  An improved mean squared error (MSE) minimization solution based on
eigenvector decomposition approach is conceived for wideband non-orthogonal
multiple-access based computation over multi-access channel (NOMA-CoMAC)
framework. This work aims at further developing NOMA-CoMAC for next-generation
multimodal sensor networks, where a multimodal sensor monitors several
environmental parameters such as temperature, pollution, humidity, or pressure.
We demonstrate that our proposed scheme achieves an MSE value approximately 0.7
lower at E_b/N_o = 1 dB in comparison to that for the average sum-channel based
method. Moreover, the MSE performance gain of our proposed solution increases
even more for larger values of subcarriers and sensor nodes due to the benefit
of the diversity gain. This, in return, suggests that our proposed scheme is
eminently suitable for multimodal sensor networks.
