In recent times, artificial intelligence (AI) has gained considerable attention as a highly
promising technology for enhancing the performance of multiple-input multiple-output …

For inverse synthetic aperture radar (ISAR) adopting the sparse stepped frequency chirp
signal (SSFCS), the target echoes are sparse in the fast time domain with unknown phase …

Underwater acoustic (UWA) channels present diverse sparsity, which is challenging for
UWA communications and underwater networked applications. Conventional methods fail to …

In this letter, we present a novel deep-learning-based network for MIMO symbol detection,
referred to as a momentum-induced detection network, MomentNet. Inspired by the …

In wireless networks, the optimization problems generally have complex constraints and are
usually solved via utilizing the traditional optimization methods that have high computational …

In wireless communication, to fully utilize the spectrum and energy efficiency of the system, it
is necessary to obtain the channel state information (CSI) of the link. However, in Frequency …

The traditional centralized baseband processing architecture is faced with the bottlenecks of
high computation complexity and excessive fronthaul communication, especially when the …

Future wireless networks are envisioned to provide ubiquitous sensing services, driving a
substantial demand for multi-dimensional non-convex parameter estimation. This entails …

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition
and reconstruction of sparse or compressible signals. In order to account for the sparsity of …

For direction-of-arrival (DOA) estimation problems in a sparse domain, sparse Bayesian
learning (SBL) is highly favored by researchers owing to its excellent estimation …