Clustering-based Joint Channel Estimation and Signal Detection for Grant-free NOMA

Salari, Ayoob; Shirvanimoghaddam, Mahyar; Shahab, Muhammad Basit; Arablouei, Reza; Johnson, Sarah J.

doi:10.1109/gcwkshps50303.2020.9367466

Cited by 12 publications

(7 citation statements)

References 53 publications

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“…It is assumed that the AP knows the partial CSI, which is the effective channel gain of each STA by using the blind energy estimation method based on the clustering algorithm such as k-means and Gaussian mixture model without the pilot signal [32]- [34]. For example, a soft k-means clustering method (expectation-maximization (EM) algorithm) can be exploited to blindly estimate the channel gains of multiple STAs in uplink CR-STLC NOMA systems, which infers the means of the Gaussian mixture model for given the number of STAs and modulation orders.…”

Section: System Modelmentioning

confidence: 99%

Performance Analysis of Uplink NOMA With Constellation–Rotated STLC for IoT Networks

Lee

Yeom

Joung

et al. 2022

IEEE Open J. Commun. Soc.

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We investigate the uplink non-orthogonal multiple access (NOMA)-based Internet-of-Things (IoT) networks, where each IoT device (station: STA) equipped with a single antenna exploits the constellation-rotated space-time line code (CR-STLC) to send signals to an access point (AP) equipped with two receive antennas. The AP decodes the signals transmitted from the STAs with the optimal joint maximum-likelihood (JML) detector. As a main result, we mathematically analyze both the bit-error-rate (BER) performance and the spatial diversity order of each STA in the two-user uplink CR-STLC NOMA system. In particular, it is shown that the two-user uplink CR-STLC NOMA system achieves the optimal diversity order regardless of the constellation rotation angle in the high signal-to-noise ratio (SNR) regime. We also consider two different rotation angle optimization techniques (dynamic rotation & fixed rotation) to improve the BER performance in the practical SNR regime, and found that the fixed rotation approach is simple but yields almost the same performance as the dynamic approach. Finally, we show that the uplink CR-STLC NOMA system achieves the spatial diversity order of 1 even when more than two STAs send packets simultaneously. It is worth noting that all STAs obtain an improved spatial diversity gain compared to that without constellation rotation.

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Section: System Modelmentioning

confidence: 99%

Performance Analysis of Uplink NOMA With Constellation–Rotated STLC for IoT Networks

Lee

Yeom

Joung

et al. 2022

IEEE Open J. Commun. Soc.

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“…We now design an expectation-maximization (EM)-based blind energy estimation (BEE) scheme for blindly estimating the transmit power and effective channel gain,

, of each STA at the AP. Briefly, we exploit the EM algorithm [ 24 ], also known as soft K -means clustering, which infers the parameters of the GMM given the number of STAs and modulation type of each STA [ 25 , 26 ]. Recall that although the constellation consisting of the superimposed M -ASK symbols from the two STAs configures

clusters as shown in the first column in Figure 1 , the signals of each STA are aligned on each axis.…”

Section: Blind Energy Estimation (Bee)mentioning

confidence: 99%

“…This is indispensable in the uplink STLC-NOMA system for blindly decoding the signals transmitted from STAs, but it has not been well-elaborated in literature [ 22 , 23 ]. Therefore, we further design a blind energy estimation (BEE) scheme for the proposed low-complexity STLC-NOMA system based on expectation-maximization (EM) for Gaussian mixture model (GMM) [ 24 , 25 ]. Although a machine learning-based blind decoding method for STLC systems has been proposed in Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems

Lee

Jung

2022

Sensors

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In this paper, we revisit a two-user space-time line coded uplink non-orthogonal multiple access (STLC-NOMA) system for Internet-of-things (IoT) networks and propose a novel low-complexity STLC-NOMA system. The basic idea is that both IoT devices (stations: STAs) employ amplitude-shift keying (ASK) modulators and align their modulated symbols to in-phase and quadrature axes, respectively, before the STLC encoding. The phase distortion caused by wireless channels becomes compensated at the receiver side with the STLC, and thus each STA’s signals are still aligned on their axes at the access point (AP) in the proposed uplink STLC-NOMA system. Then, the AP can decode the signals transmitted from STAs via a single-user maximum-likelihood (ML) detector with low-complexity, while the conventional uplink STLC-NOMA system exploits a multi-user joint ML detector with relatively high-complexity. We mathematically analyze the exact BER performance of the proposed uplink STLC-NOMA system. Furthermore, we propose a novel expectation-maximization (EM)-based blind energy estimation (BEE) algorithm to jointly estimate both transmit power and effective channel gain of each STA without the help of pilot signals at the AP. Somewhat interestingly, the proposed BEE algorithm works well even in short-packet transmission scenarios. It is worth noting that the proposed uplink STLC-NOMA architecture outperforms the conventional STLC-NOMA technique in terms of bit-error-rate (BER), especially with high-order modulation schemes, even though it requires lower computation complexity than the conventional technique at the receiver.

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“…The channel state information can be required to be estimated at the receiver side, for better BER performance. The training based estimation approaches can attain CSI with minimum complexity with high training sequences 11 . The challenges of high complexity, error propagation and high latency are tackled with parallel channel.…”

Section: Introductionmentioning

confidence: 99%

ICA-ResNet-based signal detection and optimal power allocation for CRN-NOMA system

Kumar

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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Non-orthogonal multiple access (NOMA) system is one of the assuring solutions for the upcoming wireless communication systems for achieving better spectral efficiency. The NOMA based wireless communication system is mainly transformed with cognitive radio network (CRN) to improve the performance. This research work focuses on signal detection and optimal power allocation for uplink CRN-NOMA system using optimal deep learning (DL) model. This model combines the channel state estimation process and signal detection. In the proposed work, the signal detection is carried out on the basis of allocated power to the user. Initially, the power allocation process is carried out by the Bisection water filling algorithm to achieve optimal power allocation. Then, the signal detection process is enabled by the DL model ResNet-152. Then, the metaheuristic optimization using Imperialist Competitive Algorithm (ICA) is exploited to provide optimal solution for better signal detection. The noisy channel's information like Rayleigh fading and AWGN are set for wireless transmission medium to transmit a signal. The combined model of power allocation and signal detection is carried out on MATLAB platform. The performance measures like spectral efficiency, energy efficiency, BER and average response rate of the proposed techniques are compared with the previous research works. When the value of mean harvested energy is 4.5 J  , the energy efficiency of the proposed model, deep Q learning, conventional TDMA and conventional NOMA/ TDMA are 0.74 [bit/s/Hz], 0.71 [bit/s/Hz], 0.67 [bit/s/Hz] and 0.57 [bit/s/Hz] respectively. It has been hence proved that the performance of this methodology is accurate and effective than the existing models.

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Clustering-based Joint Channel Estimation and Signal Detection for Grant-free NOMA

Cited by 12 publications

References 53 publications

Performance Analysis of Uplink NOMA With Constellation–Rotated STLC for IoT Networks

Performance Analysis of Uplink NOMA With Constellation–Rotated STLC for IoT Networks

A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems

ICA-ResNet-based signal detection and optimal power allocation for CRN-NOMA system

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