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Journal ArticleDOI

A Uniform-Forcing Transceiver Design for Over-the-Air Function Computation

TLDR
A novel uniform-forcing transceiver design is proposed for over-the-air function computation to compensate the non-uniform fading of different sensors and is able to achieve significant performance gain with low complexity.
Abstract
The future Internet-of-Things network is expected to connect billions of sensors, which incurs high latency for data aggregation. To overcome this challenge, a new technique called over-the-air function computation was recently developed to enable fusion center to receive a desired function directly. It utilizes the superposition property of wireless channel to realize the uniform summation of the desired function. In order to compensate the non-uniform fading of different sensors, we propose a novel uniform-forcing transceiver design for over-the-air function computation. A corresponding min-max optimization problem is formulated to minimize the distortion of the computation which is measured by mean squared error. Due to the non-convexity of the problem, it is relaxed to semidefinite programming first. Then, the performance of the initial solution is improved through successive convex approximation. Simulation results show that the proposed design is able to achieve significant performance gain with low complexity.

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Citations
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Journal ArticleDOI

Federated Learning via Over-the-Air Computation

TL;DR: A novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel and providing a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection.
Proceedings ArticleDOI

Over-the-Air Computation via Intelligent Reflecting Surfaces

TL;DR: In this paper, an intelligent reflecting surface (IRS) aided over-the-air computation (AirComp) system was proposed to build controllable wireless environments, thereby boosting the received signal power significantly.
Posted Content

Federated Learning via Over-the-Air Computation

TL;DR: In this article, the authors proposed a federated averaging algorithm for global model aggregation by computing the weighted average of locally updated model at each selected device, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design.
Journal ArticleDOI

Reconfigurable Intelligent Surface Empowered Downlink Non-Orthogonal Multiple Access

TL;DR: Simulation results validate the ability of an RIS in enlarging the channel-gain difference when the users’ original channel conditions are similar and the superiority of the proposed DC-based alternating optimization method in reducing the total transmit power.
Journal ArticleDOI

Wireless-Powered Over-the-Air Computation in Intelligent Reflecting Surface-Aided IoT Networks

TL;DR: This article proposes to leverage the intelligent reflecting surface (IRS) that is capable of dynamically reconfiguring the propagation environment to drastically enhance the efficiency of both downlink EB and uplink AirComp in IoT networks and demonstrates the performance gains of the proposed algorithm over the baseline methods.
References
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Journal ArticleDOI

Semidefinite Relaxation of Quadratic Optimization Problems

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Journal ArticleDOI

Compute-and-Forward: Harnessing Interference Through Structured Codes

TL;DR: In this article, the authors proposed a new strategy, compute-and-forward, that exploits interference to obtain significantly higher rates between users in a network by decoding linear functions of transmitted messages according to their observed channel coefficients rather than ignoring the interference as noise.
Journal ArticleDOI

Computation Over Multiple-Access Channels

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Journal ArticleDOI

A sequential parametric convex approximation method with applications to nonconvex truss topology design problems

TL;DR: It is shown that the approximate convex problem solved at each inner iteration can be cast as a conic quadratic programming problem, hence large scale TTD problems can be efficiently solved by the proposed method.
Journal ArticleDOI

Approximation Bounds for Quadratic Optimization with Homogeneous Quadratic Constraints

TL;DR: It is shown that a semidefinite programming (SDP) relaxation for this nonconvex quadratically constrained quadratic program (QP) provides an O(1/\ln(m)$ approximation, which is analogous to a result of Nemirovski e for the real case.
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