We introduce a novel soft-decision decoding algorithm for binary block codes named ordered reliability direct error pattern testing (ORDEPT). The proposed technique tests a list of partial error patterns (PEP)s that are arranged according to their logistic weight and completed on-the-fly based on the instantaneous received sequence and the code’s parity-check matrix. Our results, obtained for a variety of popular short high-rate codes, demonstrate that ORDEPT outperforms state-of-the-art decoding algorithms such as ordered reliability bits guessing random additive noise decoding (ORBGRAND) in terms of decoding complexity, and is hardware-favorable in terms of latency and energy consumption. The improvements carry on to the iterative decoding of product codes and convolutional product-like codes, where ORDEPT demonstrates the ability to efficiently find multiple candidate codewords and outperform state-of-the art competitors.

In this work we introduce a novel soft-decision decoding algorithm for high-rate short error-correction codes. Our results demonstrate that the proposed algorithm improves over the state-of-the-art algorithms, including recently proposed Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND), in terms of latency and decoding complexity. Specifically, we demonstrate that the ability of the proposed algorithm to find multiple candidate codewords with a reduced number of low-complexity queries makes it an efficient component decoder for iterative decoding of product-like codes.

This article addresses the problem of detecting crisis‐related messages on social media, in order to improve the situational awareness of emergency services. Previous work focused on developing machine‐learning classifiers restricted to specific disasters, such as storms or wildfires. We investigate for the first time methods to detect such messages where the type of the crisis is not known in advance, that is, the data are highly heterogeneous. Data heterogeneity causes significant difficulties for learning algorithms to generalize and accurately label incoming data. Our main contributions are as follows. First, we evaluate the extent of this problem in the context of disaster management, finding that the performance of traditional learners drops by up to 40% when trained and tested on heterogeneous data vis‐á‐vis homogeneous data. Then, in order to overcome data heterogeneity, we propose a new ensemble learning method, and found this to perform on a par with the Gradient Boosting and AdaBoost ensemble learners. The methods are studied on a benchmark data set comprising 26 disaster events and four classification problems: detection of relevant messages, informative messages, eyewitness reports, and topical classification of messages. Finally, in a case study, we evaluate the proposed methods on a real‐world data set to assess its practical value.

We introduce a low-complexity decoder with near-optimal performance for transmission over multi-antenna systems. By using lattice basis reduction for generating almost orthogonal channel matrix, we enhance the conditional decoding technique to implement a fast yet efficient decoder. The lattice-reduction-aided conditional decoder is presented as a general detection technique over fading channels to yield significant savings in complexity while achieving close to Maximum Likelihood (ML) performance. We specifically apply the decoder for two practical schemes, uncoded multi-input multi-output (MIMO) systems and the Golden code. By employing the orthogonality defect factor as a universal measure to select a near-orthogonal channel submatrix for conditional decoding, we implement an almost optimal decoder with linear complexity over quasi-static channels for the Golden code and the four-by-four uncoded MIMO system.

We introduce a low-complexity detector with near-optimal performance for transmission over multi-antenna systems. By using lattice basis reduction for generating almost orthogonal channel submatrices, we enhance the conditional optimization technique to implement a fast yet efficient detector. The lattice-reduction-aided (LRA) conditional method is presented as a general detection technique over fading channels to yield significant saving in computational complexity while achieving close to Maximum Likelihood (ML) error performance. By employing the orthogonality defect factor as a universal measure to select a near-orthogonal channel submatrix for conditional detection, we implement efficient detectors for MIMO systems. In particular, an almost optimal decoder with linear complexity for the Golden code is presented over quasi-static channels.

The impact of time asynchronism on the performance of compute-and-forward strategy in relay networks is considered. While the key idea in compute-and-forward is to decode a linear synchronous combination of the transmitted codewords, the relays receive random asynchronous versions of the combinations due to the distributed nature of the network. Over a symbol-asynchronous network, we show that by using a simple equalizer as a part of the signaling scheme, one can transform the asynchronous system into a synchronous one albeit with a smaller channel gain. With frame-asynchronism, we propose to use extra antennas at the relays to efficiently remove the asynchronous delays. By applying a linear filter with a delay-dependent structure before the decoder of compute-and-forward, the achievable rate is maximized at all signal-to-noise-ratios (SNRs).

A constant K-user interference channel in which the users are not symbol-synchronous is considered. It is shown that the asynchronism among the users facilitates aligning interfering signals at each receiver node while it does not affect the total number of degrees of freedom (DoF) of the channel. To achieve the total K/2 DoF of the channel when single antenna nodes are used, a novel interference alignment scheme is proposed wherein the alignment task is performed with the help of asynchronous delays which inherently exist among the received signals at each receiver node. When each node is equipped with M >; 1 antennas, it is argued that the same alignment scheme is sufficient to achieve the total MK/2 DoF of the medium when all links between collocated antennas experience the same asynchronous delay. The proposed alignment scheme is also extended to achieve the total DoF of X networks at the presence of the asynchronism among the nodes.

A symbol-asynchronous X network with time-invariant channel coefficients and single antenna nodes is considered. By employing the asynchronism in the design of the interference alignment, we achieve the upper bound for the total number of degrees of freedom of this network which is argued to be the same as that of the synchronous case. The asynchronism, which exists among the received signals at each receiver, results in inter-symbol-interference (ISI) among the transmitted symbols from different transmitters. This can provide the channel variation required for the vector alignment over the constant network with single antenna nodes.

The impact of symbol-asynchronism on compute-and-forward strategy in relay networks is considered. The lattice-based coding and decoding scheme presented recently by Nazer and Gastpar is applied over asynchronous distributed networks. While the key idea in compute-and-forward is to decode a linear combination of the transmitted codewords, due to distributed nature of the network, the relays receive random asynchronous versions of the combinations. It is shown that by using a simple equalizer as a part of the signaling scheme, one can transform the system to a synchronous one albeit with a smaller channel gain. The effect of asynchronism on the achievable rates is also studied, which implies a loss compared to the synchronous case. However, the resulting gap vanishes as the signal-to-noise-ratio (SNR) increases.

In multiple-input multiple-output (MIMO) systems, lattice reduction significantly improves the performance of approximate detection techniques. Taking advantage of the temporal correlation of a Rayleigh fading channel, low complexity lattice reduction is investigated in this work by adaptively updating the reduced lattice basis. We show that a careful use of previous channel realizations yields a significant saving in complexity with a minimal degradation in performance.