Tensor decompositions have proven useful for designing different transmission structures for MIMO-OFDM systems with built-in blind detection. Transmission schemes combining diversity, multiplexing, and spatial reuse of codes have been formulated by explicitly exploiting the multilinear algebraic structure of tensor decompositions at both the transmitter and the receiver.

On the one hand, modeling flexible space-time-frequency (STF) transceivers requires more general tensor decomposition structures beyond the well-known PARAFAC model. On the other hand, efficient algorithms are required for channel estimation and blind symbol decoding with guaranteed uniqueness.

Applying innovative tensor signal processing schemes to wireless technologies provides a means to exploit the space, time, and frequency dimensions of signals to design (pre)coding schemes, multi-dimensional sensing signals and blind multiuser decoding receivers.

The Tucker-(2,5) decomposition provides another layer of flexibility for STF processing by breaking down large-scale tensor structures into more manageable components, thus facilitating the design of coding schemes for MIMO systems with space-time-frequency diversity.