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Nowadays, recommender system has become one of the main tools to search for users' interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user's need of keywords. Besides, to satisfy the users' requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors' information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance. [ABSTRACT FROM AUTHOR]

The English teaching network system is a distant teaching based on the Web. This teaching method can stimulate students’ interests so that students can acquire knowledge voluntarily, and automatic test paper generation is one of the most important modules in the English teaching network system. This article first briefly introduces the architecture of the wireless sensor network and then gives a wireless sensor network teaching experiment system based on a genetic algorithm. The multiple sensor nodes in the system can form a variety of different topologies, the collected data can be sent to the user terminal through the GSM network, and the user can also control the remote sensor node through the GSM network. This paper first describes the automatic test problems, a constrained multi-objective problem, then the design of the genetic algorithm to improve the test paper and puts forward questions based on an encoding method and based on the difficulty and test points of F fitness function for dynamic adjustment of the parameters in the iterative process. Finally, it is verified by experiments that the test paper made by this method satisfies users’ requests for questions, contents, and scores, and at the same time, it also improves the running efficiency of a random optimization algorithm by 7–17 times. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) swarms have emerged as a promising technology for various applications, such as delivery, surveillance, and infrastructure inspection. An additional feature of deploying large UAV swarms is their use in mobile offloading networking. At the same time, this implies a key challenge in the efficient management of the computational and networking requirements for these offloading processes. This paper aims to fill this gap through a systematic literature review (SLR) analysing the research on distributed task offloading in UAV swarms. We conducted a systematic search of major scientific databases to identify relevant literature published between 2019 and 2023. A total of 63 papers were selected through a multistage screening process and their key contributions. This SLR aims to provide the current state of research on UAV swarm task offloading, including considerations for computation offloading, the role of UAV swarms, different aspects of UAV swarms, the number of UAVs in swarms impacting performance, and open issues. Our review also highlights UAV swarm offloading in various domains and discusses the challenges and limitations that must be addressed to ensure the widespread adoption of this technology. We outline the future research directions and potential applications of UAV swarm offloading, including its integration with other technologies. [ABSTRACT FROM AUTHOR]

This Special Issue originates from the international conference 2021 Joint EuCNC & 6G Summit (Joint European Conference on Networks and Communications and 6G Summit), which was held in June 2021 in virtual format. The Technical Programme Chairs of the conference selected the best papers and invited authors to submit an extended version of their paper by at least one-third of their length. Only the top ranked papers were invited to this Special Issue, in order to fulfil its purpose. The main target was to collect and present quality research contributions in the most recent activities related to technologies, systems and networks beyond 5G. Through this Special Issue, the state-of-the-art is presented and the new challenges highlighted, regarding the latest advances on systems and network perspectives that are already being positioned beyond 5G, bridging as well with the evolution of 5G, including applications and trials. Therefore, the motivation for this Special Issue is to present the latest and finest results on the evolution of research of mobile and wireless communications, coming, but not exclusively (since Joint EuCNC & 6G Summit is a conference open to the whole research community), from projects co-financed by the European Commission within its R&D programmes. [ABSTRACT FROM AUTHOR]

Blockchain technology has completely changed the area of cryptocurrency with a Peer-to-Peer system named Bitcoin. It can provide a distributed, transparent and highly confidential database by recording immutable transactions. Currently, the technique has obtained great research interest on other areas, including the Internet of vehicles (IoVs). In order to solve some centralized problems and improve the architecture of the IoVs, the blockchain technology is utilized to build a decentralized and secure vehicular environment. In this survey, we aim to construct a comprehensive analysis on the applications of blockchain in the IoV. This paper starts with the introduction of the IoVs and the blockchain. Additionally, some existing surveys on the blockchain enabled IoVs are reviewed. Besides, the combination of the blockchain technology and the IoVs is analyzed from seven aspects to describe how the blockchain is implemented in the IoVs. Finally, the future research directions related to the integration are highlighted. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of constructing millimeter band antennas using dielectric waveguide structures. A new type of linear antenna, incorporating metal pins on the side walls of the grooved dielectric waveguide, is proposed for generating polarization perpendicular to the waveguide axis. However, these antennas suffer from the drawback of cross-polarized radiation in directions close to the waveguide axis. To overcome this limitation, a modified antenna design with transverse polarization is introduced, featuring a closed groove waveguide with a longitudinal slot at the top of the wall. The paper provides a comparison between two types of dielectric waveguide antennas: first, waveguide antenna with grooves in the dielectric which results in longitudinal polarization, and second, waveguide antenna with quarter-wavelength pins which results in transverse polarization. Electrodynamic modeling data are provided to demonstrate the effectiveness of the proposed antennas for satellite, 5G antenna, and radar applications. Finally, an antenna of quarter-wavelength pins is proposed with a frequency of 39 GHz, a gain of 19.8 dBi, and a width of the radiation pattern of 3.2 ∘ , and a sidelobe level (SLL) of -13.3 dB has been achieved. [ABSTRACT FROM AUTHOR]

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance. [ABSTRACT FROM AUTHOR]

The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND. [ABSTRACT FROM AUTHOR]

Energy efficiency and physical-layer security are crucial considerations in the advancement of mobile edge computing systems. This paper addresses the trade-off between secure-reliability and energy consumption in finite blocklength (FBL) communications. Specifically, we examine a three-node scenario involving a user, a legitimate edge computing server, and an eavesdropper, where the user offloads sensitive data to the edge server while facing potential eavesdropping threats. We propose an optimization framework aimed at minimizing energy consumption while ensuring secure-reliability by decomposing the problem into manageable subproblems. By demonstrating the convexity of the objective function concerning the variables, we establish the existence of an optimal parameter selection for the problem. This implies that practical optimization of parameters can significantly enhance system performance. Our numerical results demonstrate that the application of FBL regime and retransmission mechanism can effectively reduce the energy consumption of the system while ensuring secure-reliability. For the quantitative analyses, the retransmission mechanism is 33.1% better than no retransmission, and the FBL regime is 13.1% better than infinite blocklength (IBL) coding. [ABSTRACT FROM AUTHOR]

This paper proposes a power scaling (PS) technique aimed at mitigating the outage floor problem commonly encountered in multi-user full-duplex (FD) non-orthogonal multiple access (NOMA) systems. Two antenna modes denominated as adaptive antenna mode (AAM) and fixed antenna mode (FAM) are utilized in this work for L FD near users in close proximity. In addition, a combined method of selecting both antenna mode and near user integrated with PS is employed to improve the overall network performance. Moreover, a power allocation between the chosen near user and far user is considered. In the low-to-moderate power regions, by AAM and FAM, we achieve twice of full diversity gain and full diversity gain, respectively. The research presents mathematical expressions for deriving the average capacity and outage probability and supports the theoretical findings with simulation-based evidence. The results of this work show that PS method not only contributes to a greater spatial diversity but also leads to superior performance compared to traditional FD NOMA systems. Moreover, our proposed method overcomes the outage floor and capacity ceiling. Furthermore, the work can be developed to 6G massive MIMO technology in multi-user FD cooperative NOMA systems. [ABSTRACT FROM AUTHOR]

A distributed ledger is a shared and synchronized database across multiple designated nodes, often referred to as miners, validators, or peers. These nodes record, distribute, and access data to ensure security and transparency. However, these nodes can be compromised and manipulated by selectively choosing which user transactions to include, exclude, or reorder, thereby gaining an unfair advantage. This is known as a miner/maximal extractable value (MEV) attack. Existing solutions can be classified into various categories, such as MEV auction platforms and time-based ordering properties, which rely on private transaction Mempools. In this paper, we first identify some architectural weaknesses inherent in the latest proposals that divide the block creation and execution roles into separate functions: block builders and block executors. The existing schemes mainly suffer from the verifiability of the decryption process, where a corrupted builder or executor can simply deny the inclusion of specific targeted transactions by exploiting the fact that all transactions are in plain format. To address this, we propose an enhanced version that incorporates a verifiable decryption process. On a very high level, within our proposal, whenever an Executor or a Builder performs a decryption, the decrypted values must be broadcasted. This enables any entity in the network to publicly verify whether the decryption was executed correctly, thus preventing malicious behavior by either party from going undetected. We also define a new adversary model for MEV and conduct a comprehensive security analysis of our protocol against all kinds of potential adversaries related to MEV. Finally, we present the performance analysis of the proposed solution. [ABSTRACT FROM AUTHOR]

Reflecting intelligent surface technology (RIS) is regarded as a key enabler of the sixth-generation (6G) communication system. It provides the ability to reshape radio channels through passively reflecting beams in a reconstructive manner. Furthermore, aerial RIS (ARIS) introduces more flexibility in providing line-of-sight (LOS) links. Unfortunately, most of the related research efforts supposed the system as a planar RIS mounted on a satellite, unmanned aerial vehicle (UAV), or balloon despite reported limitations of planar RISs. The essential problem in designing any planar RIS network resides in mutual orientation and alignment difficulty, especially under random fluctuation of position/orientation due to wind conditions or UAV wobbling in the hover state. So, this paper highlights spherical RIS (bubble) as the optimal choice for aerial beam routing where the orientation/rotation can be completely relaxed. It outperforms planar RIS in terms of RIS networking flexibility, dead zone relaxation, and coverage extension. Consequently, due to the added degrees of freedom, many new deployment scenarios/use cases are recommended such as introducing meta-bubbles as intermediate gateways between satellite and ground nodes and extending network infrastructure installation down to the client level to enhance its visibility and throughput. Simulations demonstrate the superiority of meta-bubbles in minimizing channel loss over successive multi-hop routing. [ABSTRACT FROM AUTHOR]

The future generations of communication technologies envision the transmission of signals across the millimeter wave and sub-THz spectrums. However, the characteristics of the propagation channel at such high frequencies differ from what is observed in the conventional low-frequency spectrum with for instance, the apparition of stronger phase noise (PN) induced by the Radio Frequency (RF) transceivers and more especially by the oscillators. That is why there is growing interest in evaluating and adapting the 5G new radio (5G-NR) physical layer to the presence of PN. This article is dedicated to the study of discrete Fourier transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) under uncorrelated Gaussian PN (GPN) impairments. We show that the presence of GPN induces two distortions: (i) a frequency-dependent random rotation of data, namely the subcarrier phase error (SPE) and (ii) a frequency-dependent intercarrier interference (ICI) that are analytically expressed. Then, we investigate the design of the adapted and optimal detection criterion according to the baseband model we derived in this paper. We demonstrate that (i) the proposed polar detector outperforms the conventional Euclidean detector and (ii) contrary to legacy OFDM, DFT-s-OFDM is a promising solution when strong GPN is involved. [ABSTRACT FROM AUTHOR]

The security of vehicle communication becomes increasingly important due to the transmission requirement of large-scale security and privacy data in the Internet of Vehicles (IoV). This paper investigates the physical layer secure transmission with reconfigurable intelligent surface (RIS) assistance in the coexistence of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V). It establishes a communication model for physical layer secure transmission where the eavesdropping user exists. Based on this model, we propose a V2I physical layer secure transmission rate (PLSTR) maximization problem and solve it by employing an alternating optimization algorithm based on generalized Rayleigh entropy and semi-definite relaxation programming so as to obtain optimal V2I base station precoding and RIS reflection coefficient matrix. Simulation results further validate the superiority of the proposed algorithm and analyze the impact of relevant parameters on system performance. [ABSTRACT FROM AUTHOR]

Construction of wireless infrastructure using unmanned aerial vehicle (UAV) can effectively expand the coverage and support high-density traffic of next-generation communication systems. Designing wireless systems including UAVs as aerial base stations (ABSs) is a challenging task, due to the mobility of ABSs causing time-varying nature of environmental surroundings and relative propagation paths to user equipment (UE) devices. Therefore, it is essential to have an accurate estimate of the channel for varying positioning of the UAVs. In this paper, we propose to adopt a digital twin based performance evaluation procedure for wireless systems including ABSs, providing enhanced accuracy of channel modeling for specific target deployment areas. Using ray-tracing channel models reflecting detailed building and terrain information of the transmission environment, an UAV position optimization algorithm based on reinforcement learning is presented. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm calculates the overall throughput in the digital twin and determines the desired states of the UAV. Performance evaluation results demonstrate the trajectory training ability of the algorithm and the performance advantage of the system with a reduced amount of shadow area compared to those with ground base stations (GBSs). [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new method to solve the low-entropy problem in a quasi-static environment. We utilize a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) that transmits and reflects incoming signals to users located on different sides of the surface. Specifically, we design the flow of multi-user channel probing when STAR-RIS is assisted, analyze and derive the analytical expression of the secret key capacity (SKC) of STAR-RIS-assisted system from the perspective of information theory, and finally analyze how to maximize the SKC and sum of all users under three different working protocols of STAR-RIS. The simulation results show that: (1) STAR-RIS can achieve better SKC performance than the conventional reflection/transmission RIS; and (2) the SKC is determined by the number of STAR-RIS elements, correlation coefficient, pilot length and RIS-reflected channel quality, and the time switching protocol can obtain higher SKC than the energy splitting protocol and mode switching protocol. [ABSTRACT FROM AUTHOR]

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due to the limited spectra resource, the radar needs to reuse the spectra of one BS for tracking. To suppress interference and improve performance, we propose a framework including two stages and the corresponding communication and sensing algorithms. Specifically, at the detection stage, we aim to minimize the system transmit power while maintaining the performance of both radar and communication. At the tracking stage, our goal is to maximize the radar sensing performance under a given power budget, while ensuring communication quality. Due to the complexity of the original problem, we address it by introducing auxiliary variables and proposing a penalty dual decomposition-based algorithm, enabling us to solve a more tractable form of the problem. In the inner loop, we propose a concave–convex procedure-based algorithm to handle the optimization problem, while adopting the block coordinate descent algorithm to iteratively update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Furthermore, we propose a radar target parameter estimation algorithm based on successive interference cancellation, which aims to mitigate communication interference. Finally, numerical simulations demonstrate the effectiveness of the proposed system, and the superior performance of our proposed algorithms over benchmark algorithms. [ABSTRACT FROM AUTHOR]

Due to the spectrum and complexity efficiency, the integrated radar and communications (RadCom) systems have been widely favored, in which orthogonal frequency division multiplexing (OFDM) is the most popular signal to conduct the two functions simultaneously. However, an unoptimized pulse could suffer from severe inter-carrier interference (ICI) and high out-of-band emission (OOBE), which greatly degrades the system performance. In this paper, we introduce the pulse shaping scheme dedicated to RadCom systems, in which both transmitter and receiver can adaptively design pulses with the assistance of radar estimation. We first optimize the transmitting pulse with the weighted sum of signal-to-interference-plus-noise ratio (SINR) and OOBE by employing the popular genetic algorithm. Then, we design an improved-matched pulse at the receiver for maximizing the SINR with the fmincon solver. In this way, they both utilize the readily available radar information and keep the pulse optimal even in highly dynamic scenarios, which makes the most of RadCom systems while avoiding the overhead of channel estimation and feedback. Simulations prove the feasibility of proposed scheme and reveal that the radar image and communications SINR stay close to their optimum in most cases with much lower OOBE. An improved-matched pulse can further improve the communications performance when severe ICI occurs compared with a matched pulse. [ABSTRACT FROM AUTHOR]

Large intelligent surfaces arise as an emerging technology and critical building block for sixth-generation (6G) wireless networks. Reconfigurable holographic surfaces (RHSs) have been attracting significant attention recently as active antenna arrays with the capability of forming narrow beams at low cost and complexity. This paper introduces the directional modulation (DM) concept for RHS, where a large number of elements are exploited to control not only the signal's power at the receiver but also its phase. Thus, a novel DM algorithm is proposed for RHS enables modulating a carrier wave to transmit information toward specific directions while broadcasting arbitrary signals toward other directions. Error vector magnitude results are reported for multiple users, where the directions of two users with respect to the RHS are varied. Mutual information result is also provided for 6 users to demonstrate the application of RHS for the physical layer security. Results are highly promising for future low-cost and secure spatially multiplexed communications. [ABSTRACT FROM AUTHOR]

In this paper, a multiple-input multiple-output detection structure called soft information acceleration (SIA) is proposed, which is suitable for simplifying the two-stage subspace marginalization with interference suppression (SUMIS) into one stage. The proposed one-stage method outperforms the conventional two-stage SUMIS when the subspace size is large enough. The performance advantage of the proposed SUMIS-SIA mainly results from the average number of soft information updates being equal to the 'subspace size,' instead of only once during the two-stage SUMIS detection. Thus, the SUMIS-SIA achieves a better trade-off between performance and complexity. To further reduce the complexity, a channel-shortening method based on subspace suppression is proposed. Simulation results show that the proposed channel-shortening one-stage method also outperforms SUMIS, which benefits from SIA. [ABSTRACT FROM AUTHOR]

In recent years, the behavior recognition technology based on Wi-Fi devices has been favored by many researchers. Existing Wi-Fi-based human behavior recognition technology mainly uses classification algorithms to construct classification models, which has problems such as inaccurate behavior segmentation, failure to extract deep-level features from the original data and design classification models matching the proposed features in the process of behavior recognition. In order to solve above problems, this paper proposes a window variance comparison method by combining the adaptive thresholds calculated to achieve effective segmentation of multiple discontinuous human behaviors, then uses the short-time Fourier algorithm to extract time-frequency features of individual behavior, and extracts the graph structure data from the autocorrelation matrix of time-frequency features and the features themselves. A graph neural network is built for behavior recognition. The experimental results show that the segmentation accuracy of the behavior segmentation method in the two scenes is 0.964 and 0.993, which is better than the existing threshold-based behavior segmentation methods. In addition, this paper extracts graph structure data by spectral energy change method and builds behavior recognition model by using graph neural network, and the recognition accuracy is significantly improved compared with the traditional classification algorithm. [ABSTRACT FROM AUTHOR]

As a comprehensive utilization of pipeline resources, communication intelligent manhole cover (CIMC) can effectively real-time monitor communication manhole cover and protect the safety of communication pipeline. Due to the complex working environment of manhole cover and the random error of sensor, the traditional monitoring method usual leads to frequent false alarm in actual applications. In order to ensure the monitoring service quality and improve the service efficiency, a new alarm method of CIMC with multiple event fusion in this paper via jointing analysis of multi-sensor status signals is proposed based on the equipment status signals generated by the CIMC terminal and abnormal alarm events definition. The experimental result shows that the proposed CIMC alarm method by means of multiple sensor signals in this paper can not only make up for the defect of a single sensor, but also reduces the false alarm rate caused by the random error of sensor and CIMC system. It can promote the intelligent monitoring efficiency of the manhole cover and be conducive to the construction of intelligent transportation and smart city. [ABSTRACT FROM AUTHOR]

The rapid development of infinite networks and information technology has promoted the wide deployment and rapid growth of intelligent interactive devices. However, at the same time, touch interaction technology also faces many challenges such as lack of precision. This study combines federated learning with LayerGesture technology to optimize and design a touch interaction system with higher interaction accuracy and applies it to practice. The analysis results show that with the increase in the number of iterations of the federated model, the accuracy of the human–computer recognition interaction and the amount of information contained in it increases, and the accuracy curve reaches stability at about 2800 times and is at the optimal interaction adaptation. At this point, the loss function also decreases gradually, while the loss factor tends to 0, which verifies the stability of the optimized model. According to the participants' interaction experience and experimental results, the optimized LayerGesture technique of the federated learning model has an average correctness rate of 90.4% and the lowest average selection time, while the average selection time of LayerGesture in the interaction area at the edge of the screen is 2510 ms and the average correctness rate is 93.60%, which is better than the Shift technique. In addition, the subjective survey results indicated that more participants favored the optimized LayerGesture technique. In summary, this paper's joint learning algorithm contributes to the recognition effectiveness and efficiency of intelligent interactive systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), which is built on software-defined networking (SDN), employs a paradigm known as channel reassignment. This paradigm has great potential for enhancing the communication capabilities of the network. The traffic loads may be scheduled more effectively with the help of an SDN controller, which allows for the transaction of matching channels via a single connection. The present techniques of channel reassignment, on the other hand, are plagued by problems with optimisation and cooperative multi-channel reassignment, which affect both traffic and routers. In this paper, we provide a framework for SDN–IoT in the cloud that permits multi-channel reassignment and traffic management simultaneously. The multi-channel reassignment based on traffic management is optimised via the use of a deep reinforcement learning technique, which was developed in this paper. We do an analysis of the performance metrics in order to optimise the throughput while simultaneously reducing the rate of packet loss and the amount of delay in the process. This is achieved by distributing the required traffic loads over the linked channels that make up a single connection. [ABSTRACT FROM AUTHOR]

Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate. [ABSTRACT FROM AUTHOR]

Wireless localization technology has been widely used in indoor and outdoor fields. Channel estimation based on channel state information is a hot research topic in recent years. However, due to the interference of acquisition bandwidth, noise and Doppler effect, high-resolution channel estimation is a difficult problem. In this paper, the least squares estimate the amplitude of the signal subspace projection and estimate the time delay, using wireless channel state information to delay obey exponential distribution and magnitude obey normal distribution features, and reconstruction after the signal space and sampling to the Euclidean distance between the signal space, common as gradient optimization parameters, estimate the arrival time delay of high precision. The algorithm proposed in this paper filters out the noise interference in wireless communication and improves the accuracy of channel estimation through the method of least square and gradient optimization, which provides a feasible scheme for indoor wireless localization. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a custom-designed multiband planar inverted-F antenna (PIFA) suitable for automotive applications in LTE/5G schemes operating under 6 GHz, as well as Vehicle-to-Everything (V2X) communications. The PIFA antenna has a broad bandwidth capability, resonating from 950 MHz to 6 GHz. The proposed PIFA antenna is divided into three parts: the top, front, and back, resulting in a unique and effective antenna structure. The antenna is fabricated using a substrate made of FR4 material with a dielectric constant of 4.4. The whole measurements of the antenna are 54 × 38 × 25 mm3.The proposed PIFA antenna has been tested and has achieved a voltage standing wave ratio (VSWR) of less than 2 across the entire frequency range of 950 MHz to 6 GHz. Additionally, the maximum gain achieved by the antenna is 7.08 dBi at a frequency of 5.5 GHz, 6.81 dBi at 5.2 GHz, and 6.65 dBi at 5.9 GHz. The antenna also achieved a gain of 6.67 dBi at 3.8 GHz and a gain of 3.31 dBi at 1.7 GHz. Overall, this paper presents a well-designed and effective multiband PIFA antenna that is appropriate for use in vehicular applications. The antenna ability to cover a wide range of bandwidth and achieve high gain makes it an excellent candidate for use in LTE/5G systems and V2X communications. [ABSTRACT FROM AUTHOR]

Low earth orbit (LEO) satellite communication networks require huge load capacity and information processing speed to carry global communication traffic. Inter-satellite links and the on-board processing are the key technologies to achieve this goal, but the new network architecture leads to great challenges on satellite routing. This paper designs a hybrid inter-satellite link with the same-orbit laser and the different-orbit microwave to increase the link capacity and adopts a CPU centralized scheduling to improve the utilization of computing resources. Then, this paper establishes minimum delay function by considering the inter-satellite transmission delay and the on-board processing delay. The transmission delay model bases on the orbital period, and the processing delay adopts the multi-services model, the limited-capacity single-service model, and the unlimited-capacity single-service model in the queuing theory to model the on-board CPU centralized scheduling, photoelectric converters, and electro-optical converters, respectively. Based on this model, this paper proposes an inter-satellite routing strategy with modified Q-routing algorithm. The modified algorithm uses Dijkstra algorithm to accelerate the convergence of Q-routing algorithm and retains the strong real-time performance of Q-routing algorithm. Simulations show that the delay of the modified algorithm is 83.3 % lower than that of the Dijkstra algorithm, and the larger the network and the traffic, the more obvious the advantage. [ABSTRACT FROM AUTHOR]

Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency. [ABSTRACT FROM AUTHOR]

This paper provides a methodology for the dimensioning of the access network in remote rural areas, considering the progressive introduction of cellular services in these regions. A 3G small cell (SC) network with one or several carriers deployed at the SC, fed with solar panels and connected to a backhaul with limited capacity is considered for the analysis. Because the backhaul may be inexistent or very expensive (e.g., satellite-based backhaul) the network design pursues the minimization of the required backhaul bandwidth. The required backhaul bandwidth and the required energy units (i.e., the size of the solar panels and the required number of batteries) are then obtained as an output of the dimensioning analysis. Both the backhaul minimization objective and the constraints associated with each of the carriers (low maximum radiated power and low number of users connected simultaneously) require a novel methodology compared to the classical dimensioning techniques. We also develop a procedure for switching on/off carriers in order to minimize the energy consumption without affecting the quality of service (QoS) perceived by the users. This technique allows reducing the required size of the energy units, which directly translates into a cost reduction. In the development of this on/off switching strategy, we first assume perfect knowledge of the traffic profile and later, we develop a robust Bayesian approach to account for possible error modeling in the traffic profile information. [ABSTRACT FROM AUTHOR]

Low Earth Orbit (LEO) satellite networks provide global data service coverage and has become increasingly popular. Uncoordinated access channels reduce data latency in LEO networks by allowing user terminals to transmit data packets at random times to the satellite without any coordination overhead. In this paper, packet acquisition in uncoordinated access channels of LEO networks is studied and a novel solution, called ChirpPair, is proposed, with which the satellite can detect the packets as well as estimating key parameters of the packets for data demodulation. With ChirpPair, the packet preamble consists of a chirp and its conjugate, where a chirp is a complex vector with constant magnitude and linearly increasing frequency. ChirpPair adopts a multi-stage process that gradually increases the estimation accuracy of the parameters without incurring high computation complexity. ChirpPair has been demonstrated in real-world experiments with over-the-air transmissions. ChirpPair has also been evaluated by simulations with the 3GPP New Radio (NR) Non-Terrestrial Network (NTN) channel model and the results show that ChirpPair achieves high accuracy despite its low computation complexity. [ABSTRACT FROM AUTHOR]

Conventionally, non-orthogonal multiple access (NOMA) has traditionally been implemented separately from orthogonal multiple access (OMA), aiming to improve the capacity of multi-user systems. However, a recent study has ventured beyond this conventional approach by integrating OMA and NOMA proportionally within the same system. In spite of these advancements, the consideration towards optimizing multi-user systems remains incomplete especially when user service requirements vary significantly. Therefore, this paper explores a novel layered device-to-device (D2D) partial NOMA (P-NOMA) scheme, which introduces a hybrid power-domain access method into multi-user systems. The analysis primarily focuses on evaluating both the system performance and the impact of various parameters on it. In contrast to conventional fully overlapped NOMA signals, P-NOMA signals are partially overlapped with an overlap rate that can be determined based on quality-of-service (QoS) requirements. Simulation results demonstrate that judicious utilization of P-NOMA can effectively enhance overall system performance, particularly in terms of sum rate (SR) metrics, while also flexibly accommodating diverse QoS requirements for multiple users. [ABSTRACT FROM AUTHOR]

With increasing needs for high-bitrate, ultra-reliability, spectral efficiency, power efficiency, and reducing latency in the wireless network, global studies on the sixth generation of this network began in 2020. In this paper, we will look at intelligent reconfigurable surface structure and its application in new promising physical layer technologies, such as terahertz communications and UM-MIMO systems, to support very high-bitrate and superior network capacity in the 6G wireless communications. However, terahertz communications and UM-MIMO systems are the primary research points and confront many challenges for practical implementation. They require many RF chains and create problems in terms of cost and hardware complexity which RIS can simplify hardware and reduce cost. Therefore, we will present different modeling of wireless communication systems based on RIS for different phase information. Simulation results obtained by examining SNR performance and the error probability that shows the improvement of the received signal quality. According to results, RIS-based wireless communications can become an optimized model for future wireless communication systems. [ABSTRACT FROM AUTHOR]

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

The metasurface is a promising technology that can help next-generation wireless communication systems not only improve the signal-to-noise ratio (SNR), but also increase security and mitigate interference. Further, introducing dual polarization (DP) in a metasurface can enhance its capabilities with polarization diversity, polarization multiplexing, and polarization-switched modulation. In this paper, we study a DP-metasurface-assisted single-user wireless communication system and propose a novel scheme that can improve the spectral efficiency (SE) and bit-error-rate (BER) performance compared to those of conventional schemes by exploiting the orthogonal property of dual-polarized waves. We employ the DP metasurface to increase the SNR at the receiver and create a specific phase difference between the polarized signals by controlling the transmit precoder and the phases of the metasurface reflecting elements representing some modulated bits. At the receiver, we use the recovered phase information to realign the modulated symbols in both polarizations, which are then added coherently. The simulation results demonstrate that the proposed scheme achieves significantly higher SE and BER performance than those of some closely related works. [ABSTRACT FROM AUTHOR]

This paper investigates energy-efficient communication within an integrated sensing and communication system. The system employs a dual-function radar-communication base station. This base station concurrently serves multiple mobile users for communication purposes while also performing target sensing within a designated range cell. An active reconfigurable intelligent surface (RIS) is utilized to enhance communication efficiency. The focus of this work is to optimize the communication energy efficiency by jointly allocating the transmit power and configuring the RIS elements. This optimization is achieved while satisfying constraints on both the probing power required for target sensing and the quality-of-service demands of the communication users. Two novel optimization methods are proposed, combining techniques from alternating optimization, sequential programming, and fractional programming. Through numerical simulations, the effectiveness of the developed algorithms is validated. Additionally, the performance of the system utilizing an active RIS is compared to that of a system with a passive RIS, specifically in terms of their respective communication energy efficiencies. [ABSTRACT FROM AUTHOR]

This paper focuses on radio resource management (RRM) in multi-user dual-function radar communication (DFRC) systems using orthogonal frequency division multiplexing (OFDM) waveforms. We propose two RRM schemes, one from the perspective of sum rate maximization and the other from the perspective of user fairness maximization. These optimization problems are non-convex due to the presence of mixed integer terms, making them difficult to solve. To address these challenges, we have employed a decomposition approach to transform these two complex problems into separate, more readily solvable ones. In addressing the sum rate maximization problem, we initially introduce a heuristic greedy algorithm to obtain a resource allocation scheme that satisfies radar performance requirements. Subsequently, we utilize a cyclic iterative method along with KKT conditions to solve the sum rate maximization problem for communication users. Concerning the fairness maximization problem for communication users, we similarly employ a heuristic greedy algorithm to obtain a resource allocation scheme that meets radar performance constraints. Then utilize the Lagrangian dual method to solve the multi-user fairness maximization problem for communication users. Our experimental results confirm the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we improve networks' spectral efficiency (SE), extend networks' lifetime, and maximize networks' energy efficiency (EE) of two-way full-duplex (FD) relay networks. Firstly, to improve networks' SE and to extend networks' lifetime simultaneously, we design a two-way FD relay transmission strategy with simultaneous wireless information and power transfer and direct links (DLs). The designed transmission strategy can complete a bidirectional communication in only one time slot with the exists of DLs and the energy-constrained relay node. With the designed transmission strategy, we further give the characteristics of relay amplification factor, the analysis of the designed transmission strategy, and the EE analysis of traditional half-duplex two-way amplify-and-forward relaying. Secondly, to maximize networks' EE, we present both the EE maximization problems and analyses of the designed transmission strategy with equal power allocation and optimal power allocation. To solve the EE maximization problems, we further propose the alternating optimal algorithm and give complexity analysis of the algorithm. Simulations show that our designed transmission strategy can improve the SE and EE of the networks. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

The positioning problem is interesting in a variety of applications, especially in indoor environments or in urban canyons, where the position information obtainable with traditional Global Navigation Satellite Systems is limited. In this paper, we deal with the problem of estimating, for the purposes of positioning, the time of arrival (TOA) and the angle of arrival (AOA) by processing LTE 3GPP signals, with particular attention to the uplink signals. The main contribution of this paper is the definition of new opportunistic methods to estimate the TOA and the AOA using the upstream demodulation reference signal (DM-RS) instead of the Sounding Reference Signal. We will show that the use of DM-RS and of estimation algorithms such as the Space-Alternating Generalized Expectation-Maximization and the Iterative Adaptive Approach for Amplitude and Phase estimation (IAA-APES) allows an efficient estimate of the parameters, in spite of the small, occupied bandwidth. [ABSTRACT FROM AUTHOR]

In order to degrade the inter-user interference caused by the same beam selected for different users in mmWave massive MIMO systems, this paper proposes a joint beam selection combining cuckoo search (CS) and ant colony optimization (ACO) (referred to as CSACO). Differently from the existing interference-aware beam selection, a candidate beam set (CBS) for all users is created according to the power distribution of the beamspace channel, thereby all users can be classified into non-interfering users (NIUs) and interfering users (IUs), and NIUs will be assigned the beams with large power directly, while for IUs, the beams are selected by the CSACO; in the proposed CSACO, all beams for IUs are regarded as an optimizable individual, which is continuously evolved towards the direction of sum-rate maximization. Simulation results verify that the proposed beam selection can obtain the higher sum-rate and energy efficiency compared with the existing ones. [ABSTRACT FROM AUTHOR]

With the integration of unmanned aerial vehicles (UAVs) into fifth generation (5G) networks, UAVs are used in many applications since they enhance coverage and capacity. To increase wireless communication resources, it is crucial to study the trajectory of UAV-assisted relay. In this paper, an energy-efficient UAV trajectory for uplink communication is studied, where a UAV serves as a mobile relay to maintain the communication between ground user equipment (UE) and a macro base station. This paper proposes a UAV Trajectory Optimization (UAV-TO) scheme for load balancing based on Reinforcement Learning (RL). The proposed scheme utilizes load balancing to maximize energy efficiency for multiple UEs in order to increase network resource utilization. To deal with nonconvex optimization, the RL framework is used to optimize the trajectory UAV. Both model-based and model-free approaches of RL are utilized to solve the optimization problem, considering line of sight and non-line of sight channel models. In addition, the network load distribution is calculated. The simulation results demonstrate the effectiveness of the proposed scheme under different path losses and different flight durations. The results show a significant improvement in performance compared to the existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a realistic use case in order to investigate the feasibility of a secondary service transmission over an existent satellite infrastructure. By introducing the overlay cognitive radio paradigm towards satellite communications, we compute a theoretical achievable data rate greater than 16 kbps for the secondary service, which is suitable for most M2M applications. Using simulation results, we show that this can be achieved while preserving the primary service performance. In addition, a system design framework is discussed in order to dimension such systems. [ABSTRACT FROM AUTHOR]

Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Directional Modulation (DM) techniques provide wireless communication security against passive eavesdropping by means of specific physical layer characteristics. The original symbol constellations are transmitted along pre-specified spatial direction of legitimate users, while phase-amplitude distorted symbols are transmitted along the undesired directions of eavesdropper. In this paper, a comprehensive review of DM techniques and the most recent developments in this area are discussed. An analysis from three independent Physical Layer Security (PLS) viewpoints; communications, information-theoretic and cryptographic perspective is presented. Different performance metrics in literature are compared and the need for unified PLS approach is emphasized. As DM techniques constitute a relatively new class of PLS, there is no systematic organization of these techniques so far. This paper presents a classification framework for DM comprising of two main categories; angular (1D) and range-angular (2D) techniques. The former secures data along angular direction of physical space, while the latter provides security within certain range (distance) from the transmitter along desired angular direction. Further sub-categorization is based on the under-lying physical layer parameters exploited to achieve security, i.e. space, time, frequency, phase and polarization. The proposed framework is generic, flexible and extend-able to future research. In the end, limitations of existing techniques are pointed out and research directions are suggested. [ABSTRACT FROM AUTHOR]

Person search is of great significance to public safety research, such as crime surveillance, video surveillance and security. Person search is a method of locating and identifying the queried person from a complete set of images. The main cause of false recall and missed detection in person search is the presence of person occlusion in the images. In order to improve the accuracy of person search when the person to be queried is occluded, this paper proposes an attention-based one-stage framework for person search (ABOS) using an anchor-free model as a baseline. The method uses the channel attention module to express different forms of occlusion and take full advantage of the spatial attention module to highlight the target region of the occluded pedestrians. These attention modules integrate deep and shallow features to guide the network to pay attention to the visible area of the occluded target and extract the semantic information of the pedestrians. Experimental results on CUHK-SYSU and PRW datasets show that the proposed person search method based on attention mechanism in this paper has better performance than existing methods, achieving 93.7 % of mAP on CUHK-SYSU dataset and 46.4 % of mAP on PRW dataset, respectively. [ABSTRACT FROM AUTHOR]

With the increasing utilization of space related data, the demand for spatial big data sharing and trading is growing rapidly, which promotes the emergence of spatial data market. However, in conventional data markets, both data buyers and data sellers have to use a centralized trading platform which might be dishonest. Blockchain is a decentralized distributed data storage technology, which uses the traceability and unforgeability to confirm and record each transaction, and can solve partial disadvantages of the centralized data market; unfortunately, it also introduces the problems of security and privacy. To address this issue, in this paper, we propose a blockchain-based spatial data trading framework with trusted execution environment to provide a trusted decentralized platform, including data storage, data query, data pricing, data reputation and security computing. Based on this framework, we use an auction pricing mechanism to ensure data trading authenticity and efficiency. At last, a spatial data trading framework was implemented and its effectiveness and security were verified. [ABSTRACT FROM AUTHOR]

This Special Issue originates from the international conference EuCNC 2020 (European Conference on Networks and Communications), which was planned to be held in June 2020 in Dubrovnik (Croatia), but due to the COVID-19 pandemic was changed to an Online Conference. The Technical Programme Chairs of the conference have selected the best papers and invited authors to submit an extended version of their paper, by at least one third of their length. Only the top ranked papers were invited to this Special Issue, in order to fulfil its purpose. The main target was to collect and present quality research contributions in the most recent activities related to systems and networks beyond 5G, already presenting ideas for 6G. Through this Special Issue, the state-of-the-art is presented and the new challenges are highlighted, regarding the latest advances on systems and network perspectives that are already being positioned beyond 5G, bridging as well with the evolution of 5G, including applications and trials. Therefore, the motivation for this Special Issue is to present the latest and finest results on the evolution of research of mobile and wireless communications, coming, but not exclusively (since EuCNC is a conference open to the whole research community), from projects co-financed by the European Commission within its R&D programmes. [ABSTRACT FROM AUTHOR]

5G mobile network technology together with edge computing will create new opportunities for developing novel road safety services in order to better support connected and automated driving in challenging situations. This paper studies the feasibility and benefits of localized mobile network edge applications for supporting vehicles in diverse conditions. We study a particular scenario, where vehicle sensor data processing, required by road safety services, is installed into the mobile network edge in order to extend the electronic horizon of the sensors carried by other vehicles. Specifically, we focus on a LiDAR data-based obstacle warning case where vehicles receive obstacle warnings from the mobile network edge. The proposed solution is based on a generic system architecture. In this paper, we first evaluate different connectivity and computing options associated with such a system using ns-3 simulations. Then, we introduce a proof-of-concept implementation of the LiDAR-based obstacle warning scenario together with first results from an experimental evaluation, conducted both in a real vehicle testbed environment and in a laboratory setting. As a result, we obtain first insights on the feasibility of the overall solution and further enhancements needed. [ABSTRACT FROM AUTHOR]

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%. [ABSTRACT FROM AUTHOR]