Quality Of Wireless Communication Research Articles

Online dynamic multi-user computation offloading and resource allocation for HAP-assisted MEC: an energy efficient approach

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

Influence of the parameters of the aquatic environment on the functioning of wireless channels optical underwater communication in information and measurement systems

The main challenge in creating underwater Internet of Things (UIoT) applications is to select the appropriate type of communication system, which must take into account the unique conditions of the underwater environment and data transmission requirements. Unlike terrestrial systems, where RF channels are widely used, underwater conditions require other solutions, as radio waves are significantly attenuated in the water, severely limiting the transmission range. A specific and little-studied aspect of underwater communications is the use of Underwater Wireless Optical Communications (UWOC). This paper focuses on investigating the effect of the characteristics of the aquatic environment on UWOC performance. The purpose of the study was to identify the relationship between the parameters of the aquatic environment and the UWOC channel in the interest of predicting the effectiveness of its application as part of UIoT networks, to substantiate the feasibility and prospects of using underwater optical wireless communications as part of distributed information and measurement systems. The paper presents the results of research on the relationship between the parameters of the aquatic environment and the underwater optical wireless communication channel. The interrelation of optical characteristics of the aquatic environment with the indicators of efficiency and quality of underwater optical wireless communication: normalised power, communication range, bit error rate, in turn, affecting the accuracy and reliability of the information received by the Consumer at the output of the information-measuring system. The influence of water medium turbulence on the communication indicators is assessed. It is shown that changes in the salinity of the aquatic environment as one of the factors causing turbulence negatively affects the main indicators of underwater optical wireless communication. It is shown that the assessment of the feasibility of underwater optical wireless communication should be made on the basis of a detailed analysis of the parameters of the aquatic environment of the water area in which its implementation is planned. The obtained results confirm the possibility and perspectivity of using underwater optical wireless communication as a part of distributed information-measuring systems on underwater objects located in different types of water areas.

Energy efficient resource allocation of IRS-Assisted UAV network

<p>The integration of unmanned aerial vehicle (UAV) networks with intelligent reflecting surface (IRS) technology offers a promising solution to enhance wireless communication by dynamically altering signal propagation. This study addresses the challenge of maximizing system energy efficiency (EE) in IRS-assisted UAV networks. The primary objective is to optimize power allocation and IRS reflection design to achieve this goal. To tackle the optimization problem, we employ a block coordinate descent (BCD) method, decomposing it into three subproblems: phase shift optimization, power allocation, and trajectory planning. These subproblems are iteratively solved using an improved particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed PSO algorithm effectively plans high-quality UAV trajectories in complex environments, significantly enhancing EE. The findings suggest that the IRS-assisted UAV model outperforms traditional UAV models, offering substantial improvements in wireless communication quality and EE.</p>

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

<p>With the maturity and development of new generation communication technology, the demand for wireless network communication quality is getting higher and higher. However, the existing wireless network data transmission models are greatly affected by the signal quality in the data transmission process, which seriously affects the quality of data transmission. To address the problem of signal perturbation affecting communication quality in wireless network communication, this study designs an improved perturbation compression reconstruction algorithm that can be used to process block-structured data based on the block sparse structure in the original signal, and constructs a single-user wireless network communication data transmission model based on this algorithm. The performance simulation test of the model shows that the MSE values of the model are lower than those of the conventional and perfect schemes used as comparisons under the same noise conditions and the transmitted compressed data length conditions. When the variance of Gaussian white noise is 0.125, the MSEs of the research design model, the conventional scheme and the perfect scheme are 0.031, 0.048, and 0.047, respectively. Experimental data show that the improved disturbance compressed sensing reconstruction algorithm can better reconstruct the compressed information and reduce the information loss caused by the disturbance in the compression process on the premise of removing the signal disturbance. This research result has reference significance for improving the signal quality of wireless network communication.</p> <p> </p>

A Centralized Multi-User Anti-Composite Intelligent Interference Algorithm Based on Improved Q-Learning

This paper proposes a central anti-jamming algorithm (CAJA) based on improved Q-learning to further solve the communication challenges faced by multi-user wireless communication networks in terms of external complex malicious interference. This will also reduce the dual factors restricting wireless communication quality, the impact of inter-user interference within the network, and the effect of external malicious interference on the communication system to improve multi-user wireless communication transmission. Firstly, a central base station that coordinates and allocates channels for users within the network is set up using multi-user wireless communication network architecture to constitute a centralized wireless communication network. Secondly, the multi-user system is modeled using the single-user Markov decision process in which the central base station is the main body. Finally, an improved Q-learning algorithm is used to improve overall system transmission income using the central base station, based on the network user number sequential decision action for avoiding external malicious interference. It is designed to avoid the impact of internal network interference on transmission performance during the early stage of communication, achieving overall system transmission income improvement. Simulation results show that in comparison to the existing multi-user independent Q-learning anti-jamming algorithm and the traditional orthogonal frequency-hopping scheme, the proposed algorithm significantly improves overall system transmission performance.

Application of spectral approximation to model the dynamic properties of wireless communication channels

This work is devoted to the application of approximation methods for modeling wireless radio communication channels. The rationale and prospects of the use of unmanned aerial vehicles as part of flying self-organizing networks for the transmission of high-speed information in the conditions of «smart cities» are demonstrated. The problem of using these networks is also referred, associated with the technical complexity of ensuring acceptable reliability and quality of wireless communication, associated with the multipath of signal propagation and a number of other factors. It is shown that in this aspect, the development of mathematical modeling methods of analyzing signals at the inputs of UAV radio receivers in order to evaluate their amplitude- phase transformations by a communication channel is of special significance. It has been established that the connection between the signals on the transmitting and receiving sides of an arbitrary wireless radio communication channel, assuming its linearity, can be uniquely determined by a complex transfer function in the frequency domain, which in practice is very complex and difficult to describe analytically. In this regard, an approach to its approximation by an equivalent model, described by fractional- rational functions of a complex variable, physically realized by mixed connections of various linear inertial and inertial links, is proposed, and for modeling dynamic characteristics, a numerical-a nalytical modeling technique based on the spectral method and piecewise linear approximation. A results of applying the proposed solutions are shown.

Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Recommender system provides the significant suggestion towards the effective service offers for the vast range of big data. The Internet of Things (IoT) environment exhibits the value added application services to the customer with the provision of the effective collection and processing of information. In the extension of the IoT, Internet of Medical Things (IoMT) is evolved for the patient healthcare monitoring and processing. The data collected from the IoMT are stored and processed with the cognitive system for the data transmission between the users. However, in the conventional system subjected to challenges of processing big data while transmission with the cognitive radio network. In this paper, developed a effective cognitive 5G communication model with the recommender model for the IoMT big data processing. The proposed model is termed as Ranking Strategy Internet of Medical Things (RSIoMT). The proposed RSIoMT model uses the distance vector estimation between the feature variables with the ranking. The proposed RSIoMT model perform the recommender model with the ranking those are matches with the communication devices for improved wireless communication quality. The proposed system recommender model uses the estimation of direct communication link between the IoMT variables in the cognitive radio system. The proposed RSIoMT model evaluates the collected IoMT model data with the consideration of the four different healthcare datasets for the data transmission through cognitive radio network. Through the developed model the performance of the system is evaluated based on the deep learning model with the consideration of the collaborative features. The simulation analysis is comparatively examined based on the consideration of the wireless performance. Simulation analysis expressed that the proposed RSIoMT model exhibits the superior performance than the conventional classifier. The comparative analysis expressed that the proposed mode exhibits ~3 – 4% performance improvement over the conventional classifiers. The accuracy of the developed model achieves 99% which is ~3 – 9% higher than the conventional classifier. In terms of the channel performance, the proposed RSIoMT model exhibits the reduced recommender relay selection count of 1 while the other technique achieves the relay value of 13 which implies that proposed model performance is ~4-6% higher than the other techniques.

Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning

To mitigate the effects of shadow fading and obstacle blocking, reconfigurable intelligent surface (RIS) has become a promising technology to improve the signal transmission quality of wireless communications by controlling the reconfigurable passive elements with less hardware cost and lower power consumption. However, accurate, low-latency and low-pilot-overhead channel state information (CSI) acquisition remains a considerable challenge in RIS-assisted systems due to the large number of RIS passive elements. In this paper, we propose a three-stage joint channel decomposition and prediction framework to acquire CSI. The proposed framework exploits the two-timescale property that the base station (BS)-RIS channel is quasi-static and the RIS-user equipment (UE) channel is fast time-varying. Specifically, in the first stage, we use the full-duplex technique to estimate the channel between a BS’s specific antenna and the RIS, addressing the critical scaling ambiguity problem in the channel decomposition. We then design a novel deep neural network, namely, the sparse-connected long short-term memory (SCLSTM), and propose a SCLSTM-based algorithm in the second and third stages, respectively. The algorithm can simultaneously decompose the BS-RIS channel and RIS-UE channel from the cascaded channel and capture the temporal relationship of the RIS-UE channel for prediction. Simulation results show that our proposed framework has lower pilot overhead than the traditional channel estimation algorithms, and the proposed SCLSTM-based algorithm can also achieve more accurate CSI acquisition robustly and effectively.

Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems

Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption. This letter offers a novel deep reinforcement learning (DRL) algorithm based on a location-aware imitation environment for the joint beamforming design in an RIS-aided mmWave multiple-input multiple-output system. Specifically, we design a neural network to imitate the transmission environment based on the geometric relationship between the user’s location and the mmWave channel. Following this, a novel DRL-based method is developed that interacts with the imitation environment using the easily available location information. Finally, simulation results demonstrate that the proposed DRL-based algorithm provides more robust performance without excessive interaction overhead compared to the existing DRL-based approaches.

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.

Conservative but Stable: A SARSA-Based Algorithm for Random Pulse Jamming in the Time Domain

As a dynamic jamming pattern, random pulse jamming is stochastic, sudden, and not easily perceived or addressed. Random pulse jamming can exert a negative influence on the quality of wireless communication. This paper models an anti-jamming communication under random pulse jamming as a Markov Decision Process and proposes a SARSA-based Time-domain Anti-jamming Algorithm (STAA). Differently from previous research, this study attempts to use the SARSA algorithm to counter random pulse jamming in the time domain. The proposed STAA can achieve high-quality transmission while maintaining small fluctuations in transmission performance when the risk of external interference is high.

Reconfigurable Intelligent Surfaces improved Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is the first step in the cognitive cycle and represents the most critical function in cognitive radio-based dynamic spectrum management. Recently, a new technology termed reconfigurable smart surfaces has emerged as a promising enabler of smart radio environments to control the signal propagation further and improve signal coverage and spectrum management capabilities. This paper investigates how the adoption of reconfigurable intelligent surfaces (RISs) can increase spectral efficiency in a cognitive radio environment. To this end, we optimize the parameters of the new RIS technology by determining the optimal transmit powers and the optimal number of these elements. Next, we derive expressions for the false alarm and detection probabilities of the cognitive radio (CR) node and the transmission probability and throughput. Finally, we demonstrate how the detection phase of the CR spectrum can be improved by employing RIS technology at the UP. Simulation results show that RIS achieves higher energy efficiency than SISO communication for different configurations. In addition, the RISs can significantly improve the wireless communication quality and spectrum sensing performance in a CR environment.

Better platooning toward autonomous driving: Inter-vehicle communications with directional antenna

Recently, in the researches on vehicular Internet-of-Things (IoT), platooning have received lots of attentions due to its potential to improve the fuel efficiency and driving experience. Platoon is a group of vehicles that act as smart agents, they travel collaboratively by following the leading human-driven vehicle. A vehicle in the platoon utilizes radar and wireless communication to share important information to other vehicles in the same platoon such as speed and acceleration, to realize the safe and efficient driving. The quality of wireless communication is of great importance to manage and maintain the platoons. However, in a scenario that a large number of vehicles exist, communication delay and packet loss caused by channel congestion may endanger the safe inter-vehicle distance. In this paper, we introduce inter-vehicle communication with directional antenna into platooning. By extensive simulations, we evaluate the packet delay and inter-vehicle distance in both normal driving and braking scenarios, and verify the usefulness of directional antenna in platooning for vehicular IoT.

Communication delay compensation for string stability of CACC system using LSTM prediction

Compared to traditional adaptive cruise control (ACC), cooperative ACC (CACC) can improve the response sensitivity of the following vehicles by using additional information, e.g., the acceleration of preceding vehicles, that is transmitted via inter-vehicle wireless communications. Thus, a platoon with CACC mode obtains a shorter time headway, thereby increasing road throughput while guaranteeing traffic safety. However, delays are common in wireless communication due to complex traffic conditions. The degradation of wireless communication significantly influences the string stability that refers to the attenuation of disturbance in the upstream direction of a platoon. Therefore, this study proposes the use of a deep learning method, i.e., the long short-term memory (LSTM) neural network, to predict the acceleration of the preceding vehicle by using data from onboard radar sensors. It provides the CACC platoon with another option to obtain additional information if the quality of wireless communication worsens. This type of CACC is referred to the LSTM control. Simulations proved the applicability of the LSTM control by using the next generation simulation program (NGSIM) data, in which the accuracy (goodness-of-fit index) of the LSTM prediction reached 0.766, and the LSTM control can compensate the communication delay to maintain string stability when the communication delay exceeded 0.115 s.

INFLUENCE OF DOPPLER EFFECTS ON 2×2 TO 4×4 MIMO SYSTEMS WITH DIRECT LINE-OF-SIGHT PATH BY RICIAN FADING DISTRIBUTION

Background. For multiplying the capacity of a radio link by using multiple antennas at the transmitter and receiver ends, wireless communication networks use the MIMO technique. The quality of MIMO wireless communication may worsen when either the transmitter or receiver end is in motion, or they both are in motion. To sustain high quality of links, MIMO operates, in particular, on channel state information by using orthogonal pilot sequences.Objective. The goal is to estimate the bit-error-rate performance of 2×2, 3×3, and 4×4 MIMO systems with orthogonal pilot sequences for a range of Doppler shifts by the Rician fading distribution modeling the scenario of direct line-of-sight path. The estimation is believed to answer the question on MIMO efficiency by ascertaining whether increasing the number of antennas mitigates the Doppler effect in 2×2 to 4×4 MIMO systems with direct line-of-sight path.Methods. To achieve the goal, 2×2 to 4×4 MIMO systems with channel state estimation are simulated. The simulation is configured and carried out by using MATLAB® R2019a Communications System ToolboxTM functions. The 35 subcases of the frame length and number of pilot symbols per frame are considered.Results. The near-exponential-type decreasing of the bit-error-rate performance is almost not violated by no motion, whereas further speed increments destroy it for 2×2 MIMO systems. It is revealed that too long packet transmissions by short orthogonal pilot sequences (with respect to the packet length) are prone to errors. This especially concerns 2×2 MIMO systems, where channel state estimation by short orthogonal pilot sequences appears to be ineffective or weakly effective.Conclusions. In general, to maintain an appropriate MIMO link data rate, the packet length should be shortened as the motion speed increases. Increasing the number of antennas additionally mitigates Doppler effects. S o, 4 ×4 M IMO s ystem transmissions are indeed efficient. The shortest possible packet transmissions are not affected by Doppler effects if three or four antennas are used.

Two-stage Topology Control Strategy for Software-defined UAV Networks Against Jamming Attack

Software-defined network (SDN) technology brings new methods to the autonomous control of UAV networks. Aiming at the deterioration of UAV network wireless communication quality caused by jamming attacks, we propose a two-stage topology control algorithm to improve network service quality. The first stage is the redeployment of the control plane consisted by the relay UAVs. We calculate the position of the jammers based on Received Signal Strength Indication (RSSI), and then we first redeploy the position of the Relay UAVs. The second stage is the redeployment of the data plane consisted by the mission UAVs. Mission UAVs that lose control links due to jamming are redeployed according to the historical state of controllers, and other UAVs are redeployed according to the new state of controllers. The performance metric is the weighted average of load balancing, redeployment delay and Signal to Interference plus Noise Ratio (SINR). The experimental results show that compared with the Distance Closest Migration (DCM) strategy and the Distributed Hopping Algorithm (DHA), the comprehensive performance of the proposed algorithm is better.

Low Complexity Anti-Interference Transmission Model in Massive MIMO Systems

With the development of wireless communication, wireless communication technology plays an irreplaceable role in modern instrument measurement and communication. However, the complex electromagnetic been an important factor affecting wireless communication quality with the number of devices increasing. Especially in the industrial field and modern city, electromagnetic interference (EMI) is a serious issue in wireless data transmission. Besides, user interference will also reduce the communication performance with the increasing number of equipment users. In this paper, a low-complexity data transmission model is proposed. First, the direct sequence spread spectrum (DSSS) technology is utilized to oppose narrowband electromagnetic interference and noise interference. Nevertheless, the DSSS be affected by multi-user interference (MUI) due to the number of users increasing. Based on DSSS and ZF precoding, the DSSS-ZF structure is proposed to resist interference. The simulation platform utilized the Software Defined Radio (SDR) and channel emulators (CE) to measure our DSSS-ZF model. The results show that the DSSS-ZF structure model can effectively defeat interference and improve the reliability of communication in complicated electromagnetic environment.

Performance Analysis of Wireless Communication for High-speed Trains Based on SPN

The quality of service high-speed train wireless communication is very important for high-speed railway operation. Modeling and simulation is an effective method to formulate travel plans and improve operational efficiency. In this paper, the SPN is used to establish a wireless communication model of a high-speed train for normal communication states and several system fault recovery states. The parameters are obtained through calculation, and the model is simulated and analyzed using TimeNet. The simulation results show that under the parameters required by the specification, the normal working probability of wireless communication is close to 100%, and the probability of several kinds of failure recovery is more than 95%, which meets the high reliability requirements.

Stability Analysis of Vehicle Platooning With Limited Communication Range and Random Packet Losses

Control performance of vehicle platooning relies on the information flow topology and quality of wireless communications. In this article, we investigate the constant-time-headway-spacing-policy-based vehicle platooning problem, where multiple predecessors' information is used by the following vehicles and communication impairments, i.e., limited communication range and random packet losses, are considered. In this article, first, when the leading vehicle moves at a constant speed, we obtain the sufficient and necessary conditions on sampling time, control gains, and internal lag, to ensure the stability of the vehicle platoon based on matrix polynomials' stability for ideal communications. Second, for time-independent homogeneous random packet losses, we provide the upper bound for the loss rate to maintain convergence in expectation by matrix eigenvalue perturbation theory when no input is set for lossy information. We also provide sufficient conditions to guarantee mean-square convergence for heterogeneous time-independent random packet losses and show the convergence time for any given accuracy and probability. Third, when historically latest information is used for input, the sufficient and necessary conditions are provided to ensure the internal stability and string stability by Markov jump linear system theory. Furthermore, we discuss the controller design when no feasible solution exists to guarantee the string stability. Extensive numerical results validate our analysis.

Analysis of efficient Optimization Algorithm for Chaotic Information Nodes in Wireless Networks

Aiming improve the ability of fault detection and diagnosis of wireless network communication, the network chaotic information nodes are detected accurately and efficiently. An efficient detection method for chaotic information nodes in wireless networks is proposed based on quantization tracking and identification of sensor information. Firstly, the node connectivity graph model of wireless network communication is constructed, the link transmission channel of wireless network communication is analyzed, and the topology structure of distributed node is designed. Then the distributed sensor detection technique is used to detect the node output link equalization of wireless network communication. The higher-order spectral features of the output bit sequence stream of wireless network communication channel are extracted, and the anomaly of the characteristic quantity is extracted according to the characteristic quantity. The sensor information quantization tracking and identification technology is used to detect the chaotic information nodes in wireless network. Finally, the simulation test of high efficiency optimization detection of chaotic information nodes in wireless network communication is carried out. The results show that the accuracy of efficient optimization detection for chaotic information nodes is high by using this method. The quality of wireless network communication is improved because of the good real-time performance of network interrupt detection and fault diagnosis.