5.1 Conclusion
We had studies Tomlinson-Harashima precoding optimization problem with partial channel-state-information under a Rayleigh flat fading channel. Kalman estimator is introduced for channel tracking and is combined into THP design. Kalman estimator is on the basis of random process, which has collect up all previous information and performs a robust estimation. Simulation results has shows better results compare to LMMSE-based THP in BER. Also, Kalman-based THP acts flexible as the Doppler frequency changes. From the view of calculating computation complexity, the Kalman-based THP achieve better BER and a close complexity in fast fading channel. With these features, the proposed Kalman-based THP is acceptable for application in the fast fading wireless broadcast channel.
5.2 Future Work
While in our work, we have proposed an effective Kalman-based THP algorithm, which decrease the BER evidently, we have not discuss about the accuracy for designing the autoregressive model, which can further improve the correctness of Kalman estimator.
Also, the complexity of the THP optimization has not been analyzed.
Future works might consider the design of the AR model, i.e., in (2.13), instead of us-ing the traditional AR parameters A and B, which are chosen based on Jakes’ model. In
order to approximate the real channel, the parameters can be further designed by consid-ering the channel information at the transmitter, which can be obtained by uplink training sequences. Also, future works could take account of the reduction of THP optimization.
The complexity of THP optimization can be reduced by slipping the interference mitiga-tion into two parts, which is named “cascaded THP-BD system” in [35], i.e., mitigating multi-user interference using Block Diagonalization (To orthogonalize the channels) and then mitigate inter-stream interference (the effect of multi-antenna interference) using a simpler THP.
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