Data-aided CSI estimation using affine precoded superimposed pilots in orthogonal time frequency space modulated MIMO systems
Data-aided CSI estimation using affine precoded superimposed pilots in orthogonal time frequency space modulated MIMO systems
An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.
Bayes methods, Bayesian learning, Channel estimation, Estimation, Interference, MIMO, MIMO communication, OTFS, Symbols, Uncertainty, affine precoded, channel estimation, delay-Doppler, high-mobility, sparse, superimposed, delay-doppler
4482-4498
Mehrotra, Anand
8fea1693-db94-4f75-a91d-1210fbea2fcd
Srivastava, Suraj
474a0344-2370-4e55-a434-af3e6663525f
Jagannatham, Aditya K.
6bf39c17-fdd3-4f79-9d5c-47b5e2e51098
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 August 2023
Mehrotra, Anand
8fea1693-db94-4f75-a91d-1210fbea2fcd
Srivastava, Suraj
474a0344-2370-4e55-a434-af3e6663525f
Jagannatham, Aditya K.
6bf39c17-fdd3-4f79-9d5c-47b5e2e51098
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Mehrotra, Anand, Srivastava, Suraj, Jagannatham, Aditya K. and Hanzo, Lajos
(2023)
Data-aided CSI estimation using affine precoded superimposed pilots in orthogonal time frequency space modulated MIMO systems.
IEEE Transactions on Communications, 71 (8), .
(doi:10.1109/TCOMM.2023.3280550).
Abstract
An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.
Text
Data Aided CSI Estimation using Affine Precoded Superimposed pilots in Orthogonal Time Frequency Space Modulated MIMO Systems
- Accepted Manuscript
More information
e-pub ahead of print date: 29 May 2023
Published date: 1 August 2023
Additional Information:
Funding Information:
The work of Aditya K. Jagannatham was supported in part by the Qualcomm Innovation Fellowship, and in part by the Arun Kumar Chair. Lajos Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP/W016605/1 and EP/X01228X/1 as well as of the European Research Council's Advanced Fellow Grant QuantCom (Grant No. 789028).
Publisher Copyright:
© 1972-2012 IEEE.
Keywords:
Bayes methods, Bayesian learning, Channel estimation, Estimation, Interference, MIMO, MIMO communication, OTFS, Symbols, Uncertainty, affine precoded, channel estimation, delay-Doppler, high-mobility, sparse, superimposed, delay-doppler
Identifiers
Local EPrints ID: 479200
URI: http://eprints.soton.ac.uk/id/eprint/479200
ISSN: 0090-6778
PURE UUID: 0f30ecf5-26ca-4d9b-873e-0dd3769dbccd
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Date deposited: 20 Jul 2023 16:44
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Anand Mehrotra
Author:
Suraj Srivastava
Author:
Aditya K. Jagannatham
Author:
Lajos Hanzo
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