The study of sampling signals on graphs, with the goal of building an analog of sampling for
standard signals in the time and spatial domains, has attracted considerable attention …

The graph fractional Fourier transform (GFRFT) has been applied to graph signal processing
and has become an important tool in graph signal processing. However, most of the graph …

Graph sampling set selection, where a subset of nodes are chosen to collect samples to
reconstruct a smooth graph signal, is a fundamental problem in graph signal processing …

Vertex-domain and temporal-domain smoothness of time-varying graph signals are cardinal
properties that can be exploited for effective graph signal reconstruction from limited …

Graph signal processing (GSP) extends classical signal processing methods to analyzing
signals supported over irregular grids represented by graphs. Within the scope of GSP …

In the graph signal processing literature, most methods were developed based on the
assumption of Gaussian noise since it can lead to computationally efficient and …

We propose two new least mean squares (LMS)-based algorithms for adaptive estimation of
graph signals that improve the convergence speed of the LMS algorithm while preserving its …

Graph sampling is a very effective method to deal with scalability issues when analyzing
large-scale graphs. Lots of sampling algorithms have been proposed, and sampling …

Signals typical in the real world have different modes, expressed as vectors, matrices, or
higher-order tensors. In practice, a target signal is commonly assumed to be linear in the …

This paper presents a non-orthogonal multiple access (NOMA) scheme with group detection
for uplink multiple-input multiple-output (MIMO) systems, for which an effective precoder …