Over-the-Air Computation in MIMO Multi-Access Channels: Beamforming and Channel Feedback
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To support future IoT networks with dense sensor connectivity, a technique called over-the-air-computation (AirComp) was recently developed which enables a data-fusion to receive a desired function of sensing-data from concurrent-transmissions by exploiting the superposition property of a multi-access-channel. This work aims at further developing AirComp for next-generation multi-antenna multi-modal sensor networks. Specifically, we design beamforming and channel-feedback techniques for multi-function AirComp. Given the objective of minimizing sum-mean-squared-error of computed functions, the optimization of receive-beamforming for multi-function AirComp is a NP-hard problem. The approximate problem based on tightening transmission-power constraints, however, is shown to be solvable using differential-geometry. The solution is proved to be the weighted-centroid of points on a Grassmann-manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the beamforming problem is found to have the same form as the classic problem of multicast-beamforming, establishing the AirComp-multicasting-duality. Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite-relaxation-technique. Last, building on the AirComp-beamforming solution, an efficient channel-feedback technique is designed for a data-fusion to receive the beamformer from distributed sensor transmissions of designed signals that are functions of local channel-state-information.
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