Parameterized cancellation of partial-band partial-block-duration interference for underwater acoustic OFDM

Abstract

Despite that underwater acoustic channels are well known to contain various interferences, research on interference mitigation in underwater acoustic communications has been very limited. In this paper, we deal with a wideband orthogonal frequency division multiplexing (OFDM) transmission in the presence of an external interference which occupies partially the signal band and whose time duration is shorter than the OFDM block. We parameterize the unknown interference waveform by a number of parameters assuming prior knowledge of its frequency band and time duration, and develop an iterative receiver, which couples interference detection via a generalized likelihood-ratio-test (GLRT), interference reconstruction and cancellation, channel estimation, and data detection. In addition to simulation results, we verify the receiver performance using data sets collected from two experiments. In both time-invariant and time-varying channels, the proposed iterative receiver achieves robust performance in the presence of unknown interference.

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

“…The sparse channel estimateĥ k is recovered by using the MMP or MMP-DCD algorithm. Givenĥ k , at the CV equation (14), we can obtain the CV residual ofĥ k at the k-th iteration. After N cv iterations, the CV residual vector ε is used to determine which estimate is the best candidate in the recovered set…”

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

“…The sparse channel estimateĥ k is recovered by using the MMP or MMP-DCD algorithm. Givenĥ k , at the CV equation (14), we can obtain the CV residual ofĥ k at the k-th iteration. After N cv iterations, the CV residual vector ε is used to determine which estimate is the best candidate in the recovered set…”

Non-data-aided ML SNR estimation for AWGN channels with deterministic interference

“…where 4 n = E b 4 n are the second-and fourthorder raw moments of the real/imaginary part of the Mary QAM signal {s n }, respectively. And…”

Leveraging spatial diversity to mitigate interference in underwater acoustic communication networks

“…3, identifies the portions of the received waveforms suffering from interference and then optimally combines the remaining clean portions of the signals. The interference is time and band limited, and as in [2], we assume these parameters are known. The reconstruction process must occur in the time domain since the interference occupies the same frequency band on all receivers but arrives at different times on different receivers.…”

“…Consequently, after signal detection and gross Doppler compensation, the reconstruction process begins with interference detection/suppression in the frequency domain followed by channel equalization. The algorithms developed in [2] and the references therein can be used for these tasks. The equalized received signals must then be transferred back to the time domain to remove the residual time orthogonal interference.…”

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

“…The sparse channel estimateĥ k is recovered by using the MMP or MMP-DCD algorithm. Givenĥ k , at the CV equation (14), we can obtain the CV residual ofĥ k at the k-th iteration. After N cv iterations, the CV residual vector ε is used to determine which estimate is the best candidate in the recovered set…”

Non-data-aided ML SNR estimation for AWGN channels with deterministic interference

“…where 4 n = E b 4 n are the second-and fourthorder raw moments of the real/imaginary part of the Mary QAM signal {s n }, respectively. And…”

Leveraging spatial diversity to mitigate interference in underwater acoustic communication networks

“…3, identifies the portions of the received waveforms suffering from interference and then optimally combines the remaining clean portions of the signals. The interference is time and band limited, and as in [2], we assume these parameters are known. The reconstruction process must occur in the time domain since the interference occupies the same frequency band on all receivers but arrives at different times on different receivers.…”

“…Consequently, after signal detection and gross Doppler compensation, the reconstruction process begins with interference detection/suppression in the frequency domain followed by channel equalization. The algorithms developed in [2] and the references therein can be used for these tasks. The equalized received signals must then be transferred back to the time domain to remove the residual time orthogonal interference.…”

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

“…The sparse channel estimateĥ k is recovered by using the MMP or MMP-DCD algorithm. Givenĥ k , at the CV equation (14), we can obtain the CV residual ofĥ k at the k-th iteration. After N cv iterations, the CV residual vector ε is used to determine which estimate is the best candidate in the recovered set…”

Non-data-aided ML SNR estimation for AWGN channels with deterministic interference

“…where 4 n = E b 4 n are the second-and fourthorder raw moments of the real/imaginary part of the Mary QAM signal {s n }, respectively. And…”

Leveraging spatial diversity to mitigate interference in underwater acoustic communication networks

“…3, identifies the portions of the received waveforms suffering from interference and then optimally combines the remaining clean portions of the signals. The interference is time and band limited, and as in [2], we assume these parameters are known. The reconstruction process must occur in the time domain since the interference occupies the same frequency band on all receivers but arrives at different times on different receivers.…”

“…Consequently, after signal detection and gross Doppler compensation, the reconstruction process begins with interference detection/suppression in the frequency domain followed by channel equalization. The algorithms developed in [2] and the references therein can be used for these tasks. The equalized received signals must then be transferred back to the time domain to remove the residual time orthogonal interference.…”