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Key management plays a major role in public key cryptosystem, especially using session keys changing the session key from time to time enhances the robustness of the cipher at every stage of its transmission from one end to the other. The mission of protecting the session keys against the adversary can be achieved by perfect forward secrecy (PFS). The present paper explains the digital signature algorithm with perfect forward secrecy (PFS) basing on Hyper Elliptic Curve Cryptosystem. Hyper Elliptic Curve Cryptosystem (HECC) provides equal security level as other conventional public key cryptosystems with shorter key length. The proposed digital signature algorithm strengthens the cipher in all directions of confidentiality authentication and integrity. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, positioning technology, and modern smart devices, Internet of Vehicles (IoVs) smart vehicles have brought great convenience to human production and life. Meanwhile, privacy and security issues are becoming extremely serious, with serious consequences if sensitive data such as vehicle location and trip patterns are leaked. This paper focuses on the demands for vehicular network security, especially privacy protection and existing privacy-protection techniques, including common cryptography methods and cryptography-based advanced technologies. At the same time, this paper also analyzes the advantages and challenges of these technologies in protecting privacy and network security in the Internet of Vehicles, such as the challenges of computational resource requirements and security efficiency in the implementation process, as well as the complexity of realizing effective privacy protection in the interactions among different entities. Finally, this paper envisions the development of privacy-preserving application scenarios and the prospects for crypotography-based privacy-preserving technologies. [ABSTRACT FROM AUTHOR]

In the multi hop communication of wireless sensor networks, relay stations are used to realize remote transmission. In order to avoid the air data repeating collision problem and multi-path retransmission problem of wireless signals sent by relay nodes, this paper proposes a relaying algorithm using dual buffer pool. The node buffer pool is added in the node to store the package source identifier and packet count pairs, the package buffer pool is added to the package to store paths in transmission. First, after the package is received from radio, the node compares the pairs of the SID and Count in the node buffer pool with the pairs of the package source identifier and package count in the package. If there are pairs, the package is discarded, otherwise the package source identifier and package count are added into the node buffer pool. Next step, if the node identifier is equal to the destination byte DID in package, the package will be processed in the node and terminated transmission, otherwise the package will be continued to process. Then the node compares the node identifier with the package buffer pool bytes. If there is the node identifier in the package pool, the package will be discarded, otherwise the relay node stores its own node identifier into the packet buffer pool to create a new package and transmit forwards. The algorithm is implemented on CC2530 chip and relaying experiments of wireless sensor network are carried out. The results show that the algorithm can achieve relay forwarding and avoid duplicating forwarding data collision and avoid multipaths duplicate forwarding. [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Networks (MANETs) are decentralized networks where mobile nodes communicate through radio waves without fixed infrastructure. However, they are susceptible to security attacks like the black hole attack, where malicious nodes claim optimal routes to intercept packets. This paper focuses on detecting black hole attacks within MANETs using a hybrid routing protocol. Specifically, the Modified-Zone-Based Hierarchical Link State (M-ZHLS) routing protocol is proposed and evaluated against existing protocols using simulations in NS2. The proposed method employs on-demand topology information transmission and utilizes techniques like expanding ring flooding for route discovery. Simulation results demonstrate that M-ZHLS outperforms existing protocols by reducing average delay by 5%, lowering routing overhead by 4%, and increasing delivery ratio by 4.5%. Future work aims to further enhance network performance in real-time environments. [ABSTRACT FROM AUTHOR]

Wireless Mobile Ad Hoc Networks (MANETs) present topologies where nodes act as both hosts and routes, necessitating efficient routing protocols to handle frequent route updates and transmission congestion. This paper proposes a novel routing algorithm, Fuzzy Controller Ad hoc On-demand Distance Vector (FC-AODV), to address these challenges. The FC-AODV algorithm is designed to improve packet delivery ratio, throughput, end-to-end delay, packet loss, remaining energy, and routing overhead compared to the traditional AODVprotocol. Through extensive simulations using NS 2, the proposed algorithm demonstrates superior performance across varying network scenarios. Results indicate an average increase of 12.4% in packet delivery ratio, 10.27% in throughput, and a decrease of 5.37% in end-to-end delay compared to AODV. Additionally, FC-AODV reduces packet loss by 5.57%, increases remaining energy by 6.4%, and decreases routing overhead by 6.37%. Future research directions include applying the FC-AODV concept to satellite networks, exploring the integration of shortest path algorithms, and evaluating FC-AODV in real-time network environments. [ABSTRACT FROM AUTHOR]

The term "MANET," which stands for "Mobile Adhoc Network," refers to an ad hoc wireless network incorporating routing mechanism atop an ad hoc link layer network. These networks consist of wirelessly linked mobile nodes that self- configure and self-heal. Due to frequent changes in network architecture, MANET nodes have the freedom to move. Each node in the network serves as a router, forwarding traffic to specified nodes. Broadcast algorithms in wireless ad hoc networks typically follow two main approaches: static and dynamic. In the static approach, local algorithms proactively determine each node's status based on local topology information and a globally known priority function. This paper reveals that existing local broadcast algorithms based on the static approach fail to achieve a satisfactory approximation factor for the optimum solution to an NP-hard problem. However, it is demonstrated that a constant approximation factor is attainable when relative position information is available. The hybrid broadcast method dynamically determines each node's state "on-the-fly," ensuring complete delivery and maintaining a consistent value approaching the ideal outcome as closely as possible. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) equipped with communication technologies have gained significant attention as a promising solution for providing wireless connectivity in remote, disaster-stricken areas lacking communication infrastructure. However, enabling UAVs to provide communications (e.g., UAVs acting as flying base stations) in real scenarios requires the integration of various technologies and algorithms. In particular 3D path planning algorithms are crucial in determining the optimal path free of obstacles so that UAVs in isolation or forming networks can provide wireless coverage in a specific region. Considering that most of the existing proposals in the literature only address path planning in a 2D environment, this paper systematically studies existing path-planning solutions in UAVs in a 3D environment in which optimization models (optimal and heuristics) have been applied. This paper analyzes 37 articles selected from 631 documents from a search in the Scopus database. This paper also presents an overview of UAV-enabled communications systems, the research questions, and the methodology for the systematic mapping study. In the end, this paper provides information about the objectives to be minimized or maximized, the optimization variables used, and the algorithmic strategies employed to solve the 3D path planning problem. [ABSTRACT FROM AUTHOR]

This paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to long transmission distances, making it difficult to meet the delay requirements of new power services. Therefore, to reduce the transmission delay, data transmission, storage and computation need to be performed locally. However, due to the limited resources of individual nodes in the local power wireless communication network, issues such as tight coupling between devices and resources and a lack of flexible allocation need to be addressed. The collaborative allocation of resources among multiple nodes in the local network is necessary to satisfy the multi-dimensional resource requirements of new power services. In response to the problems of limited node resources, inflexible resource allocation, and the high complexity of multi-dimensional resource allocation in local power wireless communication networks, this paper proposes a multi-objective joint optimization model for the collaborative allocation of communication, storage, and computing resources. This model utilizes the computational characteristics of communication resources to reduce the dimensionality of the objective function. Furthermore, a mouse swarm optimization algorithm based on multi-strategy improvements is proposed. The simulation results demonstrate that this method can effectively reduce the total system delay and improve the utilization of network resources. [ABSTRACT FROM AUTHOR]

Spatial Modulation(SM) and Orthogonal Frequency Division Multiplexing(OFDM) Multi-Mode Index Modulation (MM-OFDM-IM) techniques have significant advantages in Energy Efficiency(EE) and Spectral Efficiency(SE), respectively. The combination of multi-dimensional index modulation technology can apply multiple physical resources to meet the high demand for data transmission and system capacity in wireless communication systems. Therefore, SM and MM-OFDM-IM systems can be flexibly combined to construct an OFDM Multiple Mode Space Frequency Composite Index Modulation (MM-OFDM-SFCIM) based on space frequency combination. This system can retain the silent subcarriers in traditional subcarrier index modulation technique, expanding new composite coefficient dimensions and improving its SE and EE. Aiming at the complex characteristics of this system, this paper proposes a joint detection algorithm EL-REML (REML based on symbolic Energy and LLR) based on symbolic energy and log-likelihood. The simulation results show that the MM-OFDM-SFCIM system achieves about 30% improvement in SE compared to traditional space-frequency combination systems, and the EL-REML algorithm proposed in this system achieves a 3-4 dB improvement in BER (Bit Error Rate) compared to the LLR algorithm. Compared with traditional ML algorithm, this algorithm has lower computational complexity. [ABSTRACT FROM AUTHOR]

With the popularity of 5G mobile communication services, the number of users has increased dramatically, as well as the nonuniformity of user density distribution; many users are in nonideal channel conditions, so unmanned aerial vehicle (UAV) as a passive relay equipment platform has gradually entered people's vision. Intelligent reflective surfaces (IRSs) capable of reconfiguring electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we start from the point of minimizing the energy consumption and nodes of UAV passive relay; the paper proposes to install intelligent reflecting surface (IRS) on UAV as a new passive relay and establish the communication coverage model of UAV-IRS. Then, the main relationship between the coverage radius and hover height of UAV-IRS is verified. In view of different distribution densities of target users, a CK++ (cyclic k -means++) is proposed to solve the spatial deployment problem of UAV-IRS, where the optimal solution of the system is obtained through cyclic clustering. And the algorithm is verified to effectively improve the performance of urban mobile communication and user communication service quality through numerical stimulation. [ABSTRACT FROM AUTHOR]

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it's especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments. [ABSTRACT FROM AUTHOR]

With the popularization of smart devices and the continuous development of wireless communication technology, a large amount of trajectory data is recorded in wireless networks, and frequent item mining for trajectory data has become a research hotspot. In order to update the frequent trajectory set when new data is added to the trajectory database and the minimum support is changed, we propose the dynamic minimum support incremental updating PrefixSpan algorithm (DMSIU-PrefixSpan). A method is proposed in the algorithm to estimate the support of a sequence, in which the confidence of its suffix subsequence is approximated to replace its own confidence when estimating the support of a sequence. With this method, the sequence support can be calculated without scanning the database. DMSIU-PrefixSpan algorithm can make full use of the information of frequent trajectory set. When incremental trajectory data is added to the original trajectory database and the minimum support changes, the candidate sequence is generated by connecting frequent trajectory sequence and frequent 1 sequence, and the candidate sequence support is estimated using the support estimation method to discover the frequent sequences. The algorithm no longer needs to save the original trajectory data and achieves fast update of the frequent trajectory set. The accuracy of the support degree estimation and the effectiveness of the algorithm of this paper are verified by the mining experiments of the trajectory database. [ABSTRACT FROM AUTHOR]

Energy efficiency continues to be a key factor limiting the ability to deploy ad-hoc networks. Deploying an energyefficient system that maximizes network lifetime has been a major challenge for many years. Many algorithms have been proposed to solve this problem. This paper proposes a new intrusion detection system--Enhanced Adaptive 3 Acknowledgment (EA3ACK)--using the Energy Efficiency Dynamic State (EEDS) algorithm. This algorithm is designed to increase network lifetime and residual energy by constantly monitoring individual nodes in the network, thereby improving the network's quality of service. The Network Simulator (NS2) is used to implement and test the proposed system. [ABSTRACT FROM AUTHOR]

In this paper, an efficient connectivity and coverage management module for wireless sensor networks is proposed. The proposed approach is composed of centralized-based algorithms that depend on software defined network architecture. The focus in this paper is on coverage and connectivity problem under the restrictive assumptions that the controller has sufficient information about the sensor nodes in the area of interest such as nodes IDs, locations, and residential energy. This information is used to determine the active, the connection and the sleep set of nodes via two main algorithms. First, coverage algorithm uses location, residential energy and other parameters to choose coverage set which will be the active nodes in the sensing field. Second, a shortest path algorithm uses the active set produced from the coverage algorithm in order to choose the connectivity set of nodes that ensures connectivity of the entire network. The proposed approach has been compared with another related approach. Simulations results have showed that the proposed algorithm outperforms the other approach in terms of the network lifetime, coverage, and connectivity performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Satellite networks can communicate with the outside world from anywhere in the world, and multihop networks are suitable for occasions in which infrastructure is lacking or for emergencies. Heterogeneous networks formed by satellite and multihop networks can further expand the communication range of wireless networks; this expansion is conducive to communication with the outside world in remote areas and in emergency situations. However, the formation of heterogeneous networks also brings new challenges to wireless network research. To improve the transmission performance of heterogeneous networks composed of satellite and multihop networks, this paper first introduces the heterogeneous network model of satellite and multihop networks, then analyzes the bandwidth delay products of heterogeneous networks and proposes an end-to-end transmission control algorithm for heterogeneous networks. The algorithm incorporates different congestion window settings in the slow start through a threshold and through the size of the receiver notification window by increasing the amount of data transmitted in the slow start to improve the throughput of the satellite link. The algorithm then differentiates packet losses in congestion avoidance through the sizes of unacknowledged data in the heterogeneous network, using different threshold settings for different unacknowledged data sizes. The simulation results show that the proposed algorithm has some advantages over the TCP Hybla, TCP Veno and TCP Reno schemes in terms of the throughput of the satellite link, the download response time of the multihop network and the queue delay of nodes. [ABSTRACT FROM AUTHOR]

This paper proposes a CanSat system with a vehicle tracking function based on Jetson Nano, a high-performance small computer capable of operating artificial intelligence algorithms. The CanSat system consists of a CanSat and a ground station. The CanSat falls in the atmosphere and transmits the data obtained through the installed sensors to the ground station using wireless communication. The existing CanSat has the limitation of being simple data collectors, literally carrying out the mission of transmitting the collected data to the ground station as it is. The Jetson Nano-based CanSat proposed in this paper uses a pre-trained neural network model to detect the location of a vehicle in each image taken from the air in real time, and then uses a 2-axis motor to move the camera to track the vehicle. The performance of the proposed CanSat system is evaluated by measuring the accuracy and speed of vehicle tracking using self-produced image dataset. [ABSTRACT FROM AUTHOR]

Being an effective non-orthogonal multiple access (NOMA) technique, sparse code multiple access (SCMA) is promising for future wireless communication. Compared with orthogonal techniques, SCMA enjoys higher overloading tolerance and lower complexity because of its sparsity. In this paper, based on deterministic message passing algorithm (DMPA), algorithmic simplifications such as domain changing and probability approximation are applied for SCMA decoding. Early termination, adaptive decoding, and initial noise reduction are also employed for faster convergence and better performance. Numerical results show that the proposed optimizations benefit both decoding complexity and speed. Furthermore, efficient hardware architectures based on folding and retiming are proposed. VLSI implementation is also given in this paper. Comparison with the state-of-the-art have shown the proposed decoder's advantages in both latency and throughput (multi-Gbps). [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a popular technique used to solve optimization problems in WSNs due to its simplicity, high quality of solution, fast convergence and insignificant computational burden. However, iterative nature of PSO can prohibit its use for high-speed real-time applications, especially if optimization needs to be carried out frequently. This paper outlines issues in WSNs, introduces PSO and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues. The objective of this paper is to give a flavor of PSO to researchers in WSN, and to give a qualitative treatment of optimization problems in WSNs to PSO researchers in order to promote PSO in WSN applications. [ABSTRACT FROM AUTHOR]

Hardware non-idealities are among the main performance restrictions for upcoming wireless communication systems. Asymmetric hardware distortions (HWD) happen when the impairments of the I/Q branches are correlated or imbalanced, which in turn generate improper additive interference at the receiver side. When the interference is improper, as well as in other interference-limited scenarios, improper Gaussian signaling (IGS) has been shown to provide rate and/or power efficiency benefits. In this paper, we investigate the rate benefits of IGS in a two-user interference channel (IC) with additive asymmetric HWD when interference is treated as noise. We propose two iterative algorithms to optimize the parameters of the improper transmit signals. We first rewrite the rate region as an pseudo-signal-to-interference-plus-noise-ratio (PSINR) region and employ majorization minimization and fractional programming to find a suboptimal solution for the achievable user rates. Then, we propose a simplified algorithm based on a separate optimization of the powers and complementary variances of the users, which exhibits lower computational complexity. We show that IGS can improve the performance of the two-user IC with additive HWD. Our proposed algorithms outperform proper Gaussian signaling and competing IGS algorithms in the literature that do not consider asymmetric HWD. [ABSTRACT FROM AUTHOR]

With the superior development of technology, mobile robots have become an essential part of humans' daily life. Consequently, interacting and dealing with them pushes us to develop and propose different suitable Human-Robot Interaction (HRI) systems that can detect the interacted user's actions and achieve the desired output in real-time. In this paper, we propose a closed-loop smart mechanism for two main agents: the hand gloves' controller and the mobile robot. To be more specific, the developed model employs flex sensors to measure the curve of the finger. The sensor signals are then processed by aiding the Hedge Algebras Algorithm to control the movement direction and customize the speed of the mobile robot via wireless communication. Numerical simulation and experiments demonstrate that the mobile robot could operate reliably, respond rapidly to control signals, and vary its speed continually based on the different finger gestures. Besides, the control results are also compared with those obtained from the traditional fuzzy controller to prove the superiority and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In the field of intelligent anti-jamming, deep reinforcement learning algorithms are regarded as key technical means. However, the learning process of deep reinforcement learning algorithms requires a stable learning environment to ensure its effectiveness. Moreover, the inherent limitations of deep reinforcement learning algorithms mean that they can only demonstrate excellent learning capabilities on specific tasks with constant parameters. When parameters change, they can only resample and relearn to converge. In a changing jamming environment, its stability and convergence speed may be challenged, thereby affecting its robustness and generalization capabilities. Aiming at the naive yet unique similarity characteristics of the communication anti-jamming problem, this paper designs a new Meta-PPO deep reinforcement learning algorithm that combines Proximal Policy Optimization (PPO) and MAML meta-learning ideas. The proposed algorithm engrafts the principle of meta-learning used in the Model Agnostic Meta-Learning (MAML) model onto the Proximal Policy Optimization (PPO)-based schemes, enabling the communication systems to harness its prior learned experiences acquired from previous anti-jamming tasks to facilitate and speed up its optimal decision-making process when faced with incoming jamming attacks with similar features. The proposed algorithm is verified through computer simulation analyses and the results show that the proposed novel Meta-PPO algorithm can outperform traditional DQN- and PPO-based algorithms in terms of better robustness and generalization abilities, which can be used to enhance the anti-jamming capabilities of wireless communication systems. [ABSTRACT FROM AUTHOR]

Nodes localization has been a critical subject in wireless sensor network (WSN) field. As far as existing localization algorithms are concerned, distance vector hop (DV-Hop) has the advantages of no extra hardware and implementation simplicity, however its localization accuracy cannot meet some specific requirements. In order to enhance the accuracy of WSN nodes localization, a DV-Hop based localization algorithm based on nodes negotiation and multi communication radii (NNMCR DV-Hop) is proposed in this paper. Firstly, the hop counts between WSN nodes is modified from an integer to a decimal by changing communication radius of anchor nodes through nodes negotiation. By refining the hop counts, the accuracy of the estimated distance from the unknown to the anchor nodes is improved. Secondly, the calculation of the average hop size of the anchor node is abstracted into a combinatorial optimization problem which is solved by using binary particle swarm optimization (BPSO) to improve the accuracy of the estimated distance which is between the anchor and the unknown node. Finally, when calculating the coordinates of unknown nodes, only the anchor nodes with smaller hop counts are selected to participate in the calculation. Simulation experiments show that compared with the original DV-Hop as well as other improved algorithms based on DV-Hop, NNMCR DV-Hop greatly improves the localization accuracy of unknown nodes without additional hardware. [ABSTRACT FROM AUTHOR]

With the development of the computer vision field, the acquisition of scene depth information is one of the important topics in the three-dimensional reconstruction of the computer vision field, and its significance is particularly important. The purpose of this paper is to study the virtual viewpoint video synthesis for image restoration based on the intelligent algorithm of wireless network communication. Aiming at the hole problem caused by the change of occlusion relationship, this paper proposes a hole-filling method based on background recognition. A threshold segmentation algorithm is used to reduce the filling priority of foreground pixels at the boundary of the hole and fully solve the hole problem. This paper also proposes a wireless sensor network node positioning model with swarm intelligence algorithm, which combines swarm intelligence algorithm with some key issues of wireless sensor network, speeds up the convergence, and improves the traditional intelligence algorithm. According to the experimental data in this paper, the algorithm in this paper is about 20% higher than the traditional algorithm in PSNR. On SSIM, the performance of the algorithm in this paper is 4.6% higher than the traditional algorithm at most, and the lowest is 2.2%. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning approach is used to conduct an in-depth study and analysis of intelligent resource allocation in wireless communication networks. Firstly, the concepts related to CSCN architecture are discussed and the throughput of small base stations (SBS) in CSCN architecture is analyzed; then, the long short-term memory network (LSTM) model is used to predict the mobile location of users, and the transmission conditions of users are scored based on two conditions, namely, the mobile location of users and whether the small base stations to which users are connected have their desired cache states, and the small base stations select the transmission. The small base station selects several users with optimal transmission conditions based on the scores; then, the concept of game theory is introduced to model the problem of maximizing network throughput as a multi-intelligent noncooperative game problem; finally, a deep augmented learning-based wireless resource allocation algorithm is proposed to enable the small base station to learn autonomously and select channel resources based on the network environment to maximize the network throughput. Simulation results show that the algorithm proposed in this paper leads to a significant improvement in network throughput compared to the traditional random-access algorithm and the algorithm proposed in the literature. In this paper, we apply it to the fine-grained resource control problem of user traffic allocation and find that the resource control technique based on the AC framework can obtain a performance very close to the local optimal solution of a matching-based proportional fair user dual connection algorithm with polynomial-level computational complexity. The resource allocation and task unloading decision policy optimization is implemented, and at the end of the training process, each intelligent body independently performs resource allocation and task unloading according to the current system state and policy. Finally, the simulation results show that the algorithm can effectively improve the quality of user experience and reduce latency and energy consumption. [ABSTRACT FROM AUTHOR]

This paper combines the research of wireless sensor networks and sports training and proposes a wireless sensor network-based intelligent sports training system. According to the requirements of the system, this design uses the wireless sensor network system as the platform for development and the ZigBee module for wireless communication. The advantage of this system is to transmit the obtained information to the ZigBee coordinator module, and after the processing of information and the resultant decision, a nonwearable unmonitored motion training model based on visual sensing is proposed. The motion terminal collects video data streams of user motion actions and extracts features to establish HMM motion recognition algorithm to achieve recognition of motion actions, automatic counting, and intelligent scoring functions. The template matching algorithm based on dynamic time regularization and weighted Euclidean distance realizes a universal real-time motion recognition algorithm with high standard and low latency and can guide the user's motion action based on similarity calculation. The intelligent sports training system is designed and developed to maintain a high-quality human-computer interaction experience with a real-time feedback client and uploads sports data to a cloud server via the HTTP protocol, which supports real-time sports proximity query and training plan development on the website. After practical application tests, the intelligent sports training system based on the wireless sensor network proposed in this paper is stable and reliable and adds fun and competitiveness to boring sports. The research of this paper has some reference value for the application of wireless sensor networks and the research of the motion recognition algorithm. [ABSTRACT FROM AUTHOR]

With the rapid increase in the number of wireless terminals and the openness of wireless networks, the security of wireless communication is facing serious challenges. The safety and security of computer communication have always been a research hotspot, especially the wireless communication that still has a more complex architecture which leads to more safety problems in the communication system development. In recent years, more and more wireless communication systems are applied in the safety-critical field which tends to need high safety guarantees. A compiler is an important tool for system development, and its safety and reliability have an important impact on the development of safety-critical software. As the strictest method, formal verification methods have been widely paid attention to in compiler verification, but the current formal verification methods have some problems, such as high proof complexity, weak verification ability, and low algorithm efficiency. In this paper, a compiler formal verification method based on safety C subsets is proposed. By abstracting the concept of C grammar units from safety C subsets, the formal verification of the compiler is transformed into the verification of limited C grammar units. In this paper, an axiom system of first-order logic and special axioms are introduced. On this axiom system, the semantic consistency verification of C grammar unit and target code pattern is completed by means of theorem proving, and the formal verification of the compiler is completed. [ABSTRACT FROM AUTHOR]

Recent development of cognitive computing driven evolutionary techniques improve the overall quality of service and user experience in wireless communication network. This Paper consists of a feature selection method based on improvement of Crow Search Algorithm which has been used in Intrusion Detection System to limit the size of the dataset with which the system is working with and getting better results. Since IDS deals with a large data, the crucial task of IDS is to keep efficient features which represents the whole data and there is no duplicity and irrelevancy. The previous model that was proposed used the crow search algorithm in the intrusion detection system (CSA-IDS) as a model to find the optimal feature's subset and random forest as a judgement on features that are produced by the CSA-IDS. The KDD and UNSW datasets are used to evaluate the earlier proposed model. The proposed model achieved an accuracy of 99.84% for attack detection using UNSW datasets. Similarly, R2L and U2R attacks have detected accuracy of 99.97% for NSL-KDD dataset. The development of proposed model improve the overall communication services and feature selection in wireless communication network. The outcome proves that the subset of features that are obtained by using CSA-IDS fetches higher accuracy rate using a smaller number of features. [ABSTRACT FROM AUTHOR]

Aiming at the problem of mutual interference between nodes and external malicious jamming in wireless communication networks, this paper proposes an intelligent communication anti-jamming algorithm based on distributed double Q-learning. First of all, the proposed algorithm anti-jamming problem is modeled as a time-frequency two-dimensional optimization problem. The decoupled relationship between subframe and channel is used. Each node uses two Q-learning. According to the automatically received feedback signal, users choose their subframe and channel to avoid malicious external jamming and mutual interference between users. It maximizes the sum of all user throughput. Simulation results show that the proposed algorithm can effectively shorten the convergence time and improve the performance of the system. [ABSTRACT FROM AUTHOR]

Nonconvex minimax problems have attracted significant attention in machine learning, wireless communication and many other fields. In this paper, we propose an efficient approximation proximal gradient algorithm for solving a class of nonsmooth nonconvex-linear minimax problems with a nonconvex nonsmooth term, and the number of iteration to find an ε -stationary point is upper bounded by O (ε - 3) . Some numerical results on one-bit precoding problem in massive MIMO system and a distributed non-convex optimization problem demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Joint angle and frequency estimation, one of the key technologies in wireless communication and radar science, has been extensively studied by scholars. For linear arrays, this paper proposes a joint angle and frequency estimation method based on covariance reconstruction and the estimation of signal parameters via rotational invariance techniques (CR-ESPRIT). We first use the received conjugate signal to reconstruct a covariance matrix. Then, we use the least squares-ESPRIT (LS-ESPRIT) algorithm to estimate the desired frequencies. Finally, we estimate the angles according to the reconstructed matrix. The proposed method can estimate signal parameters via automatic pairing and without an additional parameter pairing process under the condition of a uniform or a nonuniform array. Moreover, this method has high estimation accuracy, excellent and stable anti-noise performance, and strong algorithmic robustness. Through a computer simulation analysis, we can confirm the reliability and validity of the proposed parameter estimation method. A comparison with other methods further proves the performance advantages of the developed method. The method in this paper can be easily applied to many signal processing contexts, such as electronic reconnaissance and wireless communication. [ABSTRACT FROM AUTHOR]

Aiming at the existing intelligent anti-jamming communication methods that fail to consider the problem that sensing is inaccurate, this paper puts forward an intelligent anti-jamming method for wireless communication under non-ideal spectrum sensing (NISS). Under the malicious jamming environment, the wireless communication system uses Q-learning (QL) to learn the change law of jamming, and considers the false alarm and missed detection probability of jamming sensing, and selects the channel with long-term optimal reporting in each time slot for communication. The simulation results show that under linear sweep jamming and intelligent blocking jamming, the proposed algorithm converges faster than QL with the same decision accuracy. Compared with wide-band spectrum sensing (WBSS), an algorithm which failed to consider non-ideal spectrum sensing, the decision accuracy of the proposed algorithm is higher with the same convergence rate. [ABSTRACT FROM AUTHOR]

Every wireless communication system comes with an innate problem of multipath propagation, which results in spreading the resultant symbols on a time scale and thus causes the symbols to overlap and end up in Inter-symbol Interference (ISI). The overall signal is distorted and the receiver is unable to recover the original signal. ISI from the signal must be removed and the signal must be brought back to its original form as it was sent or as close to it as possible; and process of equalization is used in all wireless communication system for this purpose. Two types of equalization processes are common in modern wireless communication systems. Training based equalization requires the sender block of communication system to constantly send a pilot/training signal in order to update the receiver about the original signal. The receiver removes the ISI and extracts the unadulterated signal. The second equalization process is called blind equalization and it does not require any pilot signal. The receiver only needs to know the type of constellation scheme used in modulation and then the original signal is extracted based on that information. In this paper we have thoroughly reviewed four equalization algorithms, two from each type of equalization for 16-QAM constellation and 64-QAM constellation. We came up with constellation diagrams for each equalization algorithm and comparison of BER, residual ISI and MSE for 16-QAM and 64-QAM is done through simulations. In case of LMS and RLS algorithm for 16 QAM, the performance of RLS gets slightly better than LMS at 6 dB, however, at around 12–14 dB and onwards the BER of RLS leads and there is a significantly better BER than LMS. In future, we will compare these algorithms in order to figure out the best algorithms for the current and upcoming 5G and 6G communication technologies. [ABSTRACT FROM AUTHOR]

Ad hoc wireless networks are emerging as widely used technology in various domains. Among a diversity of application armed forces use it for sensing and tracking devices, environment monitoring, patient monitoring and tracking intelligent ambiance. Security is important because of its open availability and energy consumption. Hence, it is very challenging to save energy and enhanced security in an wireless sensor network. As the triggered packets promulgate through the network towards final data accession point, it will result in the energy consumption in a low powered network. Therefore the most important aspect of such networks is the energy of the nodes, which directly affects the lifetime of the entire networks. This paper assesses the impact of two secure algorithm such as Tiny Encryption Algorithm (TEA) and Data Encryption Standard (DES) security process on ad hoc wireless network. Hence, number of evaluations are undertaken in the simulator in order to accurately establish energy consumption for above mentioned algorithms. These two algorithms are implemented using Castalia Simulator and compared on parameters, such as available energy, maximum packet size, packet header overhead, frame overhead, routing cost, energy consumed per node, etc. The comparison between the security parameters are graphically plotted with detailed analysis. [ABSTRACT FROM AUTHOR]

Purpose - This paper aims to review existing wireless sensor network (WSN) setups in various domains, focusing on affordable WSN so that it can be effectively utilised in solving the environmental problems. WSN is being explored in many applications such as home security, smart spaces, environmental monitoring, battlefield surveillance and target tracking. WSN consists of a number of tiny, low-powered, energy-constrained sensor nodes with sensing, data processing and wireless communication components. Creating a WSN setup will make the monitoring system effective and in future, it will give a roadmap for solving some common societal problems. Design/methodology/approach - Various research papers in the area of WSN have been reviewed on the basis of technologies and application on different fields. Findings - WSN was found to be the most effective solution in areas which are less explored due their hazardous nature and are difficult to reach. Originality/value - This review is based on research papers available and a recent trend in the area of WSN has been explored. [ABSTRACT FROM AUTHOR]

In this paper, we design a new branch-and-bound algorithm to solve a variant of graph partition problem called two-level graph partition problem, which is arisen in mobile wireless communications. We first exploit the two-level structure of the problem, and propose a new semidefinite relaxation for the problem. Some valid constraints are derived to enhance the tightness of the semidefinite relaxation. Furthermore, we analyze the symmetric structure of the two-level graph partition problem, and propose an effective branch pruning rule to handle the symmetry. Based on the proposed semidefinite relaxation and the symmetry handing technique, a branch-and-bound algorithm is proposed. Our numerical results show that the proposed algorithm outperforms the existing linear integer programming based algorithm on two-level graph partition problems with high density. [ABSTRACT FROM AUTHOR]

This paper develops an explicit characterization and computation of the queueing processes associated with finite buffered multiclass fluid queues and their tandem networks under strict priority and with PieceWise Constant Rate (PWCR) inputs which arise in the performance analysis of communication networks. Analytical and exact computational solutions are developed for accepted arrival, fluid level and departure processes which allow solutions for all other queueing processes. A decomposition of the multiclass queue into a hierarchical sequence of single class queues, termed Virtual Queues, is established and the solutions of each class of the original queue are computed from those of the Virtual Queues taking into account the loosely coupled dynamics of these Virtual Queues. A uniform time sampling sequence, dynamically constructed from the input rate change instances and the boundary hitting times, is employed to develop the iterative transient sample path solutions of all the queueing processes. The processes of a multiclass queue with PWCR inputs are shown to be PWCR processes. The computational solution is extended to tandem networks of multiclass queues in which a class can have different static priorities along its path. The developed algorithm is applied to solve a tandem communication network with three-state Markov modulated voice source inputs. A parametric study of the voice network in terms of long-term time-averaged behaviour of its queueing processes is carried out. [ABSTRACT FROM AUTHOR]

The use of selective partial updating method for reducing computational complexity of adaptive algorithms has been proposed recently. However, its performance analysis has not been studied as extensively and exactly as LMS or RLS algorithms for time‐varying channel estimation. This paper provides performance analysis of the low‐complexity family of affine projection algorithms based on selective partial update method on the estimation of multipath Rayleigh fading channels in the presence of carrier frequency offsets (CFO) and random channel variations. The analysis is based on the calculation of the error correlation matrix of the estimation, the mean‐square weight error (MSWE) and the mean‐square estimation error (MSE) parameters. The analysis does not use strong assumptions like small or large step‐size, and explicit closed‐form expressions for the MSE of estimation are obtained only from common hypotheses in wireless communication context. In this paper, the optimum step‐size parameters minimizing the MSE of estimation are analytically derived without any simplifying assumptions. For the sake of comparison with other analytical approaches, the performance of the introduced algorithms is also investigated using the energy conservation relation. Likewise, for exact performance analysis, all the moment terms that appear in closed form expressions for the MSE of estimation are evaluated. Simulations are conduced to corroborate the presented studies and show that the theoretical results agree well with the simulation results over non‐stationary multipath Rayleigh fading channels. [ABSTRACT FROM AUTHOR]

Wireless communications are increasingly vulnerable to malicious jamming attacks due to the inherent openness and broadcast nature. In this regard, a novel anti-jamming scheme based on knowledge graph is proposed in this paper, where the knowledge graph which contains the prior knowledge of the commonly-used jamming patterns is utilized to make the fast anti-jamming decision with low complexity. Specifically, for the known jamming patterns, the anti-jamming strategy is directly extracted based on the search from the knowledge graph by using the sensing information. On the other hand, for the unknown jamming patterns, a time-frequency domain feature extraction method is first proposed to extract the jamming feature and match the jamming patterns in the knowledge graph. As such, multiple anti-jamming strategies can be obtained by the knowledge graph search. Finally, the optimal strategy can be achieved by the two communication decision criteria-based strategy fusion. The simulation results showed that the time-frequency domain feature extraction method can well extract the time-frequency domain feature of the jamming pattern, and the proposed algorithm has high decision accuracy and robustness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The observed values of time of arrival (TOA) for the radio signals between the target and the wireless communication base stations are mainly affected by signal non-line-of-sight (NLOS) propagation in target location. TOA with NLOS makes a lot of signal noises and propagation delays, that is, location errors. For the first time, this paper focuses on the problem of modifying the Z-axis location coordinates in three-dimensional (3D) target location. A novel algorithm is proposed by establishing the modified least squares 3D location model for the accurate target location. Meanwhile, an optimal base station selection strategy is proposed by using the spectral clustering algorithm, which is based on the spatial distribution of the base stations. Compared with the existing algorithms, the proposed algorithm in this paper has better performance on the accurate target 3D location in real scenes, which has a high value of practical application. The simulations illustrate that the location error of the proposed algorithm is smaller than those of other existing algorithms based on the same simulation data and conditions. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) is seen as a supplement for fifth-generation (5G) wireless communication in short-range high data rate communication applications. A reliable VLC system relies on an accurate estimate of the position and orientation of the receiver, which corresponds to the six-dimensional positioning problem mentioned in. In this paper, we investigate the simultaneous position and orientation estimation (SPO) problem using received signal strength (RSS), for a visible light system containing multiple LEDs and multiple photodiodes (PDs) (MLMP). Although in general, the position and orientation of the receiver can be represented by a vector and a rotation matrix, respectively, the constraints imposed by the rotation matrix make the numerical optimization in the estimation process cumbersome, e.g, the commonly used constrained optimization method is often very complex and non-robust. Therefore, in this paper, we design two SPO algorithms using the principle of optimization on manifolds, which alleviates the constraints from the rotation matrix. In addition, we propose an initialization algorithm, based on the direct linear transformation (DLT) principle, to obtain an initial estimate in closed-form for the iterative algorithms. To evaluate the performance of the proposed RSS-based SPO algorithms, we derive the Cramer-Rao bound (CRB). In particular, the orientation error component of the CRB corresponds to the intrinsic CRB or the CRB on manifolds, which measures the error in the estimated rotation matrix in a physically meaningful way. Finally, computer simulations show an asymptotic tightness between the performance of the proposed algorithms and the theoretical lower bound, demonstrating the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

The C-RAN architecture has been prevailing in mobile networks for 5G and beyond. Along with the significant increase in the amount of mobile data, network operators must consider the spatio-temporal pattern of traffi c demand due to human mobility and lifestyles. It has been an important problem to efficiently deploy mobile networks avoiding the underutilization of cell capacities in off -peak times. To address this problem, in this paper we propose a concept of an adaptive C-RAN architecture toward the beyond 5G era. The proposed architecture enables flexible reconfi guration of the radio unit (RU) states and movable DU locations considering the changes in demand distribution. The DUs and RUs compose wireless relay fronthaul networks to forward fronthaul streams satisfying the strict latency requirements. This paper also proposes the joint scheduling algorithm to activate RUs, relocate DUs, and determine forwarding paths of fronthaul streams. It was confi rmed with computer simulations that mobile networks can be efficiently reconstructed with the proposed idea by activating/deactivating RUs and relocating/ migrating DUs. [ABSTRACT FROM AUTHOR]

Opportunistic routing has been extensively studied and utilized in delay/disruption-tolerant networks. The extensive use of nodes' local information, e.g., the distance to the destination or the contact frequency with the destination, in such routing schemes can cause severe security and privacy problems. Existing solutions of anonymous routing can introduce undesired overhead and fail to provide the confidentiality of the routing metric. In this paper, we propose an advanced framework for opportunistic routing schemes, providing the following properties: confidentiality of the nodes' routing metric, anonymous authentication, and efficient key agreement for pairwise communication. A comprehensive evaluation, including security analysis, efficiency analysis, and simulation evaluation, is presented to show the security and feasibility of the proposed framework. [ABSTRACT FROM PUBLISHER]

Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users’ quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method. [ABSTRACT FROM AUTHOR]

This paper considers the queue-aware optimal energy minimisation sparse beamforming design (QESB) in a downlink cloud radio access network (C-RAN) system where multi-RRH communicates with multi-user through a central computing cloud via digital front-haul links. The problem is formulated as the joint optimisation problem of the transmission energy consumption, system queue length and front-haul cost with sparse beamforming design. As we know, the beamforming design adaptive to both QSI and CSI is challenging because of the high complexity. Apart from previous works that take queue length as constraints, in this paper we directly minimise the queue length state involved joint optimisation problem with SINR constraints. A smooth function is proposed to approximate the l0-norm function which is discrete and non-convex. To overcome the challenge due to the non-convexity of the optimisation problem, the semidefinite relaxation (SDR) technology is utilised to convert the primitive problem into the difference of convex (DC) programming problem, and convex and concave procedure (CCP) algorithm is used to induce the sparsity of the beamforming control. The simulation results show that the scheme proposed by this paper can obtain a good tradeoff between system energy consumption, queue length and front-haul cost with SINR constraints in C-RAN system. [ABSTRACT FROM AUTHOR]

This paper presents a technique capable to ameliorating the impairment caused by radio channel multipath, in the estimation of the wireless communications direction of arrival (DoA). This feature is enabled by a space division multiple access approach supported by a DoA technique based on the combination of the estimates achieved at both 2.45 and 5.2 GHz. The DoA estimation relies on a comparison between the real-time received signal strength indicator and the predicted signal level distribution, which can be estimated and stored without the need of lengthy offline measurement. The experimental setup adopted for the technique validation involves an active interferer, by which arbitrary multipath levels can be implemented. An equivalent reflection coefficient signal-to-interference ratio is defined as the ratio of the unperturbed signal and the induced coherent isofrequency interferer signal, compatible with the two-ray model of the multipath impairment. The proposed technique exploits a dual-band frequency planning, and because of the combined use of the data at the two frequencies, a mean error of 2.6° is observed within a domain ranging from 0.5 to 4 m, for the entire 360° angle in the reference anechoic scenario. The 90th percentile of the cumulative error distribution is 5.4°. In the presence of a strong coherent interferer, compatible with a total reflection from the walls, the mean error is 4.9°, while the 90th percentile of the cumulative error distribution is 10.5°. In the presence of the strongest coherent interferer signal available, which is more severe than the worst case of total reflection, the mean error is 7.1°, while the 90th percentile of the cumulative error distribution is 15.1°. [ABSTRACT FROM PUBLISHER]

Noise and interference are the two most common and basic problems in wireless communication systems. The noise in wireless communication channels has the characteristics of randomness and impulsivity, so the performance of adaptive filtering algorithms based on geometric algebra (GA) and second-order statistics is greatly reduced in the wireless communication systems. In order to improve the performance of adaptive filtering algorithms in wireless communication systems, this paper proposes two novel GA-based adaptive filtering algorithms, which are deduced from the robust algorithms based on the minimum error entropy (MEE) criterion and the joint criterion (MSEMEE) of the MEE and the mean square error (MSE) with the help of GA theory. The noise interference in wireless communication is modeled by α -stable distribution which is in good agreement with the actual data in this paper. Simulation results show that for the mean square deviation (MSD) learning curve, the GA-based MEE (GA-MEE) algorithm has faster convergence rate and better steady-state accuracy compared to the GA-based maximum correntropy criterion algorithm (GA-MCC) under the same generalized signal-to-noise ratio (GSNR). The GA-MEE algorithm reduces the convergence rate, but improves the steady-state accuracy by 10–15 dB compared to the adaptive filtering algorithms based on GA and second-order statistics. For GA-based MSEMEE (GA-MSEMEE) algorithm, when GA-MSEMEE and the adaptive filtering algorithms based on GA and second-order statistics keep the same convergence rate, its steady-state accuracy is improved by 10–15 dB, and when GA-MSEMEE and GA-MEE maintain approximately steady-state accuracy, its convergence rate is improved by nearly 100 iterations. In addition, when the algorithms are applied to noise cancellation, the average recovery error of the two proposed algorithms is 7 points lower than that of other GA-based adaptive filtering algorithms. The results validate the effectiveness and superiority of the GA-MEE and GA-MSEMEE algorithms in the α -stable noise environment, providing new methods to deal with multi-channel interference in wireless networks. [ABSTRACT FROM AUTHOR]