Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks

Heo, Jehyun; Jung, Insik; Kim, Tae-Hyung; Kim, Hyunsoo; Hong, Daesik

doi:10.1109/vtcspring.2018.8417510

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…How to ensure the accuracy of channel estimation while reducing the pilot overhead is an important problem to be solved. In [4], a pilot structure based on a cyclically shifted Zadoff-Chu (ZC) sequence was proposed. The proposed algorithm uses the autocorrelation property of the ZC sequence.…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Yuan

Wang

et al. 2024

IEEE Access

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Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI). INDEX TERMS Sparse Code Multiple Access, channel estimation, sparse pilot structure, Internet of Things, massive connection, complex-valued sparse autoencoder, maximum likelihood.

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Section: Introductionmentioning

confidence: 99%

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Yuan

Wang

et al. 2024

IEEE Access

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“…As with grant-based NOMA approaches, channel estimation is equally important for the success of grant-free NOMA approaches. Papers [25][26][27][28][29][30][31][32][33][34][35][36] have considered CE in uplink NOMA for single-input-single-output (SISO)-NOMA. In the work presented in papers 25-28,31, code-domain (mostly sparse coding multiple access [SCMA]) NOMA systems are considered.…”

Section: Introductionmentioning

confidence: 99%

Successive interference cancelation‐inspired channel estimation for downlink non‐orthogonal multiple access

Sekokotoana

Takawira

Oyerinde

2021

Trans Emerging Tel Tech

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This paper proposes a channel estimation (CE) scheme composed of two successive interference cancelation-inspired approaches for a two-user power domain downlink non-orthogonal multiple access system. One approach is intended for the weaker user, and the other approach is intended for the stronger user. Both CE approaches employ a constant step-size least mean squares (CSS-LMS) algorithm, using partial pilots to estimate a Rayleigh flat fading channel with a Jake's Doppler spectrum. Closed-form analytical expressions for the CE error variance are presented for each approach and the mean square error (MSE) is used as the performance metric. Through simulation, the CE performance of the stronger user and the weaker user is assessed for different probabilities of error in detection of the unknown pilot. A reasonable probability of error in detection is then selected and employed in evaluating the CE performance of both schemes against their theoretical analyses. Overall, the theoretical analysis matches the simulation results.

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“…Since users blindly select their REs, an RE-detector at the receiver should be developed to detect the REs selected by each user. Efficient ways to estimate the user's channel coefficients can be demonstrated e.g., [152][153][154][155].…”

Section: Grant-free Scmamentioning

confidence: 99%

Designing Multidimensional Constellations and Efficient Detection Schemes for Sparse Code Multiple Access (SCMA) Systems

Vameghestahbanati¹

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Sparse code multiple access (SCMA) is a class of non-orthogonal multiple access that is proposed to support uplink machine-type communication services. In this thesis, we investigate the design of SCMA systems from two main aspects: multidimensional constellations (MdCs) and efficient detection schemes. In an SCMA system, designing MdCs plays an important role in the system performance. Since the behavior of multidimensional constellations highly depends on the type of the channel, it is crucial to employ a constellation that is suitable for a certain application. In the first part of this thesis, we highlight and review the key performance indicators (KPIs) of MdCs that should be considered in their design process for various channel scenarios. We also provide a survey on the known MdCs in the context of SCMA systems with their design criteria. The performance of some of those constellations are evaluated for uncoded and LTE turbo-coded SCMA systems under different channel conditions through extensive simulations. We then investigate the effect of the 5G low density parity check (LDPC) codes on determining KPIs in designing MdCs under various channel scenarios. Since the optimal maximum likelihood (ML) receiver for SCMA is too complex in most applications, one highly popular detection technique is the message passing algorithm (MPA), which exploits the sparsity structure of SCMA. MPA is a near-optimal technique, where its performance improves with increasing the signal-to-noise-ratio (SNR) and the number of iterations. We design novel SCMA SNR-adaptive MdCs which result in substantial performance gains (as much as 2 dB) in comparison to the best known SCMA MdCs in the literature, especially in low-to-medium SNR regions when the number of MPA iterations has to be low, and in the presence of 5G-compliant LDPC codes. Sphere decoding (SD) based detection schemes for SCMA have recently received attention due to their promising features. However, the existing SD-based schemes can only be applied to systems with constellations that possess a certain structure. In the second part of the thesis, we propose an SD-based detection scheme, namely improved SD (ISD), for SCMA that achieves the optimal ML detector for any arbitrary regular or irregular constellations. I would also like to mention the government of Ontario in acknowledgment of the Ontario Trillium scholarship which pledged a wide support to my Ph.D., Huawei Technologies research and development team, specially Dr.

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Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks

Cited by 6 publications

References 9 publications

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Successive interference cancelation‐inspired channel estimation for downlink non‐orthogonal multiple access

Designing Multidimensional Constellations and Efficient Detection Schemes for Sparse Code Multiple Access (SCMA) Systems

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