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The 5th-generation (5G) mobile networks and beyond need to support massive machine-type communications (MTC) devices with limited available radio resources. In this paper, we study the power-domain non-orthogonal multiple access (NOMA) technology to support energy-efficient massive MTC networks, where MTC devices exchange information using sporadic and low-rate short packets. We investigate the subchannel allocation and power control policy to maximize the achievable effective energy efficiency (EE) for uplink NOMA-based massive MTC networks, taking into account of short-packet communication characteristics. We model the subchannel allocation problem as a multi-agent Markov decision process and propose an efficient Q-learning algorithm to solve it. Furthermore, we obtain the optimal transmission power policy by approximating the achievable effective rate of uplink NOMA-based short packet communications. Compared with the existing OFDMA scheme, simulations validate that the proposed scheme can improve the achievable effective EE of massive MTC networks with 5.93%. [ABSTRACT FROM AUTHOR]

In the upcoming Internet-of-Things (IoT) era, the communication is often featured by massive connection, sporadic transmission, and small-sized data packets, which poses new requirements on the delay expectation and resource allocation efficiency of the Random Access (RA) mechanisms of the IoT communication stack. A grant-free non-orthogonal random access (NORA) system is considered in this paper, which could simultaneously reduce the access delay and support more Machine Type Communication (MTC) devices with limited resources. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In the DNN-MP-BSBL algorithm, the iterative message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the trained weights could alleviate the convergence problem of the MP-BSBL algorithm, especially on crowded RA scenarios. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations, indicating its advantages for low-latency grant-free NORA systems. [ABSTRACT FROM AUTHOR]

The decoupling of control functionality from the forwarding devices to the control plane in Software-Defined Networks (SDN) provides a unique platform to design a programmable and reconfigurable network. A single controller due to its limited capacity and resources may not handle heavy load traffic generated from various smart devices. In order to handle this, multiple controllers need to be deployed at the control plane so as to ensure improved efficiency and scalability of the network. The data flow by the distributed controllers fluctuates frequently which results in an uneven load distribution amongst different controllers. So, to handle the aforementioned issues, in this paper, a Load Optimization and Anomaly Detection Scheme (LOADS) is proposed. Using LOADS, the probability of switch selection is determined using the following two factors (i) distance from the switch to the controller, and (ii) resource consumption ratio of the switch to its controller. Also, an IP flow-based network anomaly detection module has been designed to classify the traffic as malicious or normal. In order to address the network anomaly, the LOADS scheme uses Access Control Policies (ACPs) on the user's behavior in the network. The proposed scheme is evaluated on Mininet emulator using POX controller with datasets of Internet Topology Zoo from BTNorthAmerica zone. The performance analysis reveals that LOADS minimizes the average execution time by 6.74% and 20.64% as compared to the existing competitive schemes, Distributed Hopping Algorithm (DHA) and Elastic Distributed Controller (ElastiCon). Also, it helps in improving the overall migration cost and response time of each controller. The proposed LOADS scheme has the migration cost of 55.1 milliseconds as compared to the ElastiCon and DHA schemes alongwith the migration cost of 110 milliseconds and 140 milliseconds respectively. In addition to the migration cost, the response time of the proposed scheme is 32.8 milliseconds as compared to DHA and ElastiCon which takes almost 90 milliseconds and 78 milliseconds respectively. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation space-time coding scheme based on the self-coding property of residual loop interference (RLI) for two-way full-duplex (FD) amplify-and-forward (AF) relaying networks is proposed. The relay and direct link cooperate to forward the received signals to destination node, forming the self-coded distributed space-time code, in which the relay performs self-coding using the RLI after implementing some self-interference cancellation techniques. Then, an iterative power allocation algorithm is presented. With the proposed power allocation algorithm, both amplifying factor and transmit powers of terminal nodes are jointly optimized to maximize the smaller signal-to-interference-plus-noise ratio (SINR) of terminal nodes under a total power constraint when estimated instantaneous channel state information is available. We theoretically demonstrate the benefits of the proposed power allocation algorithm compared to equal power allocation. Simulation results show that the proposed scheme achieves significant improvement in performance compared with existing schemes for FD relaying systems and half-duplex relaying systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint signal to interference plus noise ratio (SINR) thresholds optimization and resource allocation to maximize the sum-rate of Device-to-Device (D2D) communications while still retaining the rate requirements for active cellular users (CUs), when the inactive CUs are used as opportunistic relays under three operational modes: without using network-coding (NNC), using traditional high-layer network-coding (HNC), and using physical-layer network-coding (PNC). Under Rayleigh fading, we show that, given the selections of relays, this sum-rate maximization in no-relay scheme, NNC, HNC, and PNC opportunistic relay schemes can be formulated as a mixed integer non-linear programming (MINLP), which is NP-hard in general. To find the solution to the MINLP, we propose a two-step approach to solve the problem: 1) for each possible pairing of a D2D pair and a CU, we derive the optimal SINR thresholds to obtain the maximum transmission rate of the D2D pair while satisfying the rate requirement of the CU; 2) based on the maximum transmission rates of D2D pairs for each possible pairing in the first step, we develop a bipartite-matching method to find the optimal pairing CUs for D2D pairs. Finally, according to the solution to the MINLP, we propose an iterative relay selection algorithm to find out the relays that can further improve the sum-rate of D2D communications. Extensive simulation results demonstrate that, compared with the scenario without relaying, the NNC, HNC, and PNC opportunistic relay schemes achieve a maximum performance enhancement of 106%, 138%, and 168%, respectively. [ABSTRACT FROM AUTHOR]

Low latency and high reliability are key features of the fifth-generation (5G) machine-type communications. In this paper, we provide a stringently delay-constrained model for multi-user interference channel, and formulate the rate and power allocation problem based on Markov decision process (MDP), which is solved by value iteration algorithm. In particular, to make timely transmission of each packet after being encoded, we characterize the delay constraint on sum of buffering delay and transmission delay. Then, the rate splitting and group decoding are employed at transmitters and receivers, respectively, which can offer significant improvement of achievable rate region. In addition, we also consider the receiving latency and propose novel delay-constrained partial group decoding (DC-CPGD), where all desired messages can be decoded within allowable interference cancellation steps to effectively control this latency. Two rate allocation algorithms are proposed with limited information exchange. Numerical results demonstrate that the proposed DC-CPGD can perform close to exhaustive search, and the proposed MDP-based adaptive transmission combined with DC-CPGD can significantly reduce the transmission cost under stringent delay requirement. [ABSTRACT FROM AUTHOR]

In this paper, we investigate resource allocation schemes for Device-to-Device (D2D) communications, which aim to minimize the energy consumption of cellular users (CUs) and D2D pairs. Different from existing works where the resource allocation is performed in the premise that the sizes of orthogonal channels/resource blocks are given, we consider the case that the time resource can be dynamically adjusted according to the rate requirements of CUs and D2D pairs during the resource allocation. We first formulate the resource allocation as a mixed integer non-linear programming (MINLP). We then demonstrate that, given the selections of CUs for D2D pairs, the formulated energy minimization problem is conditionally convex, and the convexity condition is derived accordingly. If the convexity condition is not satisfied, we propose an iterative algorithm to minimize the energy consumption. Based on these results, we further develop a random-switch-based iterative (RSBI) algorithm to find the solution to the MINLP by improving the CU-selection for D2D pairs. Simulation results show that, compared with the equipotent and proportional-fair time allocation schemes, our approach can achieve an energy saving ratio of 17%–81% under various network settings. [ABSTRACT FROM AUTHOR]

The time synchronization in vehicular ad hoc networks (VANETs) has gained significant attention in recent times. In this paper, we focus on the time synchronization in large-scale VANETs and propose an adaptive beacon time synchronization (ABTS) algorithm to enhance timing message synchronization. ABTS algorithm selects the best time synchronization pairs to decrease the number of timing messages transmitted. To guarantee the reliability of synchronization, the clock offset of the connected vehicles periodically readjust to provide a better degree of synchronization accuracy with minimum energy consumption. Based on two-way timing message synchronization, we derive maximum likelihood (ML) estimation and its equivalent Cramer-Rao lower bound (CRLB) for clock offset design in the generalized sender-to-receiver system by assuming a Gaussian noise model. We propose node pair selection algorithms to improve pairwise beacon message timing synchronization. The simulation results validate that our proposed ABTS algorithm performs better than other protocols in terms of synchronization accuracy, the rate of convergence and energy efficiency. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Things (IoT) solutions are playing an important role in numerous areas, especially in smart homes and buildings, health-care, vehicles, and energy. It will continue to expand in various fields in the future. However, some issues limit the further development of IoT technologies. First, the battery-powered feature increases the maintenance cost of replacing batteries for IoT devices. Second, existing Cloud-IoT frameworks are not able to cope with emerging delay-constrained applications in the IoT system due to its centralized mode of operation and the considerable communication delay. Existing studies neither satisfy the demand for the quick response in time-constraint IoT applications nor fundamentally solving the problem of energy sustainability. Therefore, this paper studies the problem of energy sustainability and timeliness in IoT system. Based on Energy Harvesting Technologies (EHT), the Green and Sustainable Mobile Edge Computing (GS-MEC) framework is proposed to make IoT devices self-powered by utilizing the green energy in the IoT environment. In this framework, we formulate the problem of minimizing response time and packet losses of tasks under the limitation of energy queue stability to improve the timeliness and reliability of task processing. Additionally, the dynamic parallel computing offloading and energy management (DPCOEM) algorithm is designed to solve the problem based on the Lyapunov optimization technology. Finally, theoretical analysis demonstrates the effectiveness of the proposed algorithm, and the numerical result of simulation shows that the average performance of the proposed algorithm is an order of magnitude better than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that enlarges the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that our deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a machine learning based handover decision algorithm for high-speed railway systems. In a standard LTE-R network, a mobile relay (MR) on a train connects a DeNB and various onboard devices, including train controllers. The standard LTE-R handover decision scheme assumes the moving node may move back and forth. Thus, it makes the serving DeNB keep the inferior connection with the MR during the predefined Time-To-Triger, even though the MR gets closer to the cell boundary and can make a better connection with the neighbor DeNB. However, a train keeps moving forward and never moves backward during the operation. Thus, it is expected that improved quality of wireless connection can be attained if the serving DeNB starts the handover of the MR earlier than the standard by predicting the cell boundary crossing time. Especially, the Gaussian Bayesian regression model outputs the predictive distribution that contains prediction values as well as information of their uncertainty. Thus, we can decide whether to accept the prediction according to the predictive distribution. Based on the prediction, the serving DeNB decides the handover initiation time to make the MR cross the cell boundary during the handover execution. We evaluate the performance of handover decision algorithm through simulations. The simulation results show that the proposed scheme outperforms the standard handover decision algorithm and the H2-based scheme in terms of communication reliability at cell boundary regardless of the moving speed. [ABSTRACT FROM AUTHOR]

Multi-tier cellular networks are a cost-effective solution for capacity enhancement in urban scenarios. In these networks, effective mobility strategies are required to assign users to the most adequate layer. In this paper, a data-driven self-tuning algorithm for traffic steering is proposed to improve the overall Quality of Experience (QoE) in multi-carrier Long Term Evolution (LTE) networks. Traffic steering is achieved by changing Reference Signal Received Quality (RSRQ)-based inter-frequency handover margins. Unlike classical approaches considering cell-aggregated counters to drive the tuning process, the proposed algorithm relies on a novel indicator, derived from connection traces, showing the impact of handovers on user QoE. Method assessment is carried out in a dynamic system-level simulator implementing a real multi-carrier LTE scenario. Results show that the proposed algorithm significantly improves QoE figures obtained with classical load balancing techniques. [ABSTRACT FROM AUTHOR]

Of the wide palette of 5G features, ultra-wide bandwidth and large-scale antenna configuration are regarded as the essential enabling technology components for millimeter wave (mmWave) communication. Accurate knowledge of delay and angle information of multipath components is essential for many applications in mmWave systems. There is a strong need for a low computation-cost channel estimation algorithm for such systems, where typically adopted far-field and narrowband assumptions might be violated. In this paper, a generic yet novel beamspace uniform circular array (UCA) beamforming algorithm with successive cancellation scheme is proposed to jointly detect the impinging azimuth angle and delay of each multipath component. The proposed algorithm is computationally cheap and it works for ultra-wideband UCA systems in the near-field conditions as well as the traditional narrowband systems in the far-field conditions. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm, compared to the state-of-art works. [ABSTRACT FROM AUTHOR]

This paper examines the problem of predictive gear scheduling for fuel consumption minimization in connected/automated heavy trucks. The literature highlights the fuel economy benefits of such predictive scheduling, but there is a need to optimize such scheduling online, in real time. To address this need, we begin by using dynamic programming (DP) to schedule gear shifting offline, in a manner that achieves a globally optimal Pareto tradeoff between the conflicting objectives of minimizing fuel consumption and shift frequency. The computational cost of DP is unfavorable for online implementation, but we present two algorithms addressing this challenge. Both algorithms rely on the fact that in the Pareto limit where fuel consumption minimization is the sole objective, DP furnishes a simple static shift map. Our first algorithm trains a recurrent neural network to prune the shift schedule generated by this map. The second algorithm performs this pruning in a direct manner tailored to reduce the schedule's rain flow count. We simulate these algorithms for different drive cycles. Both algorithms achieve a reasonable tradeoff between fuel consumption and gear shift frequency. However, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient. [ABSTRACT FROM AUTHOR]

We consider the energy-efficient mobility-aware content caching problem with clustered small cells (SCs) in ultra-dense networks (UDNs). The UDN is regarded as one of the key solutions to overcome the explosive growth of mobile data traffic and consequential energy consumption. In addition, caching the popular contents at the edge of UDNs can further mitigate the challenges of traffic and energy. In this paper, we group the SCs into disjoint clusters and model the mobility of users between clusters as a Markov chain. We formulate the caching problem aimed at minimizing the overall energy consumption for content delivery to the moving users. Then we decompose the complicated task of energy consumption optimization in cache-enabled UDNs into two sub-problems. In the first sub-problem, we formulate the inter-cluster caching problem as minimizing the energy consumption for serving a user request by the macro base station. We show that the first sub-problem is NP-hard, and develop a polynomial-heuristic algorithm. Further, we define the mobility-oblivious version of the sub-problem, and provide a ${\textstyle {1 \over {2k }}}$ -approximation solution for the mobility-aware based on this mobility-oblivious version. In the second sub-problem, we consider the heterogeneity of small cells and define the intra-cluster caching problem. The aim of this sub-problem is the placement of content in different SCs within the cluster so that the energy consumption for the content delivery in that cluster is minimized. In particular, the second sub-problem fits the general framework of the generalized assignment problem, which allows us to exploit its rich literature, to determine the optimal solution for our problem. Through extensive simulations based on real wireless data and human mobility patterns, we demonstrate the benefits of our approach that is even up to 56% better compared to the conventional caching schemes. [ABSTRACT FROM AUTHOR]

Limited range and charging infrastructure leads to range anxiety of electric vehicle drivers. Current range estimation algorithms are deemed unreliable and large safety margins are reserved to prevent the risk of stranding. Range estimation in general depends on two factors: current battery energy content and the energy consumption forecast on the route to destination. This paper aims at improving the latter by enhancing the forecast with a notion of uncertainty. The prediction algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts. Thereby, a central part of the algorithm is the explicit evaluation of the traffic situation by classifying the traffic phases. With the help of this methodology, individual forecasts can be made more precise since they are highly dependent on surrounding traffic. To demonstrate the validity of the algorithms, the performance is evaluated using real test drive data comprising multiple drivers. On the basis of the performance evaluation, both the superiority of stochastic algorithms over deterministic predictions and the improvement of predictive performance by evaluating explicit traffic phases can be shown. Implementing the proposed methodology in modern day electric vehicles could reduce range anxiety and ultimately increase acceptance of electric mobility worldwide. [ABSTRACT FROM AUTHOR]

Fifth-generation and beyond (5G+) systems will support novel cases, and hence, require new network architecture. In this paper, network flying platforms (NFPs) as aerial hubs are considered in future 5G+ networks to provide fronthaul connectivity to small cells (SCs). We aim to find the optimal association between the NFPs and SCs to maximize the total sum rate subject for quality of service, bandwidth, and supported number of link constraints. The formulated optimization problem is an integer linear program and the optimal association between the NFPs and SCs is found using numerical solvers at the expense of high computational complexity. We propose two algorithms (centralized and distributed) to reach a sub-optimal association at reduced complexity. Simulation results show that the performance of the proposed algorithms approaches the counterpart of its optimal solution and outperforms the state-of-the-art techniques from the literature. [ABSTRACT FROM AUTHOR]

The past years have seen a rapid development of autonomous underwater vehicle-aided (AUV-aided) data-gathering schemes in underwater acoustic sensor networks (UASNs). The use of AUVs efficiently reduces energy consumption of sensor nodes. However, all AUV-aided solutions face severe problems in data collection delay, especially in a large-scale network. In this paper, to reduce data collection delay, we propose a prediction-based delay optimization data collection algorithm (PDO-DC). On the contrary to the traditional delay optimization algorithms, Kernel Ridge Regression (KRR) is utilized via cluster member nodes to obtain the corresponding prediction models. Then, the AUV can obtain all cluster data by traversing less cluster head nodes, which can effectively reduce the collection delay of the AUV. The experimental results demonstrate that the proposed method is both feasible and effective. [ABSTRACT FROM AUTHOR]

A covert spoofing algorithm of an unmanned aerial vehicle (UAV) based on GPS/inertial-navigation-system-integrated navigation is analyzed in this paper. The proposed algorithm theoretically proves the fact that when the acceleration component of the counterfeit GPS signal is the difference between the UAV's current acceleration and the spoofing control input, the UAV can be covert spoofed. To avoid adverse consequences (detection or crash) caused by frequent changes in UAV flight during the GPS spoofing attacks, the proposed algorithm requires that the deception trajectory planned by the GPS spoofer is slowly changing relative to the reference trajectory, which is pre-set by the UAV. Simulation results have verified the correctness of the proposed covert spoofing algorithm of UAV. Moreover, the spoofing effect is more distinct when the deception trajectory planning is considered. [ABSTRACT FROM AUTHOR]

In this paper, we propose the design of a uniquely factorable hexagonal constellation for a noncoherent wireless communication system with a single transmitter antenna and multiple receiver antennas [single-input multiple-output (SIMO)]. The hexagonal constellation has a densely packed 2-D “honeycomb” structure and is more energy efficient than the commonly used cross quadrature amplitude modulation (QAM) constellations. By using the hexagonal lattice formed from the Eisenstein integers and the recently developed concept of the uniquely factorable constellation (UFC) for the QAM constellation, an algorithm is developed to effectively and efficiently construct constant norm hexagonal UFCs of various sizes. In addition, an optimal energy scale is found to maximize the coding gain for the constant norm training hexagonal UFC schemes subject to a transmission bit rate constraint. Computer simulations show that the hexagonal unitary UFC proposed in this paper has the best error performance in comparison to the current literature results for the noncoherent SIMO system. [ABSTRACT FROM AUTHOR]

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence. [ABSTRACT FROM PUBLISHER]

In this paper, an initial attempt to exploit the benefits of machine learning in solving relay selection for dualhop networks has been made. From data-driven perspective, we convert relay selection to multiclass-classification problem and propose a decision-tree-based selection scheme. Input features are generated by binary quantization to the equivalent channel state information of each relay, so that the dependence of relay selection on maximization algorithm is removed. To optimize quantization parameter, we derive and maximize two splitting criteria—information gain and Gini index. Experimental results show that decision-tree-based scheme achieves comparable performance to optimal selection in terms of average end-to-end rate while reducing computational complexity and feedback amount. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA) is one type of non-orthogonal multiple access techniques for the fifth-generation wireless communication networks. Existing multiuser detection algorithms for SCMA uplink systems are based on the assumption that signals are in synchronization. However, asynchrony cannot be neglected due to the propagation delays in practical environments. In this paper, we present three detection algorithms for uplink asynchronous SCMA systems, namely belief propagation message passing algorithm, belief propagation with equal gain combining message passing algorithm, and partial phase-coherent algorithms. Simulation results show that our proposed asynchronous detection algorithms are able to offer descent bit error rate performances, with only a small performances loss compared to the synchronous case. Moreover, our proposed algorithms converge well in the uplink asynchronous SCMA systems. [ABSTRACT FROM AUTHOR]

In this paper, a novel scheme is proposed to optimize the effectiveness of the device-to-device (D2D) enabled vehicular communications (D2D-V), which are underlaid in cellular networks, where the uplink channel allocated to one cellular user (CU) is reused by the multiple D2D-V links. To develop a well-functioning D2D-V system, the sum rate of all D2D-V links is necessary to be maximized, and also the reliability of the co-channel CU to infrastructure has to be guaranteed with the CU interference constraint. Unlike the traditional statical D2D systems, the D2D-V system suffers with high mobility and channel uncertainty. Therefore, the CU interference constraint is formulated as a probabilistic function by the Bernstein approximation, the constraint attempts to address the mobile channel fluctuations. Besides, the tractable approximate constraint is reformulated as two separable structure, this reformulated constraint attempts to determine the near-optimal solutions more efficiently. Since the objective function with a logarithmic form is nonconvex, successive convex approximation is applied to transform the nonconvex problem into the convex problem. The dual decomposition is used to determine the near-optimal solutions. After the problem is reformulated, a distributed robust power control algorithm is proposed to perform the chance-constrained optimization. Numerical simulations are used to evaluate the performance of the proposed scheme. The simulation results show the impacts of system performance when applying the proposed scheme in vehicular environments with high mobility. The proposed optimization schemes are further verified by comparing with the existing methods. [ABSTRACT FROM AUTHOR]

The output characteristics and the transfer efficiencies of inductive power transfer (IPT) systems are affected by (but not limited to) the variation of parameters such as, variable load, misalignment between the primary and secondary coils, and resonance frequency detuning. This paper first analyzes the resonant conditions of all voltage-fed compensation topologies in IPT systems to achieve the load-independent voltage transfer characteristic at load-independent zero phase angle frequency. With these conditions, the parameters sensitivity to the key performance factors of the S-SP, LCC-S, LCC-P, and double-sided LCC compensated IPT systems are systematically investigated when considering the losses in coils and compensation networks. Comparisons of the quantitative sensitivity analysis and experimental results for all IPT systems are made in terms of the voltage transfer ratio and efficiency. These analyses can provide guidance for the selection of superior compensation networks to minimize the additional converter effort and simplify the control algorithm of closed-loop IPT systems. [ABSTRACT FROM AUTHOR]

The deployment of large-scale UAV clusters for cluster advantages will lead to high competition and excessive congestion of spectrum resources, which results in mutual interference. This paper investigates the interference-aware online spectrum access problem for multicluster Flying Ad-Hoc Network (FANET) under different network topologies, i.e., the locations of UAV clusters are varying. First, the problem is formulated as a data-assisted multistage channel access game with the goal of mitigating interference of all UAV clusters and decreasing channel switching cost during each slot. As we prove that the game is an exact potential game that guarantees at least one pure-strategy Nash equilibrium and interference-aware online channel preserving based concurrent best response (IOCPCBR) algorithm, an online distributed algorithm is proposed to achieve the desirable solution. Finally, the simulation results demonstrate the validity and effectiveness of the multistage channel access game as well as IOCPCBR algorithm. [ABSTRACT FROM AUTHOR]

The multimode power-split architecture for hybrid electric vehicle (HEV) powertrains is generally known for the complexity of its operation. This paper first addresses the challenge of developing an automated on-line near-optimal control strategy for these systems. Particularly, a machine learning logic based on supervised learning is developed for on-line selection of the HEV operating mode, thus the particular set of clutches to be engaged. An efficiency-based approach is then adopted to determine the optimal power-split between powertrain components. Later, the developed strategy finds integration in an optimal design methodology for multimode power-split HEVs considering the effectiveness and ease of on-line controllability. The obtained results are compared with the ones by the traditional HEV design methodology that only considers off-line energy management. The illustrated design methodology with on-line control reveals efficient at identifying the multimode HEV design that demonstrates optimal predicted fuel economy values and ease of on-line controllability simultaneously. Results suggest that the HEV design optimization procedure may produce different outcomes and demonstrate more effective when the evaluation of the on-line controlled operation is integrated. [ABSTRACT FROM AUTHOR]

Computation offloading is a promising solution to extend the capacity of robot swarms for computation-intensive applications because it allows robot swarms to benefit from the powerful computing resources of modern data centers. However, the existing computation-offloading approaches still face challenges: 1) multi-hop cooperative computation offloading, 2) joint computation offloading and routing, and 3) task slicing. In this paper, we propose a quality of service (QoS)-aware cooperative computation-offloading scheme for robot swarms using game theory. We analyze the multi-hop cooperative communication model in robot swarms and investigate the computation offloading and routing decision-making problems with the goals of both latency minimization and energy efficiency. We formulate the joint optimization problem as a multi-hop cooperative computation-offloading game and show the existence of a Nash equilibrium (NE) of the game for both unsliceable and sliceable tasks. We further propose a QoS-aware distributed algorithm to attain an NE and provide an upper bound on the price of anarchy in the game. Finally, our simulated results show that our algorithm scales well as the swarm size increases and it has a stable performance gain in various parameter settings. [ABSTRACT FROM AUTHOR]

In this paper, we propose a compressive random access (CRA) scheme using multiple resource blocks (RBs) to support massive connections for machine type communications. The proposed CRA scheme is scalable. As a result, if the number of devices increases, more RBs can be added to support them. Thanks to multiple RBs, we can employ fast retrial between RBs for re-transmissions of collided packets, which can result in short access delay. For stable CRA with fast retrial, we derive conditions (with a rate control scheme), and analyze the steady-state performance to find the throughput and delay. Through analysis and simulation results, we can see that the proposed scheme can perform better than conventional multichannel ALOHA and enjoy a tradeoff between the performance and complexity in terms of the number of RBs. [ABSTRACT FROM AUTHOR]

In cellular heterogeneous networks (HetNets), a number of distributed base stations cooperatively provide services to multiple mobile users. This paper addresses joint base-station activation and coordinated beamforming design for downlink transmission in HetNets. To this end, a mixed integer program is formulated to optimize the total power consumption of a HetNet. A novel approach based on Benders’ decomposition is then put forth to obtain the optimal solution for the problem of interest with guaranteed convergence. Building on our new formulation, a dual-subgradient algorithm is also proposed to find an approximate solution in polynomial time. The proposed approaches can be generalized to more general setups, including robust beamforming designs under channel uncertainty, and coordinated beamforming for multi-cell scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose two groups of recursive codes, namely the Hadamard recursive carrier interferometry (HRCI) codes and diagonal recursive carrier interferometry (DRCI) codes, to simultaneously reduce the peak-to-average power ratio (PAPR) and suppress the phase noises and residual carrier frequency offset (RCFO) for high-speed orthogonal frequency division multiplexing (OFDM) systems. We exploit the recursion property of the Hadamard and diagonal matrices to constitute the proposed HRCI code and DRCI code using the carrier interferometry (CI) code, and present the system model in details to spread OFDM symbols in the frequency domain. Then, we prove that both HRCI and DRCI generation matrices keep their invertibility during the recursions. As a direct result of the new code design, the phase intervals are enlarged and the signals are shifted in the time domain, thus both the PAPR and the phase noises are reduced, and the RCFO can be mitigated. Moreover, the enlarged phase intervals enable the transceiver blocks including the channel estimator, which estimate the phase noise based on the received signals, to improve the performance. Furthermore, we present the expressions for the HRCI and DRCI embedded transmitted and received signals, and derive the analytical signal-to-interference-plus-noise ratio (SINR) expressions for bit error rate (BER) performance analysis. Then, we analyze the effects of presented codes on the phase differences and provide the computational complexity analysis. Numerical simulations verify the effectiveness of our theoretical analysis of SINR. Additionally, under different parameter settings, which consider the working conditions of practical systems, we demonstrate that the presented robust HRCI and DRCI coded OFDM systems can suppress the PAPR satisfactorily and better BER performances can be achieved by the recursive CI-aided spread OFDM systems with phase noise rejection and RCFO compensation when compared with counterpart systems. [ABSTRACT FROM AUTHOR]

A $K$ -tier heterogeneous downlink millimeter wave (mmWave) cellular network with user-centric small cell deployments is studied in this paper. In particular, we consider a heterogeneous network model with user equipments (UEs) being distributed according to a Poisson cluster process (PCP). Distinguishing features of mmWave communications including directional beamforming and a sophisticated path loss model incorporating both line-of-sight and non-line-of-sight transmissions, are taken into account. Initially, we determine general expressions for the association probabilities of different tier BSs. Using tools from stochastic geometry, we then characterize the Laplace transform of the interference and derive a general expression for the signal-to-interference-plus-noise ratio coverage probability. While these expressions are applicable to any PCP, we later specialize the results to two popular PCPs, namely, first, Thomas cluster process, where the UEs are clustered around the base stations (BSs) according to a Gaussian distribution, and second, Matérn cluster process, where the UEs are scattered according to a uniform distribution. Subsequently, upper and lower bounds for the coverage probability are provided. Special cases are also addressed, providing the insight that when the cluster size grows without bound, our PCP-based model specializes to a Poisson point process-based model. Area spectral efficiency is investigated as well. Moreover, we have discussed extensions to cases in which more practical antenna gain patterns are taken into account and also the shadowing is considered. Via numerical and simulation results, we explore the effects of the beamwidth of the main lobe, the main lobe directivity gain, the biasing factor and the transmit power of the small-cell BSs to get insight on the performance in practical scenarios. Performance in dense networks is also analyzed numerically. [ABSTRACT FROM AUTHOR]

This paper uses an information-theoretic lens to view the error bound of wireless local area network (WLAN) localization, which is recognized as one of the superior candidate localization techniques in the GPS-denied environment. Interestingly, we analogize the process of WLAN localization into one of information propagation in a parallel Gaussian noisy channel, and then derive the corresponding localization error bound from the channel capacity of the analogical information propagation system. Experimental results show that compared with the widely-known Cramer-Rao Lower Bound (CRLB), the proposed approach performs better in localization error bound estimation under most of the cases of different access point deployment and reference point calibration. [ABSTRACT FROM AUTHOR]

As compared to the microwave communication networks, the theoretical analyses of system performances such as the cell coverage and the cell average data rate are more difficult due to the unique propagation path loss model for mmWave cellular communication networks. In this paper, based on the unique transmission states of the distance-dependent piecewise propagation probability functions, the analytical six-state analytical mixture distribution of the propagation loss including the distance-dependent path loss, shadowing and small-scale fading is obtained. For each state, a novel Kullback–Leibler divergence based Gaussian approximation method is proposed to model the distribution of the propagation loss. The distribution of the propagation loss including the large-scale fading and small-scale fading is approximated via a six-state Gaussian mixture model, then the closed-form expression for the signal-to-noise ratio can be easily obtained. The cell coverage is expressed as the weighted sum of the error functions, and the cell average data rate is expressed as the weighted sum of the moments of the propagation loss for the six different states. Numerical results show that the cell coverage mainly depends on the none line-of-sight transmission and the cell average data rate mainly depends on the line-of-sight transmission. [ABSTRACT FROM AUTHOR]

In millimeter wave (mm Wave) systems, the channel state fluctuates faster than that in sub-6GHz systems. Thus, the channel state dependent scheduling requires the faster feedback from a user equipment, which will generate the larger overhead. To reduce the feedback overhead, a prediction based feedback scheme is proposed under the assumption of the imperfect channel states being available at the user equipment. The proposed feedback scheme optimizes the reconstruction accuracy at a given feedback overhead. Within the scheme, the performance degradation caused by the protocol and hardware operation delay can be mitigated. The impact of the proposed scheme on improving the downlink bit rate is studied in this paper. [ABSTRACT FROM AUTHOR]

The dynamic nature of vehicular networks imposes a lot of challenges in multihop data transmission as links are vulnerable in their existence due to associated mobility of vehicles. Thus, packets frequently find it difficult to get through to the destination due to the limited lifetimes of links. The conventional broadcasting based vehicular ad-hoc network (VANET) routing protocols struggle to accurately analyze the link dynamicity due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged software defined vehicular network (SDVN) paradigm, link stability can be better scrutinized pertaining to the availability of global network information. Thus, in this paper, we introduce an optimization based novel packet routing scheme with a source routing based flow instantiation (FI) operation for SDVN. The routing framework closely analyzes the stability of links in selecting the routes and the problem is formulated as a minimum cost capacitated flow problem. Furthermore, an incremental packet allocation scheme is proposed to solve the routing problem in a less time complexity. The objective is to find multiple shortest paths which are collectively stable enough to deliver a given number of packets. The FI scheme efficiently delivers and caches flow information in the required nodes with a reduced extent of communication with the control plane. With the help of realistic simulation, we show that the proposed routing framework excels in terms of the performance over the existing routing schemes of both SDVN and conventional VANET. [ABSTRACT FROM AUTHOR]

How to stimulate the devices to participate is a key problem in the device-to-device (D2D) communications, especially when there are multiple base stations in practice. On one hand, the base station needs to lower the price to compete with other base stations for attracting more devices. On the other hand, the base station needs to increase the price to obtain more benefit. In this paper, we propose an optimal multiprincipal multiagent contract design to tackle such a challenge. Specifically, we formulate the multiprincipal contract design problem as a game theoretic optimization problem, where the objective of each principle (base station) through contract design is to maximize its overall utility obtaining from the trade with agents (devices) subject to the individual rationality and incentive compatibility constraints. By solving the game theoretic optimization problems, we can obtain the equilibrium contract where the contracts of different principals reach an equilibrium and any deviation from a single principal will lead to the degradation of its own utility. Moreover, under the equilibrium, both the individual rationality and incentive compatibility constraints are satisfied, i.e., all agents will choose the corresponding contract according to their types to reach the optimal nonnegative utilities. Simulation results show that compared with the monoprincipal contract design, both the utilities of devices and the social welfare can be improved with the multiprincipal contract design. [ABSTRACT FROM AUTHOR]

In this paper, a mixed-integer linear programming model is proposed to optimize hybrid electric vehicle (HEV) navigation modes on the city map, namely the problem of the optimal selection of navigation modes (OSNMs). The OSNMs problem of the HEV as part of the operating strategy is obtained considering a constraint set related to CO2 emissions reduction, efficient battery charging, and the optimal scheduling of deliveries. Uncertainties in the HEV navigation on urban roads are modeled using probability values assigned to an established set of traffic density values according to the levels of service. The model is implemented in a mathematical programming language (AMPL) and solved using the commercial solver CPLEX. The case study considers real data related to the Prius Prime technology and shows the effectiveness of automating the HEV navigation modes considering CO2 emissions reduction levels during an operating strategy. [ABSTRACT FROM AUTHOR]

As Vehicular Ad hoc Network (VANET) features random topology and accommodates freely connected nodes, it is important that the cooperation among the nodes exists. This paper proposes a trust model called Implicit Web of Trust in VANET (IWOT-V) to reason out the trustworthiness of vehicles. Such that untrusted nodes can be identified and avoided when we make a decision regarding whom to follow or cooperate with. Furthermore, the performance of Cooperative Intelligent Transport System (C-ITS) applications improves. The idea of IWOT-V is mainly inspired by web page ranking algorithms such as PageRank. Although there does not exist explicit link structure in VANET because of random topology and dynamic connections, social trust relationship among vehicles exists and an implicit web of trust can be derived. To accomplish the derivation, two algorithms are presented, i.e., BayesTrust and VehicleRank. They are responsible for deriving the local and global trust relationships, respectively. The simulation results show that IWOT-V can accurately identify trusted and untrusted nodes if enough local trust information is collected. The performance of IWOT-V affected by five threat models is demonstrated, and the related discussions are also given. [ABSTRACT FROM AUTHOR]

A path planning and control approach of a non-holonomic three-wheeled mobile robot (WMR) for online navigation in road following and roundabout environments is presented in this paper. We proposed a complete navigation algorithm that enables the WMR to autonomously navigate on the road with various scenarios. With such an algorithm, the robot is able to localize itself within the road environment and find a collision free-path starting from a pre-defined start position to a goal point using a novel approach called laser simulator (LS). The path planning and roundabout detection are determined based on LS and sensor fusion of a laser range finder, camera, and odometry measurements. The sensor fusion algorithm is used to remove noises and uncertainties from sensors’ data and provide optimum measurements for path planning. A robot motion control scheme is used for the purpose of controlling the kinematic parameters of WMR using a resolved acceleration control coupled with an active force control for rejecting the disturbances. Experimental results show the capability of the proposed algorithms to robustly drive the robot on the road following and roundabout environments. [ABSTRACT FROM AUTHOR]

A coordinate system is essential for the positioning and navigation services of the ground-based positioning system. The coordinate establishment requires accurate and sufficient position information of base stations. In global navigation satellite system (GNSS)-denied environments, it is challenging to determine the locations of base stations using GNSS services with GNSS signals being interfered and blocked. In this case, manual surveying constraints rapid deployment of the ground-based positioning system. By receiving ranging signals from anchors whose absolute location is known in advance, the locations of all base stations can be determined using iterative trilateration, which however causes cascading error accumulation for large networks. In this paper, using ranging signals emitted by base stations to obtain mutual distance measurements, we propose a coordinate establishment algorithm to address this problem. Introducing the Euclidean distance matrix (EDM), the problem is formulated as an EDM least squares optimization problem with the anchor information incorporated. The optimal solution is obtained via the proposed algorithm based on the shortest path algorithm and the nonlinear conjugate gradient method. Simulations and experimental results reveal that the proposed algorithm outperforms existing methods in the aspects of positioning accuracy and computational complexity in most cases. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm to evaluate the performance of the mobile edge computing system. Specifically, a new metric called computation efficiency is defined as the number of calculated data bits divided by the corresponding energy consumption. In order to compute the required data timely, we combine two schemes, local computing and data offloading, into a joint computation algorithm. An optimization problem is formulated with the objective to maximize the sum of computation efficiency among users with weighting factors. With iterative and gradient descent methods, the problem can be solved efficiently. Simulation results show that the proposed scheme outperforms traditional approaches. In addition, the tradeoff study between local computing and data offloading reveals that when data size is small, local computing plays a more important role, but when the size grows, data offloading becomes preferable. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new constellation design for a non-coherent massive single input multiple output (m-SIMO) uplink system based on M-DPSK suitable for Rician channels. Under Rician propagation, the non-coherent systems proposed until now cannot completely remove the interference. This new design takes into account the inter user and inter symbol interferences in order to convert them into useful terms that help us in the non-coherent detection. We propose an algorithm to non coherently detect the joint symbol with the information from all users leveraging the characteristics of the new constellation. In addition, the signal-to-interference-plus-noise ratio is derived and is used to present analytical bounds for the symbol error rate. It is shown that the analytical results tightly match the numerical simulation. We show that with the proposed constellation and detection algorithm we are able to leverage the line-of-sight component of the Rician propagation, obtaining a better performance than with Rayleigh channels. [ABSTRACT FROM AUTHOR]

Batched sparse (BATS) codes are a promising technique for reliable data transmission in multihop wireless networks. A BATS code consists of an outer code and an inner code, the design of which can be separated. In this paper, we investigate the design of the inner code and focus on maximizing the expected batch transfer matrix rank normalized by the total number of packets transmitted by both the source and intermediate nodes. By allowing different recoding decisions at different nodes, the problem is formulated as a nonlinear integer programming (NLIP) problem, which is non-deterministic polynomial (NP)-hard in general. We derive a noniterative formula to simplify the evaluation of the expected batch transfer matrix rank. By considering large finite fields and relaxing the integer constraints, we obtain an upper bound on the optimal value of the original maximization NLIP problem. We also derive a centralized and a decentralized algorithm to find suboptimal solutions of the NLIP problem, with different tradeoffs between complexity and performance. Numerical results show that our proposed inner codes outperform the standard BATS, and achieve very close to the upper bound, which verifies the near optimality of our algorithms. [ABSTRACT FROM AUTHOR]

The deployment of cloud and edge computing forms a three-tier mobile computing network, where each task can be processed locally, by the edge nodes, or by the remote cloud server. In this paper, we consider a cooperative three-tier computing network by leveraging the vertical cooperation among devices, edge nodes and cloud servers, as well as the horizontal cooperation between edge nodes. In this network, we jointly optimize the offloading decision and the computation resource allocation to minimize the average task duration subject to the limited battery capacity of devices. However, the formulated problem is a large-scale mixed integer non-linear optimization problem with the growing number of base stations and devices, which is NP-hard in general. To develop an efficient offloading scheme with low complexity, we conduct a series of reformulation based on reformulation linearization technology and further propose a parallel optimization framework by utilizing alternating direction method of multipliers (ADMM) method and difference of convex functions (D.C.) programming. The proposed scheme decomposes the large-scale problem into some smaller subproblems, which are done across the multiple computation units in a parallel fashion to speed up the computation process. Simulation results demonstrate that the proposed scheme can obtain a near-optimal performance with low complexity, and can reduce up to 24% of the task duration compared with other schemes. Simulation also shows how much the vertical and horizontal computation cooperations affect the system performance under different network parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a widely linear processing framework for multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) downlink systems. In the framework, a widely linear MIMO-NOMA (WL-MIMO-NOMA) model is derived by assuming that the base station transmits real-valued downlink signals. WL-MIMO-NOMA adopts complex-valued power allocation coefficients to stagger the user signals in phase. The main features of WL-MIMO-NOMA are the following: first, in general case, WL-MIMO-NOMA can remove all the inter-cluster interference and at least half of the intra-cluster interference; and second, in user pairing case, both interferences can be completely eliminated. This is distinct from the existing work where real power coefficients are used, which cannot guarantee the complete separation of the paired user signals because the signals transmitted to the paired users are overlapped in phase. In addition, the closed-form expressions of outage probabilities are derived. The phase difference of the complex power coefficients is optimized to minimize the outage probability. It is proven that, with the optimal phase difference, successive interference cancellation is unnecessary in user pairing case. Finally, the framework is extended to the mixed case of real/complex circular signals. Simulation results show that the proposed framework outperforms the existing work, and the numerical results agree well with the analytical analysis. [ABSTRACT FROM AUTHOR]

Source location privacy is a major problem in wireless sensor networks (WSNs). WSNs are usually deployed in random areas with no protection. The source location reveals valuable information about targets. If an adversary locates the source node by analyzing the traffic mode, a target can be easily attacked. In this paper, a scheme based on the cloud using multi-sinks (CPSLP) is proposed to address the issue of source location privacy. The authors propose a scheme that changes packet destinations randomly in each transmission. In addition, multiple sinks are adopted to create many routing paths. The introduction of an intermediate node renders the routing path more random and flexible. Then, a cloud-shaped fake hotspot is created to add fake packets into the WSN to confuse the adversary and provide a comprehensive privacy location. Each valuable packet is routed through a path that is quite difficult for the hotspot-locating adversary to find directly. Simulation results illustrate that the CPSLP scheme can prevent adversarial capture and maintain a high level of privacy protection at the same time. The energy consumption in this scheme exerts limited influence on the network lifetime compared with a cloud-based scheme and an all-direction random routing algorithm scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep reinforcement learning (DRL)-based method that allows unmanned aerial vehicles (UAVs) to execute navigation tasks in large-scale complex environments. This technique is important for many applications such as goods delivery and remote surveillance. The problem is formulated as a partially observable Markov decision process (POMDP) and solved by a novel online DRL algorithm designed based on two strictly proved policy gradient theorems within the actor-critic framework. In contrast to conventional simultaneous localization and mapping-based or sensing and avoidance-based approaches, our method directly maps UAVs’ raw sensory measurements into control signals for navigation. Experiment results demonstrate that our method can enable UAVs to autonomously perform navigation in a virtual large-scale complex environment and can be generalized to more complex, larger-scale, and three-dimensional environments. Besides, the proposed online DRL algorithm addressing POMDPs outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

Extended object tracking has become an integral part of many autonomous systems during the last two decades. For the first time, this paper presents a generic spatio-temporal Gaussian process (STGP) for tracking an irregular and non-rigid extended object. The complex shape is represented by key points and their parameters are estimated both in space and time. This is achieved by a factorization of the power spectral density function of the STGP covariance function. A new form of the temporal covariance kernel is derived with the theoretical expression of the filter likelihood function. Solutions to both the filtering and the smoothing problems are presented. A thorough evaluation of the performance in a simulated environment shows that the proposed STGP approach outperforms the state-of-the-art GP extended Kalman filter approach [N. Wahlström and E. Özkan, “Extended target tracking using Gaussian processes, IEEE Transactions on Signal Processing,” vol. 63, no. 16, pp. 4165–4178, Aug. 2015] with up to $\text{90}\%$ improvement in the accuracy in position, $\text{95}\%$ in velocity and $\text{7}\%$ in the shape, while tracking a simulated asymmetric non-rigid object. The tracking performance improvement for a non-rigid irregular real object is up to $\text{43}\%$ in position, $\text{68}\%$ in velocity, $\text{10}\%$ in the recall, and $\text{115}\%$ in the precision measures. [ABSTRACT FROM AUTHOR]