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The decoding performance of polar codes strongly depends on the decoding algorithm used, while also the decoder throughput and its latency mainly depend on the decoding algorithm. In this work, we implement the powerful successive cancellation list (SCL) decoder on a GPU and identify the bottlenecks of this algorithm with respect to parallel computing and its difficulties. The inherent serial decoding property of the SCL algorithm naturally limits the achievable speed-up gains on GPUs when compared to CPU implementations.

We study the problem of remote reconstruction of a continuous signal from its multiple corrupted versions. We are interested in the optimal number of samples and their locations for each corrupted signal to minimize the total reconstruction distortion of the remote signal. The correlation among the corrupted signals can be utilized to reduce the sampling rate.

Compressed sensing recovery techniques allow for reconstruction of an unknown sparse vector from an underdetermined system of linear equations. Recently, a lot of attention was drawn to the problem of recovering the sparse vector from quantized CS measurements. Especially interesting is the case, when extreme quantization is enforced that captures only the sign of the measurements. The problem becomes even more difficult if the measurements are corrupted by noise. In this paper we consider \ac{AWGN}.

We address for the first time the question of how networked agents can collaboratively fit a Morozov-regularized linear model when each agent knows a summand of the regression data. This question generalizes previously studied data-splitting scenarios, which require that the data be partitioned among the agents. To answer the question, we introduce a class of network-structured problems, which contains the regularization problem, and by using the Douglas-Rachford splitting algorithm, we develop a distributed algorithm to solve these problems.

In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how the Randomized Block Kaczmarz (RBK) method—a method for solving linear systems—works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case.

In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how the Randomized Block Kaczmarz (RBK) method—a method for solving linear systems—works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case.