Improve Transmission Efficiency Research Articles

Performance Analysis of Reconfigurable Intelligent Surface-Assisted Wireless Communication Systems Under Co-Channel Interference

Reconfigurable Intelligent Surface (RIS) can intelligently control the wireless propagation environment by adjusting the signal phase and amplitude in real time, which is considered as one of the key technologies of 6G. Although RIS-assisted wireless communication systems greatly improve transmission efficiency under ideal conditions, there are various uncertainties in actual communication systems. In this paper, RIS-assisted communication system is investigated where the RIS cannot completely eliminate the phase error and the user location is randomly distributed with uncertainty. In addition, there are some interference sources around the user, which are simplified as co-channel interference (CCI). Then we study the performance of RIS-assisted communication system, and derive the closed-form expressions for the outage probability (OP) and channel capacity of the system. Furthermore, we analyze the effects of various parameters on the OP and channel capacity. Finally Monte Carlo simulation is carried out which verify the accuracy of the derivation.

State estimation of reaction-diffusion inertial neural networks with Markovian switching channels via conjunct measurement

This paper addresses the state estimation of reaction-diffusion inertial neural networks (RDINNs) with Markovian switching channels based on conjunct measurement method. First, a suitable variable substitution is employed to transform the original second-order differential system into a first-order one, and a time-space sampled-data measurement strategy is adopted to replace the traditional continuous measurement method. As single-channel transmission may suffer from various communication deficiencies, a multi-channel switching scheme is considered in this paper to improve transmission efficiency. Then, by constructing Lyapunov-Krasovskii functional with relaxed conditions, sufficient conditions are derived to ensure that the error system is asymptotically stable. Finally, the feasibility of the proposed method is further validated by simulation results.

Image sequence decomposition via sigma-delta cellular neural network having coupled cells

In this paper, an image sequence decomposition by the sigma-delta cellular neural network (SD-CNN) with coupled cells and its composition framework is proposed. The SD-CNN, having coupled cells inspired by the second-order sigma-delta modulator, is employed for image sequence decomposition, and it enables effective image decomposition by the effects of complex dynamics. In our method, pixel luminance is described by the number of spikes within a discrete time window that corresponds to the number of SD-CNN iterations. For efficient image decomposition, a high luminance which requires a long time window, should be compensated. To solve this problem, we introduce the cumulated luminance integrator enables compensating a high luminance even with a short time window. Experimental results on various test images in the Kodak dataset and the Waterloo dataset support that all the test images can be mathematically lossless reconstructed from an image sequence decomposed via our method. It is also confirmed that the proposed method improves transmission efficiency for the Kodak dataset by approximately 80%, which is an issue with our previous method.

Semantic-Preserved Communication System for Highly Efficient Speech Transmission

Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending the semantic-related information of the source data. In this paper, we consider semantic-oriented speech transmission which transmits only the semantic-relevant information over the channel for the speech recognition task, and a compact additional set of semantic-irrelevant information for the speech reconstruction task. We propose a novel end-to-end DL-based transceiver which extracts and encodes the semantic information from the input speech spectrums at the transmitter and outputs the corresponding transcriptions from the decoded semantic information at the receiver. In particular, we employ a soft alignment module and a redundancy removal module to extract only the text-related semantic features while dropping semantically redundant content, greatly reducing the amount of semantic redundancy compared to existing methods. We also propose a semantic correction module to further correct the predicted transcription with semantic knowledge by leveraging a pretrained language model. For the speech to speech transmission, we further include a CTC alignment module that extracts a small number of additional semantic-irrelevant but speech-related information, such as duration, pitch, power and speaker identification of the speech for the better reconstruction of the original speech signals at the receiver. We also introduce a two-stage training scheme which speeds up the training of the proposed DL model. The simulation results confirm that our proposed method outperforms current methods in terms of the accuracy of the predicted text for the speech to text transmission and the quality of the recovered speech signals for the speech to speech transmission, and significantly improves transmission efficiency. More specifically, the proposed method only sends 16% of the amount of the transmitted symbols required by the existing methods while achieving about a 10% reduction in WER for the speech to text transmission. For the speech to speech transmission, it results in an even more remarkable improvement in terms of transmission efficiency with only 0.2% of the amount of the transmitted symbols required by the existing method while preserving the comparable quality of the reconstructed speech signals.

Quantum secure direct communication scheme based on the mixture of single photon and Bell state with two way authentication

In response to the demand for identity authentication in quantum secure direct communication, this paper proposes a quantum secure direct communication scheme based on a mixture of single photon and Bell state, by combining the bidirectional identity authentication. Before communication begins, both parties share a series of secret information to prepare a series of single photon and Bell state particles. Encoding four single photons and four Bell states yields eight types of encoded information, followed by identity authentication. The first step in identity authentication is to use a single photon to verify the legitimacy of the receiver. If the error exceeds the given threshold, it indicates the presence of eavesdropping. Otherwise, the channel is safe. Then, Bell state particles are used to verify the legitimacy of the sender, and the threshold is also used to determine whether there is eavesdropping. The present method is the same as previous one. If the error rate is higher than the given threshold, it indicates the existence of third-party eavesdropping. Otherwise, it indicates that the channel is secure. As for the specific verification method, it will be explained in detail in the article. Afterwards, Bell state particles are mixed with a single photon as a transmission carrier, and eavesdropping detection particles are added whenever the quantum state is sent. However, once the eavesdropper intercepts the transmitted particles, owing to incomplete information obtained, the eavesdropper is unable to recover the original information, and the eavesdropping behavior will be immediately detected, thus terminating communication. In this scheme, single photon and Bell states are fully utilized, and hybrid communication can effectively improve transmission efficiency, encoding capability, and quantum bit utilization. Security analysis shows that this scheme can resist common external and internal attacks such as interception/measurement replay attacks, auxiliary particle attacks, and identity impersonation attacks. The analysis of efficiency and encoding capacity shows that the transmission efficiency of this scheme is 1, the encoding capacity is 3 bits per state, and the quantum bit utilization rate is 1. Compared with other schemes, this scheme has significant advantages because it uses different particles for bidirectional authentication, making it more difficult for attackers to crack, and thus it has higher security than traditional schemes.

Statistical Model of Accurately Estimating Service Delay Behavior in Saturated IEEE 802.11 Networks Based on 2-D Markov Chain

To accurately estimate the service delay behavior of IEEE 802.11 networks, this paper comprehensively considers four main factors that affect the performance of IEEE 802.11 networks and establishes a service delay model with statistical characteristics. We analyzed the operation mechanism of 802.11 DCF, using the backoff stage and the backoff counter to portray the dynamic change characteristics of the system regarding the data frame transmission states. Afterward, we calculated the one-step transition probability of these states, establishing a 2-D Markov model, including the ICS procedure and the backoff procedure. Based on this model, we constructed steady-state equations to derive a relationship between the transmission probability and collision probability for each node transmission queue. By analyzing the ICS delay and the backoff delay, we obtained the probability generating function (PGF) of the average idle time. The analytical expressions of other service delays, such as the successful transmission time and collided transmission time, were derived to obtain the PGF of the total service delay. In the numerical simulation, we compared the first two statistical moments of the PGF with the Nav model, and it was found that our delay evaluation results were significantly better than the traditional evaluation results. The average service delay of the Nav model in all the scenarios was larger than that of the proposed model due to the lack of the ICS procedure in the Nav model. Since a DIFS duration is generally much shorter than a random backoff duration, our model saves the bandwidth and improves transmission efficiency.

Hydrogen detecting characteristics and an improved algorithm for data transmission of a palladium nanoparticle/amorphous InGaZnO thin film based sensor

Hydrogen gas detecting properties of a new sensing device have been comprehensively investigated and reported. The proposed sensor is synthesized by a sputtered amorphous InGaZnO (a-IGZO) thin film and thermally evaporated palladium (Pd) nanoparticles (NPs) onto a sapphire substrate. The use of Pd NPs can efficiently enhance the surface area/volume (SA/V) ratio and catalytic activity of Pd metal which yields the significant improvement on hydrogen sensing performance. The material properties related the elemental composition, height profile, surface and cross-section images have been carefully characterized. Experimentally, good hydrogen detecting behaviors including an ultra-high sensing response of 6.4×105 under introduced 1% H2/air gas with a short response (recovery) time of 3 s (11 s) and a very low detecting level of 10 ppb H2/air at 275 ℃ are acquired. The proposed sensor device exhibits good selectivity toward hydrogen gas. Novel GPFDM model is used to reduce redundant data and improve the data transmission efficiency for Internet of Things (IoT) application. The proposed algorithm uses preprocessing along with adjustable thresholds. Experimentally, the GPFDM method can effectively improve transmission efficiency without increasing computational complexity. The GPFDM method shows a prominent reduced data percentage (RDP) of 84.42% and a mean absolute percentage error (MAPE) only of 0.59% compared to the original transmitted data.

Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones.

This paper focuses on the hardware components of smartphones, namely, the use of displays and cameras in mobile devices as transmitters and receivers to establish a near-field multiple-input-multiple-output (MIMO) visible light communication (VLC) system. Based on the relationship between the grayscale values of transmitted and received signals, the physical channel responses are detected and approximated with a high-order regression to obtain the channel gain. With the constraint of bit numbers in the MIMO VLC system, an integer-type water-filling scheme was designed for bit allocation to improve transmission efficiency. The physical examinations show that bit error rate (BER) reduction can be 26.4% with Gaussian noise of 30 dB and detected channel gain compared with the equal bit allocation. The optimization of the simulation was confirmed with the bit assignments in real cases.

Transmission Efficiency Optimal Design of Spiral Bevel Gear Based on Hybrid PSOGSA (Particle Swarm Optimization—Gravitational Search Algorithm) Method

Transmission efficiency is a significant index of the transmission system. Even though much research has been carried out to calculate gear transmission efficiency, only a few of them studied spiral bevel gear due to its complexity. Moreover, spiral bevel gear does not have a “standard surface”, which means more complex coupling relations between different parameters and makes efficiency optimal design more difficult. Therefore, an instantaneous transmission efficiency computing model of a spiral bevel gear was set up based on loaded tooth contact analysis and hybrid elasto-hydrodynamic lubrication theory. Then, the particle swarm optimization–gravitational search algorithm (PSOGSA) optimal model was constructed to obtain the best parameters that maximize the average transmission efficiency of spiral bevel gears. Control parameters and machining parameters are optimized in sequence based on the proposed optimal model. The results showed that both optimal designs could help improve transmission efficiency, but the range of machining parameters is limited in a small interval because of the complex coupling relations. Therefore, the machining parameters optimization are conducted after control parameters optimization, which showed good results. Transmission efficiency was finally improved to 98.78%, which increased more than 4% at least. The proposed optimal model could also be applied into other gear design methods or even other fields.

Intelli-AR Preloading: A Learning Approach to Proactive Hologram Transmissions in Mobile AR

Mobile augmented reality (AR), which integrates virtual objects (i.e., holographic contents) with 3-D real environments in real time, has been rapidly gaining popularity in the last five years. The delivery mechanisms of these holographic contents to mobile AR devices, however, are rarely investigated. To combat bandwidth limitations that preclude providing holographic contents to user devices on-demand, in this article, we propose the intelligent AR (Intelli-AR) preloading algorithm to improve transmission efficiency in the edge-assisted network, in which edge servers proactively transmit holographic contents to the devices. Without user devices’ future motion trajectories, the Intelli-AR preloading algorithm models the user devices’ motion trajectories as Markov decision process (MDP) and adaptively learns the optimal preloading policy. The Intelli-AR preloading is decomposed into two parts and separately deployed on the edge server and the user devices to reduce the computation complexity. The Intelli-AR solution improves the ratio of successful preloading by 11.52% compared to the best baseline in the practical data set when the users’ motion trajectories tend to be more random, and by 21.97% compared to the best baseline in the data set which is synthesized from a real-life mobile AR environment.

Physics-Enhanced PCA for Data Compression in Edge Devices

In smart cities, tremendous data are generated with edge devices continuously for scientific applications, such as structural health monitoring (SHM), leading to a high bandwidth burden to edge devices. Data compression is a typical technique for reducing data size and improving transmission efficiency. However, their operations for distinguishing redundant data might be unapplicable for a domain-specific scientific analysis and distort physical quantities of raw data (i.e., mode shapes of vibration data). Besides, they are too compute-intensive to be executed in resource-constraint edge devices. In this paper, we leverage physical knowledge to enhance a lightweight data compression method for edge devices in maintaining physical quantities. In particular, we propose physics-enhanced PCA for compressing data in a dynamic system — compressing the vibration data of a structure in the context of SHM. Physical knowledge is identified from a structure and guides the compression process to preserve the mode shape, an significant physical quantity for structures. We have formally analyzed the effectiveness of our approach and performed experiments to show that physical knowledge is essential for preserving mode shapes. Concretely, experiments in numerical and real-world structures show that physics-enhanced PCA can improve the accuracy by up to 56% compared with alternative baseline.

Towards Adaptive Network Resource Orchestration for Cognitive Radio Networks

<p>This work proposes an adaptive resource orchestration system for a Wireless Local Area Network (WLAN) that is based on the operating principle of Cognitive Radio (CR) technology. By collecting environmental parameters, including the retransmission rate and the channel occupancy rate, the proposed system has “knowledge” of overall transmission behavior and can regulate transmission resources. An Adaptive Connection Assignment (ACA) mechanism is proposed for end devices; it find out target end devices with poor transmission performance, analyzes their alternative Access Point (AP) availability and causes them to change connections to improve transmission performance. An Adaptive Channel Utilization (ACU) mechanism is designed for APs to identify a target AP that is suffering from interference, to analyze its alternative channel availability and to require it to change its working channel to improve transmission efficiency. Results of simulations of various scenarios indicate that the throughput of end devices is increased by 15 to 24%, the throughput of APs is increased by 6 to 47% and the retransmission rate of APs is reduced by 0.4 to 5.3%.</p> <p> </p>

A Modified LZW Algorithm Based on a Character String Parallel Search in Cluster-Based Telemetry Data Compression

The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved, and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry systems to improve transmission efficiency and reduce the burden of required spacecraft resources, in particular regarding their transmitter power. In our primary study, a D-CLU algorithm was proposed to perform lossless compression for telemetry data, and achieve better performance. However, a limitation of this algorithm is that the compression time may become longer when the clustering head (CH) and outlier (which are compressed by LZW algorithm) numbers increase. To reduce compression delay, this paper proposed a modified character string (MCS) parallel search strategy for LZW algorithm (denoted by MCS-based LZW). The proposed MCS-based LZW algorithm designs coding principle, dictionary update rule and search strategy according to the character string matching results. Example verification and simulation results show that the proposed algorithm can effectively decrease the dictionary search times, and thus reduce the compression time.

Optimizing multipath QUIC transmission over heterogeneous paths

As a novel UDP-based transport protocol which supports stream multiplexing, QUIC is faster, more lightweight and flexible than TCP. With the prevalence of multi-homed devices such as smartphones with both WiFi and 4G/5G cellular connectivity, Multipath QUIC (MPQUIC) can effectively utilize multiple network interfaces (i.e., multiple paths) to improve transmission efficiency. Current MPQUIC implementation adopts the Lowest-RTT-First (LRF) scheduler which always selects the path with the lowest smoothed RTT among all available paths. However, we show that in networks with heterogeneous paths where network characteristics (e.g., RTT, loss rate) differ considerably, such scheduling scheme leads to unnecessary waiting on fast paths and bufferbloat, degrading overall transmission performance significantly. To use heterogeneous paths efficiently (i.e., to reduce the overall file transfer completion time), this paper proposes a novel scheduling mechanism that assigns data to paths with transfer simulation without causing much additional overhead. Extensive experiment results in Mininet demonstrate that the proposed scheduling mechanism can reduce the transfer completion time by up to 29.6% as compared to existing MPQUIC implementation.

A Two-Stage High-Order Modulation Recognition Based on Projected Accumulated Constellation Vector in Non-Cooperative B5G OSTBC-OFDM Systems

Blind modulation recognition (BMR) is a pivotal signal processing technology for cognitive receivers to reduce signaling overhead and improve transmission efficiency. Although BMR techniques have been extensively explored for single-carrier systems, only a few research have been published for OSTBC-OFDM systems. More troubling is that high-order modulation types will make the BMR harder because the constellation points are overcrowded. To remedy these flaws, we propose a two-stage high-order BMR approach that leverages low-complexity cumulants and projected accumulated constellation vector (P-ACV) to recognize high-order modulation types (e.g., 2048QAM). Firstly, zero-forcing blind equalization is employed to recover the damaged signal and enhance its feature representation. Next, the first-stage recognition module leverages the fourth-order cumulants to classify 13 modulation types into three groups, including PSK&PAM, square-shaped QAM and cross-shaped QAM groups. Finally, the second-stage recognition module distinguishes the inter-class signal in each group by P-ACV features. The temporal convolution network is built to tap the potentialities of P-ACV and speed up online inference. Numerical results demonstrate that our two-stage algorithm obtains a recognition accuracy of 97.37% when signal-to-noise-ratio (SNR) ≥ 12 dB and performs six times faster than the suboptimal method. Notably, it does not require prior knowledge, such as channel state information (CSI) and SNR conditions.

Wireless-Powered Cell-Free Massive MIMO With Superimposed Pilot Transmission

In this letter, the uplink (UL) wireless information transmission (WIT) and downlink (DL) wireless power transfer (WPT) in cell-free massive multiple-input multiple-output (CF-mMIMO) networks are investigated. Specifically, the mobile terminals (MTs) collect the radio-frequency energy emitted from the access points (APs) in the DL WPT phase and then use the harvested energy (HE) to transmit pilot and data symbols in the UL WIT phase. Considering the performance of CF-mMIMO networks with a large number of MTs is severely bottlenecked by pilot contamination, the superimposed pilot transmission (SPT) is advocated to alleviate its impact and improve transmission efficiency. Based on the SPT, closed-form UL achievable spectral efficiency (SE) and DL average HE expressions are derived. Simulation results verify the accuracies of the derived expressions and indicate that the SPT significantly outperforms the regular pilot transmission in terms of both SE and HE.

Optimal Scheduling Algorithm of Wireless Communication Packets Based on Knapsack Theory

Due to the instability of the wireless channel, data packet loss occurs during wireless communication information transmission. Wireless communication can effectively improve transmission efficiency, study the optimal transmission method of wireless communication data packets, improve the information transmission efficiency and quality of wireless communication, and realize the optimal transmission of wireless communication data packets in order to ensure the reliable transmission of data. It is crucial for increasing wireless communication quality. Natural scheduling and priority scheduling are the only two types of wireless resource scheduling algorithms available so far. These scheduling algorithms are unable to solve the problem of executing the best scheduling under the same priority constraints. This problem is solved using the knapsack theory data knapsack filling procedure. The optimal scheduling of wireless communication packets is investigated in this study using the knapsack theory. The number of destination nodes grows from two to sixteen over time, two at a time. Set the system parameters to a total of 50 original data packets, a packet loss rate of 0.4 in the optimization process, and a packet loss probability of 0.26 in the feedback connection. The KFA approach employed in this study has the highest optimization rate in the three experiments, which can be seen to reduce data packet loss, according to the simulation test. Hybrid coding of data packet transmission is used in conjunction with the buffer feedback method to increase data transmission security. At the same time, it gives practical data for wireless reactive power coded data packet transfer using the time complexity measurement algorithm.

An Interleaved Bidirectional Coupled-Inductor Based DC–DC Converter With High Conversion Ratio for Energy Storage System

In this article, an interleaved coupled-inductor (CI) based bidirectional dc–dc converter (BDC) is proposed with a higher voltage conversion ratio (VCR). In this proposed interleave CI-based BDC (ICI-BDC), the CIs can operate as both a filter inductor and a transformer simultaneously, so that the power density is improved. Moreover, as a transformer, the turns ratio of CIs is used to improve the VCR with a voltage stress reduction purpose. Furthermore, the interleaved technique can well achieve the low current ripple and reduced current stress in this topology. To control ICI-BDC, the pulsewidth modulation plus phase-shift (PPS) control is employed, where the duty cycle is used to match the two side voltages, and the phase-shift angle is used to control the amount and direction of power flow. Uniquely, a difference value is set between the two side duty cycles, and the duty cycle of the high voltage side is smaller than that of the low voltage side by a fixed value. In this proposed control method, all the switches can achieve soft-switching even at light load condition, which can improve transmission efficiency. Experimental results obtained from a 1-kW prototype show a nearly high efficiency under all loading conditions, validating the viability of the proposed ICI-BDC topology and control method.

Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things (IoT) cloud to improve transmission efficiency, which has created a novel paradigm for improving the speed of common connected objects in IoT. However, although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services, security concerns, such as a lack of reliability and collusion attacks, still exist in the outsourcing computation. In this paper, we propose a reliable, anti-collusion outsourcing computation and verification protocol, which uses distributed storage solutions in response to the issue of centralized storage, leverages homomorphic encryption to deal with outsourcing computation and ensures data privacy. Moreover, we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum, which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion attacks. The results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure, reliable, and more effective compared with state-of-the-art approaches.

Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation

During wireless video transmission, channel conditions can vary drastically. When the channel fails to support the transmission bit rate, the video quality degrades sharply. A pseudo-analog transmission system such as SoftCast relies on linear operations to achieve a linear quality transition over a wide range of channel conditions. When transmitting 3D videos over SoftCast, the following issues arise: (1) assigning the transmission power to texture and depth maps to obtain the optimal overall quality and (2) handling 3D video data traffic by dropping and re-allocating resources. This paper solves the pseudo-analog transmission resource allocation problem and improves the results by applying the optimal joint power allocation. First, the minimum and the target distortion optimization problems are formulated in terms of a power–bandwidth pair versus distortion. Then, a minimum distortion optimization algorithm iteratively computes all the possible resource allocations to find the optimal allocation based on the minimum distortion. Next, the three-dimensional target distortion problem is divided into two subproblems. In the power-distortion problem, to obtain a target distortion, the algorithm exhaustively solves the closed form of the power resource under a predefined upper-bound bandwidth. For the bandwidth-distortion problem, reaching a target distortion requires solving iteratively for the bandwidth resource closed form, given a predefined power. The proposed resource control scheme shows an improvement in transmission efficiency and resource utilization. At low power usage, the proposed method could achieve a PSNR gain of up to 1.5 dB over SoftCast and even a 1.789 dB gain over a distortion-resource algorithm, using less than 1.4% of the bandwidth.