13 accepted papers
In this paper, we present an adaptive reweighted sparse belief propagation (AR-SBP) decoder for polar codes. The AR-SBP technique is inspired by decoders that employ the sum-product algorithm for low-density parity-check codes. In particular, the AR-SBP decoding strategy introduces reweighting of th…
Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for…
In this work, we present a cloud-driven uplink framework for multiway multiple-antenna relay systems which facilitates joint linear Minimum Mean Square Error (MMSE) symbol detection in the cloud and where users are selected to simultaneously transmit to each other aided by relays. We also investigat…
In this paper, we propose a dynamic oversampling technique for asynchronous large-scale multiple-antenna systems with 1-bit analog-to-digital converters at the base station that is suitable for sustainable internet of things and cellular networks. To the best of our knowledge, this is the first pape…
Low-resolution devices are promising for systems that demand low energy consumption and low complexity as required in IoT systems. In this study, we propose a novel waveform for bandlimited channels with 1-bit quantization and oversampling at the receivers. The proposed method implies that the infor…
Large-scale multiple-antenna systems have been identified as a promising technology for the next generation of wireless systems. However, by scaling up the number of receive antennas the energy consumption will also increase. One possible solution is to use low-resolution analog-to-digital converter…
In this work, a novel rank-revealing matrix decomposition algorithm termed Compressed Randomized UTV (CoR-UTV) decomposition along with a CoR-UTV variant aided by the power method technique is proposed. CoR-UTV computes an approximation to a low-rank input matrix by making use of random sampling sch…
This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. This algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent const…
In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by the randomized sampling scheme and sub…
Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless, these methods deal with kernel expan…
In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decom…
In this work, we propose a buffer-aided distributed spacetime coding (DSTC) scheme for cooperative direct-sequence codedivision multiple access systems. We first devise a relay selection algorithm that can automatically select the optimum set of relays among both the source-relay phase and the relay…
We propose a subspace-based Widely Linear (WL) blind channel estimation scheme based on the iterative power method for the WL constrained minimum variance Code Division Multiple Access (CDMA) receiver. The novel technique approximates the noise subspace by using a matrix power and the WL processing…