Author Search Result

[Author] Baisheng DU(2hit)

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  • Unitary Precoder Design for Multi-Stream MIMO Multicasting

    Baisheng DU  Xiaodong XU  Xuchu DAI  

     
    PAPER-Wireless Communication Technologies

      Vol:
    E97-B No:11
      Page(s):
    2459-2468

    In this paper, we investigate unitary precoder design for multiple-input multiple-output (MIMO) multicasting, where multiple common data streams are sent to a group of users. Assuming that zero-forcing decision feedback equalizers (ZF-DFE) are adopted at the receiver side, we can convert the multicast channel into multiple parallel subchannels. To improve the receiving quality of all data streams, we focus on maximizing the minimal signal-to-noise ratio (SNR) of all data streams. To effectively handle this non-convex optimization problem, we first consider the special case of two data streams and derive the closed-form solution of the SNR vectors for both subchannels. Based on these results, a gradient-based iterative algorithm is developed for the proposed precoder design. For the general case, a Givens rotation-based iterative algorithm is proposed, where at each iteration the original problem of unitary precoder design is transformed into a dual-stream subproblem. Hence it can be solved efficiently by the gradient-based iterative algorithm. Finally, simulation results are presented to demonstrate the outstanding performance of the proposed design.

  • FIR Precoder Optimization Design for Broadband MIMO Multicast System

    Zilong ZHANG  Baisheng DU  Xiaodong XU  

     
    PAPER-Wireless Communication Technologies

      Vol:
    E97-B No:3
      Page(s):
    655-665

    Broadband wireless channels are frequency selective in nature. In this paper, a novel precoder with finite impulse response (FIR) structure is proposed to maximize the throughput of the multiple-input multiple-output (MIMO) frequency-selective multicast channel. An iteration mechanism is investigated to obtain the desired FIR precoding matrix. In the iterative process, two associated parameters, namely the innovation orientation and the iteration step size, are jointly derived by the convex optimization program and the traditional Gauss-Newton algorithm. Convergence and complexity analyses are presented, and the numerical simulations indicate that the proposed method outperforms the existing schemes in the moderate to high signal to noise ratio (SNR) regime.

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