Deterministic equalization and results of a DVB-T multipath equalizer for both 16-QAM and 64-QAM operation

Recent developments in the UK concerning the reception of Digital Terrestrial Television (DTT) have indicated that, as it currently stands, DVB-T receivers may not be sufficient to maintain adequate quality of digital picture information to the consumer. There are many possible reasons why such large errors are being introduced into the system preventing reception failure. It has been suggested that one possibility is that the assumptions concerning the immunity to multipath that Coded Orthogonal Frequency Division Multiplex (COFDM) is expected to have, may not be entirely accurate. Previous research has shown that multipath can indeed have an impact on a DVB-T receiver performance. In the UK, proposals have been made to change the modulation from 64-QAM to 16-QAM to improve the immunity to multipath, but this paper demonstrates that the 16-QAM performance may again not be sufficient. To this end, this paper presents a deterministic approach to equalization such that a 64-QAM receiver with the simple equalizer presented in this paper has the same order of MPEG-2 BER performance as that to a 16-QAM receiver without equalization. Thus, alleviating the requirement in the broadcasters to migrate from 64-QAM to 16-QAM Of course, by adding the equalizer to a 16-QAM receiver then the BER is also further improved and thus creating one more step to satisfying the consumers(1).

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-toaverage power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA’s nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.