Analog RF Computing for Integrated Data and AI Flows Over Wireless Networks

Wentao Yu; Vincent W. S. Wong

cs.ITOct 14, 2026

Analog RF Computing for Integrated Data and AI Flows Over Wireless Networks

Authors: Wentao Yu, Vincent W. S. Wong

Organizations: Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

Abstract

Many resource-constrained edge devices require artificial intelligence (AI) services but are not powerful enough to store or execute neural networks (NNs). Analog radio frequency (RF) computing addresses this limitation by letting a base station (BS) multicast a waveform encoding NN weights to different clients. Each client then multiplies the received weight-encoded waveform with its locally generated input-encoded waveform through a passive mixer in its RF frontend, thereby realizing the matrix-vector multiplications (MVMs) needed for AI inference with an extremely low power and memory requirement. This can enable telecom operators to provide AI inference services to the edge devices connected to their networks. In practice, however, this multicast AI flow must share the spectrum, power, and spatial resources with other data flows. This paper considers a multi-user multiple-input multiple-output (MU-MIMO) downlink scenario and studies the resulting relative aggregate data rate reduction from integrating the multicast AI flow with the unicast data flows. We formulate a joint beamforming problem for the data and AI flows to maximize the aggregate data rate subject to computing accuracy, client energy, total power, and hardware-related constraints. We develop a weighted minimum mean-square error (WMMSE)-based algorithm to solve the problem. Numerical results based on 3rd Generation Partnership Project (3GPP) channels show that the relative aggregate data rate reduction from integrating an AI flow with data flows is below 5% in representative settings. This indicates that telecom operators can use analog RF computing to provide edge AI services while providing data communication services simultaneously.

Figures & tables

Fig. 1: A multi-antenna BS serves data users via unicast data flows and AI clients via a multicast weight -encoded waveform for analog RF computing.

Fig. 2: Example for mixer-based MVM with $M=2$ and $N=3$ . (a) Weight $W_{m,n}^{(\ell)}$ modulates tone $nM+m$ . (b) Input $x_{k,n}^{(\ell)}$ modulates tone $nM$ . (c) Difference-frequency products for row $m$ align at $-m\Delta f$ and form $y_{k,m}^{(\ell)}$ .

Parameter	Description	Value
$N_{\mathrm{FFT}}$	Fast Fourier transform (FFT) size	$4096$
$B$	Occupied bandwidth	$25$ MHz
$T_{0}$	Environmental temperature	$300$ K
$\vartheta$	Guard factor	$0.33$
$M^{\prime}$	Size of row blocks	$6$
$\varpi$	CP overhead	$0.125$

TABLE I: Analog RF Computing Parameters

Wireless Resource Allocation

Wireless resource allocation assigns spectrum, power, beams, and computing capacity to meet throughput, latency, fairness, and energy goals.

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May 14, 2026J/K move · Enter open · S save

Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems

Wentao Yu, Vincent W. S. Wong

Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

Energy-Efficient ML Edge Inference Wireless Communications Wireless Resource Allocation

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Modern edge devices increasingly rely on neural networks for intelligent applications. However, conventional digital computing-based edge inference requires substantial memory and energy consumption. In analog radio frequency (RF) computing, a base station (BS) encodes the weights of the neural networks and broadcasts the RF waveforms to the clients. Each client reuses its passive mixer to multiply the received weight-encoded waveform with a locally generated input-encoded waveform. This enables wireless receivers to perform the matrix-vector multiplications (MVMs) that account for most of the computation burden in edge inference with ultra-low energy consumption. Unlike conventional downlink transmissions which are optimized for communications, analog RF computing requires a computing-centric physical layer that controls both the analog MVM accuracy and the energy consumption for inference. Motivated by this, in this paper, we propose a physical layer design framework for analog RF computing in MU-MIMO wireless systems. We derive tractable models for computing accuracy and energy consumption for inference, formulate a joint BS beamforming and client-side scaling problem subject to computing accuracy, transmit power, and hardware constraints, and develop a low-complexity algorithm to solve the non-convex problem. The proposed design provides client- and layer-specific accuracy control for both uniform- and mixed-precision inference. Simulations under 3GPP specifications show that analog RF computing can significantly reduce client-side energy consumption by nearly two orders of magnitude compared to digital computing, while mixed-precision inference requires even lower energy consumption than uniform-precision inference. Overall, these results establish analog RF computing over wireless networks as a promising paradigm for energy-efficient edge inference.

Abstract

Wireless Resource Allocation

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