LTE Uplink Power Control

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  • The Diff between the two modulations techniques, talking about the advantages of the SC-FDMA in the uplink The major advantage of using SC-FDE or SC-FDMA in the uplink is low PAPR ratio of the transmitted signal caused by DFT Precoding done at the transmitter. PAPR is basically the ratio between the peak powers to average power of the transmitted signal. PAPR is very important in relation with the power amplifier efficiency at the transmitter and in the amplifier the maximum efficiency is achieved when it operates at the saturation point. The low PAPR value will allow the amplifier to operate close to the saturation region.
  • T
  • In case of the AWGN BIFDMA and LFDMA are overlapping as both collects same diversity due to no frequency selectivity in the channel. They will experience the same channel. A little variation in the curve are due to the impact of non linearities in the system. In case of Rayleigh fading channel the schemes collects more diversity than AWGN channel due to frequency selectivity.But if we compare both, the BIFDMA collects more diversity than LFDMA as it is comprised of 4 subcarriers per block so it will be less affected by the channel.while in case of LFDMA it comprises of 128 subcarriers per block so its more affected by the channel
  • The coded performance is much better than Uncoded system. The coded system metigate with the channel errors and thus results in the low Bit error rate. In the uncoded system the three schemes curves almost overlapped but they differs on high SNR‘s due to the different diversity acheived by three schemes. In the coded system the Bit Error rates are low due to coding effect. While there is a difference in Bit Error rates in the schemes and the reason is that, IFDMA collects more diversity than BIFDMA, while BIFDMA collects more diversity than LFDMA.
  • Long term average Bit error rate for multiuser system is identical to single user Bit error rate because of no nonlinearities so there will be no out band interference.
  • Due to clipping the average energy of the signal is changed that can effect the BER so we have rescaled the clipped signal to prevent this and to see the major effects of ICI, MAI and DFT and Near Far.
  • The figure shows that as the clipping level is increased the performance becomes worse. This is expected as increasing the clipping level will cause increased non linearities and hence increased power leakage between adjacent sub carriers i.e. ICI. Rescaling ensures that the variation in BER with increased non linearities is only due to ICI and not due to any power variation of clipped signal with respect to the unclipped signal. It is noteworthy here that 10% clipping means clipping about 102 samples and 30% clipping means clipping about 307 samples of the total 1024 frequency samples of SC-FDMA symbol. Moreover the figure above also shows that the BIFDMA performs better than the LFDMA whether we have 10% or 30% clipping. This can be explained as the out of band radiation, caused due to non linear distortions produced by non linear amplifier, is dependent on the envelope of the input signal to the amplifier [15]. The envelope fluctuations of BIFDMA in turn are considerably lower than those of LFDMA when we have no over sampling and no windowing of the signal [6]. Thus these lower envelope fluctuations for BIFDMA may account for the relatively better
  • The Performance of DFT Precoded System is still better even in non linear environment as compared to the Non DFT Precoded System. In this section we analyze the performance of BIFDMA and LFDMA by introducing non linearities using two different clipping levels (10% & 30%). The figure shows the change in BER of BIFDMA and LFDMA at 10 % and 30 % clipping in presence of DFT and Non DFT Precoding. The BER of the Non DFT Precoded System is higher than that of DFT Precoded System due to the fact that the absence of DFT Precoding results in higher envelope fluctuations that would ultimately cause higher out of band interference. Furthermore the BER for BIFDMA is lower as compared to LFDMA as it has comparatively lower envelope fluctuations [6]. Another reason for the low BER for DFT Precoded system is the spreading of each data symbol over multiple subcarriers thus giving frequency diversity as mentioned before. The BER for the two schemes is higher for 30 % clipping
  • The figures above clearly indicate that increasing the number of users would worsen the BER performance for both BIFDMA and LFDMA. Again rescaling makes sure that any variation in the long term BER of multiple users relative to BER of single user is purely due to multi‐access interference. Increasing the number of users in multiuser non linear system should increase the multi‐access interference which in our case holds true but the variation in BER for different number of users and that for single user is not that evident from the figures above. More over the degradation
  • to the base station than the others. In this situation the user that is near to the base station will be receiving higher power than the ones far from it. In figure we have analyzed the near far effect in a system having eight users. In this scenario we have one user that is far away from base station while the rest of others are near to it. The affected user (farther from the BS) has been assigned 5dBs less energy as compared to the rest of seven users while we have introduced non linearities in our system by clipping 30% of the samples in the signal.
  • The performance of turbo code over AWGN channel is much better as compared to the fading channel. Moreover owing to the fact that there is no frequency selectivity in AWGN channel both the BIFDMA and LFDMA collects same frequency diversity and hence BER curves for both almost fall over each other. In case of fading channel however BIFDMA performs better than LFDMA owing to its ability to collect more frequency diversity. Non linearities on the other hand cause slight deviations in the BER curves for the two schemes in both AWGN and fading channel due to out of band interference resulting from non linearities. • In linear environments, the BER performance of single user is identical to the long term BER performance of multiple users because of lack of out of band interference as is the case in non linear environments. IFDMA performs the best in terms of BER which is followed by BIFDMA and then LFDMA. This is because of the ability of IFDMA to achieve more frequency diversity than the other two schemes. • DFT Precoding is mainly intended to keep the PAPR of the signal to a lower level and hence to ensure efficient use of HPA. However DFT Precoded system also shows an improvement in BER performance compared to non DFT Precoded system owing to the frequency diversity gain in frequency selective channels caused by spreading of each data symbol over multiple subcarriers. • BIFDMA and LFDMA perform differently in terms of their BER performances for a single user, in linear environments and in non linear environments. The reason for this difference in BER performance is the ICI that is caused due to the non linearities in the system. The ICI increases as the clipping percentage is increased that result in higher non linearities. This is evident from increased difference in the BER compared to linear case. • In non linear environments, DFT Precoded systems again have better BER performance compared to non DFT Precoded systems. The reason for this is the reduced PAPR caused by DFT Precoding which reduces not only envelope fluctuations but also reduces influence of out of band radiations caused by non linearities. Similarly BIFDMA also performs better compared to LFDMA in non linear environments, again due to lower degrades the BER performance of multiuser system compared to single user system. As the number of users is increased the BER performance worsens due to increased MAI. • In the multi‐user system with non linearities, the near far effect is evident when few users with high power (near to base station) creates interference for the users that are far away from the base
  • The performance of turbo code over AWGN channel is much better as compared to the fading channel. Moreover owing to the fact that there is no frequency selectivity in AWGN channel both the BIFDMA and LFDMA collects same frequency diversity and hence BER curves for both almost fall over each other. In case of fading channel however BIFDMA performs better than LFDMA owing to its ability to collect more frequency diversity. Non linearities on the other hand cause slight deviations in the BER curves for the two schemes in both AWGN and fading channel due to out of band interference resulting from non linearities. • In linear environments, the BER performance of single user is identical to the long term BER performance of multiple users because of lack of out of band interference as is the case in non linear environments. IFDMA performs the best in terms of BER which is followed by BIFDMA and then LFDMA. This is because of the ability of IFDMA to achieve more frequency diversity than the other two schemes. • DFT Precoding is mainly intended to keep the PAPR of the signal to a lower level and hence to ensure efficient use of HPA. However DFT Precoded system also shows an improvement in BER performance compared to non DFT Precoded system owing to the frequency diversity gain in frequency selective channels caused by spreading of each data symbol over multiple subcarriers. • BIFDMA and LFDMA perform differently in terms of their BER performances for a single user, in linear environments and in non linear environments. The reason for this difference in BER performance is the ICI that is caused due to the non linearities in the system. The ICI increases as the clipping percentage is increased that result in higher non linearities. This is evident from increased difference in the BER compared to linear case. • In non linear environments, DFT Precoded systems again have better BER performance compared to non DFT Precoded systems. The reason for this is the reduced PAPR caused by DFT Precoding which reduces not only envelope fluctuations but also reduces influence of out of band radiations caused by non linearities. Similarly BIFDMA also performs better compared to LFDMA in non linear environments, again due to lower degrades the BER performance of multiuser system compared to single user system. As the number of users is increased the BER performance worsens due to increased MAI. • In the multi‐user system with non linearities, the near far effect is evident when few users with high power (near to base station) creates interference for the users that are far away from the base
  • been able to assess the performance of IFDMA in non linear environments. In future the simulations can be carried out in presence of pulse shaping to have more realistic comparisons of the three mapping schemes in linear and non linear environments. Threshold clipping has been used to model the amplifier non linearities in this thesis work which can be replaced by more sophisticated non linearity models like the Rapp model. The impact of carrier frequency offset (CFO) and Doppler can also be included to in the future research work. Finally the channel scenario being used in this thesis work is the WINNER C2 NLOS. It can be an interesting option to use other channel scenarios.
  • LTE Uplink Power Control

    1. 1. Uplink Power Control for IMT Advanced By Mujeeb Ur Rehman (itzmujeeb@gmail.com) Fahad Muslim Examiner & Supervisor: Ass. Prof Tommy Svensson
    2. 2. Contents <ul><li>Introduction </li></ul><ul><li>Background </li></ul><ul><li>The System Model </li></ul><ul><li>Simulations & Results </li></ul><ul><li>Conclusions </li></ul><ul><li>Future Work </li></ul><ul><li>Questions </li></ul>
    3. 3. Introduction <ul><li>Thesis Objectives </li></ul><ul><ul><li>Analyzing the Channel effects on Turbo code </li></ul></ul><ul><ul><li>Analyzing the Subcarrier Mapping schemes performance (Linear and Non Linear Environments) </li></ul></ul><ul><ul><li>Analyzing the DFT Precoded and Non DFT Precoded System Performance (Linear and Non Linear Environments) </li></ul></ul>
    4. 4. Introduction <ul><li>Thesis Objectives (Cont) </li></ul><ul><ul><li>Analyzing the ICI (Inter Carrier Interference) Effect in Non Linear Environment </li></ul></ul><ul><ul><li>Analyzing the MAI (Multi Access Interference) Effect in Multi User Non Linear Environment </li></ul></ul><ul><ul><li>Analyzing the Near Far Effect in Multi User System </li></ul></ul>
    5. 5. Background <ul><li>Evolution of LTE </li></ul><ul><li>LTE to IMT Advanced </li></ul><ul><li>LTE (Long Term Evolution) </li></ul><ul><li>IMT (International Mobile Telecommunication) </li></ul>
    6. 6. The IMT Advanced System Model Channel Encoder (Turbo Encoder) Modulation (QPSK) DFT-Precoding (K-Point DFT) Demodulation (QPSK) Cyclic Prefix DFT-Deprecoding (K-Point IDFT) Sub-Carrier Mapping Remove Cyclic Prefix Channel Decoder (Turbo Decoder) OFDM Modulation (M-Point IFFT) Channel Equalization (MMSE) Sub-Carrier De-Mapping OFDM Modulation (M-Point FFT) Information Bits Decoded Bits Channel (Winner C2-NLOS) Threshold Clipper
    7. 7. System Model (Cont) <ul><li>Turbo Code </li></ul><ul><ul><li>The turbo codes are very efficient for processing longer blocks of data </li></ul></ul><ul><ul><li>They result in long delays </li></ul></ul><ul><ul><li>Major factors that affect the turbo code’s performance. </li></ul></ul><ul><ul><ul><li>Size of interleaver </li></ul></ul></ul><ul><ul><ul><li>Number of iterations </li></ul></ul></ul>
    8. 8. System Model (Cont) <ul><li>MODULATION (QPSK) </li></ul><ul><ul><li>T wo bits are assigned to each symbol </li></ul></ul><ul><ul><li>QPSK symbols have the same energy but have different phases </li></ul></ul>01 00 11 10
    9. 9. System Model (Cont) <ul><li>DFT PRECODING & DE-PRECODING </li></ul><ul><ul><li>In DFT Precoding we take the K-point FFT of the modulated symbols </li></ul></ul><ul><ul><li>DFT De-Precoding K -point IFFTof de-mapped Symbols </li></ul></ul><ul><ul><li>DFT Precoded OFDMA is also called Single Carrier Frequency Division Multiple Access (SC-FDMA) </li></ul></ul><ul><ul><li>Advantages: </li></ul></ul><ul><ul><ul><ul><li>DFT Precoded OFDMA has very small PAPR (Peak to average power ratio) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Low envelope fluctuations of OFDMA signals </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Power efficient transmission in the uplink </li></ul></ul></ul></ul>
    10. 10. System Model (Cont) <ul><li>SUBCARRIER MAPPING </li></ul><ul><ul><li> DFT Precoded symbols are mapped to Sub carriers </li></ul></ul><ul><ul><li>Localized mapping vs Distributed mapping of SC- </li></ul></ul><ul><ul><li>FDMA </li></ul></ul>Localized mapping Distributed mapping
    11. 11. System Model (Cont) <ul><li>SUBCARRIER MAPPING </li></ul><ul><ul><li>Mapping Schemes </li></ul></ul><ul><ul><li>(BIFDMA, IFDMA, LFDMA) </li></ul></ul>
    12. 12. System Model (Cont) <ul><li>OFDM & SC-FDMA </li></ul>OFDM Modulation using many subcarriers SC-FDMA using single carrier in the uplink Time Cyclic Prefix O F D M S Y M B O l O F D M S Y M B O l Frequency Amplitude Subcarrier Bandwidth Frequency Cyclic Prefix Amplitude Subcarrier Bandwidth SC-FDM Sybmol Time
    13. 13. System Model (Cont) <ul><li>Modeling Non Linearities Using Clipping Model </li></ul><ul><ul><li> Nonlinear distortion-clipping or soft limiter (SL) </li></ul></ul><ul><ul><li>Different Clipping % Levels (1%, 10%, 30%) </li></ul></ul>Threshold clipping for non linearities modeling
    14. 14. System Model (Cont) <ul><li>Power Control in Uplink </li></ul><ul><ul><li>Power Consumption </li></ul></ul><ul><ul><li>Inter and Intra Cell interference </li></ul></ul><ul><ul><li> Types of Power control (Channel Variations) </li></ul></ul><ul><ul><ul><ul><li>Slow Power Control </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Fast Power Control </li></ul></ul></ul></ul><ul><ul><li>Types of Power control (Based on Feedback) </li></ul></ul><ul><ul><ul><ul><li>Open Loop Power Control </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Closed Loop Power Control </li></ul></ul></ul></ul>
    15. 15. System Model (Cont) <ul><li>Power Control in Uplink (Cont) </li></ul><ul><ul><li>Types of Power Control (Based Upon Value of ) </li></ul></ul><ul><ul><ul><ul><li>No Path Loss Compensation </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Fractional Path loss Compensation </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Conventional Path loss Compensation </li></ul></ul></ul></ul>
    16. 16. System Model (Cont) <ul><li>Channel (C2-NLOS) </li></ul><ul><ul><li> Frequency Selective </li></ul></ul><ul><ul><li>Equalization (MMSE) </li></ul></ul>Power Delay Profile Time Frequency Response
    17. 17. Simulations & Results <ul><li>Simulations Parameters </li></ul><ul><li>Delimitations </li></ul><ul><li>Turbo Code performance over AWGN & Rayleigh Fading </li></ul><ul><li>Channel </li></ul><ul><li>Uncoded & Coded System Performance for 1, 2, 4, 8 Users </li></ul><ul><li>Performance evaluation of DFT Precoding for </li></ul><ul><li>1, 2, 4, 8 Users </li></ul><ul><li>Modeling Amplifier Non Linearities </li></ul><ul><ul><li>Inter Carrier Interference (ICI) </li></ul></ul><ul><ul><li>Performance Evaluation of DFT and No-DFT in Non Linear </li></ul></ul><ul><ul><li>Environment </li></ul></ul><ul><ul><li>Multi Access Interference in 2, 4 and 8 Users System </li></ul></ul><ul><ul><li>Multi Access Interference (MAI) with Near Far Effect </li></ul></ul>
    18. 18. Simulations Parameters WINNER C2 NLOS User Velocity=50 Km/h Coherence Bandwidth=680.27 KHz Coherence Time=5.8 ms Channel Perfect Channel Estimation MMSE Equalizer Turbo Coding, Code rate ½, [G1,G2]=[11111,10001] Coding QPSK Modulation 12 Chunk Width 4 SCs per Block 128 No of SCs per User (K) 1024 Total Number of SCs (M) 1.47ns Guard Interval 12.5ns Sampling Time 1/12.5ns Sampling Frequency 3.7GHz Carrier Frequency 80MHz Bandwidth
    19. 19. Delimitations <ul><ul><li>The cases with amplifier nonlinearities have been analyzed excluding IFDMA mapping </li></ul></ul><ul><ul><li>We have not used Pulse shaping </li></ul></ul>
    20. 20. Turbo Code performance over AWGN & Rayleigh Fading Channel BIFDMA & LFDMA Performance on AWGN Channel (1% Clipping) BIFDMA & LFDMA Performance on Rayleigh Fading Channel (1% Clipping)
    21. 21. Uncoded & Coded System Performance (Single User) Uncoded Vs Turbo Coded Performance for Single User on Rayleigh Fading Channel
    22. 22. Uncoded & Coded System Performance (2,4,8 Users) 2 Users 4 Users 8 Users
    23. 23. Performance evaluation of DFT Precoding (Single User) Performance evaluation of DFT Precoding for (Single User)
    24. 24. Performance evaluation of DFT Precoding (2,4,8 Users) 2 Users 4 Users 8 Users
    25. 25. Modeling Amplifier Non Linearities <ul><ul><li>Inter Carrier Interference (ICI) </li></ul></ul><ul><ul><li>Performance Evaluation of DFT and No-DFT Precoding </li></ul></ul><ul><ul><li>Multi Access Interference in 2, 4 and 8 Users System </li></ul></ul><ul><ul><li>Multi Access Interference (MAI) with Near Far </li></ul></ul><ul><ul><li>Effect </li></ul></ul><ul><ul><ul><li>Clipped signals have been rescaled to keep the average energy, the same as Unclipped </li></ul></ul></ul><ul><ul><ul><li>Signals. </li></ul></ul></ul>
    26. 26. Inter Carrier Interference (ICI) Inter Carrier Interference (10%, 30% and no Clipping)
    27. 27. Inter Carrier Interference (ICI) <ul><li>Results </li></ul><ul><ul><li>Clipping level is increased the performance becomes </li></ul></ul><ul><ul><li>worse </li></ul></ul><ul><ul><li>Increased power leakage between adjacent subcarriers </li></ul></ul><ul><ul><li>BIFDMA performs better than the LFDMA whether we </li></ul></ul><ul><ul><li>have 10% or 30% clipping </li></ul></ul><ul><ul><li>Envelope fluctuations of BIFDMA in turn are </li></ul></ul><ul><ul><li>considerably lower than those of LFDMA </li></ul></ul>
    28. 28. Performance Evaluation of DFT and No-DFT in Non Linear Environment <ul><ul><li>Performance Evaluation of DFT and No-DFT in </li></ul></ul><ul><ul><li>Non Linear Environment </li></ul></ul>
    29. 29. Performance Evaluation of DFT and No-DFT in Non Linear Environment <ul><li>Results </li></ul><ul><ul><li>The BER of the Non DFT Precoded System is higher </li></ul></ul><ul><ul><li>than that of DFT Precoded System </li></ul></ul><ul><ul><li>Absence of DFT Precoding results in higher envelope </li></ul></ul><ul><ul><li>fluctuations that would ultimately cause higher out of </li></ul></ul><ul><ul><li>band interference </li></ul></ul><ul><ul><li>BER for BIFDMA is lower as compared to LFDMA as it </li></ul></ul><ul><ul><li>has comparatively lower envelope fluctuations </li></ul></ul>
    30. 30. Multi Access Interference in 2, 4 & 8 Users System 2 Users 4 Users 8 Users
    31. 31. Multi Access Interference in 2, 4 & 8 Users System <ul><li>Results </li></ul><ul><ul><li>By increasing number of users, BER is </li></ul></ul><ul><ul><li>increased </li></ul></ul><ul><ul><li>The MAI is increased </li></ul></ul>
    32. 32. Multi Access Interference (MAI) with Near Far Effect Multi Access Interference with Near Far Effect (8 Users)
    33. 33. Multi Access Interference (MAI) with Near Far Effect <ul><li>Results </li></ul><ul><ul><li>The users Near to Base station with high </li></ul></ul><ul><ul><li>power creates interference to user far </li></ul></ul><ul><ul><li>away from Base station </li></ul></ul>
    34. 34. Conclusions <ul><li>The performance of turbo code over AWGN channel is </li></ul><ul><li>much better as compared to the fading channel </li></ul><ul><li>In linear Environments, the BER performance of single user </li></ul><ul><li>is identical to the long term BER performance of multiple </li></ul><ul><li>users </li></ul><ul><li>IFDMA performs the best in terms of BER which is followed </li></ul><ul><li>by BIFDMA and then LFDMA </li></ul><ul><li>DFT Precoded system shows an improvement in BER </li></ul><ul><li>performance compared to No-DFT Precoded system </li></ul>
    35. 35. Conclusions (Cont) <ul><li>BIFDMA and LFDMA perform differently in terms of their </li></ul><ul><li>BER performances for a single user, in linear environments and in </li></ul><ul><li>non linear environments </li></ul><ul><li>The ICI increases as the clipping percentage is increased due </li></ul><ul><li>to higher non linearities </li></ul><ul><li>In non linear environments, DFT Precoded systems again have better BER </li></ul><ul><li>performance compared to non DFT Precoded systems </li></ul><ul><li>As the number of users is increased the BER performance worsens due to </li></ul><ul><li>increased MAI </li></ul><ul><li>The near far effect is evident when few users with high power (near to </li></ul><ul><li>base station) creates interference for the users that are far away from the </li></ul><ul><li>base station </li></ul>
    36. 36. Future Work <ul><li>Simulations can be carried out in presence of pulse </li></ul><ul><li>shaping </li></ul><ul><li>More sophisticated non linearity models like the </li></ul><ul><li>Rapp model can be used </li></ul><ul><li>The impact of carrier frequency offset (CFO) and </li></ul><ul><li>Doppler can also be included </li></ul><ul><li>It can be an interesting option to use other </li></ul><ul><li>channel scenarios </li></ul>
    37. 37. <ul><ul><li>Questions </li></ul></ul>

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