Full-Rank and Rank-Deficient Precoding Schemes for Single-Carrier Block Transmissions

Ghogho, Mounir; Whitworth, T.; Swami, Ananthram; McLernon, Des

doi:10.1109/tsp.2009.2025153

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…In [29], information theoretic bounds are obtained for separate and joint channel and data estimations. The work in [30] focus on single carrier precoding optimization for peak to average power ratio, whereas [21], [31] is oriented towards multi-carrier transmissions. In the GSP scheme, instead of sending the same number of data symbols as the number of pilots (T ), the number of data symbols is reduced, optimized, and further precoded to avoid the possible pilot contamination in both the channel estimation and the data detection.…”

Section: Introductionmentioning

confidence: 99%

Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

Garg

Ratnarajah

2022

IEEE J. Sel. Top. Signal Process.

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In this paper, for a cell-free massive multi-input multi-output (MIMO) system assisted with intelligent reflective surface (IRS) panels, a generalized superimposed pilot (GSP) training scheme is proposed, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity approach is employed for processing, and the corresponding rate components are analyzed. It is shown that with careful design of precoding and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results, verified via simulations, shows that the centralized processing improves the data detection performance than localized processing. The pilot contamination effect, is significantly reduced due to the availability of larger number of pilots, as compared to the regular pilots transmission. For four IRS panels in the system, the proposed scheme is shown to reduce the channel estimation errors by 74% approximately.

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Section: Introductionmentioning

confidence: 99%

Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

Garg

Ratnarajah

2022

IEEE J. Sel. Top. Signal Process.

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“…Related work on precoded superimposed training: In [20], [21], precoder and pilots are optimized under the condition T ≥ K + d and found to be proportional to the sub-discrete Fourier transform (DFT) matrix. In addition to that, authors in [22] provide orthogonal precoder and pilot design using the Cramer-Rao lower bound of the channel estimation error, whereas they compare joint and individual data & channel estimations via mutual information bounds in [23].…”

Section: Introductionmentioning

confidence: 99%

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Garg

Ratnarajah

2022

IEEE Trans. Wireless Commun.

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Regular pilots in a massive multi-input multi-output (MIMO) system with large number of users suffer from the pilot contamination effect due to limited training time. In this paper, for a cell-free massive MIMO system, we have proposed a generalized superimposed pilot (GSP) scheme, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity and distributed time processing approach is employed, and the corresponding rate components are analyzed. It is shown that with careful design of precoding matrix and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results have been verified via simulations. It is also inferred that centralized processing in cell free system improves the data detection performance than localized processing. Iterative data detection at the central node also improves the MSE of data estimates. The pilot contamination effect, is significantly reduced due to availability of larger number of pilots, as compared to regular pilots transmission.

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“…If the precoding matrix is full rank, it is impossible to make the training sequence orthogonal to precoded data . However, if the precoding matrix is rank deficient, training sequence can be embedded into the precoded data in a special orthogonal manner without bandwidth expansion . Early results that involved rank‐deficient precoding schemes were the data‐dependent superimposed training (DDST) in 1 study .…”

Section: Introductionmentioning

confidence: 99%

“…14 However, if the precoding matrix is rank deficient, training sequence can be embedded into the precoded data in a special orthogonal manner without bandwidth expansion. 18 Early results that involved rank-deficient precoding schemes were the datadependent superimposed training (DDST) in 1 study. 19 With the special orthogonal constraints in rank-deficient precoding, the channel is estimated independently of the precoded data, while the training sequence is completely nulled out in symbol detection.…”

Section: Introductionmentioning

confidence: 99%

“…14 The minimum redundancy is designed to make training sequence orthogonal to the precoded data, thereby eliminating the effect of unknown data on channel estimation and obtaining the diversity at the receiver. In contrast to the developed "premultiplying" precoding mode in the previous studies, [14][15][16][17][18] a new affine precoding method, so-called postmultiplying precoding mode, is proposed to offer a greater flexibility for channel estimation and symbol detection at the cost of more coding redundancy. 16 Note that the orthogonal affine precoding schemes discussed in the previous studies [13][14][15][16][17][18] are restricted over time-invariant or slowly time-varying channels.…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal superimposed training design for doubly selective channel estimation using basis expansion models

Dou

Ren

et al. 2017

Trans. Emerging Tel. Tech.

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In this paper, we consider channel estimation for single‐carrier block transmission over doubly selective fading channel, where basis expansion models are used to describe the time‐varying channel. The affine precoding model is used as the transmission strategy, wherein the training sequence is added on the top of the precoded data prior to transmission. By enforcing a special form of orthogonality between training sequence and precoded matrix, we propose orthogonal superimposed training design for doubly selective channel that makes channel estimation decouple from symbol detection, and then the affine precoding model based on orthogonal polyphase sequence set is designed under the orthogonal conditions. To further improve the estimation performance, we exploited a joint iterative approach is used, where the detected symbols, to enhance the estimation performance. Simulations results show that the proposed scheme can yield better detection performance than the data‐dependent superimposed training and can be competitive with time‐multiplexed training scheme with higher bandwidth efficiency.

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Full-Rank and Rank-Deficient Precoding Schemes for Single-Carrier Block Transmissions

Cited by 11 publications

References 17 publications

Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Orthogonal superimposed training design for doubly selective channel estimation using basis expansion models

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