Information Transmission Rate Research Articles

Design of a multi-carrier X-ray source for communication with energy modulation information.

X-ray communication is a kind of space communication technology which uses X-ray as information carrier. In order to improve the information transmission capacity, communication rate and anti-interference ability of X-ray communication, we proposes to design a novel multi-target X-ray source. The source is composed of a fast switching module of light channels based on FPGA technology and four photoelectric X-ray tubes with different target materials: Cr, Fe, Ni, and Cu. Using Geant4 software, we determined the optimal target thickness for each material, which enabled us to fully leverage the characteristic X-rays for multi-channel signal modulation transmission. Moreover, using CST software for particle trajectory simulation and optimization of the electron beam revealed that at a tube voltage of 20 kV, the focus area measures approximately 1.2 mm×1.2 mm. The simulations show that four kinds of spectra with high distinctiveness can be generated from the Cr, Fe, Ni, and Cu targets. Within a single modulation period, these spectra can be combined in various ways to create 16 different X-ray spectra signals, thereby increasing the number of communication elements and enhancing the information transmission rate.

The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.

In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70%, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18% for the online experiment. All the experimental results demonstrated the effectiveness of the brain computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.

Efficient dual-frequency SSVEP brain-computer interface system exploiting interocular visual resource disparities

Here, we present a novel dual-frequency steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) system with a unique human–computer interaction (HCI) feature that utilizes distinct visual resource disparities between the two eyes. By leveraging subtle differences in electroencephalography (EEG) responses due to binocular disparity, our system effectively distinguishes identical frequency targets. We conducted a two-phase validation: initial offline experiments confirmed EEG differences from interocular disparity, followed by global optimization to refine the SSVEP coding and spatial arrangement, allowing for 40 distinct targets within 20 frequency combinationes. A tailored binocular-task-related component analysis (bTRCA) algorithm was developed, achieving a maximum individual information transmission rate (ITR) of 338.32 bits/min offline and an average of 224.96 bits/min, with the online version reaching an average ITR of 234.13 bits/min. These results indicate that this system is a leading candidate for dual-frequency SSVEP BCIs, opening new prospects for HCI applications.

Development of real-time brain-computer interface control system for robot

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) have been considered a prevailing non-invasive method for collecting human biomedical signals by attaching electrodes to the scalp. However, it is difficult to detect and use these signals to control an online BCI robot in a real environment owing to environmental noise. In this study, a novel state recognition model is proposed to determine and improve EEG signal states. First, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) was designed to extract EEG features along the time sequence. During this process, errors caused by the randomness of the mind or external environmental factors may be generated. Thus, an actor-critic based decision-making model was proposed to correct these errors. The model consists of two networks that can be used to predict the final signal state based on both the current signal state probability and past signal state probabilities. Subsequently, a hybrid BCI real-time control system application is proposed to control a BCI robot. The Unicorn Hybrid Black EEG device was used to acquire brain signals. A data transmission system was constructed using OpenViBE to transfer data. An EEG classification system was built to classify the BCI commands. In this experiment, EEG data from five subjects were collected to train and test the performance and reliability of the proposed control system. The system records the time spent by the robot and the moving distance. Experimental results were provided to demonstrate the feasibility of the real-time control system. Compared to similar BCI studies, the proposed hybrid BCI real-time control system can accurately classify seven BCI commands in a more reliable and precise manner. Overall, the offline testing accuracy was 87.20%. When we apply the proposed system to control a BCI robot in a real environment, the average online control accuracy is 93.12%, and the mean information transmission rate is 67.07 bits/min, which is better than those of some state-of-the-art control systems. This shows that the proposed hybrid BCI real-time control system demonstrated higher reliability, which can be used in practical BCI control applications.

The distributional properties of long nominal compounds in scientific articles: an investigation based on the uniform information density hypothesis

Abstract Nominal compounds are a structure commonly used in scientific texts. Despite their commonality, very little is known about how they are distributed in scientific articles. Based on the Uniform Information Density hypothesis, which states that speakers communicate information at a constant rate, avoiding peaks and troughs of information transmission, we predict that nominal compounds should cluster toward the end of scientific texts, be preceded by supporting text that facilitates their understanding, and be repeated often after their first use. In this paper, we examine these predictions through a quantitative and a qualitative analysis of a corpus of scientific papers from the fields of Biology, Economics and Linguistics. While our investigation did not reveal definitive findings for the first and third predictions above, it did produce supporting evidence in favor of our second prediction, thus advancing our understanding of NC use and the choices speakers make when transmitting information.

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network.

For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1-2s time window, the accuracy of CBAM-CNN is 0.0201-0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1-1s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87bit/min, which is 227.53bit/min-503.41bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.

Design of a Short Reference Differential Chaotic Shift Keying Communication System Based on Reference Signal Scrambling Transformation

A short reference differential chaotic shift keying communication system based on reference signal scrambling transformation (RSST-SR-DCSK) is proposed in this paper. The system introduces information-bit-driven scramblers at the transmission end to effectively eliminate the correlation between the reference signal and information signal. At the receiving end, a descrambler is used to restore the correlation, thereby enhancing information transmission security. Additionally, the system employs a short reference transmission mechanism to further increase the information transmission rate. The transmitter and receiver structures of the system are designed through signal structure analysis. Theoretical analyses and simulations conducted in an additive white Gaussian noise (AWGN) channel demonstrate promising performance, including a low bit error rate (BER) and a favorable data-energy-to-bit-energy ratio (DBR). Simulation verification is performed to examine the impact of parameters on the bit error rate performance. A comparative analysis with traditional DCSK and SR-DCSK systems highlights the superior performance of the RSST-SR-DCSK system.

Magneto-optical fiber-based orbital angular momentum mode converters.

Orbital angular momentum (OAM) mode division multiplexing (MDM) systems can support large-capacity and high-speed rate information transmission, in which the OAM mode conversion devices play an important role. In this paper, the mode conversion principle of magneto-optical fiber-based long-period grating (MOF-LPG) is analyzed for further developing new magneto-optical (MO) OAM mode converters, including three types of C P 01 to O A M ±1,1, O A M ±1,1 to O A M ±2,1, and O A M ±1,1 to C P 02. It is shown that the magnetic tunability of the mode converters through the propagation constants of the eigenmodes is useful for compensating for process errors and increasing the operating wavelength range. The implementation of MOF-LPGs is also discussed from the aspect of the prospective experiments.

Design of Indoor Visible Light Communication PAM4 System

Four-level pulse amplitude modulation (PAM4) can transmit more information in the same symbol interval, effectively improving the information transmission rate and frequency band utilization of visible light communication (VLC). This paper proposes a method for encoding and decoding the PAM4 signal based on FPGA. The PAM4 signal is studied and analyzed under different channel noise models. The research results show that the bit error rate (BER) performance of the PAM4 signal is the best under the additive noise model. The influence of the multiplicative noise on the BER of the PAM4 signal is much greater than that of the additive noise. The BER performance of the PAM4 signal depends on the variance of the noise data. The greater the variance, the worse the BER performance. At the same time, an indoor VLC system based on PAM4 is designed. The simulation results verify that the system scheme is feasible and provides a way to further realize high-speed signal transmission.

The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.

In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70%, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18% for the online experiment. All the experimental results demonstrated the effectiveness of the brain computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.

Information Transmission via Molecular Communication in Astrobiological Environments.

The ubiquity of information transmission via molecular communication between cells is comprehensively documented on Earth; this phenomenon might even have played a vital role in the origin(s) and early evolution of life. Motivated by these considerations, a simple model for molecular communication entailing the diffusion of signaling molecules from transmitter to receiver is elucidated. The channel capacity C (maximal rate of information transmission) and an optimistic heuristic estimate of the actual information transmission rate are derived for this communication system; the two quantities, especially the latter, are demonstrated to be broadly consistent with laboratory experiments and more sophisticated theoretical models. The channel capacity exhibits a potentially weak dependence on environmental parameters, whereas the actual information transmission rate may scale with the intercellular distance d as ∝ d-4 and could vary substantially across settings. These two variables are roughly calculated for diverse astrobiological environments, ranging from Earth's upper oceans (C ∼ 3.1 × 103 bits/s; ∼ 4.7 × 10-2 bits/s) and deep sea hydrothermal vents (C ∼ 4.2 × 103 bits/s; ∼ 1.2 × 10-1 bits/s) to the hydrocarbon lakes and seas of Titan (C ∼ 3.8 × 103 bits/s; ∼ 2.6 × 10-1 bits/s).

Performance Analysis of Routing Protocol Using Trust-Based Hybrid FCRO-AEPO Optimization Techniques

Mobile Ad hoc Networks (MANETs) offer numerous benefits and have been used in different applications. MANETs are dynamic peer-to-peer networks that use multi-hop data transfer without the need for-existing infrastructure. Due to their nature, for secure communication of mobile nodes, they need unique security requirements in MANET. In this work, a Hybrid Firefly Cyclic Rider Optimization (FCRO) algorithm is proposed for Cluster Head (CH) selection, it efficiently selects the CH and improves the network efficiency. The Ridge Regression Classification algorithm is presented in this work to sense the malicious nodes in the network and the data is transmitted using trusted Mobile nodes for the QoS enactment metric improvement. A trust-based routing protocol (TBRP) is introduced utilizing the Atom Emperor Penguin Optimization (AEPO) algorithm, it identifies the best-forwarded path to moderate the routing overhead problem in MANET. The planned method is implemented using Matlab software and the presentation metrics are packet delivers ratio, packet loss ratio (PLR), routing overhead, throughput, end-to-end delay (E2ED), transmission delay, energy consumption and network lifetime. The suggested AEPO algorithm is compared with the prevailing PSO-GA, TID-CMGR, and MFFA. The AEPO algorithm’s performance is approximately 1.5%, 3.2%, 2%, 3%, and 4% higher than the existing methods for PLR, packet delivers ratio, throughput, and E2ED and network lifetime. The sender nodes can increase their information transmission rates and reduce delays in appreciation of this evaluation. Additionally, the suggested technique has a perfect benefit in terms of demonstrating the genuine contribution of distinct nodes to trust evaluation (TE).

Information transfer rate in BCIs: Towards tightly integrated symbiosis

Objective:The information transmission rate (ITR), or effective bit rate, is a popular and widely used information measurement metric, particularly popularized for SSVEP-based Brain–Computer (BCI) interfaces. By combining speed and accuracy into a single-valued parameter, this metric aids in the evaluation and comparison of various target identification algorithms across different BCI communities. In order to calculate ITR, it is customary to assume a uniform input distribution and an oversimplified channel model that is memoryless, stationary, and symmetrical in nature with discrete alphabet sizes. To accurately depict performance and inspire an end-to-end design for futuristic BCI designs, a more thorough examination and definition of ITR is therefore required. Approach:We model the symbiotic communication medium, hosted by the retinogeniculate visual pathway, as a discrete memoryless channel and use the modified capacity expressions to redefine the ITR. We leverage a result for directed graphs to characterize the relationship between the asymmetry of the transition statistics and the ITR gain due to the new definition, leading to potential bounds on data rate performance. Main Results:On two well-known SSVEP datasets, we compared two cutting-edge target identification methods. Results indicate that the induced DM channel asymmetry has a greater impact on the actual perceived ITR than the change in input distribution. Moreover, it is demonstrated that the ITR gain under the new definition is inversely correlated with the asymmetry in the channel transition statistics. Individual input customizations are further shown to yield perceived ITR performance improvements. Finally, an algorithm is proposed to find the capacity of binary classification and further discussions are given to extend such results to multi-class case through ensemble techniques. Significance:We anticipate that the results of our study will contribute to the characterization of the highly dynamic BCI channel capacities, performance thresholds, and improved BCI stimulus designs for a tighter symbiosis between the human brain and computer systems while ensuring the efficient utilization of the underlying communication resources.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.

Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR). To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features. In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components. This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness. This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communication between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In addition, road and traffic safety have very strict requirements for low-latency, high-reliability communication, and information transmission is only valuable within a certain range, and requires timeliness. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep-Q network reinforcement learning (RL) with low-latitude fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. These agents work in concert, interact with the environment, receive appropriate observations, rewards, and ultimately learn to improve power and spectrum allocation to provide a better entertainment experience and safer driving environment for users. We also address the shortcomings in existing literature studies. Firstly, the low upper limit of transmitted data volume; Secondly, the artificial assumption that the spectrum is consistent with the number of V2V links, which is insufficient for future applications in spectrum shortage sections. We accordingly extend the amount of transmitted data while adding a scenario where the spectrum resources are relatively short, i.e., the number of V2V links is significantly larger than the amount of spectrum. Experimental results show that with proper training mechanism and reward function construction, multiple intelligence can cooperate effectively. Therefore, the total link capacity of the V2I link and the success rate of periodic security information transmission are further improved.

A method for extracting the semantic features of speech signal recognition based on empirical wavelet transform

The subject of this study is methods for improving the efficiency of semantic coding of speech signals. The purpose of this study is to develop a method for improving the efficiency of semantic coding of speech signals. Coding efficiency refers to the reduction of the information transmission rate with a given probability of error-free recognition of semantic features of speech signals, which will significantly reduce the required source bandwidth, thereby increasing the communication channel bandwidth. To achieve this goal, it is necessary to solve the following scientific tasks: (1) to investigate a known method for improving the efficiency of semantic coding of speech signals based on mel-frequency cepstral coefficients; (2) to substantiate the effectiveness of using the adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals; (3) to develop a method of semantic coding of speech signals based on adaptive empirical wavelet transform with further application of Hilbert spectral analysis and optimal thresholding; and (4) to perform an objective quantitative assessment of the increase in the efficiency of the developed method of semantic coding of speech signals in contrast to the existing method. The following scientific results were obtained during the study: a method of semantic coding of speech signals based on empirical wavelet transform is developed for the first time, which differs from existing methods by constructing a set of adaptive bandpass Meyer wavelet filters with further application of Hilbert spectral analysis to find the instantaneous amplitudes and frequencies of the functions of internal empirical modes, which will allow the identification of semantic features of speech signals and increase the efficiency of their coding; for the first time, it is proposed to use the method of adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals, which will increase the efficiency of spectral analysis by decomposing the high-frequency speech oscillation into its low-frequency components, namely internal empirical modes; the method of semantic coding of speech signals based on mel-frequency cepstral coefficients was further developed, but using the basic principles of adaptive spectral analysis with the help of empirical wavelet transform, which increases the efficiency of this method. Conclusions: We developed a method for semantic coding of speech signals based on empirical wavelet transform, which reduces the encoding rate from 320 to 192 bps and the required bandwidth from 40 to 24 Hz with a probability of error-free recognition of approximately 0.96 (96%) and a signal-to-noise ratio of 48 dB, according to which its efficiency is increased by 1.6 times as compared to the existing method. We developed an algorithm for semantic coding of speech signals based on empirical wavelet transform and its software implementation in the MATLAB R2022b programing language.

Verifiable anonymous quantum communication with authentication based on d-level single-particle states

Anonymous quantum communication (AQC) enables the secure transmission of classical and quantum messages while preserving the anonymity of the sender, receiver, or both. Qudits, quantum states with more advantages than qubits in terms of information transmission rate and eavesdropping detection, offer promising capabilities. In this paper, we propose a verifiable AQC (VAQC) protocol utilizing d-level single-particle states, establishing anonymous entanglement between a public sender and an anonymous receiver. By using quantum teleportation, the public sender can transmit private information to his/her chosen anonymous receiver. The verifiable function of the proposed protocol guarantees the legitimacy of all participants’ identities and verifies the correctness of the anonymous entanglement. Furthermore, we demonstrate that the proposed VAQC protocol satisfies the requirements of correctness, anonymity, and security. The proposed VAQC protocol exhibits extensibility and can be extended to enable private communication between an anonymous sender and an anonymous receiver, as well as anonymous entanglement among multiple participants. This work lays the foundation for practical applications in achieving d-level anonymous entanglement and anonymous transmission of qudits.

Planetary Scale Information Transmission in the Biosphere and Technosphere: Limits and Evolution.

Information transmission via communication between agents is ubiquitous on Earth, and is a vital facet of living systems. In this paper, we aim to quantify this rate of information transmission associated with Earth's biosphere and technosphere (i.e., a measure of global information flow) by means of a heuristic order-of-magnitude model. By adopting ostensibly conservative values for the salient parameters, we estimate that the global information transmission rate for the biosphere might be ∼1024 bits/s, and that it may perhaps exceed the corresponding rate for the current technosphere by ∼9 orders of magnitude. However, under the equivocal assumption of sustained exponential growth, we find that information transmission in the technosphere can potentially surpass that of the biosphere ∼90 years in the future, reflecting its increasing dominance.

Review on optical solitons for long haul transmission

Abstract Fiber optics telecommunication is the currently established backbone infrastructure for most of the information flow across the world. New services and applications are causing an exponential increase in Internet traffic, increasing transmission rate, and high bandwidth-demanding applications have been propelling the data rate per wavelength to approach the speed limit of electronics. In a few years, the current fiber optic communication system infrastructure will not be able to meet this demand because fiber nonlinearity dramatically limits the information transmission rate. Also, the dispersion phenomenon is a problem for high bit rates and long-haul optical communication systems. An easy solution to this problem is optical solitons pulses that preserve their shape over long distances. An optical soliton is formed by balancing nonlinearity (self-phase modulation) and dispersion phenomenon (group velocity dispersion). In this review article, a detailed study of optical solitons is presented along with its recent progress. Advanced numerical methods such as Darboux transform (DT), digital back propagation, split step Fourier, and forward–backward methods are also discussed in this paper which is used for solving higher-order solitons.

Effect of synchronization system errors on the reception noise immunity of amplitude-phase shift keyed signals

Objectives. An urgent task in the context of modern radio and television systems is to improve the quality and quantity of transmitted information. For example, the use of multiple amplitude-phase shift keyed (APSK) signals—16-APSK and 32-APSK—in digital satellite television systems of the Digital Video Broadcasting-Satellite2 (DVB-S2) standard made it possible to transmit 30% more data in the same frequency bands in comparison with the previous DVB-S standard. Such increases in information transmission rates impose more stringent requirements on hardware. An important role in the reception of APSK signals, as well as the signals of other coherent signal processing systems, is played by the stability of synchronization systems. The presence of operational errors can significantly reduce the quality of information reception. The aim of the present work was to analyze the effect of phase and clock synchronization errors on the reception noise immunity of APSK signals with a ring signal constellation structure.Methods. The study used statistical radio engineering methods informed by optimal signal reception theory.Results. The effect of phase and clock synchronization errors on the reception noise immunity of APSK signals having a signal constellation ring structure is analyzed. The dependencies of the bit error probability on the magnitude of the phase shift and the clock offset were characterized. The effect of synchronization errors on reception quality were compared with the known results for quadrature amplitude modulation (QAM) signals.Conclusions. At an acceptable energy loss of no more than 1 dB, the critical phase error can be considered as 2°-3°, while the critical clock error is 3-4%. A coherent receiver of APSK signals is more sensitive to the phase error of reference oscillations than a similar receiver of QAM signals, whereas clock errors have the same effect on the reception quality of these signals.