The present invention relates generally to optical communications, and more particularly, K-means clustered polyphase filtering in coherent polarization multiplexing fiber optic systems.
With the ever increasing demand for high-speed data transmissions (40 GB/S and beyond), polarization multiplexing (PolMux) fiber-optical systems with coherent detection have been the focus of constant attention. PolMux systems are able to transmit information bits through not only the amplitude but also the phase of a signal, thanks to the coherent detection techniques. Furthermore, advanced digital signal processing (DSP) technologies can be used to suppress major optical-channel distortions, such as the chromatic dispersion (CD) and polarization-mode dispersion (PMD).
The PolMux system uses high speed ADC to convert the received analog signal into a flow of digital values by sampling the analog signal periodically. We call the rate of sampled digital signals the sampling rate or sampling frequency. In order to facilitate the digital signal processing, the sampling rate is required to be twice or one and half of the symbol rate. However, in practice, the sampling frequency provided by a given ADC is pre-determined and can not be adjusted for different transmitted signals. As such, we need to convert the signal from one sampling frequency to another while changing the information carried over the signal as little as possible, which is called sample rate conversion
The combined up-sampling/down-sampling scheme is the most popular approach for the sample rate conversion, due to its simple structure and satisfying performance. The sample rate conversion is carried out in two stages, namely the up-sampling (expander) and down-sampling (decimator), as shown in
Although the combined up-sampling/down-sampling approach provides a conceptually simple framework for the sample rate conversion, there exist two major challenges against the direct implementations. First, the data rate after the up-sampling stage, i.e., w(n), could be too high to be supported by the hardware. Furthermore, the complexity of the time domain convolution operation increases quickly with the length of h(r). To cope with these two challenges, Polyphase filtering has been proposed as an efficient approach for sample rate conversion. As shown in
In one aspect of the invention, a method for clustered polyphase filtering input data converted from an optical signal converting input data from a serial form into a parallel form, permutating data symbols from the input data to form K clusters, passing the permutated data to an adder and multiplier for each cluster; and adding output of all K multipliers together to form an output.
In an alternative aspect of the invention, a method for clustered polyphase filtering input data converted from an optical signal includes clustering coefficients of a finite impulse response FIR filter into K groups; using a mean of each K group to approximate respective coefficients in the K groups; and reducing a number of multiplications for said FIR filter to K times.
These and other advantages of the invention will be apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying drawings.
The invention is directed to a K-means clustered polyphase filtering approach to deal with high speed sample rate conversion in the PolMux systems with coherent detection. It is much simpler than the conventional Polyphase filtering approach. In particular, we cluster the coefficients of the polyphase filter into K categories, and use the mean of each cluster to approximate the coefficients inside each cluster.
By properly selecting the parameter K, the resulting K-means clustered polyphase filtering approach can significantly reduce the number of distinct coefficients of the polyphase filter and thereby decrease the number of multiplications with little performance loss. For example, as shown in
The graph of
The diagram shown in
In the Clustered Polyphase filter 500, the input data will be first converted into parallel from serial form (501). The the data symbols will be permutated to form K clusters (502), and then pass to the adder (503) and multiplier (504) for each cluster. The output of all K multipliers will be added together (505) to form the final output. Notice, however, that in practice the permutation (502) can be easily achieved by the following functional block, as shown in
The invention K-means clustered polyphase filtering described above provides significant advantages and benefits. The inventive technique clusters the coefficients of the SRC FIR filter into K groups and use the mean of each group to approximate the coefficients in that group. As such, the number of multiplications for this FIR filtering is reduced to K, which could be significantly smaller than the original length of the FIR filter. In practice, the number of K can be tuned to strike a balance between the performance and the complexity. Two critical aspects of the inventive filtering are: 1) the coefficients of the FIR filters are clustered into K groups according to their distances to each other and use the mean of each group to approximate any coefficients in that filter, and 2) when perform the filtering, all variables within same cluster and added up together and then be multiplied with corresponding coefficient (501-505) in
The present invention has been shown and described in what are considered to be the most practical and preferred embodiments. It is anticipated, however, that departures may be made therefrom and that obvious modifications will be implemented by those skilled in the art. It will be appreciated that those skilled in the art will be able to devise numerous arrangements and variations, which although not explicitly shown or described herein, embody the principles of the invention and are within their spirit and scope.