ML-Type EM-Based Estimation of Fast Time-Varying Frequency-Selective Channels Over SIMO OFDM Transmissions; IEEE Access; Vol. 7
| Parent link: | IEEE Access Vol. 7.— 2019.— [P. 148265-148277] |
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| Tác giả của công ty: | |
| Tác giả khác: | , , , |
| Tóm tắt: | Title screen This paper investigates the problem of fast time-varying frequency-selective (i.e., multipath) channel estimation over single-input multiple-output orthogonal frequency-division multiplexing (SIMO OFDM)-type transmissions. We do so by tracking the variations of each complex gain coefficient using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) both in the data-aided (DA) and non-data-aided (NDA) cases. The DA maximum likelihood (ML) estimates over fast SIMO OFDM channels are derived here for the first time in closed-form expressions and hereby shown to be limited to applying over each receive antenna the DA least squares (LS) estimator tailored in [1] to fast SISO OFDM channels. This DA ML is used to initialize periodically, over a relatively large number of data blocks (i.e., with further reduced and relatively close-to-negligible pilot overhead compared to DA ML), a new expectation maximization (EM) ML-type solution we developed here in the NDA case to iteratively maximize the LLF. We also introduce an alternative regularized DA ML (RDM) initialization solution no longer requesting - in contrast to DA ML - more per-carrier pilot frames than the number of paths to further reduce overhead without incurring significant performance losses. Simulation results show that the proposed hybrid ML-EM estimator (i.e., combines all new NDA ML-EM and DA ML or RDM versions) converges within few iterations, thereby providing very accurate estimates of all multipath channel gains. Most importantly, this increased estimation accuracy translates into very significant BER and link-level per-carrier throughput gains over the best representative benchmark solution available so far for the problem at hand, the SISO DA LS technique in [1] with its new generalization here to SIMO systems. |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
2019
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| Những chủ đề: | |
| Truy cập trực tuyến: | http://earchive.tpu.ru/handle/11683/64940 https://doi.org/10.1109/ACCESS.2019.2946615 |
| Định dạng: | Điện tử Chương của sách |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663698 |
MARC
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| 200 | 1 | |a ML-Type EM-Based Estimation of Fast Time-Varying Frequency-Selective Channels Over SIMO OFDM Transmissions |f S. B. Amor, S. Affes, F. Bellili, D. N. K. Dzhayakodi (Jayakody) Arachshiladzh | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 22 tit.] | ||
| 330 | |a This paper investigates the problem of fast time-varying frequency-selective (i.e., multipath) channel estimation over single-input multiple-output orthogonal frequency-division multiplexing (SIMO OFDM)-type transmissions. We do so by tracking the variations of each complex gain coefficient using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) both in the data-aided (DA) and non-data-aided (NDA) cases. The DA maximum likelihood (ML) estimates over fast SIMO OFDM channels are derived here for the first time in closed-form expressions and hereby shown to be limited to applying over each receive antenna the DA least squares (LS) estimator tailored in [1] to fast SISO OFDM channels. This DA ML is used to initialize periodically, over a relatively large number of data blocks (i.e., with further reduced and relatively close-to-negligible pilot overhead compared to DA ML), a new expectation maximization (EM) ML-type solution we developed here in the NDA case to iteratively maximize the LLF. We also introduce an alternative regularized DA ML (RDM) initialization solution no longer requesting - in contrast to DA ML - more per-carrier pilot frames than the number of paths to further reduce overhead without incurring significant performance losses. Simulation results show that the proposed hybrid ML-EM estimator (i.e., combines all new NDA ML-EM and DA ML or RDM versions) converges within few iterations, thereby providing very accurate estimates of all multipath channel gains. Most importantly, this increased estimation accuracy translates into very significant BER and link-level per-carrier throughput gains over the best representative benchmark solution available so far for the problem at hand, the SISO DA LS technique in [1] with its new generalization here to SIMO systems. | ||
| 461 | |t IEEE Access | ||
| 463 | |t Vol. 7 |v [P. 148265-148277] |d 2019 | ||
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a OFDM | |
| 610 | 1 | |a channel estimation | |
| 610 | 1 | |a maximum likelihood estimation | |
| 610 | 1 | |a receiving antennas | |
| 610 | 1 | |a frequency estimation | |
| 610 | 1 | |a time-frequency analysis | |
| 610 | 1 | |a resistance | |
| 610 | 1 | |a оценка | |
| 610 | 1 | |a антенны | |
| 610 | 1 | |a частотно-временные методы | |
| 701 | 1 | |a Amor |b S. B. |g Souheib Ben | |
| 701 | 1 | |a Affes |b S. |g Sofiene | |
| 701 | 1 | |a Bellili |b F. |g Faouzi | |
| 701 | 1 | |a Dzhayakodi (Jayakody) Arachshiladzh |b D. N. K. |c specialist in the field of electronics |c Professor of Tomsk Polytechnic University |f 1983- |g Dushanta Nalin Kumara |3 (RuTPU)RU\TPU\pers\37962 |9 20606 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа энергетики |b Отделение электроэнергетики и электротехники |3 (RuTPU)RU\TPU\col\23505 |
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