In this paper, we introduce a new symbol detection technique for large-scale multi-input
multi-output (MIMO) systems. Based on the observation that detection errors produced by …

As a sampling paradigm to recover the sparse or compressible signals from very few
incoherent linear measurements, compressed sensing (CS) has spurred much interest in …

Ultra-reliable and low-latency communication (URLLC) has been recognized as a key
service to support delay-sensitive applications for next generation wireless systems. A …

Consider the compressed sensing setup where the support of an-sparse-dimensional signal
is to be recovered from linear measurements with a given algorithm. Suppose that the …

Recently, greedy algorithm has received much attention as a cost-effective means to
reconstruct the sparse signals from compressed measurements. Much of previous work has …

In this paper, we consider a detection problem of the underdetermined system when the
input vector is sparse and its elements are chosen from a set of finite alphabets. This …

Reliable, fast, and deterministic communications are fundamental for future industrial
wireless systems. This goal requires multiple cooperating technologies that pertain to …

A sparse superimposed vector transmission (SSVT) scheme is proposed for short-packet
ultra-reliable and low-latency communications (URLLC) in high-mobility scenarios. Unlike …

For proper matrix ensembles, it has been known that the greedy pursuit (GP) algorithms are
computationally efficient and fast to reconstruct sparse signals from far fewer linear …

In this paper, we integrate some ideas of sparse autoencoder of deep learning into
compressed sensing (CS) theory, and set up a sparse autoencoder compressed sensing …