uplink Application Network rQoS=[rR,rBER] oQoS=[oR,oBER,oCoS] Resource Allocation [User x Subcarrier] Physical Layer

OFDMA

Resource Allocation RATE: [12 6 6 8 ] BER: [1e-2 1e-2 1e-4 1e-4] QoS [12 6 6 8 ] BER: [1e-2 1e-2 1e-4 1e-4] QoS Channel Resource Allocation Subcarrier User 64-QAM 16-QAM 4-QAM

Notation

Optimal Integer Programming Subcarrier User Pc1 Subcarrier User Pc2

Motivation for Sub-optimal Algorithms IP is complex Allocation should be done within the coherence time Time increases exponentially with the number of constraints

Current Suboptimal Algorithms 2-step: Subcarrier Allocation Assume the data rate for all subcarriers Assume modulation rate is fixed Assign the subcarriers Bit Loading Greedy approach to assign the bits of user

Current Suboptimal Algorithms Subcarrier Allocation Hungarian algorithm Optimal, very complex LP approximation to IP problem Close to optimal Bit Loading Subcarrier User Subcarrier User Subcarrier User

Problems in Current Suboptimal Algorithms Subcarrier assignment and bit loading are separated Users with bad channels may need higher number of subcarriers Not iterative subcarrier assignment

Iterative Algorithm Iterative algorithm based on Assignment of bits according to highest modulation Finding the best places Distributing the assigned bits to other subcarriers or to non-assigned subcarriers Exchanging the subcarriers among user pairs for power reduction.

Iterative Algorithm Fair Selection(FS) Greedy Release(GR) Horizontal Swaping(HS) Vertical Swaping(VS)

Iterative Algorithm Start HORIZONTAL SWAP VERTICAL SWAP FAIR SELECTION Ptotal<Pmax GREEDY RELEASE Start Modulation-- HORIZONTAL SWAP VERTICAL SWAP ASSIGNMENT ITERATION

Simulation Environment Build the OFDMA system Modulations:4-QAM,16-QAM,64-QAM Independent Rayleigh fading channel to each user Number of subcarriers =128 Nodes are perfectly synchronized

CDF of total transmit power without Pmax constraint

CDF of total transmit power with Pmax constraint

Average bit SNR vs. RMS delay spread As RMS delay spread increases, the fading variation increases hence higher gains are obtained by adaptive allocation

Average bit SNR vs. number of users As the number of users increases, the probability of obtaining good channel at a subcarrier increases

Instantaneous Average bit SNR vs Time Iterative Algorithm improves its Average Bit SNR by the time.

Conclusion OFDMA Resource Allocation Optimal Algorithms Broadband Wireless Access Resource Allocation Channel Information QoS Requirement Optimal Algorithms complex Iterative Algorithms