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[Author] Xiaofang WU(2hit)

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  • Scalable Connection-Based Time Division Multiple Access Architecture for Wireless Network-on-Chip

    Shijun LIN  Zhaoshan LIU  Jianghong SHI  Xiaofang WU  

     
    BRIEF PAPER-Integrated Electronics

      Vol:
    E97-C No:9
      Page(s):
    918-921

    In this paper, we propose a scalable connection-based time division multiple access architecture for wireless NoC. In this architecture, only one-hop transmission is needed when a packet is transmitted from one wired subnet to another wired subnet, which improves the communication performance and cuts down the energy consumption. Furthermore, by carefully designing the central arbiter, the bandwidth of the wireless channel can be fully used. Simulation results show that compared with the traditional WCube wireless NoC architecture, the proposed architecture can greatly improve the network throughput, and cut down the transmission latency and energy consumption with a reasonable area overhead.

  • Adaptive Predistortion Using Cubic Spline Nonlinearity Based Hammerstein Modeling

    Xiaofang WU  Jianghong SHI  

     
    PAPER-Nonlinear Problems

      Vol:
    E95-A No:2
      Page(s):
    542-549

    In this paper, a new Hammerstein predistorter modeling for power amplifier (PA) linearization is proposed. The key feature of the model is that the cubic splines, instead of conventional high-order polynomials, are utilized as the static nonlinearities due to the fact that the splines are able to represent hard nonlinearities accurately and circumvent the numerical instability problem simultaneously. Furthermore, according to the amplifier's AM/AM and AM/PM characteristics, real-valued cubic spline functions are utilized to compensate the nonlinear distortion of the amplifier and the following finite impulse response (FIR) filters are utilized to eliminate the memory effects of the amplifier. In addition, the identification algorithm of the Hammerstein predistorter is discussed. The predistorter is implemented on the indirect learning architecture, and the separable nonlinear least squares (SNLS) Levenberg-Marquardt algorithm is adopted for the sake that the separation method reduces the dimension of the nonlinear search space and thus greatly simplifies the identification procedure. However, the convergence performance of the iterative SNLS algorithm is sensitive to the initial estimation. Therefore an effective normalization strategy is presented to solve this problem. Simulation experiments were carried out on a single-carrier WCDMA signal. Results show that compared to the conventional polynomial predistorters, the proposed Hammerstein predistorter has a higher linearization performance when the PA is near saturation and has a comparable linearization performance when the PA is mildly nonlinear. Furthermore, the proposed predistorter is numerically more stable in all input back-off cases. The results also demonstrate the validity of the convergence scheme.