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In this thesis, we investigate the problem of efficient data detection in large MIMO and high order MU-MIMO systems. First, near-optimal low-complexity detection algorithms are proposed for regular MIMO systems. Then, a family of…
In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to…
This work examines the use of two-way training in multiple-input multiple-output (MIMO) wireless systems to discriminate the channel estimation performances between a legitimate receiver (LR) and an unauthorized receiver (UR). This thesis…
In this paper, we introduce a novel algorithm that can dramatically reduce the number of antenna elements needed to accurately predict the direction of arrival (DOA) for multiple input multiple output (MIMO) radar. The new proposed…
In this paper, we propose a learning-based detection framework for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog-to-digital converters. The learning-based detection only requires counting the…
This paper investigates parametric direction-of-arrival (DOA) estimation in a particular context: i) each sensor is characterized by an unknown complex gain and ii) the array consists of a collection of subarrays which are substantially…
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed…
The recently emerged symbol-level precoding (SLP) technique has been regarded as a promising solution in multi-user wireless communication systems, since it can convert harmful multi-user interference (MUI) into beneficial signals for…
This paper presents a mutual coupling based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the…
We consider a wireless network comprising multiple singleantenna repeaters that amplify and instantaneously re-transmit received signals in a full-duplex (FD) communication setting. Specifically, we study a massive multiple-input multiple…
This paper is concerned with the performance of differential amplify-and-forward (D-AF) relaying for multi-node wireless communications over time-varying Rayleigh fading channels. A first-order auto-regressive model is utilized to…
This paper investigates downlink channel estimation in frequency-division duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems. To reduce the overhead of downlink channel estimation and uplink feedback in FDD systems,…
Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of…
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have been treated as two stand-alone components, which makes it…
Rapid detection and follow-up of electromagnetic (EM) counterparts to gravitational wave (GW) signals from binary neutron star (BNS) mergers are essential for constraining source properties and probing the physics of relativistic…
The family of constant-modulus algorithms is widely used in wireless communication systems and in radar. The classical constant-modulus adaptive (CMA) algorithm, however, fails to lock onto a single mode when used in conjunction with an…
Hybrid precoding has been recognized as a promising technology to combat the path loss of millimeter wave signals in massive multiple-input multiple-output (MIMO) systems. However, due to the joint optimization of the digital and analog…
Massive deployment of low data rate Internet of things and ehealth devices prompts us to develop more practical precoding and user selection techniques that comply with these requirements. Moreover, it is known that when the data is…
LoRa is a popular low-power wide-area network (LPWAN) technology that uses spread-spectrum to achieve long-range connectivity and resilience to noise and interference. For energy efficiency reasons, LoRa adopts a pure ALOHA access scheme,…
Deep neural networks (NNs) are considered a powerful tool for balancing the performance and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate feature extraction, high parallelism, and excellent inference…