K Giridhar

To ensure robust communication in doubly dispersive channels, Orthogonal Time Frequency and Space (OTFS) modulation has been proposed to exploit the channel in the Delay-Doppler domain. OTFS is expected to outperform conventional Orthogonal Frequency Division Multiplexing (OFDM) especially in high Doppler environments. In this work, we revisit Multi-Carrier Code Division Multiple Access (MC-CDMA) waveforms and compare them with OTFS in practical downlink Single Input Single Output (SISO) scenario with channel estimation and Peak to Average Power Ratio (PAPR) input back-off. Both the modulation schemes are compared for the same information rate, transmit power per symbol and block size MN, where M and N represent the number of Delay and Doppler bins in OTFS respectively. Simulation results highlight the fact that with channel estimation of OTFS, for constant M N with larger M and N, OTFS performs better than MC-CDMA in medium SNR links with less PAPR. In less noisy environments, both OTFS and MC-CDMA perform similarly, with OTFS having an upper hand at very high Dopplers. However, in low SNR links, MC-CDMA with grid boosting outperforms OTFS for any Doppler. Our work shows that both MC-CDMA and OTFS are good contenders for the present needs of high-speed wireless communication links, and the choice of waveform can be made based on the operating environment and system requirements.

Compressed sensing (CS) algorithms for orthogonal frequency division multiplexing (OFDM) channel estimation work best when the pilot subcarrier locations are pseudo random. However, wireless standards such as LTE typically have equi-spaced pilot structures on the downlink to also enable the estimation of various other signal parameters. Here, we propose an iterative CS algorithm for channel estimation in OFDM systems which works well even in the presence of equi-spaced pilots. Simulation results indicate that between the first and second iteration of the proposed CS algorithm, we accrue a 10 dB lower mean square error (MSE) over a SNR range of 0-30 dB. A substantial gain in also observed in the block error rate (BLER) when turbo codes are employed.

We describe an algorithm for sparse channel estimation applicable to orthogonal frequency division multiplexing systems. The proposed algorithm uses a least squares (LS) technique for channel estimation and a generalized Akaike information criterion to estimate the channel length and tap positions. This effectively reduces the signal space of the LS estimator, and hence improves the estimation performance as demonstrated using computer simulations. For example, the proposed modified LS with sparse channel-estimation algorithm has a 5-dB lower mean square error in channel estimation when compared to the conventional approach [1], which translates to approximately 0.5 dB improvement in signal-to-noise ratio at the receiver. © 2005 IEEE.