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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]

With the purpose of providing higher data rate and ultra-reliable and low-latency communications for the users, employing the small cells in the upcoming Fifth-Generation-New Radio (5G-NR) cellular networks and beyond is leading to the serious challenges in mobility management due to dynamicity of the user equipments (UEs). Among different issues related to the mobility of the UEs, the handover management is one of the key procedures to let the UEs experience a high quality of service (QoS)/quality of experience (QoE). So far, many protocols and algorithms have been proposed to enhance the mobility management in 5G-NR from various aspects, but still a thorough survey has not been incorporated to summarize the recent advances and future directions of the mentioned issue. Thus, the handover management and the corresponding challenges stand in the center of concentrations in this paper, with a glance on 4G to the advancements of 5G-NR. The challenges include QoS/QoE, throughput, delay, traffic load, the attacks during authentication process and resource allocation. To overcome the mentioned challenges, the handover procedure is evaluated by some key performance indicators such as handover ratio, handover failure, radio link failure and such like which depend on the received signal quality. The lack of new advancements and novel aspects of handover plus the separation of security and handover issues in previous works are perceived as research gaps and motivations for the current paper. In this regard, this paper aims to specify and analyze the technical issues, to provide an overview on the proposed methods and recent advances and to bring the future directions into the limelight. We categorize the concurrent standards and methods about the handover, and then, we survey the proposed algorithms including theoretical, algorithm-based and pattern-capturing approaches following the authentication process along with the vulnerabilities and the algorithms to counteract the attacks during handover. Also, we study various issues corresponding to network performance during the handover procedure. Finally, we discuss the open problems and future research directions. [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]

Sparse Code Multiple Access (SCMA) is one of Non Orthogonal Multiple Access (NOMA) candidates for 5G, the fifth generation mobile network. Multiple overlapped blocks of SCMA code are transmitted per frequency layer which compels the receiver to cancel unwanted SCMA code blocks. Thanks to the sparsity of SCMA, Message Passing Algorithm (MPA) achieves near-optimal performance. Nevertheless, this process is time-consuming and has a high degree of complexity even if Max-Log Algorithm is used. The major contribution of this paper is to propose a new decoding scheme called Hybrid Interference Cancellation (HIC) to reduce time processing without degrading a target Bit Error Rate (BER). The HIC decodes and removes the strongest user, reducing the complexity of Log Domain MPA decoding of remaining users, followed by a Low Density Parity Code (LDPC) decoder. The interference cancellation decoder is either a hard interference cancellation or a soft interference cancellation. The originality of the paper is to calculate a threshold value based on power measurements to decide whether hard decoder or soft decoder should be used to reach the BER target with a lower complexity. Results show a significant reduction in terms of complexity as compared to the traditional MPA process for the same values of BER. [ABSTRACT FROM AUTHOR]

In the complex multipath propagation environment, whether there is a line of sight (LOS) path will directly affect the positioning accuracy. Therefore, this paper proposes a single base station hybrid positioning algorithm based on LOS identification. In the algorithm, we first construct multiple features based on channel state information with LOS and without LOS in statistical distribution and use these features and gradient boosting decision tree to determine whether there is a LOS path in the environment. Then for the environment with a LOS path, we proposed a positioning method based on the estimation of signal parameters via rotation invariant technology algorithm which can be used to jointly estimate the angle of arrival and time delay of the LOS path for positioning, while for the environment without LOS path, we propose a positioning method based on adaptive genetic algorithm. Finally, a single base station hybrid positioning algorithm based on LOS identification and the corresponding positioning methods. Simulation results show that the proposed hybrid positioning algorithm can achieve high-precision positioning in the complex multipath propagation environment. [ABSTRACT FROM AUTHOR]

With the development of the information age and the maturity of Internet of Things technology, wireless sensor network has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) propagation in complicated environment and the inherent noise of the sensor will introduce errors in the measurements, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on the mean reconstruction method is proposed, which reconstructs the distance measurements from all beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is not too large. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. To make the positioning results more accurate, hypothesis test is used as NLOS identification to classify the position estimates generated from all distance combinations by least-squares. Then, the residual weighting (RWGH) method is utilized to combine the position estimates that fall into the validation region. At last, we merge the results from RWGH and REKF. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness. [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]

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]

Cognitive satellite communication (SatCom) is rapidly emerging as a promising technology to overcome the scarcity of the exclusive licensed band model in order to fulfill the increasing demand for high data rate services. The paper addresses power allocation methods for multi-operator multi-beam uplink satellite communication systems co-existing with a Ka-band terrestrial network, using cognitive radio paradigm. Such a scenario is especially challenging because of (i) the coexisting multiple SatCom operators over the cognitive band need to coordinate the use of their resources under limited inter-operator information exchange, and (ii) nonlinear onboard high power amplifier (HPA) which leads to nonlinear interference between users and beams. In order to tackle the first challenge, we propose distributed power allocation algorithms including the standard Alternate Direction Multiplier Method (ADMM); Regarding the HPA nonlinear impairment, we propose nonlinear-aware power allocation based on Signomial Programming. The proposed solutions outperform state-of-the-art in both cases. [ABSTRACT FROM AUTHOR]

Compressive data gathering (CDG) is an adequate method to reduce the amount of data transmission, thereby decreasing energy expenditure for wireless sensor networks (WSNs). Sleep scheduling integrated with CDG can further promote energy efficiency. Most of existing sleep scheduling methods for CDG were formulated as centralized optimization problems which introduced many extra control message exchanges. Meanwhile, a few distributed methods usually adopted stochastic decision which could not adapt to variance in residual energy of nodes. A part of nodes were prone to prematurely run out of energy. In this paper, a reinforcement learning-based sleep scheduling algorithm for CDG (RLSSA-CDG) is proposed. Active nodes selection is modeled as a finite Markov decision process. The mode-free Q learning algorithm is used to search optimal decision strategies. Residual energy of nodes and sampling uniformity are considered into the reward function of the Q learning algorithm for load balance of energy consumption and accurate data reconstruction. It is a distributed algorithm that avoids large amounts of control message exchanges. Each node takes part in one step of the decision process. Thus, computation overhead for sensor nodes is affordable. Simulation experiments are carried out on the MATLAB platform to validate the effectiveness of the proposed RLSSA-CDG against the distributed random sleep scheduling algorithm for CDG (DSSA-CDG) and the original sparse-CDG algorithm without sleep scheduling. The simulation results indicate that the proposed RLSSA-CDG outperforms the two contrast algorithms in terms of energy consumption, network lifetime, and data recovery accuracy. The proposed RLSSA-CDG reduces energy consumption by 4.64% and 42.42%, respectively, compared to the DSSA-CDG and the original sparse-CDG, prolongs life span by 57.3%, and promotes data recovery accuracy by 84.7% compared to the DSSA-CDG. [ABSTRACT FROM AUTHOR]

Data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering. [ABSTRACT FROM AUTHOR]

The Hemispherical Resonator Gyroscope (HRG) has many advantages such as high precision, high reliability and long life-time, it is widely used in the space-launcher and the satellites. The HRG has been mechanized to operate in the distinct operating modes, the Force to Rebalanced (FTR) mode and the Whole Angle (WA) mode. In the paper, different from the traditional control algorithm is based on average methods which usually used for the WA mode, a new electrostatic control algorithm is presented, which based on the vector composition and decomposition method to control the equivalent drive force in order to track the phase of the standing wave. The mathematics model and the control algorithm are presented in the paper, and the hardware experimental circuit system is implemented, the HRG has a range of more than 300°/s with the linearity of 40 ppm. Meanwhile, the energy transition efficiency increases by an order of magnitude. [ABSTRACT FROM AUTHOR]

Blockchain is a distributed database method of storing electronic information in digital form that makes it challenging to transform, hack, or rogue the system. Each time a new operation transpires on the Blockchain, an irreversible information of that transaction is buffered in all connected ledgers. Each distributed ledger is programmable, immutable and timestamped. This paper follows an immutable policy to ensure chronological trust-administration, security and privacy to resolve anomalies within linearly stored blocks. To prove the validity of submitted data, a consensus mechanism is required, which was attained by implementing a novel 'Rampant Smoothing Algorithm' that was encoded using Solidity (i.e., aimed at developing smart contracts). Experimental policies were aligned with Good Clinical Data Management Practices that portrayed effective 'smart contract modeling' to demonstrate resistance against majority consensus attacks by harnessing the core feature of 'privacy decentralization'. During implementation of Blockchain network, an interconnected system of six hundred nodes (i.e., data points) were gradually configured. [ABSTRACT FROM AUTHOR]

Pattern division multiple access (PDMA) is a new non-orthogonal multiple access (NOMA) technology. It is proposed to meet the challenge of 5G large-scale connectivity and high-frequency spectral efficiency. Compared with traditional orthogonal multiple access (OMA), PDMA can support more users through the allocation of non-orthogonal resources. Due to perfect aperiodic correlation, complete complementary sequence (CCS) greatly improves the spectrum efficiency of the system. It has been widely used in wireless communication and radar sensing, and it still has research value in 5G. In order to apply the advantages of CCS to NOMA communication system, this paper proposes a system model of CCS spread spectrum coding based on PDMA. CCS is used as spread spectrum code to improve the performance of PDMA communication system. At the same time, on the basis of spread spectrum technology, this paper analyzes the average power allocation algorithm and water-filling power allocation algorithm, and a dynamic power allocation algorithm based on the transmission rate and practical application is proposed. The simulation results show that the system model can effectively improve the performance of the system. [ABSTRACT FROM AUTHOR]

Network failure is categorized into the two types of software and hardware (physical layer) failure. This paper focuses on the physical layer failure in the wireless sensor and actor networks (WSANs). Actors play an important role in data processing, decision-making, and performing appropriate reactions. Single or multiple nodes failure of actors due to the explosion, energy depletion, or harsh environments, can cause multiple disjoint partitions. This paper has proposed a new computational intelligence-based connectivity restoration (CICR) method. It uses a combination of advanced computational intelligence methods to solve restoration problem. The proposed algorithm applies the novel enhanced Lagrangian relaxation with a novel metaheuristic sequential improved grey wolf optimizer (SIGWO) search space algorithm in simultaneous selection of k sponsor and p pathway nodes. The reactive proposed method aims to reduce the travel distance or moving cost and communication cost. As a result, the restored network has minimum of topology change and energy consumption. In terms of total traveled distance, CICR has 37.19%, 71.47%, and 44.71% improvement in the single-node failure averagely in comparison with HCR, HCARE, and CMH, respectively. Also, it has an average of 61.54%, 40.1%, and 57.76% improvement in comparison with DCR, PRACAR, and RTN in multiple partitions resulted from multiple nodes failure, respectively. The reliability of CICR method has improved averagely by 35.85%, 38.46%, 22.03% over HCR, CMH, and HCARE in single-node failure. In multiple nodes failure, reliability of CICR has averagely 61.54% and 20% over DCR and PRACAR, respectively. [ABSTRACT FROM AUTHOR]

Due to the deployment flexibility and broad coverage capabilities, High Altitude Platform Station is an efficiency solution of communication access for underserved areas like remote areas. However, for some special scenarios, multiple platforms may need to collaborate to overcome the effects of terrain. But the interferences introduced by multiple carriers and access links may hinder the process and restrict its practicability. In order to eliminate these interferences, a proposal on multiple carrier frequency offsets (MCFO) compensation algorithm was provided in this paper. Furthermore, this paper analyzed the lower bound of bit error ratio (BER) for cooperative HAPS systems. Simulation was also carried out, and the results validate the correctness of our theoretical analysis results. [ABSTRACT FROM AUTHOR]

One of the most significant study directions is node positioning in wireless sensor networks (WSNs). Because the existing RSSI-based triangle centroid localization technique is susceptible to the surrounding environment, this paper proposes an improved triangle centroid localization algorithm based on point-in-triangulation (PIT) criterion (ITCL-PIT) in terms of positioning accuracy and response speed. When combined with the actual placement situation in conventional triangle centroid localization, the suggested algorithm considers the estimated coordinates of the junction points as extra beacon nodes. As a result of the new beacon nodes, the size of the triangle in the junction region is decreased. Then, using the PIT criteria, keep calculating until the predicted position of the node is outside the triangle. Finally, the unknown node's coordinates are determined using the centroid approach. Based on the guaranteed response time, when the communication distance is 15–30 m, the ITCL-PIT method may enhance localization accuracy by up to five times when compared to the standard triangle centroid localization approach. Furthermore, the proposed technique has higher localization accuracy and faster response speed than the centroid iterative estimation approach, and the response time of the ITCL-PIT algorithm is reduced by around 17%. In addition, the experimental platform is built to ensure that the proposed strategy effectively lowers positioning error. [ABSTRACT FROM AUTHOR]

The three-dimensional target position is vital to automatic driving, which can efficiently detect the geometrical shape of obstacles and achieve automatic environment recognition for both indoor and outdoor scenarios. In this paper, we propose an RFID 3D localization prototype based on the polarization phase model. To eliminate the impacts of tag angle, we leverage the polarization effect to obtain polarized phases. After that, we use polarized phase decomposition to get pure phases, i.e., the phase corresponding to the line of sight path. Hence, we can calculate the raw phases using the HMFCW algorithm. Finally, we can estimate the target's 3D position according to unwrapped phase information. In this way, we implement a low-cost, high-precision, easy-for-deployment 3D position solution using commercial UHF RFID hardware. The experiments show that the method can obtain effective positioning results in various environments and achieve positioning accuracy with an average error of about 9 cm. [ABSTRACT FROM AUTHOR]

APTEEN is a typical routing protocol for wireless sensor networks, but when clustering, cluster heads are randomly selected, which makes it easy to select nodes with low residual energy as cluster heads, thus forming network holes. At the same time, in multi hop transmission, the cluster head near the sink node is overloaded due to forwarding a large amount of data. Aiming at these problems, this paper introduces PSO algorithm and proposes ADCH-EPE-APTEEN routing protocol. In order to make the particle swarm optimization algorithm more suitable for routing protocol, this paper first proposes DCA-PSO based on particle swarm optimization algorithm. DCA-PSO uses the classification adaptive change inertia weight for different states of particles in the optimization process. At the same time, dynamic learning factor is used to improve the influence of particle experience and other particle experience on convergence speed and to improve the optimization accuracy and speed of the algorithm. Secondly, when APTEEN routing protocol is networked, the cluster head is selected by DCA-PSO, in which the residual energy of nodes, the location of nodes, and the energy distribution around nodes are fully taken into consideration. Thirdly, considering the distance between nodes and the sink node, the residual energy and the energy distribution around nodes, a front area is set up in the cluster located in the upper half and the assistant cluster head is selected in the front area through the DCA-PSO algorithm. The simulation results show that the DCA-PSO algorithm can significantly improve the search speed and precision compared with the PSO algorithm. Compared with APTEEN routing protocol, the ADCH-EPE-APTEEN routing protocol can reduce the network energy consumption rate and extend network lifetime by 173%. [ABSTRACT FROM AUTHOR]

Minimizing total harmonic distortion (THD) with less system complexity and computation time is a stringent constraint for many power systems. The multilevel inverter can have low THD when switching angles are selected at the fundamental frequency. For low-order harmonic minimization, selective harmonic elimination (SHE) is the most adopted and proficient technique but it involves the non-linear transcendental equations which are very difficult to solve analytically and numerically. This paper proposes a genetic algorithm (GA)-based optimization technique to minimize the THD of cascaded H-bridge multilevel inverter. The GA is the finest approach for solving such complex equations by obtaining optimized switching angles. The switching angles are calculated by the genetic algorithm by solving the nonlinear transcendental equations. This paper has modeled and simulated a five-level inverter in MATLAB Simulink. The THD comparison is carried out between step modulation method and optimization method. The results reveal that THD has been reduced from 17.88 to 16.74% while third and fifth harmonics have been reduced from 3.24%, 3.7% to 0.84% and 3.3%, respectively. The optimization method along with LC filter significantly improves the power quality providing a complete sinusoidal signal for varying load. [ABSTRACT FROM AUTHOR]

In the past few decades, radar reflectivity data have been widely used in thunderstorm identification research. Many thunderstorm identification algorithms for ground-based weather radar have been developed. But for airborne weather radar, due to the relative scarcity of data, the thunderstorm identification research is insufficient and there are still few effective identification methods. Airborne weather radar has the realization capability of close-range detection, but most existing airborne weather radars do not have scanning capability. This paper proposes an airborne weather radar volume scan mode, under which there is a total of 31 sector scans at 31 elevations in a volume scan. And a reflectivity data simulation model of the airborne weather radar is established based on this scan strategy, then the ground-based X-band radar reflectivity data are used as input to obtain the simulated X-band airborne radar reflectivity data. Moreover, this paper studies a thunderstorm identification algorithm for the X-band airborne radar with the proposed scan mode. An improved SCI (storm cell identification) algorithm is proposed on the basis of the traditional SCI algorithm which is applicable to S-band ground-based weather radar. The results of thunderstorm identification carried out on the simulated airborne radar data show that the algorithm can effectively identify the thunderstorm cells in the mature stage and the developing stage. [ABSTRACT FROM AUTHOR]

Mobile station (MS) localization in a cellular network is appealing to both industrial community and academia, due to the wide applications of location-based services. The main challenge is the unknown one-bound (OB) and multiple-bound (MB) scattering environment in dense multipath environment. Moreover, multiple base stations (BSs) are required to be involved in the localization process, and the precise time synchronization between MS and BSs is assumed. In order to address these problems, hybrid time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurement model from the serving BS with the synchronization error is investigated in this paper. In OB scattering environment, four linear least square (LLS), one quadratic programming and data fusion-based localization algorithms are proposed to eliminate the effect of the synchronization error. In addition, the Cramer-Rao lower bound (CRLB) of our localization model on the root mean-square error (RMSE) is derived. In hybrid OB and MB scattering environment, a novel double identification algorithm (DIA) is proposed to identify the MB path. Simulation results demonstrate that the proposed algorithms are capable to deal with the synchronization error, and LLS-based localization algorithms show better localization accuracy. Furthermore, the DIA can correctly identify the MB path, and the RMSE comparison of different algorithms further prove the effectiveness of the DIA. [ABSTRACT FROM AUTHOR]

This paper investigated the spectral efficiency (SE) in massive multiple-input multiple-output systems, where all terminals have no fixed power supply and thus need to replenish energy via the received signals from the base station. The hybrid wireless energy harvesting (EH) protocol is applied for each terminal, which can switch to either existing time-switching (TS) protocol or power-splitting (PS) protocol. Based on the hybrid wireless EH protocol, a general system model is developed, which can switch to either only uplink data transmission or only downlink data transmission. As a result, a general analytical framework is formulated. Then, closed-form lower bound expressions on SE for each terminal are obtained on the uplink and downlink, respectively. According to these expressions, the joint SE of uplink and downlink maximization problem is designed with some practical constraints. As the designed optimization problem is non-linear and non-convex, it is hard to solve directly. To provide a solution, an iteration algorithm is proposed by utilizing one-dimensional search technique and successive approximation method based on geometric program. Additionally, the convergence and complexity of the proposed algorithm are discussed as well. Finally, the feasibility of the proposed algorithm is analyzed by simulations. Numerical results manifest that the proposed algorithm can provide good SE by optimizing relevant system parameters, and the system model can help to discuss the TS, PS or hybrid protocol for only uplink data transmission, only downlink data transmission or joint data transmission of uplink and downlink in the considered system. [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]

In the development of the Internet of Things technology, RFID technology plays a very important role in the application of the Internet of Things. However, due to the safety problems caused by the non-contact sensing of the RFID system, the further development of RFID technology in the application is largely hindered. In recent years, chaotic encryption has been applied in the field of cryptography by virtue of its unique characteristics, and it has received more and more attention in the security of RFID data transmission. Using the same key for encryption and decryption operations is a lightweight encryption algorithm. However, there are various problems in the application process of chaotic encryption: (1) nonlinear dynamic characteristics degradation and short-cycle cycle problems will occur under the influence of computer limited accuracy; (2) numerical conversion operations are required during application, to a certain extent It will affect the randomness of the iterative sequence; (3) During the iterative process, the iterative sequence cannot be spread over the entire value interval, and the randomness is poor. This paper proposes an improved segmented Logistic mapping encryption algorithm, uses the m-sequence to perturb initial value and sets a fixed step to change the control parameter value to generate a chaotic key stream sequence, and applies it to the RFID system data transmission security mechanism to encrypt the data. Experimental simulation and performance analysis show that the iterated chaotic sequence has good random distribution characteristics, unpredictability, and traversability. Compared to the previous improvement, the key space is increased to reach the size of 1024 space and can meet the security needs, which improve RFID data security and can effectively avoid various security problems. [ABSTRACT FROM AUTHOR]

Hybrid precoding achieves a compromise between the sum rate and hardware complexity of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most prior works on multi-user hybrid precoding only consider the full-connected structure. In this paper, a novel multi-user hybrid precoding algorithm is proposed for the sub-connected structure. Based on the improved successive interference cancellation (SIC), the analog precoding matrix optimization problem is decomposed into multiple analog precoding sub-matrix optimization problems. Further, a near-optimal analog precoder is designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, digital precoding is designed according to the block diagonalization (BD) technology. Finally, the water-filling power allocation method is used to further improve the communication quality. The extensive simulation results demonstrate that the sum rate of the proposed algorithm is higher than the existing hybrid precoding methods with the sub-connected structure, and has higher energy efficiency compared with existing approaches. Moreover, the proposed algorithm is closer to the state-of-the-art optimization approach with the full-connected structure. In addition, the simulation results also verify the effectiveness of the proposed hybrid precoding design of the uniform planar array (UPA). [ABSTRACT FROM AUTHOR]

This work investigates the unified coding and identification of smart grid IoT devices, as more and more IoT devices in smart grid need to be managed and controlled. We combine blockchain technology with 5G MEC to realize the connection of massive power IoT devices at the edge of 5G network. Due to blockchain's distributed storage and credibility, it is used to identify and register IoT devices in smart grid, ensuring the reliability and accuracy of smart grid IoT devices management. In this paper, we propose a hybrid blockchain mechanism based on 5G MEC smart grid, where both public blockchain and private blockchain are deployed on the MEC gateway/server. To facilitate the data searching and extracting, we endeavor to build a blockchain explorer indexed by IoT device identifier. After that, we study the typical consensus algorithms in the blockchain such as PoW, PoS, DPoS, PDFT, and discuss their feasibility in the hybrid blockchain. Finally, we analyzed and compared the performance of different consensus algorithms from the perspective of average computing time and average time to agreement. [ABSTRACT FROM AUTHOR]

At present, Multi-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence (MISATPS) is an advanced algorithm in the field of integrated scheduling with multi-devices-process problems. This algorithm ignores the influence of the pre-process on the post-process when solving the multi-devices-process integrated scheduling problem, which leads to the problem of poor closeness between serial processes and poor parallelism between parallel processes. This paper points out that there is no restriction of scheduling sequence between parallel processes on the same processing device. It can be scheduled flexibly of the sequence between parallel processes of the same device. Therefore, based on the scheduling scheme of MISATPS, the algorithm is improved by applying the interchange strategy and the interchange adjustment strategy of multi-device adjacent parallel process. In this way, the influence of the pre-process on the post-process is avoided, the compactness of the serial process and the parallelism of the parallel process are improved, and the scheduling result is optimized. [ABSTRACT FROM AUTHOR]

The emergence of 5G communication systems will not replace existing radio access networks but will gradually merge to form ultra-dense heterogeneous networks. In heterogeneous networks, the design of efficient vertical handoff (VHO) algorithms for 5G infrastructures is necessary to improve quality of service (QoS) and system resource utilization. In this paper, an optimized algorithm based on a multi-objective optimization model is proposed to solve the lack of a comprehensive consideration of user and network impacts during the handoff process in existing VHO algorithms. The Markov chain model of each base station (BS) is built to calculate a more accurate value of the network state that reflects the network performance. Then, a multi-objective optimization model is derived to maximize the value of the network state and the user data receiving rate. The multi-objective genetic algorithm NSGA-II is finally employed to turn the model into a final VHO strategy. The results of the simulation for the throughput and blocking rate of networks demonstrate that our algorithm significantly increases the system throughput and reduces the blocking rate compared to the existing VHO strategies. [ABSTRACT FROM AUTHOR]

This paper proposes a low complexity channel estimation algorithm for unmanned aerial vehicle three dimension multi-user multiple-input-multiple-output (3D MIMO) systems with the uniform planar array (UPA) at base station using paired spatial signatures. With the aid of antenna array theory and array signal processing, 3D channel is firstly modeled based on the angles between the direction of arrival along x- and y-axis of the UPA. And 3D MIMO channels can be projected onto the x- and y-directions, respectively. Then, channel estimation for multi-user uplinks using small amount of training resources is divided into two phases. At the first uplink preamble phase, each user is assigned the orthogonal pilot, and the paired spatial signatures and optimal rotation angle of each user through the same pilot sequence are obtained. We also propose a user grouping strategy based on three-dimension angle-division multiple access (3D-ADMA) to ensure that the user's spatial signatures do not overlap. At the second phase during several coherence times, the same pilot sequence within a group and orthogonal pilot sequences between groups are assigned, then, the channel state information of the user's x- and y-directions are recovered by the paired space signatures and optimal rotation angle of each user obtained in the preamble phase, respectively. And dynamically updating the user's paired spatial signatures and optimal rotation angle utilizes the obtained channel parameter of x- and y-directions. Finally, the channel parameter of the x- and y-directions are reconstructed by the updated user's space signatures and the optimal rotation angle, and the 3D MIMO channel estimation is obtained through the Kronecker product. Compared with the conventional channel estimation method of the 3D MIMO system under UPA using a low-rank model, the proposed methods reduce the computational complexity without degrading the estimated performance to a large extent. Furthermore, it is carried out with limited training resources, and the pilot resource overhead of the system is greatly reduced by the 3D-ADMA packet and the two-stage pilot allocation. Simulation results verified that the proposed algorithm is effective and feasible. [ABSTRACT FROM AUTHOR]

In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system. [ABSTRACT FROM AUTHOR]

Wireless sensor networks with mobile sinks enable a mobile device to move into the sensing area for the purpose of collecting the sensing data. Mobile sinks increase the flexibility and convenience of data gathering in such systems. Taking the energy consumption of the mobile sink into account, the moving distance of the mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for mobile sinks in large-scale wireless sensor network systems. According to several greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel and efficient data collection path planning scheme is proposed to reduce the moving distances and to prolong the lifetimes of mobile sinks in wireless sensor networks. Considering the communication range limitations of sensor nodes and the obstacles within sensing areas, we design an inner center path planning algorithm to reduce the moving distance for the mobile sink. A back-routing avoidance method is included to address the moving path backpropagation problem. We account for the obstacles in sensing area. The reference point of obstacle avoidance is employed to address the obstacle problem. The proposed scheme makes an adaptive decision for creating the moving path of the mobile sink. A suitable moving path planning scheme can be achieved, and the moving distance of the mobile sink can be reduced. The proposed scheme is promising in large-scale wireless sensor networks. When the number of sensor nodes in the sensing area is increased by 50, the proposed scheme yields an average moving distance that is 1.1 km shorter than that of the heuristic tour-planning algorithm, where the sensing area is 5 km × 5 km. Simulation results demonstrate that the proposed data collection path planning scheme outperforms the previously developed greedy-based scheme in terms of the moving paths and moving distances of mobile sinks in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Traditional rogue access-point (AP) detection mechanisms are employed in network administration to protect network infrastructure and organization; however, these mechanisms do not protect end users from connecting to a rogue-AP. In this paper, a rogue-AP detection technique on the mobile-user side is proposed. By using a simple method involving walking, the round-trip time (RTT) and the modulation and coding scheme values are obtained, and a more accurate transmission rate for particular RTT values is thereby calculated. Further, the cleansed data are classified using the k-means method and the cumulative distribution function for the detection process. The results demonstrate that a rogue-AP can be detected with an F-measure value of up to 0.9. In the future, the proposed algorithm can be implemented as an application installed on mobile devices so that nontechnical users can detect rogue-APs. [ABSTRACT FROM AUTHOR]

The micro-Doppler (m-D) effect which is also called micro-motion effect is a kind of physical phenomenon, which is often produced in ISAR imaging of space targets. When the space target has a micro-Doppler effect, it will produce frequency modulation for the radar echo and cause serious interference to ISAR imaging of the target. In this paper, the micro-spin model is modeled, and the echo of micro-spin model scattering point is processed respectively by short-time Fourier transform (STFT), Wigner-Vile Distribution (WVD), and pseudo-Wigner-Vile Distribution (PWV), and the micro-Doppler curve is derived. On this basis, for the space target with spin components, this paper introduces an ISAR imaging algorithm using the micro-Doppler removal based on STFT and proposes an improved algorithm based on rigid body parameter estimation, which can obtain better imaging results. [ABSTRACT FROM AUTHOR]

The resource constraint has become an important factor hindering the further development of mobile devices (MDs). Mobile cloud computing (MCC) is a new approach proposed to extend MDs' capacity and improve their performance by task offloading. In MCC, MDs send task requests to the application service operator (ASO), which provides application services to MDs and needs to determine whether to accept the task request according to the system condition. This paper studies the task admission control problem for ASOs with the consideration of three features (two-dimensional resources, uncertainty, and incomplete information). A task admission control model, which considers radio resource variations, computing, and radio resources, is established based on the semi-Markov decision process with the goal of maximizing the ASO's profits while guaranteeing the quality of service (QoS). To develop the admission policy, a reinforcement learning-based policy algorithm, which develops the admission policy through system simulations without knowing the complete system information, is proposed. Experimental results show that the established model adaptively adjusts the admission policy to accept or reject different levels and classes of task requests based on the ASO load, available radio resources, and event type. The proposed policy algorithm outperforms the existing policy algorithms and maximizes the ASO's profits while guaranteeing the QoS. [ABSTRACT FROM AUTHOR]

Energy-efficient transmission (EET) has become a very important problem in wireless communication. Optimal power allocation (OPA) is one of the general methods to achieve EET. But when OPA is only considered in EET, it maybe cannot analyze EET problems accurately. This paper aims at analyzing energy-efficient bidirectional direct and relay transmission (RT) through joint optimization of transmit power (TP) and transmit time (TT) allocation. In RT, direct links (DLs) are existed. The EET problems are given with three optimization cases: (i) maximizing sum throughput (ST) to maximize energy efficiency (EE), in such case, optimal TT (OTT), optimal relay position (ORP), and outage probability analysis are given; (ii) minimizing total energy consumption (TEC) to maximize EE, in such case, optimal TP (OTP), OTT with gradient-descent algorithm, and influences of asymmetry and asymmetry transmission tasks are given; and (iii) maximizing ST and minimizing TEC simultaneously to maximize EE, in such case, Dinkelbach's algorithm and one-by-one optimal algorithm are given. Simulation results are presented to validate theoretical analysis. Results reveal that relay technique and DLs in RT can improve system's EE. [ABSTRACT FROM AUTHOR]

In this paper, the direction of arrival (DOA) estimation of signals in the presence of impulsive noise environment is studied. Complex isotropic symmetric alpha-stable (SαS) random variables are modeled as impulsive noise, then a novel second-order statistic method that correntropy-based covariance matrix (CBCM) is defined, based on the combination of the CBCM of the array sensor outputs with the signal subspace technique (e.g., multiple signal classification (MUSIC)), which can be achieved source localization under impulsive noise environments. The Monte-Carlo simulation results illustrate the improved performance of CBCM-MUSIC for DOA estimation under a wide range of impulsive noise conditions. [ABSTRACT FROM AUTHOR]

It is very difficult to deal with the problem of error correction in random network coding, especially when the number of errors is more than the min-cut of the network. We combine a small field with rank-metric codes to solve this problem in this paper. With a small finite field, original errors are compressed to propagated errors, and their number is smaller than the min-cut. Rank-metric codes are introduced to correct the propagated errors, while the minimum rank distance of the rank-metric code is hardly influenced by the small field. It is the first time to correct errors more than the min-cut in network coding with our method using a small field. This new error-correcting algorithm is very useful for the environment such as a wireless sensor network where network coding can be applied. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the traditional DV-Hop location algorithm has a large error in locating unknown nodes, this paper proposes an improved DV-Hop algorithm based on dual communication radius. By adding a communication radius, the algorithm updates the minimum hop number obtained by the unknown node closer to the beacon node to a smaller hop value and keeps the minimum hop number information of unknown nodes farther away. This method reflects the gap between the actual distances in terms of hop number, which solves the problem of the large difference between the actual distances of the same hops to a certain extent. Therefore, it is more advantageous to estimate the more accurate average jump distance and can calculate the more precise distance between them and obtain the more precise coordinates of unknown nodes. MATLAB simulation results show that the improved algorithm based on dual communication radius DV-Hop can effectively reduce the location error and improve the location accuracy and stability compared with the traditional DV-Hop location algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive algorithm to detect and track targets with low grazing angle is addressed. For this purpose, an orthogonal frequency division multiplexing (OFDM) radar signal is employed through an edge computing framework over the radar platform. However, detecting the targets in the low grazing angle area is a great challenge due to severe multipath reflection effects. The Earth's curvature geometry model is presented as the multipath propagation model. Based on the fact that the different scattering centers of a target resonate at different frequencies, we use the optimized OFDM waveform and propose a novel target tracking procedure for low grazing angle target tracking scenarios. The obtained results show that using an OFDM radar waveform provides a more uniform detection coverage in the presence of multipath propagation such that this will fill in the nulls. Finally, simulations are used to compare the performance of the proposed OFDM waveform with the conventional equal-power, the generalized likelihood ratio (GLR)-based and single-carrier waveforms. [ABSTRACT FROM AUTHOR]

Cloud computing is a novel computing paradigm, which connects plenty of computing resources and storage resources via Internet. Cloud computing provides a number of high-quality services, such as cloud storage, outsourcing computing, and on-demand self-service, which have been widely accepted by the public. In cloud computing, by submitting their tasks to cloud, plenty of applications share huge computation and storage resources. However, how to schedule resource efficiently is a big challenge in cloud computing.In this paper, we propose a SLA-aware resource algorithm to enable cloud storage more efficiently. In our scheme, we take advantage of the back-end node space utilization and I/O throughput comprehensively simultaneously. We compare and contrast the existing scheduling storage policies by implementing those algorithms. The extensive tests show that our algorithm achieves a considerable improvement in terms of violation rate and the number of used hosts. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) in wireless communication have received significant interests recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. In this paper, we optimize the location of UAVs to maximize their coverage areas. We propose a modified artificial bee colony (MABC) incorporating bee number reallocation and a new search equation. In the proposed algorithm, a greater number of bees in the population are assigned to execute local searches near food sources to enhance solution accuracy, and the bees are guided by elite vectors to enable the algorithm to rapidly converge to a potentially globally optimal position. The deployment of UAVs is thus obtained. The experimental results show that the proposed algorithm achieves great improvements over the traditional algorithm. [ABSTRACT FROM AUTHOR]

Map-matching is the process of matching the GPS locus to the road network on the digital map. However, due to the most existing map-matching algorithms that are based on high sampling rate, when the sampling interval is increased, the correct rate of the algorithm will be greatly reduced. Based on this, this paper proposed a new algorithm of map-matching for low sampling rate GPS trajectories. The algorithm gave full consideration to the road network of the geometric structure and topological structure and the mutual influence between adjacent points (time, speed information) by calculating the probability of each trajectory point of candidate points to determine matching results. At the end of this paper, we use the data of Beijing UCAR Inc.'s car in a case study. This case demonstrates: For low sampling rate matching track points in the complex road, the algorithm has a good uptime, and an exact match was found. [ABSTRACT FROM AUTHOR]

In this paper, we propose a compressive sensing (CS)-based channel estimation technique for asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) visible light communications (VLC) in 5G systems. We proposed a modified version of sparsity adaptive matching pursuit (SAMP) algorithm which is named as self-aware step size sparsity adaptive matching pursuit (SS-SAMP) algorithm. It utilizes the built-in features of SAMP and with additional ability to select step size according to the present situation, hence term self-aware, can provide better accuracy and low computational cost. It also does not require any prior knowledge of the sparsity of the signal which makes it self-sufficient. CS-based algorithms such as orthogonal matching pursuit (OMP), SAMP, and our proposed SS-SAMP were implemented on ACO-OFDM VLC. The paper is supported by simulation results which demonstrate performance of proposed scheme in terms of bit error rate (BER), mean square error (MSE), computational complexity, and key VLC parameter (LED nonlinearity, shot noise, thermal noise, channel response, and peak-to-average power ratio (PAPR). It is shown that the SS-SAMP is a good candidate for ACO-OFDM-based VLC that are mobile and have limited processing power, based on its performance and computational complexity. [ABSTRACT FROM AUTHOR]

In the context of Self-Organizing Networks (SON), this paper presents a novel cell degradation compensation algorithm based on handover margin modifications. The fault considered in this paper is a weak coverage use case so that the faulty cell will be active during the compensation, but its power will be abnormally lower than it should. Unlike the extensively studied cell outage use case, in this case, the modification of the faulty cell's parameters can be considered as part of the compensation algorithm. In particular, this paper proposes to modify the handover margins for cell degradation compensation. To analyze the proposed algorithm, a set of simulations has been carried out using an LTE simulator with a realistic scenario. Results show that the proposed algorithm is able to improve the degradation caused by the fault with a low effect in the neighboring cells under different conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a stream selection algorithm to deal with the interference and increase the sum capacity in the context of heterogeneous networks where cells of different types coexist. Due to the different transmit powers between the macro and small cells, interference levels are also different. Since the small cells are densely deployed, fully connected interference network between small cells is considered in this paper. The proposed algorithm performs the selection of a stream sequence among a predetermined set of sequences. Those selected sequences are the ones that mostly contribute to the sum rate when performing the exhaustive search. As a consequence, since the search space is reduced, the complexity is significantly decreased. These stream sequences form a regular structure where the first stream is associated to a pico user. Another distinguishing property of the proposed approach is that the stream sequences include at least one stream for each user. When selecting the streams, the channel matrices of the unselected streams are projected orthogonally to the virtual transmit and virtual receive channels of the selected stream in order to align the interference in the null space of these virtual channels. The performance evaluations are carried out by considering different scenarios with different numbers and locations of pico cells. It is shown that the proposed method can significantly reduce the computational complexity while achieving a very close performance to the exhaustive search. [ABSTRACT FROM AUTHOR]

In a 3rd Generation Partnership Project Long Term Evolution-Advanced (3GPP LTE-A) uplink, user equipment (UE) has a maximum transmission power limit defined by the UE power class. Generally, the cell edge UE has a higher probability to be constrained by the maximum transmission power level owing to the compensation of the large pathloss. When the UE transmission power is constrained by the maximum level, allocating a higher number of physical resource blocks (PRBs) than the UE power capability can afford will reduce the transmission power to be allocated per PRB, resulting in inefficient use of power resources. To avoid this power inefficiency, the uplink transmission power can be controlled according to the number of PRBs allocated using the power headroom report-based power efficient resource allocation (PHR-PERA) scheme proposed in this paper. Furthermore, adaptive open-loop power control (OL-PC) based on the signal-to-interference-plus-noise ratio (SINR) and the uplink interference is used to improve the cell capacity. By the uplink power control employing the proposed PHR-PERA scheme, the macro and femto UE throughputs were increased by 49.9 and 5 %, respectively, compared to the case of conventional fractional power control (FPC). Additional gains of 21.9 and 4.8 % for macro and femto UE throughputs, respectively, were achieved by adaptive OL-PC. The performance of fast closed-loop power control (CL-PC) based on the received SINR is also evaluated in this paper. The simulation results demonstrate that CL-PC supports OL-PC by compensating the fading effect for the UE uplink SINR to meet the target SINR. [ABSTRACT FROM AUTHOR]

Due to lack of the coverage of 3G/4G network, satellite communication which costs excessively is the main approach used in ocean to provide network service. Ocean mobile delay tolerant network (OMDTN) can provide low-cost data transmission service in the network by utilizing the contact chances of moving vessels. Spatio-temporal contact pattern is one of the key metrics to improve the efficiency of the routing algorithm in OMDTN. Some researches have been carried out on human handheld device and vehicular ad hoc networks (VANETs). However, the vessel's trajectory data is distributed and stored disorderly, which makes traditional contact pattern detection algorithm cannot be directly applied. In this paper, we design a parallel algorithm named VSTP based on MapReduce to detect spatio-temporal contact pattern from trajectories of over 2000 vessels. Studying the vessels' trajectories and the contact records, we observe that the vessels' contact pattern including inter-contact time distribution and contact times distribution is in sharp contrast to the study on human handheld device and VANETs. Our results can provide the guidelines for the design of data routing protocols on OMDTN and give a new solution to overcome the difficulty of ocean network coverage. [ABSTRACT FROM AUTHOR]

Underwater acoustic channel (UAC) is one of the most challenging communication channels in the world, owing to its complex multi-path and absorption as well as variable ambient noise. Although adaptive equalization could effectively eliminate the inter-symbol interference (ISI) with the help of training sequences, the convergence rate of equalization in sparse UAC decreased remarkably. Besides, channel estimation algorithms could roughly figure out channel impulse response and other channel parameters through several specific mathematical criterions. In this paper, a typical channel estimation method, least square (LS) algorithm, is applied in adaptive equalization to obtain the initial tap weights of least mean square (LMS) algorithm. Simulation results show that the proposed method significantly enhances the convergence rate of the LMS algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study a K-user fading multiple access channel (F-MAC), with and without an eavesdropper (Eve). In the system without Eve, we assume that each user knows only its own channel gain and is completely ignorant about the other users' channel state. The legitimate receiver sends a short acknowledgement message Acknowledge (ACK) if the message is correctly decoded and a No Acknowledge (NACK) if the message is not correctly decoded. Under these assumptions, we use game theoretic learning setup to make transmitters learn about the power allocation under each state. We use multiplicative weight no-regret algorithm to achieve an ε-coarse correlated equilibrium. We also consider the case where a user can receive other users' ACK/NACK messages. Now, we can maximize a weighted sum utility and achieve Pareto optimal points. We also obtain Nash bargaining solutions, which are Pareto points that are fairer to the transmitting users. Fairness among users is quantified using Jain's index. With Eve, we first assume each user knows only its own channel gain to the receiver as well as to Eve. The receiver decides whether to send an ACK or a NACK to the transmitting user based on the secrecy-rate condition. We use the above developed algorithms to get the equilibrium points. Next, we study the case where each user knows only the distribution of the channel state of Eve. Finally, we also consider the system where the users do not know even the distribution of the Eve's channel. [ABSTRACT FROM AUTHOR]