Research On The Equalzation In Filtered Multitone Modulation Systems

Multi-carrier modulation has been one of the physical layer key technologies of the next generation wideband communication due to its robustness to multi-path and high bandwidth efficiency, etc. The current research in multi-carrier modulation mainly focuses on OFDM (Orthogonal Frequency Division Multiplexing) and FMT (Filtered Multi-tone Modulation). OFDM has simple equalization, whereas the ICI (Inter-channel Interference) results in the performance degradation because the orthogonality is prone to be destroyed by the frequency offset. The essential difference between FMT and OFDM is the FMT's non-overlapping sub-channel characteristics which bring a set of merits such as negligible ICI, good anti-frequency offset performance, no CP (Cyclic Prefix) and virtual carrier. However, the non-overlapping sub-channel characteristics are realized by the non-perfect reconstruction prototype filter, which inevitably introduce ISI (Inter-symbol Interference), and ISI will be future increased especially when FMT is applied in wireless multi-path environment. Thus, the receiver must eliminate ISI by equalization. This dissertation mainly makes a deep research on the equalization in the FMT systems, including subchannel equalization, Turbo iterative equalization and blind equalization.Several characteristics of the wireless fading channel and the system model of the FMT are firstly analyzed. The principle of FMT is introduced according to the filter technology, and the fast algorithm based on IDFT (Inversed Discrete Fourier Transform) and DFT (Discrete Fourier Transform), the efficient structure of poly-phase filter are presented by means of graphic illustration. And then the matrix representation of the FMT is deduced. Moreover, the objects of the FMT equalizer over flat fading channel, frequency selective fading channel and time varying frequency selective fading channel are especially analyzed.A notable feature of FMT system is the small ICI and the ISI in the received signal can be eliminated by independent per subchannel equalization. Therefore, the dissertation then focuses on the subchannel frequency domain equalization algorithms in FMT system. Firstly, four algorithms based on DFE (Decision Feedback Equalization) are analyzed and these ABR (Achievable Bit Rate) and BER (Bit Error Ratio) performance are simulated and compared, the coefficients of the four DFE equalizers are also deduced according to the MMSE (Minimum Mean Square Error) criterion. However, the equalizer coefficients satisfying the MMSE criterion cannot guarantee the maximum SINR (Signal-to-interference-plus-noise Ratio), corresponding the optimal BER or ABR performance since the BER performance is related to the received signal's SINR. According to the above-mentioned analyses, the CS-FMT (Critically Sampled FMT) subchannel linear equalization algorithm based on MMSE criterion is analyzed, and the subchannel linear equalization algorithms based on MSINR (Maximum the Signal-to-interference-plus-noise Ratio) criterion and MSB (Maximum Subchannel Bit-rate) criterion are proposed respectively by deducing the expression of the CS-FMT's SINR and ABR. Simulation results show that the SINR, ABR and BER performance of the equalization algorithms based on MSINR and MSB criterion and these superiority in comparison with those of the linear and DFE equalization algorithm based on MMSE criterion. Moreover, the FLANN (Functional Link Artificial Neural Networks) are introduced into the subchannel equalization in FMT system to combat the nonlinear distortion of the channel, and a novel learning algorithm based on EKF (Extended Kalman Filter) is proposed to train the FLANN networks. Simulation results indicate that the proposed algorithm can simultaneously improve the convergence and steady-state error performance by comparing the performance of BP (Back Propagation) learning algorithm.Making full use of the redundant information from channel coding in the iterative process, turbo equalization has better performance, in which the equalization and decoding are jointly processed. Introducing turbo equalization into FMT system, the dissertation respectively proposes a kind of turbo equalization algorithm for the known channel and the unknown channel. When the channel is known, and the subchannel is flat, the channel fading can be compensated by a one tap per subchannel equalizer, whereas the ISI caused by the prototype filers can be eliminated by the turbo equalizer. When the channel is unknown, a FMT receiver scheme using joint channel estimation and turbo equalization is presented by applying the iterative channel estimation algorithm based on soft information. Simulation results show that the proposed turbo equalization algorithms can yield great improvements in BER performance compared with the DFE equalizer whenever the channel is known or unknown.Blind equalizers do not require a training sequence in the whole communication process. Lastly, the dissertation researches the application of blind equalization algorithm in FMT systems. Constructing a kind of new error function and building a nonlinear function between the step-size factor and the error function, a novel MS-MCMA (modified step-size blind equalization algorithm) blind equalization algorithm is proposed to alleviate the contradiction between convergence speed and steady-state error of the MCMA (Modified Constant Modulus Algorithm). Simulation results show that the proposed algorithm can simultaneously improve convergence speed and reduce the steady-state error. And then, the proposed blind equalization algorithm is used in FMT system. Simulation results show that the subchannel blind equalizer adopting MS-MCMA can perfectly eliminate ISI and the MS-MCMA can simultaneously improve convergence speed by one time and reduce the steady-state error by comparing the performance of the MCMA.