Enforcing Statistical Orthogonality in Massive MIMO Systems via Covariance Shaping
This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipments (UEs) exhibit high spatial […]
Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems
The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However MMW […]
Massive MIMO With Radio Stripes for Indoor Wireless Energy Transfer
Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous Internet of Things (IoT) deployments. In this work we […]
Deep Learning for Massive MIMO Uplink Detectors
Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a […]
Massive Wireless Energy Transfer With Statistical CSI Beamforming
Wireless energy transfer (WET) is a promising solution to enable massive machine-type communications (mMTC) with low-complexity and low-powered wireless devices. Given the […]
Application of Deep Learning to Sphere Decoding for Large MIMO Systems
Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO […]
ADMM-Based Infinity-Norm Detection for Massive MIMO
In this article, we propose a novel data detection algorithm and a corresponding VLSI design for massive multiuser (MU) multiple-input–multiple-output (MIMO) wireless […]
On Approximate Matrix Inversion Methods for Massive MIMO Detectors
Massive multiple-input multiple-output (MIMO) systems have been proposed to meet the user demands in terms of performance and quality of service (QoS). […]
Massive MIMO Detection Techniques
Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next […]