Least Mean Squares Channel Estimation for Downlink Non-Orthogonal Multiple Access

Sekokotoana, Lehlohonolo Edwin; Takawira, Fambirai; Oyerinde, Olutayo O.

doi:10.1109/africon46755.2019.9133868

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The adaptive adjustment of the A-FSD decoder needs the cooperation of the channel estimation algorithm. Typical SNR estimation methods for AWGN channels include M2M4 estimation [27,28], singular value decomposition [29] and data fitting algorithm [30]. In this paper, we adopt the data fitting algorithm with simple calculation and accurate estimation.…”

Section: It Can Be Expressed Asmentioning

confidence: 99%

Improved encoding structure and decoding algorithms for spinal codes

Huang

Wang

2022

J. of Syst. Eng. Electron.

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To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding (A-FSD) algorithm with lower complexity for spinal codes is proposed. In the A-FSD algorithm, a flexible threshold parameter is set by a variable channel state to narrow the scale of nodes accessed. On this basis, a new decoding method of AFSD with early termination (AFSD-ET) is further proposed. The AFSD-ET decoder not only has the ability of dynamically modifying the number of stored nodes, but also adopts the early termination criterion to curtail complexity. The complexity and related parameters are verified through a series of simulations. The simulation results show that the proposed spinal codes with tail-biting and the AFSD-ET decoding algorithms can reduce the complexity and improve the decoding rate without sacrificing correct decoding performance.

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Section: It Can Be Expressed Asmentioning

confidence: 99%

Improved encoding structure and decoding algorithms for spinal codes

Huang

Wang

2022

J. of Syst. Eng. Electron.

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“…A novel linear estimation for NOMA system is proposed to improve the average effective signal-tointerference-and-noise ratio (SINR) of one strong user while guaranteeing a bounded average effective SINR of the weak user [3]. A least mean squares (LMS)-based channel estimation approach is presents for NOMA system [4]. These estimations are an improvement on the traditional methods, and they are incapable of capturing the change in the complicated channel information.…”

Section: Introductionmentioning

confidence: 99%

Laplacian Densely connected Channel Estimation Network in NOMA System

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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Non-orthogonal multiple access (NOMA) is an essential technology in wireless communications because it can increase the number of users. However, it needs the accurate channel state information (CSI) to achieve successive interference cancellation (SIC) and the signal detection. To that end, we propose a laplacian densely connected channel estimation network in NOMA system. The network integrates the laplacian pyramid structure between the dense connected blocks to reconstruct the complete channel matrix based on channel matrix at pilot positions. The Laplacian pyramid structure can gradually increase the size of channel matrix which make full use of channel matrix information of different sizes, and the dense connected network reuse the network information to enhance the network performance. The 3rd Generation Partnership Project (3GPP) channel models and mean square error as estimation error are adopted to evaluate the network performance. The estimation error shows that the proposed network is better than least squares (LS) estimation with linear interpolation, and competitive to the minimum mean square error (MMSE) estimation.

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“…Recently, some scholars have studied the NOMA system under realistic channel estimation. The least mean squares (LMS) algorithm is proposed to estimate the channel information of the power domain NOMA system [14]. In [15], the channel estimation error of linear minimum mean square error (LMMSE) algorithm is smaller than that of weighted least square (WLS) algorithm for uplink power domain NOMA systems.…”

Section: Introductionmentioning

confidence: 99%

Sparse Signature Matrix Optimization for NOMA under Imperfect CSI

Yang

2021

Preprint

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Non-orthogonal multiple access (NOMA) can support the rapid development of the Internet of Things (IoT) with its potential to support high spectral efficiency and massive connectivity. The low-density superposition modulation (LDSM) scheme is one of the NOMA schemes and uses the sparse signature matrix to reduce multiple access interferences (MAI). In order to improve the NOMA system performance in practice, this paper focuses on designing the sparse signature matrix with a large girth for LDSM under imperfect channel state information (CSI). Based on the orthogonal pilot and linear minimum mean square error (LMMSE) estimation, the LDSM optimized by bare-bone particle swarm optimization (BBPSO) algorithm has a larger girth and can gather more accurate information in the process of iterative decoding convergence. An extrinsic information transfer (EXIT) chart analysis is designed for the LDSM-OFDM system as a theoretical analysis tool. The simulation results show that the optimized LDSM outperforms the reference LDSM system, bringing about a 0.5 dB performance gain.

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Least Mean Squares Channel Estimation for Downlink Non-Orthogonal Multiple Access

Cited by 5 publications

References 8 publications

Improved encoding structure and decoding algorithms for spinal codes

Improved encoding structure and decoding algorithms for spinal codes

Laplacian Densely connected Channel Estimation Network in NOMA System

Sparse Signature Matrix Optimization for NOMA under Imperfect CSI

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