7 research outputs found
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
Beamforming design for intelligent reflecting surface (IRS)-assisted
multi-user communication (IRS-MUC) systems critically depends on the
acquisition of accurate channel state information (CSI). However, channel
estimation (CE) in IRS-MUC systems causes a large signaling overhead for
training due to the large number of IRS elements. In this paper, taking into
account user mobility, we adopt a deep learning (DL) approach to implicitly
learn the historical line-of-sight (LoS) channel features and predict the IRS
phase shifts to be adopted for the next time slot for maximization of the
weighted sum-rate (WSR) of the IRS-MUC system. With the proposed predictive
approach, we can avoid full-scale CSI estimation and facilitate low-dimensional
CE for transmit beamforming design such that the signaling overhead is reduced
by a scale of , where is the number of IRS elements. To this
end, we first develop a universal DL-based predictive beamforming (DLPB)
framework featuring a two-stage predictive-instantaneous beamforming mechanism.
As a realization of the developed framework, a location-aware convolutional
long short-term memory (CLSTM) graph neural network (GNN) is developed to
facilitate effective predictive beamforming at the IRS, where a CLSTM module is
first adopted to exploit the spatial and temporal features of the considered
channels and a GNN is then applied to empower the designed neural network with
high scalability and generalizability. Furthermore, in the second stage, based
on the predicted IRS phase shifts, an instantaneous CSI-aware fully-connected
neural network is designed to optimize the transmit beamforming at the access
point. Simulation results demonstrate that the proposed framework not only
achieves a better WSR performance and requires a lower CE overhead compared
with state-of-the-art benchmarks, but also is highly scalable in the numbers of
users.Comment: 30 pages, 14 figures, journal pape
Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems with Limited Feedback Using ESPAR Antennas
We consider an opportunistic cognitive radio (CR) system consisting of a
primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx),
where SUtx is equipped with an electrically steerable parasitic array radiator
(ESPAR) antenna with the capability of choosing one beam among M beams for
sensing and communication, and there is a limited feedback channel from SUrx to
SUtx. Taking a holistic approach, we develop a framework for integrated
sector-based spectrum sensing and sector-based data communication. Upon sensing
the channel busy, SUtx determines the beam corresponding to PU's orientation.
Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected
beam corresponding to the strongest channel between SUtx and SUrx. We formulate
a constrained optimization problem, where SUtx-SUrx link ergodic capacity is
maximized, subject to average transmit and interference power constraints, and
the optimization variables are sensing duration, thresholds of channel
quantizer at SUrx, and transmit power levels at SUtx. Since this problem is
non-convex we develop a suboptimal computationally efficient iterative
algorithm to find the solution. Our results demonstrate that our CR system
yields a significantly higher capacity, and lower outage and symbol error
probabilities, compared with a CR system that its SUtx has an omni-directional
antenna.Comment: This paper has been submitted to IEEE Transactions on Cognitive
Communications and Networkin
Reliable and Efficient Cognitive Radio Communications Using Directional Antennas
Cognitive Radio (CR) is a promising solution that enhances spectrum utilization by allowing an unlicensed or Secondary User (SU) to access licensed bands in a such way that its imposed interference on a license holder Primary User (PU) is limited, and hence fills the spectrum holes in time and/or frequency domains. Resource allocation, which involves scheduling of available time and transmit power, represents a crucial problem for the performance evaluation of CR systems. In this dissertation, we study the spectral efficiency maximization problem in an opportunistic CR system. Specifically, in the first part of the dissertation, we consider an opportunistic CR system where the SU transmitter (SUtx) is equipped to a Reconfigurable Antenna (RA). RA, with the capabilities of dynamically modifying their characteristics can improve the spectral efficiency, via beam steering and utilizing the spectrum white spaces in spatial (angular) domain. In our opportunistic CR system, SUtx relies on the beam steering capability of RA to detect the direction of PU\u27s activity and also to select the strongest beam for data transmission to SU receiver (SUrx). We study the combined effects of spectrum sensing error and channel training error as well as the beam detection error and beam selection error on the achievable rates of an opportunistic CR system with a RA at SUtx. We also find the best duration for spectrum sensing and channel training as well as the best transmit power at SUtx such that the throughput of our CR system is maximized subject to the Average Transmit Power Constraint (ATPC) and Average Interference Constraint (AIC). In the second part of the dissertation, we consider an opportunistic Energy Harvesting (EH)-enabled CR network, consisting of multiple SUs and an Access Point (AP), that can access a wideband spectrum licensed to a primary network. Assuming that each SU is equipped with a finite size rechargeable battery, we study how the achievable sum-rate of SUs is impacted by the combined effects of spectrum sensing error and imperfect Channel State Information (CSI) of SUs–AP links. We also design an energy management strategy that maximizes the achievable sum-rate of SUs, subject to a constraint on the average interference that SUs can impose on the PU