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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]

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 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]

This paper proposes a quantitative evaluation algorithm based on a comprehensive analysis of material's shape, texture, color, style, and other characteristics using indoor wireless networking design and cloud data to improve the application effectiveness of ecological environment materials. It also adopts a data-driven method to quantitatively evaluate the application effectiveness of ecological environment materials using indoor wireless network design based on preliminary feature information. Specifically, this work reconstructs the feature space of ecological environment materials by extracting dimensionality features related to the quantitative evaluation, and analyzes the mutual information features of big data fusion. Moreover, a quantitative regression analysis and statistical model construction is used to evaluate the application effectiveness by using preliminary feature information as a constraint condition. In addition, this study employs the spatial effect element feature analysis method to quantitatively analyze and rigorously test the application effectiveness assessment. The empirical results show that the proposed quantitative evaluation algorithm has good accuracy and high reliability, which can effectively improve the application quality of ecological environment materials in indoor wireless network design. [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]

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]

The transmitting signal of wireless communication system is impaired by the nonlinearity of RF power amplifier (PA) which leads to signal distortion and spectrum spillover, by which the signal transmission quality is affected. Digital predistortion (DPD) is an efficient and economical way to correct the nonlinear effects of power amplifiers. The recursive least square (RLS) recognition algorithm is commonly used to extract the correction coefficients of the DPD model, and the accuracy of the extraction directly affects the system performance. In this paper, a new variable forgetting factor identification algorithm (new variable forgetting factor recursive least square, NVFFRLS) is proposed for recursive least square (RLS) identification algorithm. The 64-QAM signal is combined with a memory polynomial (MP) predistortion model for predistortion system simulation. The experimental results show that, compared with the RLS identification algorithm and two kinds of variable forgetting factor RLS identification algorithms, the algorithm has smaller estimation error, faster convergence, and better tracking capability, stability, and adaptability; the predistortion system based on NVFFRLS identification algorithm can compensate the nonlinear memory effects of power amplifier more effectively. [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]

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]

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 uses a line-following approach to study the longitudinal and lateral problems in vehicle platooning. Under this setup, we assume that inter-vehicle distance sensing is unreliable and propose a cooperative control strategy to render the platoon less vulnerable to these sensing difficulties. The proposed control scheme uses the velocity of the predecessor vehicle, communicated through a Vehicle-to-Vehicle technology, to avoid significant oscillations in the local speed provoked by tracking using unreliable local distance measurements. We implement the proposed control algorithm in the RUPU platform, a low-cost experimental platform with wireless communication interfaces that enable the implementation of cooperative control schemes for mobile agent platooning. The experiments show the effectiveness of the proposed cooperative control scheme in maintaining a suitable performance even when subject to temporal distortions in local measurements, which, in the considered experimental setup, arise from losing the line-of-sight of the local sensors in paths with closed curves. [ABSTRACT FROM AUTHOR]

Wireless powered communication network (WPCN) is a promising technique to resolve the power constraint issue faced by wireless nodes at the same time it also provides green and a safer solution. This paper studies, mutual sharing between WPCN and simultaneous wireless information and power transfer model where optimal resource allocation occurs between licensed/bandwidth group and an unlicensed/power group to enhance the system performance. The main aim of this paper is to maximize system weighted sum rate and minimize power consumption during transmission by optimal time allocation and energy beamforming vector allocation. These multiobjective combinatorial problems are solved using joint metaheuristic particle swarm optimization-cuckoo search algorithm to provide an optimal solution. The results are compared using a conventional mathematical optimization algorithm to set as a baseline for performance parameters. The obtained results demonstrate that the efficient joint metaheuristic approach provides a significant gain in the system performance and also offers a computationally less complex approach for solving a complicated non-convex multiobjective problem of wireless powered network. [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]

We address a dynamic covering location problem of an unmanned aerial vehicle base station (UAV-BS), in which the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and nonconvex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer nonlinear programming formulation that is nonconvex in nature and propose a Lagrangean decomposition algorithm (LDA) to solve this formulation. Because of the high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a continuum approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms. Summary of Contribution: This paper addresses a facet of mixed integer nonlinear programming formulations. Dynamic facility location problems (DFLPs) arise in a wide range of applications. However, classical DFLPs typically focus on the two-dimensional spaces. Emerging technologies in wireless communication and some other promising application areas, such as smart grids, have brought new location problems that cannot be solved with classical approaches. For practical reasons, many research attempts to solve this new problem, especially by researchers whose primary research area is not OR, have seemed far from analyzing the characteristics of the formulations. Rather, solution-oriented greedy heuristics have been proposed. This paper has two main objectives: (i) to close the gap between practical and theoretical sides of this new problem with the help of current knowledge that OR possesses to solve facility location problems and (ii) to support the findings with an exhaustive computational study to show how these findings can be applied to practice. [ABSTRACT FROM AUTHOR]

Cognitive Radio Wireless Sensor Networks (CRWSNs) promise optimized spectrum utilization but face challenges in sustaining energy balance, particularly due to the emergence of “hot spots.” In CRWSNs, Cluster Heads (CHs) closer to the sink experience higher traffic as compared to those farther away, primarily due to their role in data collaboration and relaying to the sink. This leads to early depletion of their energy reserves and potentially causing the network to partition creating hot spots or energy holes. Effective clustering algorithms are needed to mitigate these hot spots. The main objective of the paper is to propose a novel clustering scheme titled “Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm” to address hot spot issues in CRWSNs. UCEHA partitions the network into clusters based on sink proximity, selecting CHs considering node energy, communication channels, neighbors, and sink distance. An enhanced spectrum-aware AODV mechanism facilitates efficient data routing. To test and validate the proposed methodology, extensive experimentations were conducted and the results demonstrate UCEHA’s superiority over existing methods, exhibiting reduced energy consumption (average 19%), improved network load balance (average 26%), increased network lifetime (average 40%), and enhanced throughput (average 8%). These results highlight the effectiveness of UCEHA algorithm in addressing energy imbalance and hot spot issues in CRWSNs, ultimately leading to enhanced network performance and longevity. [ABSTRACT FROM AUTHOR]

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters. [ABSTRACT FROM AUTHOR]

The rise in the usage of wireless communication increases the cellular communication by the same rate. With the continuation of this situation, the density in data traffic has the potential to cause problems in the near future. Coping with spectral efficiency-energy efficiency trade-off using massive MIMO systems is considered to be a reasonable solution to this problem. In this paper, cellular communication simulations were performed in cases with different number of users, number of antennas and transmission power of massive MIMO systems and then non-dominated solutions are determined. Multi-objective bat algorithm has been used to make this process much shorter. At last stage, performance of this algorithm is compared with various intelligent optimization algorithms and with ideal non-dominated solutions. When the algorithms are compared with each other, it is seen that multi-objective bat algorithm has the best performance among them. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent reflecting surface (IRS) is deployed to assist the terahertz (THz) communications. The sum-rate of user equipments (UEs) is maximized while guaranteeing the rate requirement of each UE. A block coordinate searching (BCS) algorithm is proposed to jointly optimize the IRS’s coordinates, phase shifts, THz sub-bands allocation and power control. Specifically, the relaxation with penalties based (RPB) algorithm is developed to obtain feasible coordinates of the IRS and guarantee the monotonicity of objective value. In addition, the IRS phase shifts are formulated as closed-form expressions with introduced pricing factors. Simulation results show that the proposed scheme can significantly enhance system performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Emergent wireless communication standards, which are employed in different transmission environments, support various modulation schemes. High-order constellations are targeted to achieve high bandwidth efficiency. However, the complexity of the symbol-by-symbol Maximum A Posteriori (MAP) algorithm increases dramatically for these high-order modulation schemes. In order to reduce the hardware complexity, the suboptimal Max-Log-MAP, which is the direct transformation of the MAP algorithm into logarithmic domain, is alternatively implemented. In the literature, a great deal of research effort has been invested into Max-Log-MAP demapping. Several simplifications are presented to meet with specific constellations. In addition, the hardware implementations dedicated for Max-Log-MAP demapping vary greatly in terms of design choices, supported flexibility and performance criteria, making them a challenge to compare. This paper explores the published Max-Log-MAP algorithm simplifications and existing hardware demapper designs and presents an extensive review of the current literature. In-depth comparisons are drawn amongst the designs and different key performance characteristics are described, namely, achieved throughput, hardware resource requirements and flexibility. This survey should facilitate fair comparisons of future designs, as well as opportunities for improving the design of Max-Log-MAP demappers. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are widely considered as key enablers for future wireless networks due to their advantages, such as high mobility and flexible deployment. In this paper, the UAV assisted secure communication system is investigated, where the UAV is deployed as the mobile jammer to prevent the eavesdropper from overhearing the confidential message. With the objective of maximizing the secrecy rate, a joint optimization problem involving the trajectory and transmit power of UAV, as well as the transmit power of source node is formulated. Moreover, the effects of Non‐Fly Zone (NFZ) and imperfect estimation on the location of eavesdropper are also taken into consideration. As the original problem is hardly trackable, the worst case secrecy rate (WCSR) assumption is first employed to bypass the uncertainty brought by the estimation error. Then a block coordinate descent (BCD) based algorithm is proposed to decompose the problem into three sub‐ones, where the trajectory of UAV, the transmit power of UAV and the transmit power of source can be obtained in an iterative manner. Simulation results reveal that the proposed algorithm can improve the secrecy performance significantly. In addition, the robustness of the proposed algorithm under the estimation error can also be verified. [ABSTRACT FROM AUTHOR]

Multicarrier transmission strategy provides great benefits in wireless communication technology. For this reason, all of the waveform contenders proposed for the fifth generation and beyond technologies are built on this strategy. Universal filtered multicarrier (UFMC) is one of the foremost representatives of the related waveform contenders. However, utilizing the multicarrier transmission strategy causes the UFMC to generate transmission signals with high peak-to-average power ratio (PAPR), which leads to serious deteriorations in the communication quality. In order to solve this issue, it is crucial to reduce the PAPR of transmission signal before giving it to the wireless channel. With the aim of developing an efficient PAPR reduction scheme for the UFMC waveform, we first developed a new discrete version of the invasive weed optimization (DIWO) algorithm and integrated it to one of the classical PAPR reduction techniques called partial transmit sequence (PTS) as a phase optimizer. By doing so, we obtained an advanced scheme called DIWO–PTS. The function of DIWO algorithm in this new scheme is to optimize the phase sequences in discrete space. The usage of DIWO as a phase optimizer enables PTS to reach better phase combinations in less number of searches. Thus, it becomes possible to boost the PAPR reduction performance of the PTS scheme to a quite high level. Simulation results support the advantage of DIWO-based phase optimization. In the simulations, neither of the algorithms used for comparison in our paper could reach the performance level achieved by DIWO algorithm due to its efficient optimization capability. [ABSTRACT FROM AUTHOR]

IOT information is always subjected to attacks, because component of the IOT system always unsupervised for most of time, also due to simplicity of wireless communication media, so there is high chance for attack, lastly, IOT is constraint device in terms of energy and computation complexity. So, different research and study are proposed to provide cryptographic algorithm. In this paper, a new image encryption is proposed based on anew chaotic map used to generate the binary key. The proposed map is three dimensional map, which is more sensitive to initial condition, each dimension of the 3-D chaotic map is depended on the others dimension, which may increase the randomness of the behavior trajectory for the next values and this gives the algorithm the ability to resist any attacks. At first, 3-D chaotic map is proposed, which is very sensitive for initial condition, the three dimension is depended on each other, which make the system more randomness, then the produced sequences is converted on binary key by using mod operation. The original image is scrambled based on mod operation to exchange the row and interleaving them, the same operations are repeated for column of the image. Later, the image is divided into blocks of size (8*8) and scrambled by using negative diagonal scan, the final pixels are converted into binary sequences, which are XORed with the generated key to produce the encrypted image. The experiment is performed on different images with different properties and tested with different metrics such as entropy, correlation, key sensitivity, number of pixel change rate (NPCR) and histogram of the original and encrypted images. T results shows that the proposed encryption algorithm is more efficient and outperform other methods. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network is an essential building block for Internet of Things (IoT) due to its usage for collecting and sensing data by the sensor nodes in a nearby environment. Energy efficiency is an important requirement for these networks as the sensors used are short on power and memory. In this paper, an enhanced energy‐efficient clustering protocol is proposed that works on three major phases under network setup: grid formation and grid head selection, clustering, and cluster head (CH) selection. Clustering is achieved using nature‐inspired grey wolf optimization (GWO) algorithm, and CH selection is accomplished using fuzzy inference‐based system. Simulation is performed in MATLAB software, and the protocol is evaluated in terms of network lifetime, the first node dead (FND), half node dead (HND), and last node dead (LND). Additionally, a prototype modeling of IoT using the Thingspeak IoT Platform provided by Mathworks is also integrated with the proposed protocol to demonstrate its usage in IoT applications. The simulation results indicated that the proposed scheme attained better performance in preserving energy and extending network lifetime. [ABSTRACT FROM AUTHOR]

Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi‐Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multi‐dimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two‐level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one‐person and two‐person is 99.6% and 94.6% respectively. [ABSTRACT FROM AUTHOR]

Summary: A novel multiband pinwheel fractal antenna with monopole configuration is proposed. The pinwheel fractal antenna has been fabricated on a commercially available FR4 substrate with a thickness of 1.6 mm. This paper proposes a novel pinwheel antenna with a 50% reduction in the dimension of width with a reduced level of return loss. The proposed antenna has multiband characteristics covering the bands of GSM (900 MHz), DCS (1710 GHz), PCS (1900 MHz), UMTS (1920 MHz), and WLAN (2.4–5.9 GHz) applications. The projected antenna resonates at multiple frequencies with acceptable antenna gain and directivity. The proposed antenna design is optimized by particle swarm optimization (PSO) algorithm. The parametric studies and the design rule for the proposed fractal have been reported. The prototypes of the proposed multiband antenna have been demonstrated. The measured and simulated results ensure the multi‐resonance characteristics and authorize the projected design method and gratify the necessities of handheld wireless communication systems. The performance analysis proves that the proposed novel pinwheel antenna provides reduced return loss. [ABSTRACT FROM AUTHOR]

The efficient use of resources in wireless communications has always been a major issue. In the Internet of Things (IoT), the energy resource becomes more critical. The transmission policy with the aid of a coordinator is not a viable solution in an IoT network, since a node should report its state to the coordinator for scheduling and it causes serious signaling overhead. Machine learning algorithms can provide the optimal distributed transmission mechanism with little overhead. A node can learn by itself by utilizing the machine learning algorithm and make the optimal transmission decision on its own. In this paper, we propose a novel learning Medium Access Control (MAC) protocol with learning nodes. Nodes learn the optimal transmission policy, i.e., minimizing the data and energy queue levels, using the Q-learning algorithm. The performance evaluation shows that the proposed scheme enhances the queue states and throughput. [ABSTRACT FROM AUTHOR]

Summary: To design new revolutionary wireless communication systems, orthogonal frequency‐division multiplexing (OFDM)‐based massive multiuser (MU) multiple‐input multiple‐output (MIMO) has been shown to be the most promising technology to significantly enhance the spectral, energy, and hardware efficiencies. However, massive MU‐MIMO‐OFDM transmitters exhibit signal with high peak‐to‐average power ratio (PAPR). Accordingly, the nonlinearity of the radio frequency (RF) power amplifier (PA), which causes the most severe hardware impairment, is expected to be a low‐cost and energy‐efficient component to enable cost‐ and energy‐efficient massive MU‐MIMO‐OFDM BS deployments, generating harmful in‐band distortion and out‐of‐band (OOB) emissions. In this paper, we develop a PAPR‐aware downlink transmission scheme in an OFDM‐based large‐scale MU‐MIMO. Linear precoding of data and peak‐canceling signals (PCSs) are employed to reduce the PAPRs of the transmitted signals by exploiting the excess degrees of freedom (DoFs) provided by equipping the base station (BS) by a large number of transmit antennas. Specifically, we design PCSs to be added to the frequency‐domain precoded data signals, with the goal of reducing the PAPRs of their time‐domain counterpart signals. Most importantly, the added PCSs have to lie in the null spaces of their associated MIMO channel matrices such that they do not cause any MU interference (MUI) and OOB radiation. In this regard, an efficient algorithm is developed, which is based on different data and PCSs precoders, and the corresponding achievable PAPR reduction and bit error rate (BER) performance are analyzed. Moreover, to optimize a tradeoff between performance and complexity, linear precoders based on matrix polynomials (M‐POLYs) and gradient‐iterative approaches are studied for both data and PCSs precoding. Simulation results reveal that these latter provide similar performance as the regularized zero‐forcing (RZF) and orthogonal projection null space (OPNS)‐based data and PCSs precoders, while they need much lower computational complexity. The substantial PAPR reduction provided by the proposed algorithm offers interesting insights for the design of energy‐efficient massive MU‐MIMO‐OFDM systems. [ABSTRACT FROM AUTHOR]

This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large. [ABSTRACT FROM AUTHOR]

The primary objective of cognitive radio network is to effectively utilize the unused spectrum bands. In cognitive radio networks, spectrum sharing between primary and secondary users is accomplished using either underlay or interweave cognitive radio approach. Non orthogonal multiple access (NOMA) is the proven technology in the present wireless developments, which allows the coexistence of multiple users in the same orthogonal block. The new paradigm cognitive radio NOMA (CR-NOMA) is one of the potential solutions to fulfill the demands of future wireless communication. This paper emphasizes on practical implementation of NOMA in cognitive radio networks to enhance the spectral efficiency. The goal is to increase the throughput of the secondary users satisfying the quality of service (QOS) requirements of primary users. To achieve this, we have presented the optimized power allocation strategy for underlay downlink scenario to support the simultaneous transmission of primary and secondary users. Furthermore, we have proposed QOS based power allocation scheme for CR-NOMA interweave model to support the coexistence of multiple secondary networks. Also, the changes adopted in implementing superposition coding (SC) and successive interference cancellation (SIC) for CR-NOMA are highlighted. Finally, simulation results validate the mathematical expressions that are derived for power allocation coefficient and outage probability. [ABSTRACT FROM AUTHOR]

Sorted QR decomposition (SQRD) has been extensively adopted for various multiple‐input‐multiple‐output (MIMO) detectors, in which the sorting process incurs severe latency when it comes to larger‐scale MIMO situations. This paper proposes a group‐SQRD (GSQRD) algorithm to alleviate the latency problem of general SQRD architectures for larger‐scale MIMO systems. Via predictively sorting a group of 4 columns at one stage, the GSQRD could eliminate the processing latency by 41% for decomposing 16×16 complex‐valued matrices. Additionally, this percentage even rises up to 68% for decomposing 128×128 matrices. To analyse the side effects, the GSQRD is applied in various MIMO detectors in a simulation link, which exhibits a negligible performance degradation for MIMO detection. Moreover, GSQRD is a hardware‐friendly algorithm because the division and square root operations in GSQRD are converted to multiplications for simplifying the hardware implementation. Based on this algorithm, two corresponding hardware architectures, which contains 2 and 4 columns respectively in a sorting group, are also implemented with 65‐nm CMOS technology. These architectures can work at 513 MHz to decompose 16×16 complex‐valued matrices. The processing latencies are respectively 0.32 and 0.26 μs, superior to the state‐of‐art designs. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]

The recent decades have seen the growth in the fields of wireless communication technologies, which has made it possible to produce components with a rational cost of a few cubic millimeters of volume, called sensors. The collaboration of many of these wireless sensors with a basic base station gives birth to a network of wireless sensors. The latter faces numerous problems related to application requirements and the inadequate abilities of sensor nodes, particularly in terms of energy. In order to integrate the different models describing the characteristics of the nodes of a WSN, this paper presents the topological organization strategies to structure its communication. For large networks, partitioning into sub-networks (clusters) is a technique used to reduce consumption, improve network stability and facilitate scalability. [ABSTRACT FROM AUTHOR]

Wireless communication technology is increasingly gaining popularity, with aerial wireless sensor networks being highly regarded for their high mobility, ability to handle special environments, and wide coverage. To enhance the performance of aerial wireless sensor networks, the study of area coverage algorithms is crucial. However, most existing coverage algorithms primarily focus on the coverage area without considering coverage quality. To address this limitation, this paper proposes a coverage algorithm based on coverage quality for regional coverage by unmanned aerial vehicles (UAVs). The algorithm incorporates the importance and priority of the coverage area, utilizes an adaptive spatial distribution function for weight distribution, and employs horizontal and vertical control strategies to strike a balance between maximum coverage and optimization of coverage quality. Simulation experiments are conducted to validate the effectiveness of the proposed algorithm. This research contributes to the field by emphasizing the significance and impact of considering coverage quality in aerial wireless sensor networks, thereby enhancing their overall performance. [ABSTRACT FROM AUTHOR]

If each node in a wireless network has information about only its 1-hop neighborhood, then what are the limits to performance? This problem is considered for wireless networks where each communication link has a minimum bandwidth quality-of-service (QoS) requirement. Links in the same vicinity contend for the shared wireless medium. The conflict graph captures which pairs of links interfere with each other and depends on the MAC protocol. In IEEE 802.11 MAC protocol-based networks, when communication between nodes i and j takes place, the neighbors of both i and j remain silent. This model of interference is called the 2-hop interference model because the distance in the network graph between any two links that can be simultaneously active is at least 2. In the admission control problem studied in the present paper, the objective is to determine, using only localized information, whether a given set of flow rates is feasible. While distance- d distributed algorithms have been analyzed for the 1-hop interference model, an open problem in the literature is to extend these results to the K -hop interference model, and the present work initiates the generalization to the K -hop interference model. We show that the centralized version of the problem is NP-hard and then investigate distributed, low-complexity solutions for this problem. We propose a distributed algorithm for this problem where each node has information about only its 1-hop neighborhood. The worst-case performance of the distributed algorithm, i.e. the largest factor by which the performance of this distributed algorithm is away from that of an optimal, centralized algorithm, is analyzed. Lower and upper bounds on the suboptimality of the distributed algorithm are obtained, and both bounds are shown to be tight. The exact worst-case performance is obtained for some ring topologies. The performance of the distance-1 distributed algorithm is compared with that of the row constraints, and these two distributed algorithms are shown to be incomparable. [ABSTRACT FROM AUTHOR]

• A curve considering local traffic flow characteristics is generated from NGSIM dataset for searching for the optimal gains. • A new String Stability Index (SSI) is proposed as a quantitative evaluation indicator for string stability. • An improved Particle Swarm Optimization algorithm is used to dynamically tune the optimal gains under different delays. • Three groups of simulation experiments are conduced to verify the performance of the proposed algorithm. Due to the inevitable existence of wireless communication delay, the string stability of cooperative adaptive cruise control (CACC) platoon systems may not be guaranteed if the controller gains are not tuned in time. Most relevant studies discussed how to determine the stable region of controller gains under different communication delays, but seldom told us how to select the optimal gains. Some simulation-based approaches just gave out the feasible gains to meet CACC string stability conditions under different communication delays by using standard input signal (e.g. Sine wave, step function) as the leading vehicle acceleration curve. Unfortunately, as the above approaches did not consider the local traffic characteristics, these feasible gains were not robust enough and even failed when tackling a realistic environment. To this end, this paper proposes a dynamic controller gains tuning algorithm to ensure CACC string stability. First, a synthesized leading vehicle acceleration curve is obtained by performing an inverse fast Fourier transform (IFFT) on the average frequency spectra of 230 vehicles' acceleration from the next-generation simulation (NGSIM) dataset, which is highly related to the local traffic flow characteristics of the specified road. Then, the ratio of the ∞ -norm of the spacing error between the ego vehicle and the preceding vehicle is set as the objective function, and with the synthesized curve as the input of the vehicle platoon, we apply an improved particle swarm optimization (PSO) algorithm to dynamically tune the gains of the CACC controller when the wireless communication delay changes. Hereby, the improved PSO utilizes the nonlinear decreasing inertia weight to accelerate the convergence speed of the algorithm. Finally, three groups of detailed simulation experiments are conducted to verify the effectiveness and performance of the dynamic optimal gains generated by the synthesized curve on string stability and assess the convergence speed of the improved PSO. The experimental results reveal that the proposed algorithm outperforms the traditional methods and can significantly suppress the disturbance along the direction upstream of the platoon. [ABSTRACT FROM AUTHOR]

The quality of direction of arrival (DOA) estimation is affected by factors such as noise and interferences in wireless communication and other systems. By employing the benefits of cyclostationary signal analysis, which includes immunity to noise and interferences, in this paper, a simple high-resolution DOA estimation method is proposed. Most of the cyclostationary-based DOA estimation methods depend on the statistical properties of the received signals (e.g., the cyclic correlation matrix). Unlike them, in the proposed method, the received signal directly enters a DOA estimation loop containing a normalized least mean square (NLMS) adaptive algorithm. The method has high resolution and performance and low computational complexity in comparison with its counterparts. Moreover, it is befitting for signals in the presence of noise (Gaussian and impulsive) and interferences. Simulation results validate the efficiency of the proposed method from different aspects in comparison with other related studies. [ABSTRACT FROM AUTHOR]

Nowadays, the demand for communication multi-carriers' channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases U = 8 , the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10−2, which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10−1, changes from 2.09 dB for the analytical to 1.68 dB for the random SLM. [ABSTRACT FROM AUTHOR]

The nature of wireless propagation may reduce the QoS of the applications, such that some packages can be delayed or lost. For this reason, the design of wireless control applications must be faced in a holistic way to avoid degrading the performance of the control algorithms. This paper is aimed at improving the reliability of wireless control applications in the event of communication degradation or temporary loss at the wireless links. Two controller levels are used: sophisticated algorithms providing better performance are executed in a central node, whereas local independent controllers, implemented as back-up controllers, are executed next to the process in case of QoS degradation. This work presents a reliable strategy for switching between central and local controllers avoiding that plants may become uncontrolled. For validation purposes, the presented approach was used to control a planar robot. A Fuzzy Logic control algorithm was implemented as a main controller at a high performance computing platform. A back-up controller was implemented on an edge device. This approach avoids the robot becoming uncontrolled in case of communication failure. Although a planar robot was chosen in this work, the presented approach may be extended to other processes. XBee 900 MHz communication technology was selected for control tasks, leaving the 2.4 GHz band for integration with cloud services. Several experiments are presented to analyze the behavior of the control application under different circumstances. The results proved that our approach allows the use of wireless communications, even in critical control applications. [ABSTRACT FROM AUTHOR]

Cooperative Adaptive Cruise Control (CACC) is an advanced technique for organizing and managing a vehicle platoon, which employs the Vehicle-to-Vehicle/Vehicle-to-Infrastructure (V2V/V2I, or V2X) wireless communication to minimize the inter-vehicle distance while guaranteeing string-stability. Consequently, the conventional CACC system relies heavily on the quality of communications, which means that the regular CACC platoon is sensitive to the communication failure. Therefore, in this paper, a Safety Reinforced Cooperative Adaptive Cruise Control (SR-CACC) strategy is proposed to resist unexpected communication failure. Different from the regular CACC system, the safety enhanced platoon control system is embedded with a dual-branch control strategy. When a fatal wireless communication failure is detected and confirmed, the SR-CACC system will automatically activate the alternative sensor-based adaptive cruise control strategy. Moreover, to make the transforming process smooth, a linear smooth transition algorithm is added to the SR-CACC system. Then, to verify the performance of the proposed SR-CACC system, we conducted a simulation experiment with a heterogonous platoon constructed with eight vehicles. The experiments results reveal that, under the extremely poor communication environment, the proposed SR-CACC strategy can significantly improve the safety performance of the organized vehicle platoon. [ABSTRACT FROM AUTHOR]

Free space optical (FSO) communication is a very attractive solution to substitute or complement radio frequency-based wireless technologies for providing high-speed wireless connections. FSO-based network consists of a lot of optical nodes, whose transmission power is concentrated in a narrow volume. Considering the directionality of FSO communications, the outage of a node in the network may result in reduced link connectivity and availability, thus causing coverage hole. Once the coverage hole, the accuracy of network monitoring data would not only be reduced, but much of the not-yet-used resource would also be abandoned in the network resulting in energy waste. In this paper, we propose an energy-efficient recovery algorithm for FSO network based on reflections. The proposed algorithm adopts the classified repair mode based on the communication conditions prior to dying of the node. If the dead node is a relay node, the mirror node is used as the repair node in order to reduce the energy consumption of data forwarding, and an evaluation model is established by combining the coverage and the energy to determine the repair location. However, if the dead node is not a relay node, the ordinary node is used as repair node. The location of the repair node is found based on the coverage and the geometric relationship to ensure the maximum coverage. The simulation results show that, the proposed algorithm not only sustains coverage but also reduces the network energy consumption and prolongs the network's lifetime. [ABSTRACT FROM AUTHOR]

• An optimal focusing matrix construction criterion uses the signal subspace to avoid the error caused by complex angle pre-estimation. • To reduce the computational complexity, a PCA neural network is used to obtain the signal subspace through self-organizing learning. • To further reduce the complexity, we simplify the training of the PCA neural network, which conducts self-organized learning through the limited iteration of the network. • To enhance the DOA estimation accuracy, we employ the AIC to accurately estimate the dimension of the signal subspace. Massive multiple-input multiple-output (MIMO) has become one of the most promising technologies for wireless communication systems, in which the direction-of-arrival (DOA) plays an important role in interference cancellation and transmission reliability. However, the challenge of conventional wideband DOA estimation algorithms is that their high computational complexity brings great difficulties for their effective application in massive MIMO systems. In this paper, we propose a wideband low-complexity DOA estimation algorithm based on a principal component analysis (PCA) neural network for massive MIMO systems. First, a new criterion for constructing a focusing matrix is proposed to avoid complex angle pre-estimation. To further reduce the complexity of the eigenvalue decomposition (EVD), we propose a signal subspace estimation algorithm based on PCA, which uses only a limited amount of self-organizing learning to estimate the weights of the network and the signal subspace and does not require prior sample training. Moreover, to increase the estimation accuracy, we use the Akaike information criterion (AIC) to divide signal and noise subspaces accurately. The theoretical analysis and simulation results show that the computational complexity of the proposed algorithm is effectively reduced, and the angles of wideband sources can be accurately estimated in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Widespread use of the internet has resulted in a massive rise in the data rate of a wireless communication system. Eventually, to mitigate the effect of inter-symbol interference (ISI) in multipath wireless channels, designing a channel equalizer is becoming more demanding. For severe non-linear distortion, the neural network (NN) based non-linear channel equalizers provide superior performance than the adaptive filter based linear equalizers. The NN equalizers are generally trained with back-propagation algorithm which has limitations of slower convergence, local minima stagnation and sensitivity to initial parameters. To overcome these limitations, this paper proposes a training scheme using Cuckoo Search Algorithm (CSA) for functional link artificial NN (FLANN) based channel equalizers. The proposed training scheme has a better ability to escape from local minima, higher exploitation and exploration capabilities. To choose the optimum values of the parameters, the sensitivity analysis of the CSA based approach is performed with its key parameters. Furthermore, three non-linear channels have been simulated to demonstrate the equalization performance of the CSA based training scheme and the results have been compared with recent and well - established algorithms. The simulations confirm that the proposed training scheme performs substantially better than existing metaheuristic algorithms in terms of BER and MSE performance. To show the robustness of the CSA based method, the burst error scenario has been considered and results proved that the method is more successful in handling such scenarios when compared to other methods. The performance of the proposed scheme has been validated for a wide range of signal-to-noise ratio through simulation studies and it is observed that the scheme outperforms the other algorithms in poor SNR conditions as well. Also, to examine the statistical significance of the results provided by the proposed scheme, the Wilcoxon test is performed and the test reveals that the obtained results are statistically significant. [ABSTRACT FROM AUTHOR]

The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability. [ABSTRACT FROM AUTHOR]

In recent years, due to the strong mobility, easy deployment, and low cost of unmanned aerial vehicles (UAV), great interest has arisen in utilizing UAVs to assist in wireless communication, especially for on-demand deployment in emergency situations and temporary events. However, UAVs can only provide users with data transmission services through wireless backhaul links established with a ground base station, and the limited capacity of the wireless backhaul link would limit the transmission speed of UAVs. Therefore, this paper designed a UAV-assisted wireless communication system that used cache technology and realized the transmission of multi-user data by using the mobility of UAVs and wireless cache technology. Considering the limited storage space and energy of UAVs, the joint optimization problem of the UAV's trajectory, cache placement, and transmission power was established to minimize the mission time of the UAV. Since this problem was a non-convex problem, it was decomposed into three sub-problems: trajectory optimization, cache placement optimization, and power allocation optimization. An iterative algorithm based on the successive convex approximation and alternate optimization techniques was proposed to solve these three optimization problems. Finally, in the power allocation optimization, the proposed algorithm was improved by changing the optimization objective function. Numerical results showed that the algorithm had good performance and could effectively reduce the task completion time of the UAV. [ABSTRACT FROM AUTHOR]