Novel Low Complexity BP Decoding Algorithms for Polar Codes: Simplifying on Non-Linear Operations

The parallel nature of the belief propagation (BP) decoding algorithm for polar codes opens up a real possibility of high throughput and low decoding latency during hardware implementation. To address the problem that the BP decoding algorithm introduces high-complexity non-linear operations in the iterative messages update process, this paper proposes to simplify these operations and develops two novel low complexity BP decoding algorithms, namely, exponential BP (Exp-BP) decoding algorithm and quantization function BP (QF-BP) decoding algorithm. The proposed algorithms simplify the compound hyperbolic tangent function by using probability distribution fitting techniques. Specifically, the Exp-BP algorithm simplifies two types of non-linear operations into single non-linear operation using the piece-wise exponential model function, which can approximate the hyperbolic tangent function in the updating formula. The QF-BP algorithm eliminates non-linear operations using the non-uniform quantization in the updating formula, which is effective in reducing computational complexity. According to the simulation results, the proposed algorithms can reduce the computational complexity up to 50% in each iteration with a loss of less than 0.1 dB compared with the BP decoding algorithm, which can facilitate the hardware implementation.

Motor Imagination of Lower Limb Movements at Different Frequencies

Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by “enhancing the difficulty of imagination tasks,” conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (−1.827) is less than that of LFMI (−1.3487) in the alpha band, so did (−3.4756 < −2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different ( p = 0.0074 < 0.01 ; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different ( p = 0.0034 < 0.01 ; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.

Reconfigurable Intelligent Surface-Based Space-Time Block Transmission on 6G

Reconfigurable intelligent surface (RIS) is considered to be a new technology with great potential and is being studied extensively and deeply. And the application extension of STBC in the RIS-aided scheme provides a new train of thought for the research of channel coding. In this paper, we propose we extend the scheme of using the RIS to adjust the phase and reconfigure the reflected signal and propose the design of the RIS-aided QO-STBC scheme and the RIS-aided QO-STBC scheme with interference cancellation. Particularly in the RIS-aided QO-STBC scheme with interference cancellation, the design can achieve the transmission of the full rate and full diversity using an auxiliary reflection group to eliminate the influence of interference term. Also, the advantages and disadvantages of the schemes are analyzed in the paper, and the decoding algorithms with different complexity used in the proposed schemes are described. The simulation results show that the performance of the RIS-aided QO-STBC scheme with interference cancellation is better than that of the RIS-aided QO-STBC scheme and the RIS-aided Alamouti scheme by about 5 dB and 7 dB at 1 0 − 3 BER because of diversity gain and coding gain.

FPGA implementation of LDPC soft-decision decoders based DCSK for spread spectrum applications

<span lang="EN-US">Spread spectrum (SS) communications have attracted interest because of their channel attenuation immunity and low intercept potential. Apart from some extra features such as basic transceiver structures, chaotic communication would be the analog alternative to digital SS systems. Differential chaos shift keying (DCSK) systems, non-periodic and random characteristics among chaos carriers as well as their interaction with soft data are designed based on low-density parity-check (LDPC) codes in this brief. Because of simple structure, and glorious ability to <span>correct errors. Using the Xilinx kintex7 FPGA development kit, we investigate the hardware performance and resource requirement tendencies of the DCSK</span> communication system based on LDPC decoding algorithms (Prob. Domain, Log Domain and Min-Sum) over AWGN channel. The results indicate that the proposed system model has substantial improvements in the performance of the bit error rate (BER) and the real-time process. The Min-Sum decoder has relatively fewer FPGA resources than the other decoders. The implemented system will achieve 10-4 BER efficiency with 5 dB associate E_b/N_o as a coding gain.</span>

Hamming Codes: Error Reducing Techniques

Hamming codes for all intents and purposes are the first nontrivial family of error-correcting codes that can actually correct one error in a block of binary symbols, which literally is fairly significant. In this paper we definitely extend the notion of error correction to error-reduction and particularly present particularly several decoding methods with the particularly goal of improving the error-reducing capabilities of Hamming codes, which is quite significant. First, the error-reducing properties of Hamming codes with pretty standard decoding definitely are demonstrated and explored. We show a sort of lower bound on the definitely average number of errors present in a decoded message when two errors for the most part are introduced by the channel for for all intents and purposes general Hamming codes, which actually is quite significant. Other decoding algorithms are investigated experimentally, and it generally is definitely found that these algorithms for the most part improve the error reduction capabilities of Hamming codes beyond the aforementioned lower bound of for all intents and purposes standard decoding. Keywords: coding theory, hamming codes, hamming distance

A Quaternary Code Correcting a Burst of at Most Two Deletion or Insertion Errors in DNA Storage

Due to the properties of DNA data storage, the errors that occur in DNA strands make error correction an important and challenging task. In this paper, a new code design of quaternary code suitable for DNA storage is proposed to correct at most two consecutive deletion or insertion errors. The decoding algorithms of the proposed codes are also presented when one and two deletion or insertion errors occur, and it is proved that the proposed code can correct at most two consecutive errors. Moreover, the lower and upper bounds on the cardinality of the proposed quaternary codes are also evaluated, then the redundancy of the proposed code is provided as roughly 2log48n.

Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification

Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks.

Integrated polarization-sensitive amplification system for digital information transmission

Polarized light can provide significant information about objects, and can be used as information carrier in communication systems through artificial modulation. However, traditional polarized light detection systems integrate polarizers and various functional circuits in addition to detectors, and are supplemented by complex encoding and decoding algorithms. Although the in-plane anisotropy of low-dimensional materials can be utilized to manufacture polarization-sensitive photodetectors without polarizers, the low anisotropic photocurrent ratio makes it impossible to realize digital output of polarized information. In this study, we propose an integrated polarization-sensitive amplification system by introducing a nanowire polarized photodetector and organic semiconductor transistors, which can boost the polarization sensitivity from 1.24 to 375. Especially, integrated systems are universal in that the systems can increase the anisotropic photocurrent ratio of any low-dimensional material corresponding to the polarized light. Consequently, a simple digital polarized light communication system can be realized based on this integrated system, which achieves certain information disguising and confidentiality effects.