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Text-based emotion prediction is an important task in the field of affective computing. Most prior work has been restricted to predicting emotions corresponding to a few high-level emotion classes. This paper explores and experiments with various techniques for fine-grained (27 classes) emotion prediction $^\dagger$ † . In particular, 1) we present a method to incorporate multiple annotations from different raters, 2) we analyze the model's performance on fused emotion classes and with sub-sampled training data, 3) we present a method to leverage the correlations among the emotion categories, and 4) we propose a new framework for text-based fine-grained emotion prediction through emotion definition modeling. The emotion definition-based model outperforms the existing state-of-the-art for fine-grained emotion dataset GoEmotions. The approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. We show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the model's generalization capability. [ABSTRACT FROM AUTHOR]

In this letter, a combined encryption and error-control coding scheme based on turbo codes (TCs) is proposed to enhance the security of the transmission. It relies on convolutional encoders that operate over Galois fields ${\textrm {GF}}(4)$ and include variable encryption polynomials, whose coefficients are randomly selected every $L$ steps from a set of optimal polynomials. This type of encoder has been studied in previous papers, where a certain correspondence between the randomness of the encoded sequence and the performance of the TC was conjectured. In this work, a scheme with a varying length $L$ is proposed to increase the output randomness and, therefore, the security of the sequence to be transmitted. That is to say, the number of steps $L$ , over which one of the polynomials is iterated, is a random variable. The randomness of the output and the security of the TC are quantified using differential block entropy, showing that it is possible to improve the security of the transmitted data without compromising the corresponding bit error rate (BER). [ABSTRACT FROM AUTHOR]

This paper investigates intelligent reflecting surface based backscatter communication (IRS-BackCom), in order to realize computational task offloading of energy-constrained mobile edge computing network in a self-sustainable manner. Specifically, the system operation is divided into two phases. In the first one, the ambient signal energy from a power beacon (PB) either provides the energy supply of local computing and energy harvesting circuits, or flows into the energy storage, when reaching the IRS. In the second one, the stored energy is used to enable IRS-BackCom for partial computational data offloading and energize local computing circuit. Based on this, the maximization problem of sum computational bits is formulated. By jointly optimizing the beamforming vector at the PB, the backscatter matrix at the IRS, the time scheduling of two-phase process, as well as the time of local computing, sum computational bits are maximized. In addition, this paper proposes element clustering to realize BackCom, so as to reduce the control and computation complexity of IRS. According to different operating mechanisms, two cluster operation modes are considered, namely independent cluster operation mode and joint cluster operation mode. Simulation results demonstrate the achievable sum computational bits by the proposed IRS-BackCom schemes. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a well-accepted technology for future Wi-Fi systems to improve spectral efficiency. NOMA allows an access point (AP) to simultaneously serve multiple devices by splitting the transmit power between them. Using the superposition coding the AP combines signals of different frames with predefined power weights. However, as it is already known, the phase noise caused by hardware imperfections limits the performance of NOMA transmissions. The paper proposes a practical, easy-to-implement, and backward-compatible method to rotate the constellation of the low power signal in order to make it more robust to the phase noise. A mathematical model is developed to find such a rotation angle that minimizes the bit error rate (BER) of the low power frame. A new NOMA Wi-Fi prototype testbed is developed with the new feature of constellation rotation ability, which is used to validate the performance of our proposed method. Many experimental results show the proposed constellation rotation approach significantly decreases the BER in high signal-to-noise scenarios. [ABSTRACT FROM AUTHOR]

The performance of the recently proposed orientational beamforming (OBF) system for ultra-wideband (UWB) signals degrades seriously in multiuser situation. Although various techniques have been proposed in the literature to suppress multiuser interference in the time-hopping UWB system, there has been no research on interference suppression in the OBF UWB system. In this paper, we propose a nonlinear OBF system via a modified radial basis function (RBF) neural network, named the RBF-OBF system, to reject orientational UWB interferences caused by multiuser transmission in the OBF system. The RBF neural network is selected mainly because of its simple structure, convenient training process, and fast convergence speed. However, the conventional Euclidean-distance-based RBF neural network is not suitable for the training process used in this work, as the training samples are noiseless orientational steering vectors in frequency domain. Therefore, a modified RBF neural network is proposed in this paper, which evaluates the similarity between an input vector and the center vector of a hidden layer neuron by their signed complex correlation coefficient. Numerous simulations demonstrate that compared to the conventional OBF system, the proposed RBF-OBF system can significantly reduce the bit error rates (BERs) under different noise and interference situations. The BER reduction rate of the proposed RBF-OBF system is higher than 92% in additive white Gaussian noise channel and higher than 89% in line-of-sight multipath channel. [ABSTRACT FROM AUTHOR]

Signed quadrature spatial modulation (SQSM) was recently introduced to enhance the throughput of spatial modulation (SM) in multiple-input multiple-output (MIMO) systems. This strategy involves independently transmitting the real and imaginary components of a symbol and its inverse from four independent antennas. In this paper, an extended SQSM (ESQSM) system is proposed. It combines $K$ SQSM constellations and transmits them from the same transmit-antenna set with the aim of realizing a $K$ -fold improvement in the spectral efficiency (SE). The proposed ESQSM employs the average power of the $K$ SQSM constellations as an additional dimension for transmission. Performance of the proposed ESQSM surpasses that of other state-of-art SM systems such as the QSM, DSM, IQSM SQSM, and EQSM by 13 dB, 11 dB, 10.55 dB, 10 dB, and 6.8 dB, respectively. In this paper, we also propose an efficient ESQSM with a multi-user (ESQSM-MU) system inspired by the ESQSM ability in increasing the SE. The proposed ESQSM-MU system offers a significant bit error rate (BER) improvement and a higher capacity (i.e., $5K$ -users) compared to the current non orthogonal multiple access-SM (NOMA-SM) and SM-assisted multi-antenna NOMA (SM-AMA-NOMA) systems. Moreover, an efficient low-complexity detector is introduced for the ESQSM signal detection, promising a 91.2% reduction in the computational complexity of ESQSM and ESQSM-MU systems relative to the maximum likelihood (ML) with a trivial loss in performance. [ABSTRACT FROM AUTHOR]

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme. [ABSTRACT FROM AUTHOR]

Considering the highly dynamic nature of the vehicular environment and the delay-sensitive wireless medium, enabling secure and reliable vehicle-to-vehicle communication becomes a very challenging task. In this paper, we propose a novel system design that uses blockchain technology to establish a high-level trust-management successful trustworthy cooperative vehicular wireless communication. The proposed system is a cross-layer approach that optimizes the best-relay selection process allowing only trustworthy relays to participate in data transmission. In this work, blockchain stores real-time information about relaying, transmitting, and receiving vehicles. The information includes channel characteristics and participation quality. The information is vetted and verified by mining vehicles. The result guides the relay selection process to block untrustworthy vehicles from participation. The entire system is comprehensively modeled and mathematically analyzed. Simulations using throughput and Bit Error Rate (BER) as evaluation metrics demonstrated the effectiveness and efficacy of the presented approach in enabling reliable wireless communication in presence of maliciously behaving vehicles. On average, the overall system throughput rate increased by $3bps$ , the BER was reduced by almost by $2dB$ , and the percentage of false messages decreased by 90% considering high Signal to Noise Ratio (SNR). [ABSTRACT FROM AUTHOR]

This paper presents techniques that are expected to improve the bit error rate (BER) performance of Unmanned Aerial Vehicles (UAVs) communication systems. The decode-and-forward (DF)-based cooperative multi-hop Rician network using space shift keying (SSK) modulation is employed for this purpose. The SSK modulation and cooperative communication are merged and used together. This enhances the BER performance for multiple-input multiple-output (MIMO) channels, thus accentuating its significance as an important technique for consistently reliable communication methodology while, at the same time, eliminating wastage of energy. The new technique is named as high performance (HP) due to not only BER performance improvement but also increase of the diversity of the communication system. This new model is further used for multi-hop networks and the resulting BER characteristics are assessed for possible enhanced performance in Rician channels. The paper also showcases the generalized-n-hop BER equation for various multi-hop DF-based cooperative communication systems in diverse UAV models. The Monte Carlo simulations further confirm the theoretical results that already exist for transmit-receive antennas. This work also underlines the fact that the number of hops in a communication system has no bearing whatsoever on the BER performance diversity value. [ABSTRACT FROM AUTHOR]

Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transformer encoder, which has brought about significant improvements in the performance of many NLP tasks. Though the Transformer encoder may effectively capture general information in its resulting representations, the backbone information, meaning the gist of the input text, is not specifically focused on. In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily. Our explicit text compression approaches use dedicated models to compress text, while our implicit text compression approach simply adds an additional module to the main model to handle text compression. We propose three ways of integration, namely backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the backbone information into Transformer-based models for various downstream tasks. Our evaluation on benchmark datasets shows that the proposed explicit and implicit text compression approaches improve results in comparison to strong baselines. We therefore conclude, when comparing the encodings to the baseline models, text compression helps the encoders to learn better language representations. [ABSTRACT FROM AUTHOR]

Existing linguistic steganalysis methods share a similar conceptual learning paradigm: namely, learning specific features derived from a word and its surrounding words, then passing them to the classifier. However, these features are unable to deal with the impact of the association between words and the corpus, which gives rise to significant limitations. In this paper, we discover the missing link between the words and the corpus, and accordingly propose a framework of linguistic steganalysis named LS-BGAT. Specifically, we fine-tune a large-scale pre-training BERT as the local feature extraction model and employ Graph Attention Network (GAT) as the global feature extraction model. Through ensuring collaboration between the local BERT-based features and the global GAT-based features in the joint prediction layer, we combine rich semantic and syntactic information at the sentence level with underlying global information at the corpus level. Furthermore, we extend the binary classification of steganalysis to multi-category classification, thereby enhancing practicality. We empirically substantiate the effectiveness and universality of LS-BGAT on three tasks. [ABSTRACT FROM AUTHOR]

Neuro-spike communication (NSC) is a communication method that includes electrical communication process and molecular communication process, which has been investigated extensively in recent years. The vesicle release process has a great influence on the accuracy of NSC systems. So the modeling of the vesicle release process has become a hot spot. There exist different vesicle release mechanisms, including univesicular release (UVR) case, multivesicular release (MVR) case and hybrid vesicle release (HVR) case. When a spike arrives, the UVR case refers to that at most one vesicle can be released. The MVR case refers to that more than one vesicle can be released. The HVR case is a mixed case of the UVR and MVR cases. This paper proposes the modeling of these three vesicle release cases. The theoretical analysis is conducted to compare them in terms of efficiency. Simulations are performed to evaluate the impacts of main vesicle releasing parameters on signal transmission accuracy. The simulation results show that the HVR model can effectively improve the transmission accuracy compared with UVR and MVR models under some conditions. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mobile molecular communication (MC) system consisting of two mobile nanomachines, a transmitter and a receiver, propelled by a positive drift velocity and Brownian motion in a realistic blood-vessel-type flow regime. Considering the nonlinear movement of the nanomachines, an extended Kalman filter is employed to estimate the distance from the transmitter. Furthermore, based on the predicted distance, to keep the number of received molecules for bit 1 at a stable level, we employ power control on the number of transmitted molecules based on the distance between the transmitter and the receiver and the residual molecules in the channel from the previous transmission. Finally, the optimal detection threshold is obtained by minimizing the error probability. It is verified that a fixed optimal detection threshold can be effective for the power control scheme in the mobile MC. The bit error rate (BER) performance of our scheme is verified via simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a precoded faster-than-Nyquist (FTN) signaling technique for time-domain single-carrier index-modulated symbol transmission. More precisely, eigenvalue decomposition (EVD) precoding is adopted for the FTN transmission of data bits modulated by single-carrier time-domain index modulation (IM). While the FTN scheme increases the spectral efficiency and data rate by increasing the density of transmit symbols, the time-domain IM works towards the same objective while maintaining symbol sparsity. We analytically derive the constrained capacity of the proposed system. Our simulation results show that the proposed scheme has better bit error ratio (BER) performance than the conventional FTN-IM scheme, particularly for the scenario of a higher packing ratio. With the proposed scheme, a 2.5 dB performance gain is observed at the BER of 10−4, where the packing ratio is 0.7 and the roll-off factor is 0.5 in the channel-uncoded scenario. We further analyze our scheme’s performance by characterizing its minimum Euclidean distance and inter-symbol interference. Furthermore, we present a three-stage serially concatenated iterative detection architecture for the proposed EVD-FTN-IM signaling scheme. It is demonstrated in our simulations that a near-capacity BER performance is achievable in the proposed turbo-coded FTN-IM scheme. [ABSTRACT FROM AUTHOR]

Orthogonal Time Frequency Space (OTFS) modulation is a recently proposed scheme for time-varying narrowband channels in terrestrial radio-frequency communications. Underwater acoustic (UWA) and ultra-wideband (UWB) communication systems, on the other hand, confront wideband time-varying channels. Unlike narrowband channels, for which time contractions or dilations due to Doppler effect can be approximated by frequency-shifts, the Doppler effect in wideband channels results in frequency-dependent non-uniform shift of signal frequencies across the band. In this paper, we develop an OTFS-like modulation scheme – Orthogonal Delay Scale Space (ODSS) modulation – for handling wideband time-varying channels. We derive the ODSS transmission and reception schemes from first principles. In the process, we introduce the notion of $\omega$ -convolution in the delay-scale space that parallels the twisted convolution used in the time-frequency space. The preprocessing 2D transformation from the Fourier-Mellin domain to the delay-scale space in ODSS, which plays the role of inverse symplectic Fourier transform (ISFFT) in OTFS, improves the bit error rate performance compared to OTFS and Orthogonal Frequency Division Multiplexing (OFDM) in wideband time-varying channels. Furthermore, since the channel matrix is rendered near-diagonal, ODSS retains the advantage of OFDM in terms of its low-complexity receiver structure. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the precoder design for multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system with one-bit digital-to-analog converters (DACs). In order to form the dual-functional beam-pattern, we formulate the precoding problem as a weighted optimization problem with the constant modulus constraint, which aims at minimizing the average error power and guaranteeing radar waveform similarity. The problem is divided into three sub-problems corresponding to the multiple variables, i.e., the precoding factor, transmit signal matrix, and radar waveform matrix. Due to the discrete and non-convex properties of the optimization problem, we propose a multi-variable alternating minimization (MVAM) framework to achieve the near-optimal solutions. The precoding factor and radar waveform can be solved in closed-forms. For the transmit signal matrix, we devise a binary particle swarm optimization-simulated annealing (BPSO-SA) algorithm to obtain it under the MVAM framework. Extensive simulations validate the effectiveness of the proposed approach under various scenarios, including the case without perfect channel state information. The simulation results show that, compared with existing non-linear precoders, the proposed approach achieves 7dB SNR gain at the bit error rate of 10−4 in the 8-antenna system, and the gain of SNR is 0.2dB in the massive MIMO system with 128 antennas. [ABSTRACT FROM AUTHOR]

The joint transmission scheme of polar codes and sparse code multiple access (SCMA) has been regarded as a promising technology for future wireless communication systems. However, most of the existing polar-coded SCMA (PC-SCMA) systems suffer from high latency caused by the feedback iteration and list decoding. In addition, the error performance of PC-SCMA systems is unsatisfactory for ultra-reliable transmission. Inspired by the compelling benefits of non-binary polar codes, in this paper, we design a non-binary polar-coded SCMA (NB-PC-SCMA) system with a free order matching strategy to address the issues of delay and reliability. Specifically, we first formulate a joint factor graph for NB-PC-SCMA and propose a non-binary successive cancellation list (NB-SCL) and damping based joint iterative detection and decoding (NSD-JIDD) multiuser receiver to improve the BER and latency performance. Then, a lazy-search based NB-SCL (L-NB-SCL) decoding is proposed to reduce the computational complexity by simplifying the path search pattern of the list decoder. After that, we modify the update of user nodes for SCMA detection to improve the convergence error and finally propose the improved NSD-JIDD (ISD-JIDD) algorithm, which can avoid redundant operations by exploiting L-NB-SCL decoding. Simulation results show that the proposed NB-PC-SCMA system achieves better bit error rate (BER) performance and considerable latency gain when compared to its counterparts. In particular, the proposed ISD-JIDD can achieve similar BER performance of NSD-JIDD with less complexity. [ABSTRACT FROM AUTHOR]

The weighted-type fractional Fourier transform (WFRFT)-based hybrid carrier (HC) system has been proposed to bridge the gap between orthogonal frequency division multiplexing (OFDM) system and single-carrier system with frequency domain equalization (SC-FDE). In this paper, we propose a novel HC system based on efficient fast-convolution (FC) without a cyclic prefix to meet the requirements of complex scenarios towards future communications. The proposed system can achieve flexible optimization of system performance, such as minimizing out-of-band emission (OOBE), peak to average power ratio (PAPR) and bit error rate (BER). This efficient FC based hybrid carrier (FCHC) system is among the most competitive waveforms. The closed-form BER expressions are derived and analyzed over additive white Gaussian noise (AWGN) and frequency-selective fading channels. A modified equalizer over frequency-selective channels is also provided to resist the inter-block interference (IBI). Moreover, the banded minimum mean-square error (MMSE) equalization based on FC can be embedded in the proposed system over doubly-selective channels, and then the interference caused by fading channels can be further suppressed through the combination of FC and HC. The simulation results demonstrate the superiority of the proposed system over the conventional HC system in terms of OOBE, PAPR and BER. The significant flexibility and combined performance advantages make the proposed system a potential scheme for future communications. [ABSTRACT FROM AUTHOR]

A zero forcing (ZF) assisted single layer beamforming is developed for spatial modulation (SM) and space shift keying (SSK) modulation multiple input multiple output (MIMO) systems, termed as SZF-SM/SZF-SSK, to achieve both transmit beamforming and receive diversity gain in this paper. Specifically, the proposed SZF-SM/SZF-SSK maps part of the bits to spatial domain, simultaneously forming a ZF precoding assisted single layer data both in multiple input single output (MISO) and MIMO systems, that differs greatly from conventional ZF beamforming assisted MIMO systems. With MISO configuration, the ZF precoder of SZF-SM/SZF-SSK converges to the maximum ratio transmission (MRT), achieving only the transmit beamforming gain. With MIMO configuration, SZF-SM/SZF-SSK tranceiver could be further modified along with the MIMO channel to achieve single layer transmission and joint detection, attaining both the transmit beamforming and receive diversity gain in SM-MIMO system. The theoretical analysis and the simulation results of the proposed SZF-SM/SZF-SSK with MISO and MIMO configurations are presented in this paper respectively, which show an attractive performance gain over the conventional SM/SSK. Compared with the conventional ZF beamforming assisted MIMO systems and some other CSI assisted SM-MIMO schemes, the proposed scheme exhibits better performance, especially under highly correlated MIMO channels, rendering a new and practical way to achieve low rank MIMO transmission with good performance and high data rate. [ABSTRACT FROM AUTHOR]

In conventional star-topology LoRa networks, the gateways are expected to collect the data from all the nodes nearby. However, a major challenge for the conventional LoRa system is the performance degradation due to the long-range communication over fading channels. To resolve the challenging issue, this paper investigates a two-hop amplify-and-forward relaying LoRa network in a two-dimension plane, where random nodal distances are considered. Moreover, a relay-selection mechanism is developed for the proposed system. Based on the best relay-selection protocol, the analytical bit-error-rate (BER) and asymptotic BER expressions, achievable diversity order, coverage probability, and throughput of the proposed system are derived over the Nakagami- $m$ fading channel. Furthermore, to maximize the throughput of the proposed system, a two-dimensional resource allocation optimization problem (i.e., the spread factor selection and power allocation optimization) is formulated and investigated. The proposed optimal spread factor selection and power allocation scheme is verified to outperform the two baseline schemes. Simulation and numerical results show that although the proposed system reduces the throughput compared to the conventional LoRa system, it significantly improves the BER and coverage probability. Hence, the proposed system can be considered as a promising technique for low-power, long-range and highly reliable Internet-of-Things applications. [ABSTRACT FROM AUTHOR]

Intensity modulation and direct detection (IM-DD) based optical wireless communication (OWC), requires the modulated signal to be real and non-negative. To satisfy the requirements, this paper proposes two types of mixed orthogonal frequency division multiplexing (X-OFDM) waveforms. The Hermitian symmetry (HS) characteristic of the sub-carriers in the frequency domain, guarantees the signal in the time domain to be real, which reduces the spectral efficiency to 1/2. For the odd sub-carriers in the frequency domain, the signal in the time domain after the inverse fast fourier transform (IFFT) is antisymmetric. For the even sub-carriers in the frequency domain, the signal in the time domain after the IFFT is symmetric. Based on the antisymmetric and symmetric characteristics, the two types of X-OFDM waveforms are designed to guarantee the signal in the time domain to be non-negative. With $M$ sub-carriers in the frequency domain, the generated signal in the time domain has $3M/2$ points, which further reduces the spectral efficiency to 1/3. The numerical simulations show that, the two types of X-OFDM waveforms greatly enhance the power efficiency considering the OWC channel with the signal-dependent noise and/or the signal-independent noise. [ABSTRACT FROM AUTHOR]

In this paper, we study a model that mimics the programming operation of memory cells. This model was first introduced by Lastras-Montano et al. for continuous-alphabet channels, and later by Bunte and Lapidoth for discrete memoryless channels (DMC). Under this paradigm we assume that cells are programmed sequentially and individually. The programming process is modeled as transmission over a channel, such that it is possible to read the cell state in order to determine its programming success, and in case of programming failure, to reprogram the cell again. Reprogramming a cell can reduce the bit error rate, however this comes with the price of increasing the overall programming time and thereby affecting the writing speed of the memory. An iterative programming scheme is an algorithm which specifies the number of attempts to program each cell. Given the programming channel and constraints on the average and maximum number of attempts to program a cell, we study programming schemes which maximize the number of bits that can be reliably stored in the memory. We extend the results by Bunte and Lapidoth and study this problem when the programming channel is either discrete-input memoryless symmetric channel (including the BSC,BEC, BI-AWGN) or the $Z$ channel. For the BSC and the BEC our analysis is also extended for the case where the error probabilities on consecutive writes are not necessarily the same. Lastly, we also study a related model which is motivated by the synthesis process of DNA molecules. [ABSTRACT FROM AUTHOR]

This paper investigates a novel intelligent reflecting surface (IRS)-based symbiotic radio (SR) system architecture consisting of a transmitter, an IRS, and an information receiver (IR). The primary transmitter communicates with the IR and at the same time assists the IRS in forwarding information to the IR. Based on the IRS’s symbol period, we distinguish two scenarios, namely, commensal SR (CSR) and parasitic SR (PSR), where two different techniques for decoding the IRS signals at the IR are employed. We formulate bit error rate (BER) minimization problems for both scenarios by jointly optimizing the active beamformer at the base station and the phase shifts at the IRS, subject to a minimum primary rate requirement. Specifically, for the CSR scenario, a penalty-based algorithm is proposed to obtain a high-quality solution, where semi-closed-form solutions for the active beamformer and the IRS phase shifts are derived based on Lagrange duality and Majorization-Minimization methods, respectively. For the PSR scenario, we apply a bisection search-based method, successive convex approximation, and difference of convex programming to develop a computationally efficient algorithm, which converges to a locally optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithms and show that the proposed SR techniques are able to achieve a lower BER than benchmark schemes. [ABSTRACT FROM AUTHOR]

Data sketches (or summaries) are widely used to efficiently process large data sets and obtain relevant information, such as the number of distinct elements, their frequency or the similarity of several sets. Since sketches provide probabilistic estimates (hence accomplishing considerable reductions in storage and computational requirements), it seems that approximate hardware is appropriate for implementation providing an estimate rather than the exact value and enabling further improvements in metrics. In this paper, the use of voltage scaling in an approximate nanoscale FinFET-based memory for implementing sketches is studied and evaluated. To mitigate the errors introduced by the approximate memory, techniques to protect several widely used sketches when implemented in such voltage-scaled FinFET memory, are proposed. The proposed schemes preserve the sketches functionality with a negligible impact on the accuracy of their estimates and tolerate large bit error rates for sketches such as Minhash and HyperLogLog. The analysis and simulation results show that the intrinsic error tolerance of data sketches can be exploited to provide accurate estimates even when the memory suffers many errors due to the voltage scaling of the supply. Voltage scaling enables large power savings for Minhash and HyperLogLog. These initial results suggest that the use of approximate computing for data sketches is an interesting area for further research. [ABSTRACT FROM AUTHOR]

This paper proposes a novel bit loading algorithm to multicarrier non-orthogonal multiple access (NOMA) systems to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally a non-deterministic polynomial-time (NP)-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users, and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, in this paper, we propose an efficient bit loading algorithm for multicarrier NOMA systems and compare the complexity and throughput with a corresponding OMA system. The obtained results show that for the case of NOMA, bit loading provides an additional degree of freedom that allows non-uniform spectrum sharing among the users. In particular scenarios, the system may experience hybrid modes of operation, where the system switches between NOMA and OMA. The obtained numerical results show that NOMA can outperform OMA by 100% in terms of spectral efficiency, for a two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high complexity associated with the bit error rate (BER) computation in the case of NOMA. [ABSTRACT FROM AUTHOR]

This paper presents the design and bit error rate (BER) analysis of a phase-independent non-orthogonal multiple access (NOMA) system. The proposed NOMA system can utilize amplitude-coherent detection (ACD) which requires only the channel amplitude for equalization purposes. In what follows, three different designs for realizing the detection of the proposed NOMA are investigated. One is based on the maximum likelihood (ML) principle, while the other two are based on successive interference cancellation (SIC). Closed-form expressions for the BER of all detectors are derived and compared with the BER of the coherent ML detector. The obtained results, which are corroborated by simulations, demonstrate that, in most scenarios, the BER is dominated by multiuser interference rather than the absence of the channel phase information. Consequently, the BER using ML and ACD are comparable for various cases of interest. The paper also shows that the SIC detector is just an alternative approach to realize the ML detector, and hence, both detectors provide the same BER performance. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the design and analysis of the Analog Fountain Code (AFC) for short packet communications. We first propose a density evolution (DE) based framework, which tracks the evolution of the probability density function of the messages exchanged between variable and check nodes of AFC in the belief propagation decoder. Using the proposed DE framework, we formulate an optimisation problem to find the optimal AFC code parameters, including the weight-set, which minimises the bit error rate at a given signal-to-noise ratio (SNR). Our results show the superiority of our AFC code design compared to existing designs of AFC in the literature and thus the validity of the proposed DE framework in the asymptotically long block length regime. We then focus on selecting the precoder to improve the performance of AFC at short block lengths. Simulation results show that lower precode rates obtain better realised rates over a wide SNR range for short information block lengths. We also discuss the complexity of the AFC decoder and propose a threshold-based decoder to reduce the complexity. [ABSTRACT FROM AUTHOR]

One of the main problems faced by communication systems is the presence of skip-zones in the targeted areas. With the deployment of the fifth-generation mobile network, solutions are proposed to solve the signal loss due to obstruction by buildings, mountains, and atmospheric or weather conditions. Among these solutions, reconfigurable intelligent surfaces (RIS), which are newly proposed modules, may be exploited to reflect the incident signal in the direction of dead zones, increase communication coverage, and make the channel smarter and controllable. This paper tackles the skip-zone problem in terrestrial free-space optical (T-FSO) systems using a single-element RIS. Considering link distances and jitter ratios at the RIS position, we carry out a performance analysis of RIS-aided T-FSO links affected by turbulence and pointing errors, for both heterodyne detection and intensity modulation-direct detection techniques. Turbulence is modeled using the Gamma-Gamma distribution. We analyze the model and provide exact closed-form expressions of the probability density function, cumulative distribution function, and moment generating function of the end-to-end signal-to-noise ratio. Capitalizing on these statistics, we evaluate the system performance through the outage probability, ergodic channel capacity, and average bit error rate for selected binary modulation schemes. Numerical results, validated through simulations, obtained for different RIS positions and link distances ratio values, reveal that RIS-based T-FSO performs better when the RIS module is located near the transmitter. [ABSTRACT FROM AUTHOR]

Molecular communication, as an emerging research direction, has emerged in the field of communication, which usually combined with nanotechnology and bio-related knowledge. In the direction of communication channel research, the most widespread model for a molecular communication channel is the diffusion-based channel, where the information-carrying molecules propagate randomly in the medium based on Brownian motion. Multi-input multi-output (MIMO) technology is often used to improve communication quality in the traditional communication field. Compared with the SISO model, which only has inter-symbol interference (ISI) as the interference source, the interference in MIMO communication model includes ISI as well as inter-link interference (ILI), which emerges when receiver receive other transmitters’ molecules. In this paper, MIMO communication models are built, based on diffusion channel, CSK, probabilistic theory, considered with ISI and ILI, to establish the calculation formula of related bit error rate, And the influence of relevant parameters in the model on bit error rate is studied. Then, SISO and SIMO models will be built to compare with MIMO models. Last, self-adaptive dual threshold algorithm is proposed to reduce BER of the ${2}\times {2}$ MIMO system. Simulation results show that the proposed algorithm has better performance on reducing BER than other approaches. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access based cooperative relaying system (NOMA-CRS) has been proposed to alleviate the decay in spectral efficiency of the conventional CRS. However, existing NOMA-CRS studies assume perfect successive interference canceler at the relay and mostly investigate sum rate whereas the error performance has not been taken into consideration. In this paper, we analyze error performance of the NOMA-CRS and the closed-form bit error probability (BEP) expression is derived over Nakagami-m fading channels. Then, thanks to the high performance of machine learning (ML) in challenging optimization problems, a joint power sharing-power allocation (PS-PA) scheme is proposed to minimize the bit error rate (BER) of the NOMA-CRS. The proposed ML-assisted optimization has a very low online implementation complexity. Based on provided extensive simulations, theoretical BEP analysis is validated. Besides, the proposed ML-aided PS-PA provides minimum BER (MBER) and outperforms previous PA strategies for the NOMA-CRS notably. [ABSTRACT FROM AUTHOR]

Large blackouts in power grids are often the consequence of uncontrolled failure cascades. The ability to predict the failure cascade process in an efficient and accurate manner is important for power system contingency analysis. In this paper, we propose to apply the influence model for the prediction and screening of failure cascades in large scale DC and AC power networks. Then, the trained influence model is applied to some large power grids with thousands of buses and transmission lines. The prediction performance is evaluated in four different aspects, from the global perspective of the overall failure size to the individual information regarding the link failure time. The results show that under limited training samples, the proposed framework is capable of predicting the failure cascade size with a 7% error rate, the final state of links with a 10% error rate, and the failure time within 1 time unit for both the DC and AC model. One major advantage of the proposed method is that it can reduce the computational time of the failure cascade prediction by a few orders of magnitude with limited compromise in accuracy, as compared to the power flow based contingency analysis. Another important feature of the proposed method is that the trained influence parameters can reveal the critical initial contingencies. This information is very helpful for identifying the worst contingency scenarios for system operators. [ABSTRACT FROM AUTHOR]

This paper evaluates the practically achievable best error performance obtained from a dimmable visible light communication (VLC) system that conveys information using binary modulations, such as on-off keying (OOK). As compared to previous studies that address the ideal performance of dimmable VLC systems only over reliable channels with arbitrarily small error probability, this paper presents the best achievable error probability of various configurations with the dimming targets, signal quality, and data rate. The numerical results establish the ultimate decoding error performance targeted for dimmable VLC systems and are expected to provide a practical guideline for the enhanced VLC system design. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a promising candidate for future mobile networks as it enables improved spectral-efficiency, massive connectivity and low latency. This paper derives exact and asymptotic bit error rate (BER) expressions under Rayleigh fading channels for NOMA systems with arbitrary number of users and arbitrary number of receiving antennas and modulation orders, including binary phase-shift keying and rectangular/square quadrature amplitude modulation. Furthermore, the power coefficients’ bounds, which ensure users’ fairness, and solve the constellation ambiguity problem, are derived for $N=2$ and 3 users cases with any modulation orders. In addition, this paper determines the optimal power assignment that minimizes the system’s average BER. These results provide valuable insight into the system’s BER performance and power assignment granularity. For instance, it is shown that the feasible power coefficients range becomes significantly small as the modulation order, or $N$ , increases, where the BER performance degrades due to the increased inter-user interference. Hence, the derived expressions can be crucial for the system scheduler in allowing it to make accurate decisions of selecting appropriate $N$ , modulation orders, and power coefficients to satisfy the users’ requirements. The presented expressions are corroborated via Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Adaptive optics (AO) has the potential to mitigate the effect of atmospheric turbulence and improve the performance of orbital angular momentum (OAM)-based optical wireless communication (OAM-OWC) links. Here, we propose a single-intensity-measurement phase retrieval algorithm (SPRA)-based AO technique of compensating for the distortion of the OAM beam. The only parameter required by the SPRA wave-front sensor is the intensity of the probe beam in the Fourier domain, which substantially simplifies the AO system. We first derive an analytical expression to characterize the expansion of probe beam in OAM-OWC links and then determine the diameter constraints as the apriori information of the SPRA required for guaranteeing a certain compensation performance. The simulation results illustrate that the SPRA-AO approach can indeed correct a distorted OAM beam both in a single-channel scenario and in multiplexed OAM-OWC systems. The bit error rate can be improved by orders of magnitude with the aid of SPRA-AO compensation. Furthermore, we establish noise models of AO-based OAM-OWC systems and analyze the robustness of the SPRA-AO technique. In a nutshell, this paper provides new insights for the applications of AO and forms the theoretical basis of employing probe beams in OAM-OWC systems. [ABSTRACT FROM AUTHOR]

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following $\mu $ -law normalization. The method was tested for within-subject and cross-subject conditions. We trained the model with two hard sample mining methods, Gradient Harmonizing Mechanism (GHM) and Online Hard Sample Mining (OHEM). The proposed method was compared with classic approaches, including long short-term memory (LSTM) and Temporal Convolutional Network (TCN) as well as a recent method called Long Exposure Convolutional Memory Network (LE-ConvMN). Correlation coefficient (CC), normalized root mean square error (NRMSE) and time costs were used as performance metrics. Our method (sBERT-OHEM) achieved state-of-the-art performance in cross-subject evaluation as well as high performance in subject-specific tests on the Ninapro dataset. The above tests are based on the same randomly selected 10 subjects. Generally, in the cross-subject situation, with the increasing of the subjects’ number, it unavoidably leads to the decline of the performance. While the performance of our method on 38 subjects was significantly higher than the other methods on 10 subjects in cross-subject conditions, which further verified the advantage of our methods. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) have recently become more significant to intelligent transport systems (ITS). Recent works focused on improving throughput and decreasing delay. In this paper, a novel MIMO-OFDM based MAC Protocol is proposed for VANETs to enhance their performance. Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are integrated to exploit the benefit of both MIMO and OFDM. To accord MIMO with OFDM, a novel access mechanism is proposed. 3D Markov chain model-based analytical study is presented, considered a non-saturated condition. Nakagami-m fading channels are taken into account in the study. Parameters affecting performance are taken into consideration, and relationships are derived. The probability of successful transmission, probability of outage, bit error rate (BER), throughput, and delay expressions are achieved. Moreover, numerical results are presented, which verify analytical studies. [ABSTRACT FROM AUTHOR]

In this paper, a vehicular pairing scheme is proposed to balance the resource distribution in non-orthogonal multiple access (NOMA) based vehicular networks. The proposed BVP: balanced vehicular pairing scheme chooses pairs based on fair inter-vehicle distances through the base station (BS) for mobile NOMA users with high mobility. BVP reduces the biased pairing and the disparity of resource utilization by each pair, resulting in fewer signal decoding errors than the existing state-of-the-art solutions. The exhaustive evaluation in Matlab-based network simulator, using real vehicular traffic traces from OpenStreetMap (OSM) and simulation of urban mobility (SUMO) traffic simulator shows that the proposed BVP outperforms existing schemes. The proposed solution reduces throughput and pair sum rate standard deviation by 2 ~ 3 b/s/Hz for every device and every resource, while also significantly reducing the decoding bit error rate. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-layer heterogeneous network to realize the remote monitoring and advanced warning for infectious diseases spread by airborne pathogens, whose detection can be considered as a binary detection problem. To intuitively study the detection process, we abstract it as a molecular communication via diffusion (MCvD) model and uncover that one of the key factors to impact the detection performance is inter-symbol interference (ISI), i.e., the viral aerosol from other biological entities. Therefore, overcoming ISI is imperative to ensure reliable detection. In the abstracted MCvD model, the detection performance can be described by the bit error rate (BER) performance. Following this assumption, we propose to optimize the detection interval to minimize the impact of ISI while ensuring the accurate detection of the transmitted information symbol, which is suitable for both the absorbing and passive receivers. For tractability, based on the signal-to-interference difference (SID) and signal-to-interference-and-noise amplitude ratio (SINAR), we design a modified-SINAR (mSINAR) to measure BER performance for the MCvD system with a variable detection interval. Besides, we derive the optimal detection interval in closed-form. Using simulation results, we show that in terms of BER, our proposed mSINAR scheme is superior to the competitive schemes, and performs similarly to the scheme with optimal intervals determined by the exhaustive search. [ABSTRACT FROM AUTHOR]

In many papers, random modulation of power electronic converters is described as an electromagnetic interference (EMI) mitigation technique, which contributes to the assurance of electromagnetic compatibility (EMC) of systems consisting of power electronic converters and serial transmission-based control and measuring devices. However, experimental results as well as theoretical analyses of physical phenomenon suggest that the EMI reduction is ostensible and results from the measuring procedure adopted in standards. This article presents the first known approach to compare the influence of deterministic and random modulation of transmission errors rate in serial communication systems. The comparison is based on the especially developed mathematical models, which are validated experimentally, using a simple setup with a dc/dc converter injecting a noise into the USB/RS232 converter representing a serial transmission scheme. The comparison reveals that for the studied switching frequency range from 10 kHz to 1 MHz, on average, there is no significant difference between random and deterministic modulations with respect to the probability of error rate in serial communication systems. Thus, in such systems, random modulation should not be considered as a valid EMC-assuring technique. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel deep Efficient Relational Sentence Ordering Network (referred to as ERSON) by leveraging pre-trained language model in both encoder and decoder architectures to strengthen the coherence modeling of the entire model. Specifically, we first introduce a divide-and-fuse BERT (referred to as DF-BERT), a new refactor of BERT network, where lower layers in the improved model encode each sentence in the paragraph independently, which are shared by different sentence pairs, and the higher layers learn the cross-attention between sentence pairs jointly. It enables us to capture the semantic concepts and contextual information between the sentences of the paragraph, while significantly reducing the runtime and memory consumption without sacrificing the model performance. Besides, a Relational Pointer Decoder (referred to as RPD) is developed, which utilizes the pre-trained Next Sentence Prediction (NSP) task of BERT to capture the useful relative ordering information between sentences to enhance the order predictions. In addition, a variety of knowledge distillation based losses are added as auxiliary supervision to further improve the ordering performance. The extensive evaluations on Sentence Ordering, Order Discrimination, and Multi-Document Summarization tasks show the superiority of ERSON to the state-of-the-art ordering methods. [ABSTRACT FROM AUTHOR]

In symbiotic radio (SR), a secondary transmitter (STx) transmits messages by modulating its information over the radio frequency (RF) signals received from a primary transmitter (PTx), and in return, the secondary transmission provides multipath gain to the primary transmission. In this paper, we are interested in the fundamental mutualistic mechanism between the primary and secondary transmissions, which describes the condition through which the two systems can benefit each other. Since the symbol period ratio $K$ between secondary and primary transmissions is an important system parameter that affects the mutualistic symbiosis, we first derive the theoretical performance in terms of bit error rate (BER) for both primary and secondary transmissions for arbitrary $K$ by using QPSK modulation scheme at the PTx and BPSK modulation scheme at the STx as an example setup. Then we the obtain closed-form expressions for the condition on $K$ to enable mutualistic symbiosis in SR, which is not related to the specific channel realizations but determined by the average strengths of the direct and backscatter links when the number of receiving antennas is large. Meanwhile, we analyze the average BER performance and the diversity orders for both transmissions in the high signal-to-noise-ratio (SNR) regime. Extensive simulations and numerical results are provided to verify the accuracy of our theoretical analysis and demonstrate the interrelationship between the primary and secondary transmissions. [ABSTRACT FROM AUTHOR]

This paper describes a new non-charge-based data storing technique in NAND flash memory called watermark that encodes read-only data in the form of physical properties of flash memory cells. Unlike traditional charge-based data storing method in flash memory, the proposed technique is resistant to total ionizing dose (TID) effects. To evaluate its resistance to irradiation effects, we analyze data stored in several commercial single-level-cell (SLC) flash memory chips from different vendors and technology nodes. These chips are irradiated using a Co-60 gamma-ray source array for up to 100 krad(Si) at Sandia National Laboratories. Experimental evaluation performed on a flash chip from Samsung shows that the intrinsic bit error rate (BER) of watermark increases from $\mathbf {\sim }0.8$ % for TID = 0 krad(Si) to $\mathbf {\mathrm {\sim }}1$ % for TID = 100 krad(Si). Conversely, the BER of charge-based data stored on the same chip increases from 0% at TID = 0 krad(Si) to 1.5% at TID = 100 krad(Si). The results imply that the proposed technique may potentially offer significant improvements in data integrity relative to traditional charge-based data storage for very high radiation (TID $\mathbf { > } 100$ krad(Si)) environments. These gains in data integrity relative to the charge-based data storage are useful in radiation-prone environments, but they come at the cost of increased write times and higher BERs before irradiation. [ABSTRACT FROM AUTHOR]

Linear companding transform (LCT) is one of the most attractive schemes to deal with the inherent problem of high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems, due to its effectiveness and low complexity. In this paper, a generalized continuous piecewise linear companding (CPLC) transform is proposed. The companding and decompanding functions of the proposed scheme consist of multiple continuous linear segments, and the total number of segments is generalized to be any integer not less than four. These linear segments can provide much adaptability for function shaping, leading to high flexibility for performance tradeoffs among PAPR reduction, bit error rate (BER), out-of-band (OOB) rejection and algorithmic complexity. The continuity of the companding function helps to reduce OOB radiations. Several optimization problems with different targets under the constraint condition of constant average power are constructed. Permissible ranges of key parameters, relationships among these parameters and their influence on algorithmic performance are analyzed. Parameter selecting strategies are also provided. In addition, an iterative receiving method is designed to reduce system BER effectively at the receiver. Simulation results validate the performance superiorities of the proposed scheme to that of other typical companding schemes. [ABSTRACT FROM AUTHOR]