Related papers: Massive MIMO As an Extreme Learning Machine
This paper considers antenna impedance estimation based on training sequences at MIMO receivers. The goal is to firstly leverage extensive resources available in most wireless systems for channel estimation to estimate antenna impedance in…
This paper investigates an uplink multiuser massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which $K$ users with a single-antenna communicate with one base station (BS) with $n_r$…
The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of…
Large-scale multi-user multiple-input multiple-output (MU-MIMO) systems and cloud radio access networks (C-RANs) are considered promising technologies for the fifth generation (5G) of wireless networks. In these technologies, the use of…
Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog…
We consider a fully digital massive multiple-input multiple-output architecture with low-resolution analog-to-digital/digital-to-analog converters (ADCs/DACs) at the base station (BS) and analyze the performance trade-off between the number…
Based on a new equivalent model of quantizer with noisy input recently presented in [23], we propose a new low complexity receiver that takes into account the nonlinear distortion (NLD) generated by Analog to Digital converter (ADC) with…
A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive number of connected devices and…
Accurate channel knowledge is critical in massive multiple-input multiple-output (MIMO), which motivates the use of channel prediction. Machine learning techniques for channel prediction hold much promise, but current schemes are limited in…
Massive connectivity for extra large-scale multi-input multi-output (XL-MIMO) systems is a challenging issue due to the near-field access channels and the prohibitive cost. In this paper, we propose an uplink grant-free massive access…
In this paper, we investigate learning-based maximum likelihood (ML) detection for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). To overcome the significant dependency of…
Multi-user Multiple-Input Multiple-Output (MIMO) offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified…
Spatial filtering based on multiple-input multiple-output (MIMO) processing is a powerful method for jammer mitigation. In principle, a MIMO receiver can null the interference of a single-antenna jammer at the cost of only one degree of…
Low-resolution analog-to-digital converters (ADCs) have emerged as an efficient solution for massive multiple-input multiple-output (MIMO) systems to reap high data rates with reasonable power consumption and hardware complexity. In this…
Due to the implementation bottleneck of training data collection in realistic wireless communications systems, supervised learning-based timing synchronization (TS) is challenged by the incompleteness of training data. To tackle this…
We consider large MIMO systems, where by `{\em large}' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems will be of immense interest because of the very high spectral efficiencies…
The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs…
This paper studies the fundamental problem of learning energy-based model (EBM) in the latent space of the generator model. Learning such prior model typically requires running costly Markov Chain Monte Carlo (MCMC). Instead, we propose to…
We consider a massive MIMO system where the array at the access point reaches a dimension that is much larger than the array in current systems. Transitioning to an extremely large dimension and hence large number of antennas implies a need…
Innovation in the physical layer of communication systems has traditionally been achieved by breaking down the transceivers into sets of processing blocks, each optimized independently based on mathematical models. Conversely, deep learning…