Burst-mode electronic dispersion compensation in long reach PONs

Long reach passive optical networks (LR-PONs), which integrate fibre-to-the-home with metro networks, have been the subject of intensive research in recent years and are considered one of the most promising candidates for the next generation of optical access networks. Such systems ideally have reaches greater than 100km and bit rates of at least 10Gb/s per wavelength in the downstream and upstream directions. Due to the limited equipment sharing that is possible in access networks, the laser transmitters in the terminal units, which are usually the most expensive components, must be as cheap as possible. However, the requirement for low cost is generally incompatible with the need for a transmitter chirp characteristic that is optimised for such long reaches at 10Gb/s, and hence dispersion compensation is required. In this thesis electronic dispersion compensation (EDC) techniques are employed to increase the chromatic dispersion tolerance and to enhance the system performance at the expense of moderate additional implementation complexity. In order to use such EDC in LR-PON architectures, a number of challenges associated with the burst-mode nature of the upstream link need to be overcome. In particular, the EDC must be made adaptive from one burst to the next (burst-mode EDC, or BM-EDC) in time scales on the order of tens to hundreds of nanoseconds. Burst-mode operation of EDC has received little attention to date. The main objective of this thesis is to demonstrate the feasibility of such a concept and to identify the key BM-EDC design parameters required for applications in a 10Gb/s burst-mode link. This is achieved through a combination of simulations and transmission experiments utilising off-line data processing. The research shows that burst-to-burst adaptation can in principle be implemented efficiently, opening the possibility of low overhead, adaptive EDC-enabled burst-mode systems.

Equalisation with adaptive time lag

In an adaptive equaliser, the time lag is an important parameter that significantly influences the performance. Only with the optimum time lag that corresponds to the best minimum-mean-square-error (MMSE) performance, can there be best use of the available resources. Many designs, however, choose the time lag either based on preassumption of the channel or simply based on average experience. The relation between the MMSE performance and the time lag is investigated using a new interpretation of the MMSE equaliser, and then a novel adaptive time lag algorithm is proposed based on gradient search. The proposed algorithm can converge to the optimum time lag in the mean and is verified by the numerical simulations provided.

Equalisation with adaptive time lag

In an adaptive equaliser, the time lag is an important parameter that significantly influences the performance. Only with the optimum time lag that corresponds to the best minimum-mean-square-error (MMSE) performance, can there be best use of the available resources. Many designs, however, choose the time lag either based on preassumption of the channel or simply based on average experience. The relation between the MMSE performance and the time lag is investigated using a new interpretation of the MMSE equaliser, and then a novel adaptive time lag algorithm is proposed based on gradient search. The proposed algorithm can converge to the optimum time lag in the mean and is verified by the numerical simulations provided.

Equalisation of time variant multipath channels using amplitude banded LMS algorithms

For the purpose of equalisation of rapidly time variant multipath channels, we derive a novel adaptive algorithm, the amplitude banded LMS (ABLMS); which implements a nonlinear adaptation based on a coefficient matrix. Then we develop the: ABLMS algorithm as the adaptation procedure for a linear transversal equaliser (LTE) and a decision feedback equaliser (DFE) where a parallel adaptation scheme is deployed. Computer simulations demonstrate that with a small increase of computational complexity, the ABLMS based parallel equalisers provide a significant improvement related to the conventional LMS DFE and the LMS LTE in the case of a second order Markov communication channel model.

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.