1 research outputs found
Stochastic Beamforming for Reconfigurable Intelligent Surface Aided Over-the-Air Computation
Over-the-air computation (AirComp) is a promising technology that is capable
of achieving fast data aggregation in Internet of Things (IoT) networks. The
mean-squared error (MSE) performance of AirComp is bottlenecked by the
unfavorable channel conditions. This limitation can be mitigated by deploying a
reconfigurable intelligent surface (RIS), which reconfigures the propagation
environment to facilitate the receiving power equalization. The achievable
performance of RIS relies on the availability of accurate channel state
information (CSI), which however is generally difficult to be obtained. In this
paper, we consider an RIS-aided AirComp IoT network, where an access point (AP)
aggregates sensing data from distributed devices. Without assuming any prior
knowledge on the underlying channel distribution, we formulate a stochastic
optimization problem to maximize the probability that the MSE is below a
certain threshold. The formulated problem turns out to be non-convex and highly
intractable. To this end, we propose a data-driven approach to jointly optimize
the receive beamforming vector at the AP and the phase-shift vector at the RIS
based on historical channel realizations. After smoothing the objective
function by adopting the sigmoid function, we develop an alternating stochastic
variance reduced gradient (SVRG) algorithm with a fast convergence rate to
solve the problem. Simulation results demonstrate the effectiveness of the
proposed algorithm and the importance of deploying an RIS in reducing the MSE
outage probability.Comment: 6pages, 3 figure