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Analysis of hybrid adaptive/non-adaptive multi-user OFDMA systems with imperfect channel knowledge

Analysis of hybrid adaptive/non-adaptive multi-user OFDMA systems with imperfect channel knowledge. Alexander Kühne. Motivation (I). f. OFDMA. Multiple access scheme for future radio systems Offers the possibility to allocate time-frequency resources to different users. t. Adaptive OFDMA:

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Analysis of hybrid adaptive/non-adaptive multi-user OFDMA systems with imperfect channel knowledge

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  1. Analysis of hybrid adaptive/non-adaptive multi-user OFDMA systems with imperfect channel knowledge Alexander Kühne

  2. Motivation (I) f • OFDMA • Multiple access scheme for future radio systems • Offers the possibility to allocate time-frequency resources to different users t • Adaptive OFDMA: • Adaptive subcarrier allocation and modulation • Advantages: • Exploitation of multi-user diversity • Good performance with perfect channel knowledge • Disadvantages: • Instantaneous channel knowledge required at the transmitter • Non-adaptive OFDMA: • Fixed subcarrier allocation and modulation • Advantages: • Exploitation of frequency diversity • No instantaneous channel knowledge at transmitter required • Disadvantages: • No optimal channel exploitation possible 1 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  3. Motivation (I) f • OFDMA • Multiple access scheme for future radio systems • Offers the possibility to allocate time-frequency resources to different users t • Adaptive OFDMA: • Adaptive subcarrier allocation and modulation • Advantages: • Exploitation of multi-user diversity • Good performance with perfect channel knowledge • Disadvantages: • Instantaneous channel knowledge required at the transmitter • Non-adaptive OFDMA: • Fixed subcarrier allocation and modulation • Advantages: • Exploitation of frequency diversity • No instantaneous channel knowledge at transmitter required • Disadvantages: • No optimal channel exploitation possible 1 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  4. Motivation (I) f • OFDMA • Multiple access scheme for future radio systems • Offers the possibility to allocate time-frequency resources to different users t • Adaptive OFDMA: • Adaptive subcarrier allocation and modulation • Advantages: • Exploitation of multi-user diversity • Good performance with perfect channel knowledge • Disadvantages: • Instantaneous channel knowledge required at the transmitter • Non-adaptive OFDMA: • Fixed subcarrier allocation and modulation • Advantages: • Exploitation of frequency diversity • No instantaneous channel knowledge at transmitter required • Disadvantages: • No optimal channel exploitation possible 1 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  5. Motivation (II) • Combine both access schemes to a hybrid OFDMA system f - Resource for non-adaptive transmission - Resource for adaptive transmission Frequency multiplexing t • Problems: User specific imperfect channel knowledge • How to decide which user is served adaptively or non-adaptively? • How to allocate the resources? • How to select the applied modulation schemes? 2 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  6. Outline • Assumptions • Hybrid OFDMA • Problem formulation • SNR threshold problem • User serving problem • Considering overhead • Performance evaluation • Conclusions 3 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  7. Assumptions • System assumptions • Single cell scenario: One BS with nT transmit antennas and U MSs with nR receive antennas each • TDD-OFDMA with N subcarriers • Orthogonal Space Time Block Coding (OSTBC) or Transmit Antenna Selection (TAS) at the transmitter and Maximum Ratio Combining (MRC) at the receiver • Different user demands Du f • Channel model • Resource unit consisting of Q subcarriers in frequency and MT OFDMA symbols in time • Temporally correlated block fading • Resource unit based Channel Quality Information (CQI): Q t MT • Imperfect CQI due to time delays and estimation errors 4 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  8. Problem formulation hybrid OFDMA • Preprocessing: Impairment parameters User demand vector User serving vector SNR threshold vector 5 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  9. Problem formulation hybrid OFDMA • Preprocessing: • Problem formulation: 6 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  10. Problem formulation hybrid OFDMA • Preprocessing: SNR threshold problem • Problem formulation: 6 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  11. Problem formulation hybrid OFDMA • Preprocessing: User serving problem • Problem formulation: 6 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  12. Problem formulation hybrid OFDMA • Preprocessing: User serving problem • Problem formulation: 6 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  13. Two types of adaptive/non-adaptive resource allocation • Non-Adaptive First (NAF) allocation f • First, the resource units of the non-adaptive users are allocated following an round robin approach • Second, the remaining resource units are allocated following the WPFS policy t Non-adaptive user adaptive users 7 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  14. Two types of adaptive/non-adaptive resource allocation • Non-Adaptive First (NAF) allocation f • First, the resource units of the non-adaptive users are allocated following an round robin approach • Second, the remaining resource units are allocated following the WPFS policy • Adaptive First (AF) allocation • First, all resource units are allocated to the adaptive users applying WPFS • Second, the worst of these selected resource units are re-allocated to non-adaptive users t Non-adaptive user adaptive users 7 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  15. SNR threshold problem • Goal: To optimally adjust the modulation scheme SNR thresholds for each possible in dependency of the impairment parameters and the different user demands • Adjustment of the weighting factors applying WPFS to fulfill user demands • Analysis of the distribution of the SNR values of allocated resource units • Derivation of analytical expressions of the average user data rate and bit error rate (BER) using the CQI error models together with SNR distributions • Maximization of the user data rate subject to the target BER using the analytical expressions by adjusting the SNR thresholds 8 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  16. SNR threshold problem • Goal: To optimally adjust the modulation scheme SNR thresholds for each possible in dependency of the impairment parameters and the different user demands • Adjustment of the weighting factors applying WPFS to fulfill user demands • Analysis of the distribution of the SNR values of allocated resource units • Derivation of analytical expressions of the average user data rate and bit error rate (BER) using the CQI error models together with SNR distributions • Maximization of the user data rate subject to the target BER using the analytical expressions by adjusting the SNR thresholds 8 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  17. Adjustment of the weighting applying WPFS • Assumptions: • Each user demands Du resource units with • Users having the same demand are grouped in demand groups with i=1,..,G • Resource units for adaptive users are allocated following WPFS policy: • Question: How to adjust p such that Du is fulfilled for each user? • No direct relation between pu and Du • Different antenna techniques (OSTBC-TAS) and adaptive/non-adaptive resource allocation schemes (NAF-AF) • Solution: 9 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  18. Weighting for WPFS • Example with 10 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  19. Weighting for WPFS • Example with 10 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  20. Maximizing user data rate • For both OSTBC and TAS as well as NAF and AF analytical expressions for the average user data rate and BER of an adaptively served user u can be derived: - SNR thresholds - Number of TX/RX antennas - Channel estimation error variance - User serving vector - Correlation coefficient (time delay) - User demand vector 11 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  21. Maximizing user data rate by means of user-wise SNR threshold optimization • Assumptions: Parameters are known at the BS • Solution: • Non-adaptive users: Problem reduces to a one-dimensional search for the proper modulation scheme • Adaptive users: Lagrange multiplier approach to determine SNR threshold vector 12 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  22. User serving problem • For each possible user serving realization , the maximum achievable data rate of adaptively and non-adaptively served users subject to the target BER are determinable • Problem: Find that which maximizes the system data rate while fulfilling the minimum user data rate requirement for NAF and AF • Assumption: • There are 2U possible user serving realizations 13 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  23. Algorithms • Exhaustive Search (ES) algorithm: • Check all 2U possible solutions • Unpractical for a large number of users • Reduced Complexity (RedCom) algorithm: • Notice: User data rate and BER of adaptively served users do not depend on itself, but on the number of adaptive users in the different demand groups • Not all 2U combinations have to be tested but only the tuples • Example: 14 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  24. Algorithms • Exhaustive Search (ES) algorithm: • Check all 2U possible solutions • Unpractical for a large number of users • Reduced Complexity (RedCom) algorithm: • Notice: User data rate and BER of adaptively served users do not depend on itself, but on the number of adaptive users in the different demand groups • Not all 2U combinations have to be tested but only the tuples • Example: 14 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  25. Algorithms • Exhaustive Search (ES) algorithm: • Check all 2U possible solutions • Unpractical for a large number of users • Reduced Complexity (RedCom) algorithm: • Notice: User data rate and BER of adaptively served users do not depend on itself, but on the number of adaptive users in the different demand groups • Not all 2U combinations have to be tested but only the tuples • Example: 14 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  26. Algorithms • Exhaustive Search (ES) algorithm: • Check all 2U possible solutions • Unpractical for a large number of users • Reduced Complexity (RedCom) algorithm: • Notice: User data rate and BER of adaptively served users do not depend on itself, but on the number of adaptive users in the different demand groups • Not all 2U combinations have to be tested but only the tuples • Example: • for G=U: • for G=1: Further complexity reduction possible exploiting monotonic behavior of data rate with respect to number of adaptive users 14 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  27. Complexity 15 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  28. Considering overhead • So far, no pilot and signaling overhead has been taken into account • To achieve realistic results and for a fair comparison, it is important to incorporate the overhead in the overall system performance • Pilot and signaling overhead effects both downlink and uplink • Introduction of frame structure to identify the amount of required pilot and signaling overhead in hybrid systems • Introduction of an effective data rate taken into account the overhead in both up- and downlink 16 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  29. Considering overhead – Superframe structure LSF – superframe length MT – time frame size in OFDMA symbols TS – ODFMA symbol duration for NAF LSF≥ 1 for AF LSF = 1 17 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  30. Considering overhead – Effective user data rate • For a given user set of one can formulate the effective user data rate of adaptively or non-adaptively served users as a weighted sum of uplink and downlink data rates: • The effective system data rate can be also maximized using the RedCom algorithm 18 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  31. Performance evaluation – Simulation Parameters 19 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  32. Performance evaluation – Neglecting overhead (I) 20 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  33. Performance evaluation – Neglecting overhead (I) 20 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  34. Performance evaluation – Neglecting overhead (I) 20 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  35. Performance evaluation – Neglecting overhead (II) 21 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  36. Performance evaluation – Neglecting overhead (III) 22 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  37. Performance evaluation – Considering overhead (I) 23 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  38. Performance evaluation – Considering overhead (II) 24 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

  39. Conclusions • Analytical expressions for the average user data rate and BER of a hybrid OFDMA system • for two different adaptive/non-adaptive resource allocation schemes NAF and AF • applying OSTBC and TAS in combination with MRC • for different user demands • assuming imperfect CQI • Maximization of system data rate subject to target BER and minimum user data rate by solving the SNR threshold and the user serving problem • Consideration of pilot and signaling overhead • Hybrid OFDMA systems outperform conventional pure adaptive or pure non-adaptive OFDMA systems for increasing user-dependent imperfect CQI even when considering overhead 25 18.03.10 | Alexander Kühne, Technische Universität Darmstadt, Communications Engineering Lab

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