Ofdma 1

1,158 views

Published on

Published in: Technology, Business
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,158
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
40
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • 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.
  • BWA is alternative to DSL technologies
    Physical layer should mitigate non LOS environments in indoor.
    802.16 10-66GHz
    802.16a 2-11GHz
  • 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.
  • Ofdma 1

    1. 1. QoS Aware Adaptive Subcarrier Allocation in OFDMA Systems Mustafa Ergen & Sinem Coleri {ergen,csinem}@eecs.berkeley.edu University of California Berkeley
    2. 2. Introduction      Motivation Orthogonal Frequency Division Multiple Access(OFDMA) OFDMA System Resource Allocation Problem Algorithms     Optimal Suboptimal Simulation Conclusion
    3. 3. Motivation  Broadband Wireless Access  Ex:  IEEE 802.16, Wireless MAN OFDM  Eliminates  OFDMA InterSymbol Interference
    4. 4. OFDM Diagram
    5. 5. OFDM-TDMA Multiuser OFDM Time Subcarrier OFDM-FDMA Time    OFDM-TDMA OFDM-FDMA OFDMA Subcarrier OFDMA Time User 1 User 2 User 3 … … Subcarrier
    6. 6. 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
    7. 7. Resource Allocation ri ar bc su Assumptions:  Base station knows the channel  Base station informs the mobiles for allocation er Base Station user
    8. 8. 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
    9. 9. OFDMA
    10. 10. Resource Allocation RATE: BER: [12 6 6 8 ] [1e-2 1e-2 1e-4 1e-4] Resource Allocation Subcarrier r es U Channel QoS 64-QAM 16-QAM 4-QAM
    11. 11. Notation Transmit Power : Pkc,n = f k (ck , n ) α k2,n user : k ∈ {1,..., K } subcarrier : n ∈ {1,..., N } assigned bit : c ∈ {0,1,..., M } k, n channel gain : α 2 k, n No  −1 BER M − QAM : f (c) = Q ( 3  4 2  ) (2c − 1) 
    12. 12. K min γ k ,n ,c N M ∑∑∑ f ( ck , n ) k =1 n =1 c =1 subject to Rk = α N 2 k ,n γ k ,n ,c for γ k ,n ,c ∈{0,1} M ∑∑ c k ,n .γ k ,n ,c for all k , n =1 c =1 and 0 ≤ K M ∑∑ γ k , n ,c Pc2 Pc3 r es U Integer Programming ≤ 1, for all n. Subcarrier Subcarrier Subcarrier k =1 c =1 es U  Pc1 r es U Optimal r es U Subcarrier
    13. 13. Motivation for Sub-optimal Algorithms IP is complex  Allocation should be done within the coherence time  Time increases exponentially with the number of constraints 
    14. 14. 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
    15. 15. Current Suboptimal Algorithms Subcarrier  Hungarian algorithm Optimal, very complex   LP approximation to IP problem  Bit Loading For each k , repeat the following Rk times : n = arg min ∆Pk ,n (ck ,n ) n∈S k c k ,n =c Subcarrier Close to optimal r es U  r es U Subcarrier Allocation k ,n +1 evaluate ∆P (c ). k ,n k ,n r es U  Subcarrier
    16. 16. 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
    17. 17. 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.
    18. 18. Iterative Algorithm Fair Selection(FS)  Greedy Release(GR)  Horizontal Swaping(HS)  Vertical Swaping(VS) 
    19. 19. Iterative Algorithm Start ASSIGNMENT Modulation-- ITERATION PA W L A C T REV S I GREEDY RELEASE PA W L AT N OZ R OH S I Ptotal<Pmax FAIR SELECTION
    20. 20. 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
    21. 21. CDF of total transmit power without Pmax constraint
    22. 22. CDF of total transmit power with Pmax constraint
    23. 23. Average bit SNR vs. RMS delay spread As RMS delay spread increases, the fading variation increases hence higher gains are obtained by adaptive allocation
    24. 24. Average bit SNR vs. number of users As the number of users increases, the probability of obtaining good channel at a subcarrier increases
    25. 25. Instantaneous Average bit SNR vs Time Iterative Algorithm improves its Average Bit SNR by the time.
    26. 26. Conclusion  OFDMA  Broadband  Wireless Access Resource Allocation  Channel Information  QoS Requirement  Optimal Algorithms  complex  Iterative Algorithms

    ×