5 research outputs found
FDD massive MIMO channel spatial covariance conversion using projection methods
Knowledge of second-order statistics of channels (e.g. in the form of
covariance matrices) is crucial for the acquisition of downlink channel state
information (CSI) in massive MIMO systems operating in the frequency division
duplexing (FDD) mode. Current MIMO systems usually obtain downlink covariance
information via feedback of the estimated covariance matrix from the user
equipment (UE), but in the massive MIMO regime this approach is infeasible
because of the unacceptably high training overhead. This paper considers
instead the problem of estimating the downlink channel covariance from uplink
measurements. We propose two variants of an algorithm based on projection
methods in an infinite-dimensional Hilbert space that exploit channel
reciprocity properties in the angular domain. The proposed schemes are
evaluated via Monte Carlo simulations, and they are shown to outperform current
state-of-the art solutions in terms of accuracy and complexity, for typical
array geometries and duplex gaps.Comment: Paper accepted on 29/01/2018 for presentation at ICASSP 201
Hybrid data and model driven algorithms for angular power spectrum estimation
We propose two algorithms that use both models and datasets to estimate
angular power spectra from channel covariance matrices in massive MIMO systems.
The first algorithm is an iterative fixed-point method that solves a
hierarchical problem. It uses model knowledge to narrow down candidate angular
power spectra to a set that is consistent with a measured covariance matrix.
Then, from this set, the algorithm selects the angular power spectrum with
minimum distance to its expected value with respect to a Hilbertian metric
learned from data. The second algorithm solves an alternative optimization
problem with a single application of a solver for nonnegative least squares
programs. By fusing information obtained from datasets and models, both
algorithms can outperform existing approaches based on models, and they are
also robust against environmental changes and small datasets.Comment: Paper accepted for presentation at IEEE Globecom 2020 - fixed typo in
Eq. (15
Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey
Joint radar and communication (JRC) has recently attracted substantial
attention. The first reason is that JRC allows individual radar and
communication systems to share spectrum bands and thus improves the spectrum
utilization. The second reason is that JRC enables a single hardware platform,
e.g., an autonomous vehicle or a UAV, to simultaneously perform the
communication function and the radar function. As a result, JRC is able to
improve the efficiency of resources, i.e., spectrum and energy, reduce the
system size, and minimize the system cost. However, there are several
challenges to be solved for the JRC design. In particular, sharing the spectrum
imposes the interference caused by the systems, and sharing the hardware
platform and energy resource complicates the design of the JRC transmitter and
compromises the performance of each function. To address the challenges,
several resource management approaches have been recently proposed, and this
paper presents a comprehensive literature review on resource management for
JRC. First, we give fundamental concepts of JRC, important performance metrics
used in JRC systems, and applications of the JRC systems. Then, we review and
analyze resource management approaches, i.e., spectrum sharing, power
allocation, and interference management, for JRC. In addition, we present
security issues to JRC and provide a discussion of countermeasures to the
security issues. Finally, we highlight important challenges in the JRC design
and discuss future research directions related to JRC
A survey on reconfigurable intelligent surfaces: wireless communication perspective
Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed