Adjacent Symbols Research Articles

Precedence Table Construction Algorithm for CFGs Regardless of Being OPGs

Operator precedence grammars (OPG) are context-free grammars (CFG) that are characterized by the absence of two adjacent non-terminal symbols in the body of each production (right-hand side). Operator precedence languages (OPL) are deterministic and context-free. Three possible precedence relations between pairs of terminal symbols are established for these languages. Many CFGs are not OPGs because the operator precedence cannot be applied to them as they do not comply with the basic rule. To solve this problem, we have conducted a thorough redefinition of the Left and Right sets of terminals that are the basis for calculating the precedence relations, and we have defined a new Leftmost set. The algorithms for calculating them are also described in detail. Our work’s most significant contribution is that we establish precedence relationships between terminals by overcoming the basic rule of not having two consecutive non-terminals using an algorithm that allows building the operator precedence table for a CFG regardless of whether it is an OPG. The paper shows the complexities of the proposed algorithms and possible exceptions to the proposed rules. We present examples by using an OPG and two non-OPGs to illustrate the operation of the proposed algorithms. With these, the operator precedence table is built, and bottom-up parsing is carried out correctly.

Complex-valued recurrent neural network equalizer with low complexity for a 120-Gbps 50-km optical PAM-4 IM/DD system.

This paper introduces a novel complex-valued recurrent neural networks equalizer (RNNE) designed for a 120-Gbps, 50-km optical 4-level pulse-amplitude modulation (PAM-4) intensity modulation and direct detection (IM/DD) system. By mapping adjacent symbols of PAM-4 signals onto the complex domain, the correlation between two adjacent symbols of PAM-4 signals can be preserved. Based on experimental results, the proposed complex-valued RNNE outperforms the traditional real-valued RNNE with a 1.38-dB system power budget gain at the 7% overhead forward error correction BER threshold of 3.8 × 10-3. We believe that complex-valued RNNE has an advantage over real-valued RNNE in processing real-valued signals in IM/DD systems.

Encryption of messages by the sum of a real variable functions.

The article proposes a cryptosystem with symmetric keys, where the keys are functions of a real variable. These functions can be either continuous or discrete and must satisfy certain constraints. The number of key functions is determined by the number of binary digits that encode a character in the ASCII table. Each binary digit has its own key function. The cipher of a character is represented by a one-dimensional array of real numbers. These numbers are obtained by summing the key functions, which correspond to “1” in the ASCII code of the character. The amplitudes of these functions are random and unknown to the receiving party. Decryption is a multi-level process, in which integral disproportion functions are calculated at each level. To increase the cryptographic strength, the encryption/decryption process involves a permutation of the key-functions according to a secret scheme agreed upon by both parties. Computer simulation has demonstrated the high cryptographic resistance of the proposed system to the determination of the coefficients within the key functions, as well as to the rearrangement of the key functions themselves. It is shown that adjacent identical symbols in an encrypted message have different ciphers, which also complicates hacking the system.

A novel SLM scheme for peak-to-average power ratio reduction in WOLA-OFDM system

This paper addresses high peak-to-average power ratio (PAPR) issue in the recently invented weighted overlap and add-orthogonal frequency division multiplexing (WOLA-OFDM) system. The selective mapping (SLM), one of the most widely used PAPR reduction schemes in OFDM-based multicarrier transmission strategies, can be used to reduce the PAPR of WOLA-OFDM signals. However, the overlapping of symbol extensions in the WOLA-OFDM transmitter considerably deteriorates the performance of SLM. To overcome this problem, we have developed an efficient SLM scheme named dual symbol optimization-SLM (DSO-SLM) for the WOLA-OFDM system. In this new scheme, instead of reducing the PAPR of adjacent symbols separately as in conventional SLM, each symbol is evaluated together with its overlapping predecessor symbol while searching for the optimal transmission signal with minimized PAPR. With the usage of DSO procedure, it becomes possible to enhance the performance of conventional SLM in the WOLA-OFDM system.

On the Robustness to Doppler Rate of the Modified LoRa using two Spreading Factors

In [1], we proposed a modified LoRa using two spreading factors (SF’s) in order to independently set the frequency difference of adjacent LoRa symbols and the LoRa symbol period, and demonstrated that the use of two spreading factors could make it less sensitive to sampling frequency error while enabling low power transmission. In this paper, as an extension of [1], we will show that our scheme has also robustness to the Doppler rate through computer simulations.

Estimation of Dispersive High-Doppler Channels in the RIS-Aided mmWave Internet of Vehicles

Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for improving the spectral-and energy-efficiency of millimeter-wave (mmWave) Internet of vehicles (IoV) communications, but the conception of their accurate channel estimation poses. Hence the existing estimation methods mainly focus on time-invariant channels, while ignoring the Doppler effect induced by the high-velocity vehicles, which will lead to significant performance degradation. In this paper, we investigate the problem of channel estimation in RIS-aided mmWave IoV systems considering the deleterious Doppler effect. Firstly, we derive the expression of the time-varying cascaded two-hop multiple-path channels, where each delay tap is subject to multiple paths instead of having a simple one-to-one correspondence. In order to decouple the paths, the problem is formulated in the delay-domain by a series of transformations and the cascaded two-hop channel can be estimated at each delay tap. Then we propose a pair of estimation strategies by considering different hardware constraints depending on the number of receiver antennas at the base station (BS). When a large receiver array is employed at the BS, we can exploit its high angular selectivity for distinguishing each resolvable path at a certain delay tap because they arrive from different directions. However, this cannot be achieved for small arrays, given their more limited angular resolution. Thus, the RIS reflection patterns are delicately designed for distinguishing multiple resolvable paths. After separating the paths, Doppler estimation can be performed by calculating the phase difference of the adjacent symbols. Our simulation results demonstrate the superior performance of the proposed methods within a wide range of Doppler shifts.

Enhanced Performance of Asymmetrically Clipped DC-Biased Optical OFDM Systems Using Adjacent Symbol Detection

Enhanced Performance of Asymmetrically Clipped DC-Biased Optical OFDM Systems Using Adjacent Symbol Detection

Cluster-aware channel estimation with deep learning method in deep-water acoustic communications.

In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.

TWENTY SIMILARITY FUNCTIONS OF TWO FINITE SEQUENCES

The article discusses the various numerical functions that determine the degree of "similarity" of the two given final sequences. These similarity measures are based on the concept we define of embedding in a sequence. A special case of such an attachment is the usual sub-subsequence. Other cases further require equality of distances between adjacent sub-sequence symbols in both sequences. This is generalization of the concept of a sequence segment (substring) in which these distances are unit. In addition, equality of distances from the beginning of the sequences to the first embedding symbol or from the last embedding symbol to the end of the sequences may be required. Except these last two cases, the attachment can be in a sequence several times. The literature uses functions such as the number of common attachments or the number of attachment occurrence pairs in a sequence. In addition to them, we enter three more functions: the sum of the lengths of total investments, the sum of the minima of the number of occurrences of a common embedding in both sequences and the similarity function based on the largest number of symbols of the common embedding. Intotal, 20 numerical functions are considered, for 17 of which algorithms (including new ones) of polynomial complexity are proposed, for two more functions, algorithms have exponential complexity with reduced a measure of degree. The Conclusion gives a brief comparative description of these investments and functions.

An Adaptive Sparse Channel Estimation Algorithm for OFDM Systems Based on Distributed Compressive Sensing

The current greedy iterative pursuit algorithms for sparse channel estimation in an orthogonal frequency division multiplexing (OFDM) system based on compressive sensing (CS) or distributed CS (DCS) have the disadvantages of relying on channel priori information as halting condition and having a low support searching efficiency. Under DCS framework, this letter proposes a unique halting condition for greedy algorithms by exploiting the delay correlation between adjacent symbol channels. Additionally, we present a segmented pruning strategy that supports to select multiple atoms in a single iteration to improve the support searching efficiency. Simulation results show that our algorithm can achieve more robust sparsity-adaptive channel estimation with reduced computational complexity compared to the traditional methods.

High accurate and modulation transparent frequency offset compensation scheme using data-aided feedback structure in coherent optical communication systems

The high-order modulation format has the advantages of high spectral efficiency and large system capacity. However, the traditional frequency offset estimation and compensation algorithms, such as fast Fourier transform-based frequency offset estimation (FFT-FOE) that relies on a large number of complex FFT operations and differential frequency offset estimation (Diff-FOE) that relies on finite phase difference between adjacent symbols, are difficult to meet the frequency offset estimation requirements for any high-order modulation format. In this work, a novel FOE scheme using QPSK training symbols aided feedback structure is proposed. The performance of the proposed FOE scheme is evaluated in a polarization multiplexing QPSK-aided 32/64-quadrature amplitude modulation (QAM) simulation platforms with a symbol rate of 28 GBaud. The results show that has the technical advantages of low complexity, suitable for arbitrarily high-order modulation, tolerance for laser phase noise and wide tracking frequency offset range.

Design of a Channel Robust Radio Frequency Fingerprint Identification Scheme

Radio frequency fingerprint (RFF) identification is an emerging device authentication technique that exploits the hardware imperfections resulting from the manufacturing process. Due to the varying impact of the wireless channel during RFF training and test stages, it is challenging to design channel-independent RFF techniques. This article designs a channel robust RFF identification scheme by leveraging the different spectrum of adjacent signal symbols, named the Difference of the Logarithm of the Spectrum (DoLoS), which does not rely on a single RFF feature or requires additional manipulation of the devices under test. Specifically, DoLoS exploits the fact that two different symbols in a packet exhibit different RFF features but have a similar channel response during the channel coherence time. We implemented the DoLoS with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system as a case study. We carried out extensive experiments using seven Wi-Fi devices of the same model in different wireless channel environments, including 12 data collection positions in two completely different environments. Compared with conventional RFF identification schemes that do not eliminate channel effects, our scheme is robust to channel variations and the highest identification accuracy is 99.02% in the single-environment evaluation and 97.05% in the cross-environment evaluation.

Evaluation of Non-Coherent Signal Detection Techniques for Mobile Molecular Communication.

In recent years, there have been more and more research on molecular communication (MC). Because the deployment of mobile nanomachines may be required in some applications of MC, research on mobile MC has become a trend. The signal detection schemes for static MC are no longer applicable due to the time varying channel impulse response (IR), which is caused by the mobile characteristics of nanomachines. In this paper, a low complexity and non-coherent detection scheme is proposed for mobile scenario, which is based on the energy difference between two adjacent symbols. Most of the existing signal detection methods do not consider inter-symbol interference (ISI). Compared with those methods, the proposed scheme can achieve signal detection utilizing ISI without knowing channel state information (CSI). The bit error rate (BER) performance of the proposed method is investigated under different conditions through simulations. Besides, the influence of mobility features of nanomachines on the signal detection accuracy is also investigated in detail. The simulation results demonstrate that the BER performance of the proposed scheme outperforms the latest signal detection scheme for short-distance mobile MC system with high velocity. Consequently, the detection scheme proposed in this paper can reduce the influence of nanomachines' mobility and has the potential to be used in mobile MC systems.

Can HRV Predict Prolonged Hospitalization and Favorable or Unfavorable Short-Term Outcome in Patients with Acute Ischemic Stroke?

The aim of this study was to assess whether the heart rate variability (HRV) could predict a favorable or unfavorable stroke outcome. The endpoint was based on the National Institutes of Health Stroke Scale (NIHSS). The patient's health condition was assessed upon discharge from the hospital. An unfavorable stroke outcome was defined as death or NIHSS ≥ 9, while NIHSS < 9 meant a favorable stroke outcome. The studied group consisted of 59 patients with acute ischemic stroke AIS (mean age of 65.6 ± 13.2; 58% were females). An original and innovative non-linear measure was used to analyze HRV. It was based on symbolic dynamics consisting of comparing the "length of the longest words" in the night recording of HRV. "The length of the longest word" meant the longest sequence of identical adjacent symbols possible for a patient. An unfavorable stroke outcome occurred in 22 patients, whereas the majority of patients (37) had a favorable stroke outcome. The average hospitalization time of patients with clinical progression was 29 ± 14 days, and with favorable outcomes was 10 ± 3 days. Patients with long words (more than 150 adjacent RR intervals having the same symbol) were hospitalized no longer than 14 days and they had no clinical progression. The patients with a favorable stroke outcome were characterized by longer words. Our pilot study may be the beginning of work on the development of a non-linear, symbolic method as a predictor of prolonged hospitalization and increased risk of clinical progression in patients with AIS.

Design and Development of Improved Coyote Optimization-based Adaptive Equalization Technique for ECG Signal Transmission

Biological signals such as an electrocardiogram (ECG) are broadcast directly through wireless devices and mobile networks during patients' real-time activities and over limitless distances. However, the transmission of signals through a limited band channel or over multi-propagation suffers from limitations such as inter-symbol interference (ISI). In channel output, the adjacent symbols smudge and integrate with each other by degenerating during error analysis. Equalization filters are used to improve such kinds of deformation. A new adaptive equalization approach is proposed in this paper to extract real broadcasted signals from Gaussian noise-distorted ECG signals using the enhanced meta-heuristic algorithm. The weight optimization strategy of the "adaptive equalization technique" is implemented using oppositional searched coyote optimization algorithm (OS-COA). The main purpose of adapting the improved meta-heuristic-based adaptive equalization technique is to decrease error in the section of receivers, thus ensuring the received ECG signal is error-free. The obtained outcomes are examined to evaluate the performance of error metrics like "mean square error (MSE), and convergence rate" of the introduced model and for comparison with other existing equalization approaches. According to the experimental evaluation, the recommended adaptive linear equalization technique has better extraction performance than other blind and nonlinear equalization approaches.

Modified SC Decoding for BDPSK Modulated Polar Codes

In this letter, we derive a modified successive cancellation (SC) decoder for binary differential shift keying (BDPSK) modulated polar codes. The proposed decoder takes the phase differences of adjacent even-indexed BDPSK symbols as additional inputs, and the simulations show that the proposed decoder could improve the error performance of polar coded BDPSK systems.

Capacity Analysis and Data Detection of OvTDM-MIMO System

It is well known that the channel capacity of traditional MIMO systems is positively correlated with the number of antennas, so MIMO systems usually use more antennas to meet higher system capacity requirements, which is increasingly conflicting with the miniaturization requirements of portable devices. To overcome the above problems, a new OvTDM-MIMO system is proposed, which can effectively improve the capacity of the system. This system introduces the overlapped time division multiplexing (OvTDM) technology into the MIMO system, which can further improve the channel capacity of the MIMO system by using a smaller number of antennas. This paper introduces the transceiver model of the OvTDM-MIMO system in detail and derives the channel capacity of the system based on mutual information theory. Compared with a conventional MIMO system, the OvTDM-MIMO system has the ability to improve channel capacity. Then, this paper proposes an OvTDM-MIMO symbol detection scheme based on an improved low-complexity Orthogonal Approximate Message Passing (OAMP) algorithm to solve the symbol detection problem caused by symbol correlation. The main design idea of this scheme is to use symbol correlation as the detection constraint and realizes joint symbol detection by combining adjacent symbols, avoiding the problem of excessive matrix size caused by Kronecker product operation. The simulation results show that the proposed detection algorithm can achieve similar performance to traditional MIMO system detection algorithms with low computational complexity.

Transition‐limited pulse‐amplitude modulation technique for high‐speed wireline communication systems

AbstractThis paper presents a transition‐limited pulse‐amplitude modulation (TLPAM) signaling method to enable a high data rate and robust wireline communications. TLPAM signaling addresses the impact of high intersymbol interference (ISI) ratios in conventional M‐ary PAM signaling methods by limiting the maximum voltage transition level between adjacent symbols. The implementation of a TLPAM signaling encoder is realized by setting back the most significant bits (MSBs) in the queue. The correlation between TLPAM's maximum transition level, effective data rate, and eye width/height is analyzed with various channel loss parameters, followed by characterization and measurement results with a realistic channel setup. The analysis and experimental results reveal the effectiveness of the proposed TLPAM signaling scheme for achieving a high data rate with minimal interference.

Nonlinear Impairment Compensation Using Transfer Learning-Assisted Convolutional Bidirectional Long Short-Term Memory Neural Network for Coherent Optical Communication Systems

By combining the nonlinear impairment features derived from the first-order perturbation theory, we propose a nonlinear impairment compensation (NLC) scheme based on the transfer learning-assisted convolutional bidirectional long short-term Memory (CNN-BiLSTM) neural network structure. When considering the correlation of nonlinear impairment between preceding and succeeding consecutive adjacent symbols on the current moment symbol and integrating the multidimensional feature extraction and time memory characteristics of CNN-BiLSTM, the nonlinear impairment information contained in the input feature can be fully utilized to accurately predict the nonlinear impairment showing significant compensation effect. Meanwhile, transfer learning (TL) is introduced to greatly reduce the complexity of the scheme on the basis of high compensation performance. To verify the effectiveness of the proposed scheme, we construct single-channel (SC) and 5-channel 28 GBaud polarization division multiplexing 16 quadrature amplitude modulation (PDM-16QAM)/85 GBaud PDM-64QAM simulation systems, and SC and 3-channel 28 GBaud PDM-16QAM experimental systems. The experimental results show that when compared with simple recurrent neural network (SRNN) NLC and DBP 20 steps per span (DBP20StPs), the Q-factor gain of our scheme is about 1 dB and 1.7 dB in the SC system, and about 1.1 dB and 1.5 dB in the 3-channel system at the optimal launch power, respectively. It is interesting to highlight that, by applying TL to the simulation and experimental systems, our scheme based on only 5% of the training samples can achieve compensation performance comparable to or higher quality than retraining at various launch powers.

LSTM Attention Neural-Network-Based Signal Detection for Hybrid Modulated Faster-Than-Nyquist Optical Wireless Communications.

In order to improve the accuracy of signal recovery after transmitting over atmospheric turbulence channel, a deep-learning-based signal detection method is proposed for a faster-than-Nyquist (FTN) hybrid modulated optical wireless communication (OWC) system. It takes advantage of the long short-term memory (LSTM) network in the recurrent neural network (RNN) to alleviate the interdependence problem of adjacent symbols. Moreover, an LSTM attention decoder is constructed by employing the attention mechanism, which can alleviate the shortcomings in conventional LSTM. The simulation results show that the bit error rate (BER) performance of the proposed LSTM attention neural network is 1 dB better than that of the back propagation (BP) neural network and outperforms by 2.5 dB when compared with the maximum likelihood sequence estimation (MLSE) detection method.