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Modeling OFDM Radio Channel Sachin Adlakha EE206A Spring 2001.

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Presentation on theme: "Modeling OFDM Radio Channel Sachin Adlakha EE206A Spring 2001."— Presentation transcript:

1 Modeling OFDM Radio Channel Sachin Adlakha EE206A Spring 2001

2 Modeling Multi carrier Systems  SNR for different Carrier is different.  Adaptive Bit Loading Optimizing Constellation Size and Transmit Power requires knowledge of each sub channel SNR.  Higher Layers like Link layer and MAC Layer require channel state information Adapt Retransmission and Error Control Schemes Adapt Frame Length Defer Transmission Channel State dependent Scheduling Thus Simple model of Loaded Multi Carrier is imperative.

3 Basic Multi Carrier System INPUT BITSTREAM S/PP/S En- Code De- Code OUTPUT BITSTREAM N-Parallel Channels X+

4 Performance Expressions  Assuming each sub carrier to be Quadrature Amplitude Modulated (QAM) we have Here is the constellation size, A is the loss propagation factor, is the channel gain factor and represents transmit and noise power.

5 Single parameter model  To optimize the number of bit on each channel a Lagrange Multiplier based optimization technique is used Also called Water filling Assign bits to each sub channel so that performance is same on each channel  Using this technique and pervious equations we get Here is the average constellation size and is the average power

6 Single parameter model (contd..)  Thus the multi carrier system is completely modeled in terms of a single parameter, which depends on the knowledge of bit assignments and channel gains.  This parameter is independent of SNR, and thus characterize the entire BER-SNR curve.  Any higher layer can now perfectly characterize the current state of the channel through this parameter. Encode Decode X+ INPUT OUTPUT

7 Model Verification SNR (dB) BER

8 Applications  Channel state dependent Scheduling Takes into account channel state The receivers calculate their parameters and send it back to the transmitter. Since this parameter represents the channel state, the transmitter can perform scheduling using this parameter.  Frame Length Adaptation The optimal frame length is dependent on the BER. Since fully specifies the BER, the link layer can perform frame length adaptation.

9 Stochastic Modeling  The previous model was deterministic i.e. given channel gain factors the model compresses the channel state to single parameter.  However this parameter can also be generated as a random variable to simplify simulation of loaded multi carrier systems  The distribution of logarithm of is derived using chi- square distribution for channel gains  This distribution is derived for water filling case only and is approximated for practical bit loading algorithms

10 Mean b av

11 Variance N

12 Statistics for practical Loading Algorithms The distribution for for water filling case is log- normal distribution For practical loading algorithm the mean is augmented to take into account the deviations for practical algorithms The variance however remains approximately same

13 Conclusions A loaded multi carrier system can be regarded as a single carrier one with single flat fading parameter. The overall system performance is governed by this parameter for different SNR. The distribution of this parameter is also obtained allowing easy modeling of multi carrier system for the purpose of simulation This modeling allows for compact interaction between physical layer and higher layer

14 Reference The entire presentation is based on following papers –“Model of Loaded Multi carrier system for Simulation and Channel State Aware Protocols” by Curt Schurgers and Mani B Srivastava. –“Single Parameter Model for Loaded Multi carrier Systems” by Curt Schurgers and Mani B Srivastava. Thanks to Curt Schurgers for providing the graphs used in the presentation

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