Frequency domain direct adaptive turbo equalization based on block least mean square for underwater acoustic communications

Jing, Lianyou; Zheng, Tonghui; He, Chengbing; Long, Chao; Liu, Xin; Yin, Hongxi

doi:10.1016/j.apacoust.2022.108631

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…, x e K ] is obtained with the IDFT of X e . Subsequently, the estimated mean and variance of the marginal distribution obtained by (24) and (25) are fed back to (15), and the EP is executed until the maximum EP self-iteration is reached. On the basis of the estimated a posteriori probability of the transmitted symbols and the Bayesian principle, the extrinsic information L e E (c k,q ) is calculated as…”

Section: Expectation Propagation Interference Cancellation Fdtementioning

confidence: 99%

“…(Step 3). On the basis of the estimated a posteriori information, the extrinsic information of equalization is calculated by using (24) and is then inputted into the decoder. (Step 4).…”

Section: Procedures Of the Ifdte With Iterative Channel Estimationmentioning

confidence: 99%

“…Also, a hybrid frequency–time domain turbo equalizer (FTD-TEQ) was proposed to benefit from two turbo equalizers to solve the slow-convergence problem at different iterative stages for UWACs with comprehensive experimental investigations [ 23 ]. Furthermore, the block implementation of least mean square based frequency domain direct adaptive turbo equalization was proposed for use in UWACs [ 24 ] and it can be combined with excellent Shannon capacity, approaching channel codes for the better performance and security of turbo equalization. Then, security-oriented Polar coding can be adopted based on channel-gain-mapped frozen bits [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Frequency Domain Turbo Equalization with Expectation Propagation Interference Cancellation in Underwater Acoustic Communications

Jiang,

Tang,

Zhao

et al. 2023

Sensors

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This paper proposes an improved frequency domain turbo equalization (IFDTE) with iterative channel estimation and feedback to achieve both a good performance and low complexity in underwater acoustic communications (UWACs). A selective zero-attracting (SZA) improved proportionate normal least mean square (SZA-IPNLMS) algorithm is adopted by utilizing the sparsity of the UWAC channel to estimate it using a training sequence. Simultaneously, a set-membership (SM) SZA differential IPNLMS (SM SZA-DIPNLMS) with variable step size is adopted to estimate the channel status information (CSI) in the iterative channel estimation with soft feedback. In this way, the computational complexity for iterative channel estimation is reduced effectively with minimal performance loss. Different from traditional schemes in UWACs, an IFDTE with expectation propagation (EP) interference cancellation is adopted to estimate the a posteriori probability of transmitted symbols iteratively. A bidirectional IFDTE with the EP interference cancellation is proposed to further accelerate the convergence. THe simulation results show that the proposed channel estimation obtains 1.9 and 0.5 dB performance gains, when compared with those of the IPNLMS and the l0-IPNLMS at a bit error rate (BER) of 10−3. The proposed channel estimation also effectively reduces the unnecessary updating of the coefficients of the UWAC channel. Compared with traditional time-domain turbo equalization and FDTE in UWACs, the IFDTE obtains 0.5 and 1 dB gains in the environment of SPACE’08 and it obtains 0.5 and 0.4 dB gains in the environment of MACE’04 at a BER of 10−3. Therefore, the proposed scheme obtains a good BER performance and low complexity and it is suitable for efficient use in UWACs.

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Section: Expectation Propagation Interference Cancellation Fdtementioning

confidence: 99%

“…(Step 3). On the basis of the estimated a posteriori information, the extrinsic information of equalization is calculated by using (24) and is then inputted into the decoder. (Step 4).…”

Section: Procedures Of the Ifdte With Iterative Channel Estimationmentioning

confidence: 99%