639 research outputs found
Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning
Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity
Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System
In this paper, we consider an intelligent reflecting surface (IRS)-aided
cell-free massive multiple-input multiple-output system, where the beamforming
at access points and the phase shifts at IRSs are jointly optimized to maximize
energy efficiency (EE). To solve EE maximization problem, we propose an
iterative optimization algorithm by using quadratic transform and Lagrangian
dual transform to find the optimum beamforming and phase shifts. However, the
proposed algorithm suffers from high computational complexity, which hinders
its application in some practical scenarios. Responding to this, we further
propose a deep learning based approach for joint beamforming and phase shifts
design. Specifically, a two-stage deep neural network is trained offline using
the unsupervised learning manner, which is then deployed online for the
predictions of beamforming and phase shifts. Simulation results show that
compared with the iterative optimization algorithm and the genetic algorithm,
the unsupervised learning based approach has higher EE performance and lower
running time.Comment: 6 pages, 4 figure
Deep learning-based optimization for reconfigurable intelligent surface-assisted communications
Proceedings of: 2022 IEEE Wireless Communications and Networking Conference (WCNC), 10-13 April 2022, Austin, USA.Reconfigurable Intelligent Surfaces (RISs) are an emerging technology in the evolution towards the Sixth Generation (6G) of mobile communications. They are capable of enhancing the overall system performance and extending the coverage of the existing cells. They are built by a large amount of low-cost meta-elements that can be configured by tuning their phase shifts, and hence, the channel response can be constructively combined and forwarded to some specific direction. Many algorithms have been proposed to obtain the optimum phase shifts, generally assuming a single-carrier system and/or a medium-size RIS to constrain the complexity of the optimization process. In this work, we propose a flexible and scalable unsupervised learning model, capable of obtaining the best phase shifts for any scenario. Our proposal is able to handle multi-carrier waveforms and very large-size RIS, considering both continuous and discrete phase shifts. Besides, we also propose the use of clustering to reduce further the complexity while maintaining the performance. A comparison in terms of achievable rate and time execution is provided in order to show the superiority of our proposal against the existing solutions.This work has been funded by the Spanish National projects IRENE-EARTH (PID2020-115323RB-C33 / AEI/ 10.13039/501100011033) and AMATISTA (CDTI IDI20200861)
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
Hybrid beamforming (HBF) and antenna selection are promising techniques for
improving the energy efficiency~(EE) of massive multiple-input
multiple-output~(mMIMO) systems. However, the transmitter architecture may
contain several parameters that need to be optimized, such as the power
allocated to the antennas and the connections between the antennas and the
radio frequency chains. Therefore, finding the optimal transmitter architecture
requires solving a non-convex mixed integer problem in a large search space. In
this paper, we consider the problem of maximizing the EE of fully digital
precoder~(FDP) and hybrid beamforming~(HBF) transmitters. First, we propose an
energy model for different beamforming structures. Then, based on the proposed
energy model, we develop an unsupervised deep learning method to maximize the
EE by designing the transmitter configuration for FDP and HBF. The proposed
deep neural networks can provide different trade-offs between spectral
efficiency and energy consumption while adapting to different numbers of active
users. Finally, to ensure that the proposed method can be implemented in
practice, we investigate the ability of the model to be trained exclusively
using imperfect channel state information~(CSI), both for the input to the deep
learning model and for the calculation of the loss function. Simulation results
show that the proposed solutions can outperform conventional methods in terms
of EE while being trained with imperfect CSI. Furthermore, we show that the
proposed solutions are less complex and more robust to noise than conventional
methods.Comment: This preprint comprises 15 pages and features 15 figures. Copyright
may be transferred without notic
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
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
The propagation of sound in a shallow water environment is characterized by
boundary reflections from the sea surface and sea floor. These reflections
result in multiple (indirect) sound propagation paths, which can degrade the
performance of passive sound source localization methods. This paper proposes
the use of convolutional neural networks (CNNs) for the localization of sources
of broadband acoustic radiated noise (such as motor vessels) in shallow water
multipath environments. It is shown that CNNs operating on cepstrogram and
generalized cross-correlogram inputs are able to more reliably estimate the
instantaneous range and bearing of transiting motor vessels when the source
localization performance of conventional passive ranging methods is degraded.
The ensuing improvement in source localization performance is demonstrated
using real data collected during an at-sea experiment.Comment: 5 pages, 5 figures, Final draft of paper submitted to 2018 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
15-20 April 2018 in Calgary, Alberta, Canada. arXiv admin note: text overlap
with arXiv:1612.0350
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