11 research outputs found
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
Cell-free massive MIMO is emerging as a promising technology for future
wireless communication systems, which is expected to offer uniform coverage and
high spectral efficiency compared to classical cellular systems. We study in
this paper how cell-free massive MIMO can support federated edge learning.
Taking advantage of the additive nature of the wireless multiple access
channel, over-the-air computation is exploited, where the clients send their
local updates simultaneously over the same communication resource. This
approach, known as over-the-air federated learning (OTA-FL), is proven to
alleviate the communication overhead of federated learning over wireless
networks. Considering channel correlation and only imperfect channel state
information available at the central server, we propose a practical
implementation of OTA-FL over cell-free massive MIMO. The convergence of the
proposed implementation is studied analytically and experimentally, confirming
the benefits of cell-free massive MIMO for OTA-FL
Optimal Linear Precoding for Indoor Visible Light Communication System
Visible light communication (VLC) is an emerging technique that uses
light-emitting diodes (LED) to combine communication and illumination. It is
considered as a promising scheme for indoor wireless communication that can be
deployed at reduced costs while offering high data rate performance. In this
paper, we focus on the design of the downlink of a multi-user VLC system.
Inherent to multi-user systems is the interference caused by the broadcast
nature of the medium. Linear precoding based schemes are among the most popular
solutions that have recently been proposed to mitigate inter-user interference.
This paper focuses on the design of the optimal linear precoding scheme that
solves the max-min signal-to-interference-plus-noise ratio (SINR) problem. The
performance of the proposed precoding scheme is studied under different working
conditions and compared with the classical zero-forcing precoding. Simulations
have been provided to illustrate the high gain of the proposed scheme.Comment: 5 pages, 4 figures, accepted for publication in ICC proceedings 201
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
Deep learning has been widely adopted for channel state information
(CSI)-fingerprinting indoor localization systems. These systems usually consist
of two main parts, i.e., a positioning network that learns the mapping from
high-dimensional CSI to physical locations and a tracking system that utilizes
historical CSI to reduce the positioning error. This paper presents a new
localization system with high accuracy and generality. On the one hand, the
receptive field of the existing convolutional neural network (CNN)-based
positioning networks is limited, restricting their performance as useful
information in CSI is not explored thoroughly. As a solution, we propose a
novel attention-augmented residual CNN to utilize the local information and
global context in CSI exhaustively. On the other hand, considering the
generality of a tracking system, we decouple the tracking system from the CSI
environments so that one tracking system for all environments becomes possible.
Specifically, we remodel the tracking problem as a denoising task and solve it
with deep trajectory prior. Furthermore, we investigate how the precision
difference of inertial measurement units will adversely affect the tracking
performance and adopt plug-and-play to solve the precision difference problem.
Experiments show the superiority of our methods over existing approaches in
performance and generality improvement.Comment: 32 pages, Added references in section 2,3; Added explanations for
some academic terms; Corrected typos; Added experiments in section 5,
previous results unchanged; is under review for possible publicatio
Max-Min SINR in Large-Scale Single-Cell MU-MIMO: Asymptotic Analysis and Low Complexity Transceivers
This work focuses on the downlink and uplink of large-scale single-cell
MU-MIMO systems in which the base station (BS) endowed with antennas
communicates with single-antenna user equipments (UEs). Particularly, we
aim at reducing the complexity of the linear precoder and receiver that
maximize the minimum signal-to-interference-plus-noise ratio subject to a given
power constraint. To this end, we consider the asymptotic regime in which
and grow large with a given ratio. Tools from random matrix theory (RMT)
are then used to compute, in closed form, accurate approximations for the
parameters of the optimal precoder and receiver, when imperfect channel state
information (modeled by the generic Gauss-Markov formulation form) is available
at the BS. The asymptotic analysis allows us to derive the asymptotically
optimal linear precoder and receiver that are characterized by a lower
complexity (due to the dependence on the large scale components of the channel)
and, possibly, by a better resilience to imperfect channel state information.
However, the implementation of both is still challenging as it requires fast
inversions of large matrices in every coherence period. To overcome this issue,
we apply the truncated polynomial expansion (TPE) technique to the precoding
and receiving vector of each UE and make use of RMT to determine the optimal
weighting coefficients on a per-UE basis that asymptotically solve the max-min
SINR problem. Numerical results are used to validate the asymptotic analysis in
the finite system regime and to show that the proposed TPE transceivers
efficiently mimic the optimal ones, while requiring much lower computational
complexity.Comment: 13 pages, 4 figures, submitted to IEEE Transactions on Signal
Processin
Over-The-Air Federated Learning under Byzantine Attacks
Federated learning (FL) is a promising solution to enable many AI
applications, where sensitive datasets from distributed clients are needed for
collaboratively training a global model. FL allows the clients to participate
in the training phase, governed by a central server, without sharing their
local data. One of the main challenges of FL is the communication overhead,
where the model updates of the participating clients are sent to the central
server at each global training round. Over-the-air computation (AirComp) has
been recently proposed to alleviate the communication bottleneck where the
model updates are sent simultaneously over the multiple-access channel.
However, simple averaging of the model updates via AirComp makes the learning
process vulnerable to random or intended modifications of the local model
updates of some Byzantine clients. In this paper, we propose a transmission and
aggregation framework to reduce the effect of such attacks while preserving the
benefits of AirComp for FL. For the proposed robust approach, the central
server divides the participating clients randomly into groups and allocates a
transmission time slot for each group. The updates of the different groups are
then aggregated using a robust aggregation technique. We extend our approach to
handle the case of non-i.i.d. local data, where a resampling step is added
before robust aggregation. We analyze the convergence of the proposed approach
for both cases of i.i.d. and non-i.i.d. data and demonstrate that the proposed
algorithm converges at a linear rate to a neighborhood of the optimal solution.
Experiments on real datasets are provided to confirm the robustness of the
proposed approach.Comment: arXiv admin note: substantial text overlap with arXiv:2111.0122
Max-min SINR low complexity transceiver design for single cell massive MIMO
International audience—In this paper, we consider a uniform circular array (UCA) of directional antennas at the base station (BS) and the mobile station (MS) and derive an exact closed-form expression for the spatial correlation present in the 3D multiple-input multiple-output (MIMO) channel constituted by these arrays. The underlying method leverages the mathematical convenience of the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre polynomials. In contrast to the existing results, this generalized closed-form expression is independent of the form of the underlying angular distributions and antenna patterns. Moreover, the incorporation of the elevation dimension into the antenna pattern and channel model renders the proposed expression extremely useful for the performance evaluation of 3D MIMO systems in the future. The simulation results not only verify the derived analytical expression but also highlight the dependence of the spatial correlation on channel and array parameters. An interesting interplay between the mean angle of departure (AoD), angular spread and the positioning of antennas in the array is demonstrated
Polynomial expansion of the precoder for power minimization in large-scale MIMO systems
International audience—This work focuses on the downlink of a single-cell large-scale MIMO system in which the base station equipped with M antennas serves K single-antenna users. In particular, we are interested in reducing the implementation complexity of the optimal linear precoder (OLP) that minimizes the total power consumption while ensuring target user rates. As most precoding schemes, a major difficulty towards the implementation of OLP is that it requires fast inversions of large matrices at every new channel realizations. To overcome this issue, we aim at designing a linear precoding scheme providing the same performance of OLP but with lower complexity. This is achieved by applying the truncated polynomial expansion (TPE) concept on a per-user basis. To get a further leap in complexity reduction and allow for closed-form expressions of the per-user weighting coefficients, we resort to the asymptotic regime in which M and K grow large with a bounded ratio. Numerical results are used to show that the proposed TPE precoding scheme achieves the same performance of OLP with a significantly lower implementation complexity
Power Efficient Low Complexity Precoding for Massive MIMO Systems
International audienceThis work aims at designing a low-complexity precoding technique in the downlink of a large-scale multiple-input multiple-output (MIMO) system in which the base station (BS) is equipped with M antennas to serve K single-antenna user equipments. This is motivated by the high computational complexity required by the widely used zero-forcing or regularized zero-forcing precoding techniques, especially when K grows large. To reduce the computational burden, we adopt a precoding technique based on truncated polynomial expansion (TPE) and make use of the asymptotic analysis to compute the deterministic equivalents of its corresponding signal-to-interference-plus-noise ratios (SINRs) and transmit power. The asymptotic analysis is conducted in the regime in which M and K tend to infinity with the same pace under the assumption that imperfect channel state information is available at the BS. The results are then used to compute the TPE weights that minimize the asymptotic transmit power while meeting a set of target SINR constraints. Numerical simulations are used to validate the theoretical analysis