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In this paper, we consider the over-the-air computation of sums.Specifically, we wish to compute $M\geq 2$ sums$s_m=\sum_{k\in\mathcal{D}m}x_k$ over a shared complex-valued MAC at once withminimal mean-squared error ($\mathsf{MSE}$). Finding appropriate Tx-Rx scalingfactors balance between a low error in the computation of $s_n$ and theinterference induced by it in the computation of other sums $s_m$, $m\neq n$.In this paper, we are interested in designing an optimal Tx-Rx scaling policythat minimizes the mean-squared error $\max_{m\in[1:M]}\mathsf{MSE}_m$ subjectto a Tx power constraint with maximum power $P$. We show that an optimal designof the Tx-Rx scaling policy $\left(\bar{\mathbf{a}},\bar{\mathbf{b}}\right)$involves optimizing (a) their phases and (b) their absolute values in order to(i) decompose the computation of $M$ sums into, respectively, $M_R$ and $M_I$($M=M_R+M_I$) calculations over real and imaginary part of the Rx signal and(ii) to minimize the computation over each part -- real and imaginary --individually. The primary focus of this paper is on (b). We derive conditions(i) on the feasibility of the optimization problem and (ii) on the Tx-Rxscaling policy of a local minimum for $M_w=2$ computations over the real($w=R$) or the imaginary ($w=I$) part. Extensive simulations over a single Rxchain for $M_w=2$ show that the level of interference in terms of $\DeltaD=|\mathcal{D}_2|-|\mathcal{D}_1|$ plays an important role on the ergodicworst-case $\mathsf{MSE}$. At very high $\mathsf{SNR}$, typically only thesensor with the weakest channel transmits with full power while all remainingsensors transmit with less to limit the interference. Interestingly, we observethat due to residual interference, the ergodic worst-case $\mathsf{MSE}$ is notvanishing; rather, it converges to $\frac{|\mathcal{D}_1||\mathcal{D}_2|}{K}$as $\mathsf{SNR}\rightarrow\infty$.

Robust Interference Management for SISO Systems with Multiple Over-the-Air Computations