Pilot Pouring in Superimposed Training for Channel Estimation in CB-FMT

Cyclic block filtered multi-tone (CB-FMT) is a waveform that can be efficiently synthesized through filter-bank in the frequency domain. Although main principles have been already established, channel estimation has not addressed yet. This because of assuming existing techniques based on pilot symbol assisted modulation (PSAM), implemented OFDM-like schemes, reused. However, PSAM leads to an undesirable loss data-rate. In this paper, alternative method inspired by superimposed training (ST) concept, namely pouring ST (PPST), proposed. PPST, pilots are over data taking advantage particular spectral characteristics CB-FMT. Exploiting sub-channel spectrum, symbols poured those resources unused for transmission. shaping also exploited at receiver carry out estimation, enhancing estimates exhibit low interference contribution. Furthermore, domain resource mapping strategy and proposed enable accurate strongly frequency-selective channels. The parameters scheme optimized minimize mean squared error (MSE). Finally, several numerical results illustrate performance advantages technique as compared other alternatives.