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Grant-free random access is promising in achieving massive connectivity with sporadic transmissions in massive machine type communications (mMTC) for internet of things (IoT) applications, where the hand-shaking between the access point (AP) and users is skipped, leading to high multiple access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still res...

This paper studies the channel estimation for wideband multiple-input multiple-output (MIMO) systems equipped with hybrid analog/digital transceivers operating in the millimeter-wave (mmWave) or terahertz (THz) bands. By exploiting the low-rank property of the concatenated channel matrix of the delay taps, we formulate the channel estimation problem as a low-rank matrix sensing (LRMS) problem and solve it using a low-complexity generalized conditional gradient-alternating minimization (GCG-ALTMIN) algorithm. This LRMS-based solution can accommodate different precoder/combiner and training stru...

We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detect...

Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc. To unleash the full potentials of RIS-aided communications, acquiring accurate channel state information is crucial, which however is very challenging. For RIS-aided multiple-input and multiple-output (MIMO) communications, the existing channel estimation methods have computational complexity growing rapidly with the number of RIS units N (e.g., in the order of N2 or N3) and/or have special requirements on the matri...

This work deals with the combined effect of nonlinear distortions and inter-channel interference in millimeter wave multi-input multi-output (MIMO) communications. Deep neural networks (DNNs) can be used to handle the effect, but they often require a large number of pilot symbols, hindering their applications. With the aim of online training using a relatively small number of pilot symbols, we design a deep neural network (DNN) architecture carefully, which consists of a fully connected linear hidden layer and a non-fully connected nonlinear hidden layer. The linear hidden layer is used to dea...