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—In this paper we focus on compressive sampling (CS) based ultra wideband (UWB) differential detection. We formulate an optimization problem to jointly recover the sparse received UWB signals as well as the differentially encoded data symbol. We utilize an alternating direction method of multipliers (ADMoM) to solve this joint optimization problem. Our(More)
Compressive sampling (CS) based energy detectors are developed for ultra-wideband (UWB) pulse position modulation (PPM), in multipath fading environments so as to reduce the sampling complexity at the receiver side. Due to sub-Nyquist rate sampling, the CS process outputs a compressed version of the received signal such that the original signal can be(More)
—Compressive sampling (CS) based multiple symbol differential detectors are proposed for impulse-radio ultra-wideband signaling, using the principles of generalized likelihood ratio tests. The CS based detectors correspond to two communication scenarios. One, where the signaling is fully synchronized at the receiver and the other, where there exists a(More)
—In this paper, energy detectors are developed for wideband and ultra-wideband (UWB) pulse position modulation (PPM). Exact bit error probability (BEP) formulas are derived under different assumptions about the channel. More specifically, we present an expression for the instantaneous BEP for a specific channel realization, as well as an expression for the(More)
In this paper, compressive sampling (CS) based energy detectors are developed for sparse communication signals, namely, pulse-position modulation (PPM) and frequency shift-keying (FSK) signals so as to reduce the complexity and sampling rate at the receiver. We focus on noncoherent detection, thereby avoiding the channel estimation step. Exact bit error(More)
We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements. We tackle the issue of varying group size via the group-sparse least absolute shrinkage selection operator (LASSO) as well as via latent group LASSO regularizations. We achieve smoothness in the signal via fusion. We develop low-complexity solvers(More)
We propose maximum a posteriori (MAP) based noncoherent differential detector for ultra-wideband (UWB) impulse radio (IR) signals, received at a sub-Nyquist sampling rate. We build our detector for a Laplacian distributed multipath channel, which models sparsity. Our MAP based detector outperforms differential detectors based on other state-of-the-art(More)
In this paper, a method for the estimation of the spatial rainfall distribution over a specified service area from a limited number of path-averaged rainfall measurements is proposed. The aforementioned problem is formulated as a nonnegativity constrained convex optimization problem with priors that influence both sparsity and clustering properties of the(More)