Polar-coded modulation based on the amplitude phase shift keying constellations

Zhou, Dekun; Dai, Jincheng; Niu, Kai; Dong, Chao; Sun, Jingyuan; Zhang, Yi; Guan, Hao

doi:10.1109/cc.2017.8068774

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research contribution regarding GA implementation architecture for SCL and SC decoding for polar codes has good references. However, most research did not focus on GA implementation architecture and ASK modulation with either SC or SCL decoder for their polar codes algorithm [21][22][23][24]. Besides, several research papers were proposed over a decade ago [25], [26].…”

Section: Related Research On Polar Codesmentioning

confidence: 99%

The Effect of Different Decoding Techniques With Gaussian Approximation on the Performance of Polar Codes

Suhaimi

Shuhaimi

Mohamad

2022

AEJ

View full text Add to dashboard Cite

In the last ten years, polar code research has piqued the interest of firms and researchers, particularly in the communication industry. Polar codes have been utilised as a coding method for the fifth-generation wireless standard (5G). However, the polar decoder does not adequately correct errors in successive cancellation (SC) decoding when dealing with short- to intermediate-length codes. However, SC decoding can correct errors more efficiently by using sequential cancellation list (SCL) decoding. The main drawback of SCL is its higher cost due to computational complexity and throughput. The present research investigates the effect of Gaussian approximation (GA) and different decoding approaches on the performance of polar codes. First, SC and SCL decoders are developed utilising amplitude shift keying modulation; a decoder using GA is then integrated. According to simulation data, the SCL, both with and without GA, exhibits a better block error rate (BLER) than SC. The maximum difference between the SCL decoder and SC decoder is 0.6 dB at BLER=0.1 for N=2048. Furthermore, at BLER=5.6 x 10-6, the SCL decoder with GA performs better than the SC decoder for block lengths, N=1024, with a maximum difference of 2.72 dB. When the polar decoder with GA is utilised, enhancements are observed in polar code performance for various list sizes and block lengths, although time complexity is increased.

show abstract

Section: Related Research On Polar Codesmentioning

confidence: 99%

The Effect of Different Decoding Techniques With Gaussian Approximation on the Performance of Polar Codes

Suhaimi

Shuhaimi

Mohamad

2022

AEJ

View full text Add to dashboard Cite

show abstract

“…At present, AMC approaches can be roughly categorized into maximum likelihood methods, statistical pattern recognition methods and deep learning (DL) recognition methods. In likelihood-based ratio recognition methods, the modulation recognition problem is regarded as a hypothesis test problem, and a likelihood function is used to make the decision to realize signal classification [8], [9]. The statistical pattern recognition method compares the modulation characteristics with the theoretical features [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Modulation Recognition System Based on Software-Defined Radio and Multi-Skip Residual Neural Network

Lin¹,

Yan²,

Zhang³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Communication signal modulation recognition has important research value in the fields of cognitive electronic warfare, communication countermeasures and non-collaborative communication. However, traditional signal recognition methods usually suffer some drawbacks, such as low accuracy, poor scalability, dependence on expert characteristics, and poor applicability to real-world environments. Therefore, in this paper, a real-time modulation recognition system based on deep learning and softwaredefined radio (SDR) technology is designed. In the first step, an improved residual neural network is designed. A multi-skip residual stack (MRS) is designed to preserve more initial residuals information on the multiscale feature map, which can simultaneously learn the deep and shallow characteristics of the signal. Then, a multi-skip residual network is designed with the MRS as the basic unit, and the network is trained using an adaptive moment estimation optimization algorithm. Finally, the network is tested on public datasets. In the second step, the network is embedded in a SDR platform composed of a GNU Radio and an universal software radio peripheral to realize real-time recognition of the input signal. Experiments show that this system has strong real-time capabilities, high recognition accuracy and considerable robustness.

show abstract