A narrowband interference (NBI) estimation and mitigation method based on compressive sensing (CS) for communication systems with repeated training sequences is investigated in this letter. The proposed CS-based differential measuring method is performed through the differential operation on the inter-block-interference-free regions of the received adjacent training sequences. The sparse NBI signal can be accurately recovered from a time-domain measurement vector of small size under the CS framework, without requiring channel information or dedicated resources. Theoretical analysis and simulation results show that the proposed method is robust to NBI under multi-path fading channels.

A simple yet effective time domain correlation channel estimation method is proposed for multiple-input multiple-output (MIMO) systems over dispersive channels. It is known that the inherent co-channel interference (CCI) and inter-symbol interference (ISI) coexist when the signals propagate through MIMO frequency selective channels, which renders the MIMO channel estimation intractable. By elaborately devising the quasi-orthogonal training sequences between multiple antennas which have constant autocorrelation property with different cyclic shifts in the time domain, the interferences induced by ISI and CCI can be simultaneously maintained at a constant and identical value under quasi-static channels. As a consequence, it is advisable to implement the joint ISI and CCI cancelation by solving the constructed linear equation on the basis of the correlation output with optional correlation window. Finally, a general and simplified closed-form expression of the estimated channel impulse response can be acquired without matrix inversion. Additionally, the layered space-time (LST) minimum mean square error (MMSE) (LST-MMSE) frequency domain equalization is briefly described. We also provide some meaningful discussions on the beginning index of the variable correlation window and on the cyclic shift number of m-sequence of other antennas relative to the first antenna. Simulation results demonstrate that the proposed channel estimation approach apparently outperforms the existing schemes with a remarkable reduction in computational complexity.

This paper proposes a new channel estimation method and a new interference cancellation scheme for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in the presence of intersymbol interference (ISI). The proposed channel estimation method uses special training sequences (TSs) to have a desirable crest-factor of the transmitted training signal, and to prevent the influence of ISI on the channel estimation performance. By using the recommended training sequences, the ill-conditioned problem of the least square (LS) filter integrated in the proposed channel estimator can be avoided. The proposed interference cancellation scheme uses the estimated channel coefficients and the channel state information (CSI) to reproduce the interference components, which are then iteratively cancelled from the received signals. To reduced the error-floor of the demodulated symbols using for the calculations of the interference components, the so-called remodulation technique is also included in the proposed interference cancellation scheme. Simulation results show that the proposed channel estimation method outperforms conventional channel estimation methods, especially in the presence of ISI and if the signal-to-noise ratio (SNR) is larger than 15 dB. The combination of the proposed method with a space-time block code (STBC) to combat the interference influences results in an excellent system performance in terms of symbol error ratio (SER). In comparison with a STBC MIMO-OFDM system with sufficient guard interval (GI), this combination gains 1.52 dB of SNR at the same SER of 1.110-6 even after performing only one iteration of interference cancellation.