  In this paper, we consider fast wireless data aggregation via over-the-air
computation (AirComp) in Internet of Things (IoT) networks, where an access
point (AP) with multiple antennas aim to recover the arithmetic mean of sensory
data from multiple IoT devices. To minimize the estimation distortion, we
formulate a mean-squared-error (MSE) minimization problem that involves the
joint optimization of the transmit scalars at the IoT devices as well as the
denoising factor and the receive beamforming vector at the AP. To this end, we
derive the transmit scalars and the denoising factor in closed-form, resulting
in a non-convex quadratic constrained quadratic programming (QCQP) problem
concerning the receive beamforming vector.Different from the existing studies
that only obtain sub-optimal beamformers, we propose a branch and bound (BnB)
algorithm to design the globally optimal receive beamformer.Extensive
simulations demonstrate the superior performance of the proposed algorithm in
terms of MSE. Moreover, the proposed BnB algorithm can serve as a benchmark to
evaluate the performance of the existing sub-optimal algorithms.
