32 research outputs found
Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning
In this study, we propose a digital over-the-air computation (OAC) scheme for
achieving continuous-valued (analog) aggregation for federated edge learning
(FEEL). We show that the average of a set of real-valued parameters can be
calculated approximately by using the average of the corresponding numerals,
where the numerals are obtained based on a balanced number system. By
exploiting this key property, the proposed scheme encodes the local stochastic
gradients into a set of numerals. Next, it determines the positions of the
activated orthogonal frequency division multiplexing (OFDM) subcarriers by
using the values of the numerals. To eliminate the need for precise
sample-level time synchronization, channel estimation overhead, and channel
inversion, the proposed scheme also uses a non-coherent receiver at the edge
server (ES) and does not utilize a pre-equalization at the edge devices (EDs).
We theoretically analyze the MSE performance of the proposed scheme and the
convergence rate for a non-convex loss function. To improve the test accuracy
of FEEL with the proposed scheme, we introduce the concept of adaptive absolute
maximum (AAM). Our numerical results show that when the proposed scheme is used
with AAM for FEEL, the test accuracy can reach up to 98% for heterogeneous data
distribution.Comment: Accepted for publication in IEEE Transactions on Wireless
Communications. arXiv admin note: substantial text overlap with
arXiv:2209.1100
Wireless Federated -Means Clustering with Non-coherent Over-the-Air Computation
In this study, we propose using an over-the-air computation (OAC) scheme for
the federated k-means clustering algorithm to reduce the per-round
communication latency when it is implemented over a wireless network. The OAC
scheme relies on an encoder exploiting the representation of a number in a
balanced number system and computes the sum of the updates for the federated
k-means via signal superposition property of wireless multiple-access channels
non-coherently to eliminate the need for precise phase and time
synchronization. Also, a reinitialization method for ineffectively used
centroids is proposed to improve the performance of the proposed method for
heterogeneous data distribution. For a customer-location clustering scenario,
we demonstrate the performance of the proposed algorithm and compare it with
the standard k-means clustering. Our results show that the proposed approach
performs similarly to the standard k-means while reducing communication
latency.Comment: This work has been accepted for presentation at IEEE MILCOM 202
Majority Vote Computation With Complementary Sequences for Distributed UAV Guidance
This study introduces a novel non-coherent over-the-air computation (OAC)
scheme aimed at achieving reliable majority vote (MV) calculations in fading
channels. The proposed approach relies on modulating the amplitude of the
elements of complementary sequences (CSs) based on the sign of the parameters
to be aggregated. Notably, our method eliminates the reliance on channel state
information at the nodes, rendering it compatible with time-varying channels.
To demonstrate the efficacy of our approach, we employ it in a scenario where
an unmanned aerial vehicle (UAV) is guided by distributed sensors, relying on
the MV computed using our proposed scheme. The experimental results confirm the
superiority of our approach, as evidenced by a significant reduction in
computation error rates in fading channels, particularly with longer sequence
lengths. Meanwhile, we ensure that the peak-to-mean-envelope power ratio of the
transmitted orthogonal frequency division multiplexing signals remains within
or below 3 dB.Comment: This work has been accepted for presentation at IEEE MILCOM 202
Over-the-Air Computation over Balanced Numerals
In this study, a digital over-the-air computation (OAC) scheme for achieving
continuous-valued gradient aggregation is proposed. It is shown that the
average of a set of real-valued parameters can be calculated approximately by
using the average of the corresponding numerals, where the numerals are
obtained based on a balanced number system. By using this property, the
proposed scheme encodes the local gradients into a set of numerals. It then
determines the positions of the activated orthogonal frequency division
multiplexing (OFDM) subcarriers by using the values of the numerals. To
eliminate the need for a precise sample-level time synchronization, channel
estimation overhead, and power instabilities due to the channel inversion, the
proposed scheme also uses a non-coherent receiver at the edge server (ES) and
does not utilize a pre-equalization at the edge devices (EDs). Finally, the
theoretical mean squared error (MSE) performance of the proposed scheme is
derived and its performance for federated edge learning (FEEL) is demonstrated.Comment: 6 pages, 3 figures, Accepted to GLOBECOM'2022 Workshops: Workshop on
Wireless Communications for Distributed Intelligenc
Hybrid 3D Localization for Visible Light Communication Systems
In this study, we investigate hybrid utilization of angle-of-arrival (AOA)
and received signal strength (RSS) information in visible light communication
(VLC) systems for 3D localization. We show that AOA-based localization method
allows the receiver to locate itself via a least squares estimator by
exploiting the directionality of light-emitting diodes (LEDs). We then prove
that when the RSS information is taken into account, the positioning accuracy
of AOA-based localization can be improved further using a weighted least
squares solution. On the other hand, when the radiation patterns of LEDs are
explicitly considered in the estimation, RSS-based localization yields highly
accurate results. In order to deal with the system of nonlinear equations for
RSS-based localization, we develop an analytical learning rule based on the
Newton-Raphson method. The non-convex structure is addressed by initializing
the learning rule based on 1) location estimates, and 2) a newly developed
method, which we refer as random report and cluster algorithm. As a benchmark,
we also derive analytical expression of the Cramer-Rao lower bound (CRLB) for
RSS-based localization, which captures any deployment scenario positioning in
3D geometry. Finally, we demonstrate the effectiveness of the proposed
solutions for a wide range of LED characteristics and orientations through
extensive computer simulations.Comment: Submitted to IEEE/OSA Journal of Lightwave Technology (10 pages, 14
figures