Towards Spectral Efficiency Enhancement for IoT Aided Smart Transportation: A Compressive OFDM Transmission and Regularized Recovery Approach

The increasing number of vehicles brings ubiquitous connectivity and huge information interaction, implementing with limited spectrum resource. Focusing on the higher spectral efficiency requirement, a compressive OFDM system is proposed in this paper. The idea of compressing the transmission of OFDM signal for spectral efficiency enhancement origins from GP extrapolation algorithm for bandlimited signal. In the proposed scheme, a truncation filter with deliberately designed compressed ratio and truncation mode is performed on the OFDM signal to generate the compressive OFDM signal. At the receiver, up-sampling and iterative extrapolation are conducted to recover from the partial signal. Simulation results show that the compressive OFDM signal could be compressed up to 0.5, presenting better compressive capability than the typical nonorthogonal SEFDM system. Further considering the ill-posed problem caused by the noise, a regularization approach is adopted to retain the convergence of recovery. Moreover, the proposed compressive OFDM system possesses the spectrally efficient advantage than SEFDM system. At the compressed ratio 0.5, the compressive OFDM system possesses better BER than SEFDM. At 10dB E b /N 0 , the throughput rate of the compressive OFDM is 2 times and 1.6 times higher than OFDM and SEFDM, respectively.