Turbo Channel Covariance Conversion in Massive MIMO Frequency Division Duplex Systems

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

interesting and excellent paper

if indeed the proposed algorithm has an advantage as shown in simulations over OAMP and PLV, then the results would be oncouraging

nevertheless, the differences between proposed, OAMP and PLV are significant but not dramatic, and what appears to be missing is an estimate of the differences in effort  required for the 3 resp. 4 cases identified for APS and DL CCM

The relative effort reqruired would give an indication on whether the proposed algorithm could be considered a candidate for general use, or would be limited to situations with more extreme requirements 

It would be welcomed if the research would be followed by experimental validation (a wish, not a criticism)

 

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper iterates between orthogonal approximate message
passing (OAMP) and multikernel adaptive filtering (MKAF), to estimate thechannel covariance matrix in the downlink of a MIMO communication channel.

The paper follows some interesting approach. However, there also a number of open issues.

1) OAMP might not be "orthogonal" at all. In line 207, the authors choose a linear MMSE approach to decorrelate the matrix W. However, the additive noise term "destroys" the orthogonality. To be discussed.

2) the authors take the expectation of the squared APS vector with respect to signal and noise. Note that in (25), the authors state that Z is parameter and not variable. Hence, the expectation w.r.t.
Z seems to be wrong. This has likely impact
on subsequent equations. To be updated in the manuscript.

3) In line 216, the authors state "For the convenience of later derivation, we assume that the coefficients of ηmmse
t (X + τtZ) and X are equal, "  This is a very large restriction. What is the rational for doing that? and what is the impact? To be tackled.

4) the proposed algorithm iterates between OAMP and MKAF until "The maximum number of iterations T is reached."   what is the objective function that is monitored during Algorithm 3?  A convergency analysis is missing (to be added).

5) The literature list does a good job.  However, there have been notable message passing algorithms for MIMO around that should be mentioned like the Bayesian approach in DOI 10.1186/s13638-016-0786-y (EURASIP Journal on Wireless Communications and Networking), Deep Learning approach DOI 10.1109/TWC.2023.3321667 (IEEE TWCOM) and generalized Approximate Message Passing  DOI 10.1109/TCOMM.2019.2892719 (IEEE TCOM) and many more. The literature list should be updated.

Comments on the Quality of English Language

minor issues

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper addresses a problem in massive MIMO FDD systems—estimating the downlink channel covariance matrix using the angular power spectrum reciprocity. The paper is well-organized and makes a meaningful contribution to the field. Below is a detailed review of the paper from several aspects.

1. While the manuscript introduces a novel Turbo-CCC algorithm, it needs clearer elaboration on precisely how this approach improves upon existing algorithms. Explicit comparisons or quantified benefits over recent similar methods should be better highlighted.
2. The literature review lacks sufficient discussion on recent advancements in sparse estimation and complexity reduction techniques relevant to MIMO systems. It is recommended to enhance this section by discussing relevant research, for example, citing "Low Complexity MIMO-FBMC Sparse Channel Parameter Estimation for Industrial Big Data Communications,2021" to adequately address recent advances in low complexity methods.
3. The iterative procedure’s convergence criteria or conditions for terminating iterations are not explicitly stated. The authors need to clearly define these criteria to ensure replicability and practical implementation.
4. Although the experimental validation is thorough, it does not provide sufficient information about computational complexity or runtime analysis. This omission makes it difficult to judge the practical applicability of the proposed algorithm.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

the authors tackled all open issues raised by the reviewer.