Proposal and performance analysis of a novel preamble-based SNR estimation algorithm

Malik, Saransh; Portugal, Sherlie; Seo, Changwoo; Kim, Cheolsung; Hwang, Intae

doi:10.1109/tsp.2011.6043764

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…Moreover, accurate estimation plays important role for the optimal modulation selection. However, there have been studies that proposed approaches to mitigate incurred throughput penalties in DA-SNR by exploiting synchronization preamble [15,16,19,25,26]. Many preamble structures have been proposed for time synchronization in OFDM system [27][28][29].…”

Section: Related Workmentioning

confidence: 99%

“…The DA-SNR estimator uses transmitted pilot data to estimate SNR at the receiver. This method increases estimation accuracy at the cost of decreased system throughput [14][15][16][17]19]. However, the NDA-SNR estimator blindly estimates SNR without prior transmitted data.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the SNR estimation in frequency domain would incur computational complexity of the Fast Fourier Transform (FFT) computation. From the literature so far, SNR estimation algorithms for MIMO-OFDM system were derived using two methods, namely maximum likelihood [17] and second order moment criteria [14][15][16]19]. The derivation of the SNR expression based on these methods involved probabilistic methods which is high in complexity, and required multiplication and addition operations in SNR estimation in opposed to SNR estimation using autocorrelation methods.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Modulation in STBC AssistedMIMO-OFDM System using Preamble-BasedSNR Estimator

Manzoor,

Othman

2023

Preprint

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This paper presents a preamble-based time domain SNR estimator for an adaptive modulation of STBC assisted MIMO-OFDM system employing \(M\)-PSK. The modified Constant Amplitude Zero Autocorrelation (CAZAC) preamble structure is exploited in the preamble-based time domain SNR estimator, which exhibits estimated SNR difference of about 0.09 dB compares to the actual SNR for SUI-5 channel. It is observed that the difference between the estimated SNR using the CAZAC-MIMO SNR estimator and the actual SNR is smaller compared to that of the estimated SNR using the benchmarking SNR estimator and its performance is further improved as the diversity order of the system increases. The proposed adaptive modulation of STBC assisted MIMO-OFDM system achieves BER and channel throughput improvement, benefited from the employment of the proposed SNR estimator. At BER\(=10^{-4}\), the proposed adaptive modulation system outperforms the corresponding non-adaptive system employing 8-PSK by about 2 dB. Meanwhile, the performance comparison between the proposed adaptive modulation system and the corresponding adaptive modulation of single-input single-output (SISO)-OFDM system demonstrated significant BER and channel throughput improvement, benefiting from spatial diversity of \(2 \times 2\) MIMO-OFDM system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Adaptive Modulation in STBC AssistedMIMO-OFDM System using Preamble-BasedSNR Estimator

Manzoor,

Othman

2023

Preprint

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“…The ACK/NACK signals, used in Hybrid Automatic Repeat Request (HARQ) scheme [1], [2], can be used to identify the breaking point SINRs, but this procedure also depends on a high error rate prospect. There are several approaches available in the literature to estimate the SINR using data-aided (DA) classes [3], [4], [5], [6]. The results presented in the literature show that the performance of the SINR estimation improves when previously known transmit data is used in the receiver side.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique for Link Adaptation Based on Inner Receiver Statistics

Kagami¹,

Figueiredo²,

Lopes³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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The fast-moving evolution of new technologies is driving the future mobile networks to a challenging pursuit for high throughput and reliability. Emerging services will need precise indicators to address contrasting requisites and link adaptation is a cardinal element. The received signal measurements must provide complete information to perform appropriate and timely decisions. Most communication systems employ statistics based on error rates, but it is not sufficient to accomplish all requirements. Additional information is necessary to define the best parametrization for each application. This paper indicates an approach to achieve this goal by monitoring processes directly into the physical layer decoding.

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“…In OFDM systems, this measurement can be performed using pilot carriers [Khan et al 2017], cyclic prefix [Baumgartner et al 2014] or preambles [Malik et al 2011]. Basically, the benefit of such methods is the use of pre-known transmitted information to estimate the SINR on the receiver side.…”

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Deep Neural Network for Signal-to-Interference-Plus-Noise Ratio Estimation

Kagami

Mendes

2021

Anais Do I Workshop De Redes 6G (W6G 2021)

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Mobile network technology has been driven by a huge demand for throughput and reliability to support new emerging services. The quality of service is based on measurements of indicators with a high level of precision. Accurate controlling of parameters to fulfil the quality requirements will be essential for future applications. In LTE and 5G standards, the Channel Quality Indicator can be calculated using different algorithms. It is key to determine the best coding and modulation as well as the power control. Thus, it depends on the exact signal-to-noise ratio estimation. MSE based on hard-decision has a very low computational cost, however, it can insert non-linearities. This paper proposes a neural network to estimate an SINR from a modified MSE function.

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Proposal and performance analysis of a novel preamble-based SNR estimation algorithm

Cited by 3 publications

References 7 publications

Adaptive Modulation in STBC AssistedMIMO-OFDM System using Preamble-BasedSNR Estimator

Adaptive Modulation in STBC AssistedMIMO-OFDM System using Preamble-BasedSNR Estimator

A Novel Technique for Link Adaptation Based on Inner Receiver Statistics

A Low-Complexity Deep Neural Network for Signal-to-Interference-Plus-Noise Ratio Estimation

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