  In this paper, we study the impact of computational complexity on the
throughput limits of the {\color{black}fast Fourier transform (FFT)} algorithm
for {\color{black}orthogonal frequency division multiplexing(OFDM)} waveforms.
Based on the spectro-computational {\color{\corcorrecao}complexity} (SC)
analysis, {\color{\corcorrecao} we verify that the complexity of an $N$-point
FFT grows faster than the number of bits in the OFDM symbol.} Thus, we show
that FFT nullifies the OFDM throughput on $N$ unless the $N$-point discrete
Fourier transform (DFT) problem verifies as $\Omega(N)$, which remains a
"fascinating" open question in theoretical computer science. Also, because FFT
demands $N$ to be a power of two $2^i$ ($i>0$), the spectrum widening leads to
an exponential complexity on $i$, i.e. $O(2^ii)$. To overcome these
limitations, {\color{\corcorrecao} we consider the alternative frequency-time
transform formulation of vector OFDM (V-OFDM), in which an $N$-point FFT is
replaced by $N/L$ ($L$$>$$0$) smaller {\color{\corcorrecao}$L$-point} FFTs to
mitigate the cyclic prefix overhead of OFDM. Building on that, we replace FFT
by the straightforward DFT algorithm to release the V-OFDM parameters from
growing as powers of two and to benefit from flexible numerology (e.g., $L=3$,
$N=156$). Besides, by setting $L$ to $\Theta(1)$, the resulting solution can
run linearly on $N$ (rather than exponentially on $i$) while sustaining a non
null throughput as $N$ grows. }
