Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without a good proposal distribution can perform poorly, in particular in high dimensions. We propose nested sequential Monte Carlo, a methodology that generalizes the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. 

The paper derives the stability bound of the initial mean-square deviation of an adaptive filtering algorithm based on minimizing the 2 L th moment of the estimation error, with L being an integer greater than 1. The analysis is done for a time-invariant plant with even input probability density function. Dependence of the stability bound on the algorithm step-size, type of the noise distribution, signal-to-noise ratio (SNR), and L is studied.

Although massive multiple-input multiple-output (MIMO) promises high spectral efficiency, there are several issues that significantly limit the potential gain of massive MIMO, such as severe inter-cell interference, huge channel state information (CSI) overhead/delay, high cost and power consumption of RF chains, and user fairness. 

Much effort has been devoted to recovering sparse signals from one-bit measurements in recent years. However, it is still quite challenging to recover signals with high fidelity, which is desired in practical one-bit compressive sensing (1-bit CS) applications. We introduce the notion of Schur-concavity in this paper and propose to construct signals by taking advantage of Schur-Concave functions , which are capable of enhancing sparsity.