Normal priors with unknown variance (NUV) have long been known to promote sparsity and to blend well with parameter learning by expectation maximization (EM). In this paper, we advocate this approach for linear state space models for applications such as the estimation of impulsive signals, the detection of localized events, smoothing with occasional jumps in the state space, and the detection and removal of outliers.The actual computations boil down to multivariate-Gaussian message passing algorithms that are closely related to Kalman smoothing. We give improved tables of Gaussian-message computations from which such algorithms are easily synthesized, and we point out two preferred such algorithms.

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Abstract-Sphere decoding (SD) is a promising means for implementing high-performance data detection in multiple-input multiple-output (MIMO) wireless communication systems. In this paper, we focus on the register transfer level implementation of SD with minimum area-delay product for application in wideband MIMO communication systems, such as IEEE 802.11n, where multiple SD cores need to be instantiated. The basic architectural considerations and the proposed optimizations are explained based on hard-output SD, but are also applicable to soft-output SD. Corresponding VLSI implementation results (for both hard-output and soft-output SD) show an improvement in the area-delay product by almost 50 % compared to that of other SD implementations reported in the literature.

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