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The application of network slicing to mobile edge computing (MEC) systems has aroused great interests from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of the longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes. [ABSTRACT FROM AUTHOR]

Network slicing has been considered as a promising candidate to provide customized services for vehicular applications that have extremely high requirements of latency and reliability. However, the high mobility of vehicles poses significant challenges to resource management in such a stochastic vehicular environment with time-varying service demands. In this paper, we develop an online network slicing scheduling strategy for joint resource block (RB) allocation and power control in vehicular networks. The long-term time-averaged total system capacity is maximized while guaranteeing strict ultra-reliable and low-latency requirements of vehicle communication links, subject to stability constraints of task queues. The formulated problem is a mixed integer nonlinear stochastic optimization problem, which is decoupled into three subproblems by leveraging Lyapunov optimization. In order to tackle this problem, we propose an online algorithm, namely JRPSV, to obtain the optimal RB allocation and power control at each time slot according to the current network state. Furthermore, rigorous theoretical analysis is conducted for the proposed JRPSV algorithm, indicating that the system capacity and the system average latency obey a $[ {{\mathcal O}({1/V}),{\mathcal O}(V)} ]$ trade-off with the control parameter $V$. Extensive simulation results are provided to validate the theoretical analysis and demonstrate the effectiveness of JRPSV as well as the impacts of various parameters. [ABSTRACT FROM AUTHOR]

The emerging orthogonal time frequency space (OTFS) modulation technique has shown its superiority to the current orthogonal frequency division multiplexing (OFDM) scheme, in terms of its capabilities of exploiting full time-frequency diversity and coping with channel dynamics. The optimal maximum a posteriori (MAP) detection is capable of eliminating the negative impacts of the inter-symbol interference in the delay-Doppler (DD) domain at the expense of a prohibitively high complexity. To reduce the receiver complexity for OTFS scheme, this paper proposes a variational Bayes (VB) approach as an approximation of the optimal MAP detection. Compared to the widely used message passing algorithm, we prove that the proposed iterative algorithm is guaranteed to converge to the global optimum of the approximated MAP detector regardless the resulting factor graph is loopy or not. Simulation results validate the fast convergence of the proposed VB receiver and also show its promising performance gain compared to the conventional message passing algorithm. [ABSTRACT FROM AUTHOR]

In this paper, under unforeseen circumstances, a dynamics-constrained global-local (DGL) hybrid path planning scheme incorporating global path planning and local hierarchical architecture is created for an autonomous surface vehicle (ASV) with constrained dynamics. By encapsulating ASV safety area into Theta*-like heuristics, global path planning algorithm is developed to optimally generate sparse waypoints which are sufficiently clear to constraints. To deal with dynamically unforeseen environments, a local hierarchy is established by fuzzy decision-making (FDM) and fine dynamic window (FDW) layers, which are responsible for large- and close-range collision avoidance, respectively, by governing surge and yaw velocity guidance signals. With the aid of the FDW, constrained dynamics pertaining to the ASV, i.e., actuatable surge/yaw velocities and accelerations, are elaboratively embedded into local path planning, which in turn governs trackable collision-avoidance local path. By inserting virtual waypoints onto the globally optimal path, a seamless interface between global and local path-planning mechanism is devised, and thereby contributing to the entire DGL hybrid path planning scheme. Simulations and comparisons in various real-world geographies demonstrate the effectiveness and superiority of the proposed DGL hybrid path planning scheme. [ABSTRACT FROM AUTHOR]

This paper presents a realistic feasibility of radio frequency identification (RFID) based self-localization of objects in real environments using simple infrastructure composed of chipless tags. These tags are designed as arrays of dielectric resonators (DR) in order to overcome the range limitations of the traditional chipless tags, where the DR array structure of the tags boosts the radar cross section (RCS) and, consequentially, extends the range coverage. Furthermore, an efficient algorithm for tag detection is proposed considering the environmental and hardware impairments that alter the backscattered signal from the tag. In this system, the reader interrogates the reference nodes, i.e. chipless tags, by a frequency-sweep RF signal, and the backscattered signals from the tags are captured and analyzed at the reader for detection, ranging and location estimation. A testbed is implemented with fabricated chipless RFID tags in order to evaluate the validity and accuracy of the proposed self-localization system. Experimental measurements based simulations are conducted in several scenarios in order to evaluate the proposed system. Moreover, an object (the reader) is moved to several locations in an interrogation area of $2.5\times 1.2$ m, and, afterwards, its location is estimated. The averaged measured location error achieved based on the proposed system is approximately 5 cm with a variation of 0.9 cm as a minimum observed error and 7 cm as a maximum error. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the scheduling design of a mobile edge computing (MEC) system, where active mobile devices with computation tasks randomly appear in a cell. Every task can be computed at either the mobile device or the MEC server. We jointly optimize the task offloading decision, uplink transmission device selection and power allocation by formulating the problem as an infinite-horizon Markov decision process (MDP). Compared with most of the existing literature, this is the first attempt to address the transmission and computation optimization with random device arrivals in an infinite time horizon to our best knowledge. Due to the uncertainty in the device number and location, the conventional approximate MDP approaches addressing the curse of dimensionality cannot be applied. An alternative and suitable low-complexity solution framework is proposed in this work. We first introduce a baseline scheduling policy, whose value function can be derived analytically with the statistics of random mobile device arrivals. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. The complexity of deriving the sub-optimal policy is reduced dramatically compared with conventional solutions of MDP by eliminating the complicated value iteration. To address a more general scenario where the statistics of random mobile device arrivals are unknown, a novel and efficient algorithm integrating reinforcement learning and stochastic gradient descent (SGD) is proposed to improve the system performance in an online manner. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant. [ABSTRACT FROM AUTHOR]

This paper studies the information-energy ($\mathrm{I}$ - $\mathrm{E}$) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models. [ABSTRACT FROM AUTHOR]

This paper demonstrates a real world case study of a new method for robust simultaneous estimation of two parameters using multiple data sources. The method is used to simultaneously estimate vehicle mass and road gradient. No additional sensors are required beyond those that would normally be found on a vehicle controller network. The estimation algorithm combines components driven by observer state error and also directly by the parameter error using a sliding-mode inspired regressor structure. The algorithm incorporates a novel information fusion method that is integral to the regressor structure and a supervised data-rejection system to limit estimation activity in periods of recognised error-promoting activity. The estimation method has been demonstrated in real time on a modified production passenger car platform. It has been shown to be effective at robustly predicting road gradient and offering more reliable and stable prediction of vehicle mass than existing estimation methods employed in the same multi-parameter estimation context. The estimator allows prediction of vehicle mass whose limiting factor is the bandwidth and accuracy of the available driveline torque information, not the algorithm itself, allowing the identification of a 150 kg change in mass on a 2000 kg vehicle in this case study. [ABSTRACT FROM AUTHOR]

Linear and non-linear data-detection and precoding algorithms for wideband massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems that rely on orthogonal frequency-division multiplexing (OFDM) or single-carrier frequency-division multiple access (SC-FDMA) often require computation of the Gram matrix for each active subcarrier. Computing the Gram matrix for each active subcarrier, however, results in excessively high computational complexity. In this paper, we propose novel, approximate algorithms that significantly reduce the complexity of Gram-matrix computation by simultaneously exploiting channel hardening and correlation across subcarriers. We show analytically that a small fraction of Gram-matrix computations in combination with approximate interpolation schemes are sufficient to achieve near-optimal error-rate performance at low computational complexity in massive MU-MIMO systems. We furthermore demonstrate that our approximate interpolation algorithms are more robust against channel-estimation errors than exact Gram-matrix interpolation algorithms that require high computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a reliable algorithm is proposed, which aims at providing the desired reliability, avoiding the data loss due to probable failures, and guaranteeing the energy consumption balancing among all fog nodes (FNs) in the fog-enabled Internet of Things (IoT) networks. To this end, first, a new optimization model is defined to find the best transmission power and the best transmission rate of each terminal node (TN) based on the channel conditions and the desired reliability of the network. Then, the best FN is assigned to each TN according to the topology potential between FNs and TNs, such that there exists an energy consumption balancing among all FNs. Simulation results reveal that the proposed algorithm improves the network performance in terms of reliability and loss ratio, while maintaining the energy consumption balancing among all FNs. [ABSTRACT FROM AUTHOR]

This paper introduces a new torque vectoring (TV) algorithm with regards to enhancing the cornering performance of the electronic-four-wheel drive (e-4WD) vehicles. The proposed TV algorithm aims to distribute the in-wheel motor (IWM) torques to the left and right wheels assisting the vehicle in following the driver's intended trajectories. The proposed TV algorithm first included a yaw rate controller reference for neutral-steering which leads to the improvement of vehicle cornering agility. Thereafter, in-vehicle sensors and a standalone global positioning system (GPS) combined with a smooth sliding mode controller (SMC) were used based on a vehicle bicycle model to generate desired yaw moment values to track the reference. Finally, a novel torque distribution algorithm using the Daisy-chaining allocation, which is suitable for redundant actuator configuration, was utilized. Noteworthy, the Daisy-chaining allocation algorithm takes into account a torque operation area with the characteristics of IWM. In this manner, based on the vehicle bicycle model and using the CARSIM interface, the simulations, evaluation and verifications of the proposed control technique were done. Thereafter, considering a real-car based experiments with various driving scenarios, the effectiveness of the proposed TV algorithm was evaluated. It was confirmed that some evaluation factors in terms of the cornering performance were improved. The following main contributions makes the proposed TV algorithm be a meaningful solution to enhance the cornering performance of e-4WD vehicles: 1) improvement of both smoothness and convergence rate of control action, 2) a practical and intuitive way to distribute IWM torques, and 3) high applicability to mass-produced vehicles. [ABSTRACT FROM AUTHOR]

This paper investigates UAV-to-UAV (unmanned aerial vehicle) communications with a turning angle constraint, in which the information transmission time is minimized by joint UAV flight path planning and transmit power control. Considering the communication throughput requirement, peak transmit power, energy consumption, UAV flight speed, collision avoidance, and turning angle constraints, the joint UAV flight path planning and transmit power control problem is formulated as a non-convex optimization. Different from majority of prior studies that separately optimize the UAV flight path and transmit power, we develop a successive convex approximation based time minimization algorithm that simultaneously updates the UAV path and transmit power. We further prove that the proposed algorithm is guaranteed to converge. In addition, an initialization scheme is designed to start the proposed time minimization algorithm by solving an energy minimization problem. Simulation results verify that the proposed time minimization algorithm can shorten the completion time length and reduce the computational complexity compared with state of the art methods. [ABSTRACT FROM AUTHOR]

Security is a critical element to the existing Internet of Things (IoT) deployment, where any user may actively or passively attack the content sharing of others reusing the same channel. As most smart devices are carried by human, we may leverage their owners’ social trust to avoid being intercepted by untrusted users, which conforms to the Social Internet of Things (SIoT) paradigm. In this paper, we propose a secure content sharing (SCS) scheme to strike the trade-off between security and quality of experience (QoE) by exploring the social trust. Firstly, to dynamically extract the social trust, the random walk strategy is employed for prediction based on the proposed “User-Content-Social Group” graph which models users’ preference over time. Given the social trust value, we propose a hierarchical game model to decouple the optimization problem into two sub-problems: user pairing and channel selection. More specifically, the user pairing sub-problem is formulated as a matching sub-game with peer effect, and the embedded rotation-swap matching algorithm can accommodate the dynamics caused by mutual interference. The second sub-problem can be formulated as a secure channel selection sub-game with the directed hypergraph being game space, which is proved to be an exact potential game. Then, we design an uncoupled-user concurrent learning algorithm (UUCL) to search for the optimal pure Nash equilibrium, and thereby the global optimum of this sub-game is achieved. Finally, simulation results generated on realistic social dataset verify that our proposed scheme can notably enhance the security without sacrificing users’ QoE. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communication is a key enabler that connects vehicles to neighboring vehicles, infrastructure and pedestrians. In the past few years, multimedia services have seen an enormous growth and it is expected to increase as more devices will utilize infotainment services in the future i.e. vehicular devices. Therefore, it is important to focus on user centric measures i.e. quality-of-experience (QoE) such as video quality (resolution) and fluctuations therein. In this paper, a novel joint video quality selection and resource allocation technique is proposed for increasing the QoE of vehicular devices. The proposed approach exploits the queuing dynamics and channel states of vehicular devices, to maximize the QoE while ensuring seamless video playback at the end users with high probability. The network wide QoE maximization problem is decoupled into two subparts. First, a network slicing based clustering algorithm is applied to partition the vehicles into multiple logical networks. Secondly, vehicle scheduling and quality selection is formulated as a stochastic optimization problem which is solved using the Lyapunov drift plus penalty method. Numerical results show that the proposed algorithm ensures high video quality experience compared to the baseline. Simulation results also show that the proposed technique achieves low latency and high-reliability communication. [ABSTRACT FROM AUTHOR]

In this paper, we study the reconfigurable intelligent surface (RIS) based downlink multi-user system where a multi-antenna base station (BS) sends signals to various users assisted by the RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only the large-scale fading gain is required at the BS and a limited number of phase shifts can be realized by the finite-sized RIS. To maximize the sum rate, we propose a hybrid beamforming scheme where the continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively. An iterative algorithm is designed for beamforming and theoretical analysis is provided to evaluate how the size of RIS influences the achievable rate. Simulation results show that the RIS-based system can achieve a good sum-rate performance by setting a reasonable size of RIS and a small number of discrete phase shifts. [ABSTRACT FROM AUTHOR]

Off-road vehicles always experience serious uncertain longitudinal and lateral slips when running on soft and slope terrains, and some parameters of the vehicles, such as the cornering stiffness and the slip of wheels, are always not constants. In this paper, control strategies for the torque of each wheel and the rear-wheel-steering angle are proposed to maintain a stable velocity and approach an ideal reference model for the off-road vehicle by using second-order sliding mode (SOSM) techniques. An observer is constructed to estimate the actual sideslip angle of the vehicle with the consideration of uncertainties and disturbances of the system. Then, with conditions of bounded uncertainties and disturbances, composite super-twisting (ST) controllers combined with a velocity controller are designed to generate the total torque, the differential torque, and the rear-wheel-steering angle. On this basis, the proposed controllers have been verified to lead good robustness for maintaining the stable velocity and approaching the ideal reference model by using an optimal torque allocation controller at a lower layer. In comparison with conventional yaw moment controllers without the rear-wheel-steering control, the proposed controllers are shown to be more effective. [ABSTRACT FROM AUTHOR]

As the first provably capacity-achieving codes, polar codes have been selected as the coding scheme for 5G enhanced mobile broadband (eMBB) control channels. Furthermore, if the bottleneck in decoding complexity is addressed, polar codes are still promising for long code length scenarios in the future with superior error-correcting performance. Considering the demand of cloud radio access network (C-RAN) in 5G system, the low-latency software polar decoder is studied in this paper. Two novel algorithms are proposed to reduce the latency of the successive cancellation list (SCL) decoding, including the segmented list-pruning (SLP) algorithm and the modified distributed sorting (MDS) algorithm. Moreover, the proposed SLP algorithm improves the decoding performance compared to existing segmentation schemes. In terms of the software implementation, our decoder is nearly $20\%$ faster than the state-of-the-art flexible software polar list decoder. [ABSTRACT FROM AUTHOR]

For the prominent superiorities in energy and spectrum efficiency, Device-to-Device (D2D) communication has become a hot topic among the 5th generation (5G) technologies. However, the widely adopted mode that one or more D2D links reuse one cellular user‘s uplink spectrum may lead to severe limitation on D2D communication rate, especially when cellular user's uplink spectrum is very limited. In this paper, we will face the high rate requirements of D2D pairs (DPs), with the help of carrier aggregation technology, each DP can reuse the uplink spectrum of multiple cellular users when needed. More practically, we consider that only statistical channel state information (CSI) of certain communication links is available here. Then, we formulate the problem to minimize the total power consumption of the mobile devices to obtain an energy-efficient resource allocation result, including spectrum and power allocation. Meanwhile, the quality of service (QoS) requirements of cellular and high-rate D2D communications are both ensured. The formulated problem is mathematically a non-convex mixed-integer nonlinear programming (MINLP) problem, which is NP-hard. To solve it, we first tighten the constraints and deploy transformation methods on it to make it convex. Then, we propose a two-layer algorithm, the independent-power greedy-based outer approximation (IPGOA), to solve the transformed problem. Besides, to handle the involved uniform power allocation circumstance in the carrier aggregation process, a uniform-power GOA (UPGOA) algorithm, which can be regarded as a simplified version of IPGOA, is also proposed. The simulation results show that in different scenarios, our proposed scheme is approximately optimal. [ABSTRACT FROM AUTHOR]

As an essential part of vehicle networks, the Vehicle to Infrastructure (V2I) needs the support of millimeter wave and massive MIMO technologies to enable high data rate applications, such as automated driving, real-time high-quality multimedia services and so on. As the scale of the antenna array increases, the complexity of the beamforming and channel estimation algorithms under high mobility conditions also increases significantly. In particular, highly robust beamforming methods need to cope with fast changing transmission environments. In this paper, we adopt a biological inspired self-adaptive selection algorithm called attractor selection algorithm (ASA) to support uplink beamforming. The ASA requires only a little feedback information from the Road Side Infrastructure (RSI) to perform fast beam training, hence making the transmission link more stable. The simulation results indicate that the proposed ASA-assisted algorithm can significantly reduce the time required to achieve a timely beam training, which would be essential for V2I high communications under high mobility conditions. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITSs), the communication between the base station (BS) and the vehicles can form a virtual multiple-input multiple-out put (VMIMO) vehicle communication system. This paper addresses the issue of channel estimation in a virtual MIMO system that simultaneously adopt a smart antenna (SA). First, based on quaternion theory, a novel algorithm named quaternion noncircular MUSIC (QNC-MUSIC) is proposed, and direction of arrival (DOA) estimation is achieved for circular and noncircular signals. Then, a dimension reduction procedure is performed for the parameter search by employing the partial derivative of the spectrum function with respect to the noncircular phase; thus, a novel dimensional reduction noncircular MUSIC (DRNC-MUSIC) algorithm with a low computational complexity is proposed for DOA and polarization estimation of circular signals and noncircular signals. Finally, numerical examples are provided to demonstrate the parameter estimation performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The application of high directional beam in millimeter wave leads to a new downlink inter-cell interference (ICI) characteristic that the ICI is high only if the beam of a neighboring cell points towards a user in local cell. This characteristic indicates that the ICI can be avoided if the beams in the network are scheduled coordinately. In this paper, we investigate the time-domain beam scheduling problem for downlink ICI avoidance by modeling the entire network as a graph, where the number of time slots occupied by each beam is considered as a constraint to guarantee service quality. The beams in each cell are classified according to the neighboring cells they may interfere with. If two adjacent cells simultaneously use the beam which may interfere with each other, beam collision occurs, leading to strong ICI. Based on graph theory, we propose a least beam collision (LBC) algorithm to minimize the number of beam collisions, and we prove that this LBC algorithm is capable of acquiring the global minimum beam collision solution. Our simulation results verify that the strong ICI between two neighboring cells can be efficiently eliminated, which benefits the transmission reliability and the network's sum rate. [ABSTRACT FROM AUTHOR]

In this paper, we consider a wireless-powered network with co-channel interference where the transmitters control their transmit power and receivers harvest wireless energy using a time switching policy. Considering the interference channels among multiple nodes, we jointly optimize the transmit power and energy harvesting time to maximize the energy efficiency of the network. To solve this non-convex optimization problem, we first design an iterative algorithm based on a typical optimization technique, and then, propose a learning algorithm based on a neural network with a proper loss function. Simulation results show that the proposed learning algorithm can achieve a near-optimal energy efficiency with reducing the computational complexity, compared to an iterative algorithm with a suboptimal performance. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a promising paradigm to connect massive number of devices in future wireless communications while satisfying various quality of service (QoS) requirements. In this paper, we consider a QoS-constrained IoT system operating with finite blocklength (FBL) codes to support low latency communications. Two data arrival models are considered, namely, constant-rate arrival and ON-OFF discrete-time Markov arrival. The throughput performance is studied for both arrival models under statistical queuing constraints and deadline limits. For the scenario with instantaneous channel state information (CSI), we derive the QoS-constrained throughput expressions for both arrival models. Subsequently, an instantaneous-CSI-driven optimal power control algorithm is proposed to maximize the throughput, while guaranteeing a certain reliability target. In addition, we consider a scenario with only average CSI being available at the transmitter and propose to apply hybrid automatic repeat request (HARQ) schemes to improve the FBL performance. The decoding error probability and the outage probability are first characterized, following which the distribution of transmission period is derived. Furthermore, the throughput expressions are provided for both types of arrivals. Via numerical analysis, the impact of error probability, fixed transmission rate, coding blocklength, and QoS constraints on the throughput is studied. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) is considered as a novel paradigm to enhance the safety of automated vehicles and intelligent transportation systems (ITS). The computation offloading strategies are the key point of VEC, and the effect of time-varying channels cannot be ignored during the task transmission period. This paper investigates the utility maximization problem with task delay requirement constraints, in which the influence of time-varying channel on the task offloading strategies during the task offloading period is considered. The time-varying fading channel leads to the time-varying spectrum efficiency (SE), so the previous offloading strategies are questionable when the additional uncertain allocated bandwidth is taken into account. To deal with it, we first propose a linearization based Branch and Bound (LBB) algorithm to solve the fixed SE problem without considering the time-varying channel characteristics. Considering the complexity of the LBB algorithm, a closest rounding integer (CRI) algorithm is proposed to solve the fixed SE problem. Then, based on the resource allocation strategies of the fixed SE problem, we propose the LBB based computation offloading (LBBCO) algorithm and the CRI based computation offloading (CRICO) algorithm to solve the original problem for both the static tasks and dynamic tasks. The proposed LBBCO/CRICO algorithms are also applicable to multi-vehicle and multi-task scenarios. Furthermore, we analyze the effect of small-scale fading on the proposed offloading strategies. The simulation results show that the average utilities of LBBCO and CRICO algorithms have a small gap by 3.93% and 6.13% only to the upper bound, respectively. Meanwhile, the proposed LBBCO and CRICO algorithms can outperform the previous state-of-the-art solution by 4.52% and 2.38%, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the beamforming and resource allocation problem in a multiuser full duplex (FD) system with delay-awareness. We design a power-efficient algorithm to minimize the long-term sum transmit power under delay constraints. We do this by jointly optimizing the uplink transmit power and the downlink beamforming vectors while satisfying the long-term stability constraints on the queue buffers for the downlink and uplink users as well as quality of service constraints. Due to the stochastic nature of the problem, we exploit the classic drift-plus-penalty function and subsequently simplify the problem into a difference of convex functions. Building upon the transformed problem, we propose two algorithm designs, one, that exploits the users with good channel conditions for efficient resource allocation, and the other that ensures delay-fairness among all users based on the max-min formulation. Simulation results show significant gains achieved by the proposed FD schemes compared with the baseline HD schemes. [ABSTRACT FROM AUTHOR]

Mobile video traffic has experienced exponential growth in recent years due to increasing prevalence of mobile devices and continuous advancement of wireless communications. However, network fluctuations often occur when transmitting video data, which greatly impact the quality of experience (QoE) of mobile users. Buffering technique has been widely adopted to mitigate the effect of network fluctuations on QoE for mobile users by prefetching certain video data to mobile devices in advance. However, mobile users often abort video watching before the videos are fully watched and such early departure behavior can result in considerable wastage of buffered video data. In this paper, we study buffer-aware resource allocation for multiuser mobile video streaming system by jointly considering video users early departure behavior and wireless channel fluctuation property. We formulate a stochastic optimization problem in which the objective is to reduce both the amount of wasted buffered data and the video re-buffering time. The problem is then transformed into a series of deterministic per-slot problems by using Lyapunov optimization theory. We propose an online buffer-aware resource allocation algorithm to solve the per-slot optimization problems. After proving the convexity of the per-slot optimization problem, we further design a fast-convergent iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) to perform each of the per-slot resource allocations. Simulation results show that our proposed algorithm has very low running time, near optimal performance, and is able to achieve a good trade-off between QoE improvement and data wastage reduction. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a new computing paradigm with great potential to enhance the performance of user equipment (UE) by offloading resource-hungry computation tasks to lightweight and ubiquitously deployed MEC servers. In this paper, we investigate the problem of offloading decision and resource allocation among multiple users served by one base station to achieve the optimal system-wide user utility, which is defined as a trade-off between task latency and energy consumption. Mobility in the process of task offloading is considered in the optimization. We prove that the problem is NP-hard and propose a heuristic mobility-aware offloading algorithm (HMAOA) to obtain the approximate optimal offloading scheme. The original global optimization problem is converted into multiple local optimization problems. Each local optimization problem is then decomposed into two subproblems: a convex computation allocation subproblem and a non-linear integer programming (NLIP) offloading decision subproblem. The convex subproblem is solved with a numerical method to obtain the optimal computation allocation among multiple offloading users, and a partial order based heuristic approach is designed for the NLIP subproblem to determine the approximate optimal offloading decision. The proposed HMAOA is with polynomial complexity. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate its excellent performance. [ABSTRACT FROM AUTHOR]

Sparse Code Multiple Access (SCMA) has been proposed for the newest generation of communication systems, considering overloaded non-orthogonal multiple access. To deal with the overload, SCMA exploits the sparsity feature with the aid of receivers based on the messsage-passing algorithm (MPA). In this paper, we review a low-complexity MPA receiver based on edge selection and gaussian approximation (ESGA). We improve this receiver by proposing a threshold-based criterion for the edge selection (TB-ESGA). With the threshold, the number of interfering neighbors considered in the calculation of each message of the MPA becomes a random variable. We derive the probability of selecting a given number of edges in our strategy and also an expression for the average complexity order, as a function of the threshold. We also show that the proposed threshold results in a better trade-off between complexity and error-rate performance. [ABSTRACT FROM AUTHOR]

Deploying a Drone Base Station (DBS) over a hotspot area is a promising solution to improve the user Quality of Service (QoS) by helping the Macro Base Station (MBS) transmit traffic to the users. Essentially, the DBS, which works as a relay node between the users and the MBS, can increase the users’ data rates by virtue of more likely short-distance Line of Sight (LoS) communication links. Furthermore, deploying DBS is more cost-effective and flexible as compared to deploying small cells. The DBS can employ Free Space Optical (FSO) links for backhauling between the DBS and the MBS. In this paper, we study the 3D deployment and resource allocation of a DBS in a given hotspot area with the objective of maximizing the throughput in the access link under the constraint of user QoS, the capacity of the backhaul link, and total available bandwidth and power. To solve the problem, we first decompose the primal problem into two subproblems, i.e., the 3D DBS placement problem and the resource allocation problem. Second, we propose a cyclic iterative algorithm to solve the two sub-problems separately and use the output of one as the input of the other. The performance of the algorithm is demonstrated via extensive simulations. [ABSTRACT FROM AUTHOR]

Resonant Beam Charging (RBC) is a long-range, high-power, mobile, and safe Wireless Energy Transfer (WET) technology, which can provide wireless power for mobile devices like Wi-Fi communications. Due to the wireless energy transmission decay in RBC systems, the charging power received per device relies on the distance-dependent energy transmission channel. To extend battery life of all devices, this paper develops a Channel-Dependent Charge (CDC) scheduling algorithm to control receivers’ charging power, order and duration. Each receiver is assigned a dynamic scheduling coefficient, which is the product of the battery's remaining energy and the energy transmission channel. The resultant optimal charging order is to charge the receiver with the minimum scheduling coefficient first per equal unit-length time slot. It is shown analytically and experimentally that the CDC algorithm achieves higher charging performance than other scheduling algorithms including the Round-Robin Charge (RRC) scheduling algorithms. In a word, the CDC scheduling algorithm offers a viable approach to extending mobile devices’ battery life while accounting for varying RBC transmission channels. [ABSTRACT FROM AUTHOR]

Opportunistic beamforming (OBF) is an attractive research topic in wireless communications, which can provide multi-user diversity with a low complexity. The conventional OBF technique requires a large number of waiting users, and cannot simultaneously support multiple users. In order to solve the mentioned problem, this paper proposes a downlink multi-user multi-weight opportunistic beamforming non-orthogonal multiple access (MW-OBF-NOMA) system. An objective function is formulated to maximize the sum spectrum efficiency (SE) of the proposed system, however, it is a non-convex issue and intractable to derive an optimal solution. To solve this non-convex issue, we divide it into two sub-optimal issues and solve them one-by-one. Based on the solutions of the two sub-optimal issues, a joint iterative resource allocation algorithm is presented. Moreover, we discuss the influences of signal-to-noise ratio (SNR) estimation, and deduce a closed-form expression of bit error ratio (BER). Numerical results show that our proposed system can simultaneously serve multiple users without extra resources, and achieve an improved sum SE with a low complexity. [ABSTRACT FROM AUTHOR]

Low latency/delay is one of the most critical requirements for the application of vehicular networks. However, frequent real-time information update caused by vehicles high mobility is liable to aggravate the delay. Meanwhile, the task migration between different vehicular edge computing (VEC) servers results in an amount of delay if the computing cannot be completed before the vehicle moves out of the coverage of the current VEC server. In this paper, the problem is concluded as when and to whom to offload the task for VEC, which is formulated as a finite horizon Markov decision process (MDP) to minimize the delay with respect to the communication, computing, handover and migration. Through characterizing the time-space correlation of vehicles mobility, the curse of dimensionality problem in MDP is resolved. Meanwhile, a general expression of the transition probabilities is derived. On this basis, the specific results of highway, 2-D street and real-data scenarios are provided as well. For practical implementation considerations, the transition probabilities are commonly uncertain primarily due to random driver behavior, inaccurate sample data and complex path environment. Under this uncertain environment,a robust time-aware MDP-based task offloading algorithm (RTMDP) is proposed, which has been proved to perform well even under the high uncertain transition probabilities by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a recursive sparse channel estimation algorithm in the presence of impulse noise. Firstly the channel impulse response and impulsive noise are jointly viewed as an unknown sparse vector. Then a novel recursive Kalman filtering based compressed sensing algorithm for joint channel and impulsive noise estimation is proposed by using the first order autoregressive model for tracking slowly time varying wireless channel. This algorithm can be extended also to quasi-static, block-fading scenario conveniently. Simulation results illustrate the efficiency of the proposed techniques in terms of the mean square error and bit error rate performance. [ABSTRACT FROM AUTHOR]

Resource Allocation (RA) is one of the important factors in network function virtualization (NFV) deployment. As physical (PHY) layer resources are limited, e.g., transmitted energy and channel uses, the RA problem at the PHY layer for NFV deployment has become a fast-growing problem, especially for supporting ultra-reliable and low-latency communications (URLLC). Moreover, different nodes in NFV have different requirements for end-to-end communication, e.g., a controller has more stringent reliability requirements than does a logical node. There is a need for efficient and robust RA algorithms at the PHY layer for NFV deployment. To illustrate these challenges, we consider an up-link (UL) transmission protocol for NFV deployment, in which wireless transmissions with short packets are considered, and both the packet length and the transmission power are adjustable. Then, for three NFV deployment scenarios, we formulate three RA problems as three optimization problems to obtain the optimal parameters. Since these optimization problems are highly non-convex and they include excessive constraint conditions, the global optimal solutions are hard to obtain and are even infeasible for the conventional heuristic algorithms due to their low convergence efficiency. To address these problems, in this paper, the intelligent scheme of the modified shuffled frog-leaping algorithm (MSFLA) based on improved extremal optimization (EO) is applied to design RA algorithms. Three RA algorithms are designed for three NFV deployment scenarios to evaluate the quality of the solutions produced by the MSFLA-EO scheme. We perform simulations of three proposed RA algorithms in terms of various performance parameters. The experimental results are encouraging and demonstrate the efficiency of the proposed RA algorithms. [ABSTRACT FROM AUTHOR]

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique. [ABSTRACT FROM AUTHOR]

This paper investigates joint secure beamforming design and receive antenna selection problem for large-scale multiple-input-multiple-output (MIMO) wiretap channels. A Branch-And-Bound (BAB) based algorithm is utilized to search the optimal receive antenna subset under both perfect and imperfect channel state information (CSI) scenarios. First, for the perfect CSI scenario, we formulate the secrecy rate optimization problem, and an equivalent monotonically decreasing objective function is established to make the problem suitable for efficient BAB search. In each search, we propose using the weighted mean squared error minimization (WMMSE) method to tackle the optimization problem. Then, we generalize the framework to the imperfect CSI case, and the robust problem is designed to maximize the worst-case secrecy rate, the problem is challenging to solve due to the channel uncertainties. A cutting-set method is devised to solve this non-convex problem by updating the worst-case secrecy rate and channel uncertainties alternately. To reduce the design complexity, we also present an antenna selection based on the alternating optimization (AO) method. The validity of the proposed antenna selection algorithms is evaluated through numerical simulations. [ABSTRACT FROM AUTHOR]

Magnetic Induction (MI) communication is effective in underground tunnels for emergency rescue vehicle due to the small-size antenna. It can highly benefit from a cooperative decode-and-forward (DF) relay to achieve a higher data rate. However, its channel gain is extremely position-and-orientation-selective. The unreachable space increases the complexity of the antenna deployment. To find the best antenna position and orientation (PO) of the relay achieving the higher data rate, this paper formulates the optimization problem of the relay MI antenna PO with tunnel constraints. To solve the problem more quickly, we propose to use geometric modeling to eliminate the tunnel constraints and develop a random-search algorithm achieving a fast convergence and excellent global search ability. Simulations show that the proposed algorithm can quickly converge to one optimum which signifies a noticeable improvement of data rate for vehicle MI systems with weak signals. [ABSTRACT FROM AUTHOR]

In order to maximize the utilization rate of the Mobile Wireless Chargers (MWCs) and reduce the recharging delay in large-scale Rechargeable Wireless Sensor Networks (WRSNs), a type of Cost-Balanced Mobile Energy Replenishment Strategy (CBMERS) is proposed in this paper. Firstly, nodes are assigned into groups according to their remaining lifetime, which ensures that only the ones with lower residual energy are recharged in each time slot. Then, to balance energy consumption among multiple MWCs, the moving distance as well as the power cost of the MWC are taken as constraints to get the optimal trajectory allocation scheme. Moreover, by further adjusting the amount of energy being replenished to some sensor nodes, it ensures that the MWC have enough energy to fulfill the recharging task and return back to the base station. Experiment results show that, compared with the Periodic recharging strategy and the Cluster based Multiple Charges Coordination algorithm (C-MCC), the proposed method can improve the recharging efficiency of MWCs by about 48.22% and 43.35%, and the average waiting time of nodes is also reduced by about 55.72% and 30.7%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for vision-based Simultaneous Localization and Mapping (SLAM) on a high curved track without pose-based landmarks. The proposed approach combines an Iterative Closest Points (ICP) method and Stochastic Gradient Descent (SGD) optimization and comprises four main steps. First, a Kalman filter with a simple circular lane model is used to estimate the road curvature using images from a front camera. Then, the vehicle position and orientation are reconstructed by using the yaw rate and longitudinal speed from inertial sensors. Drift and misalignment of the constructed map are corrected using ICP under the assumption that the vehicle continuously travels the same track. The final map is obtained using SGD optimization, which enforces curvature matching. We evaluate the performance of the proposed algorithm with an environment of the winding track of Hyundai-Kia California Proving Ground (CPG) located in Southern California and the customized ellipsoidal track. The experimental results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In drone assisted mobile networks, a drone mounted base station (DBS) is deployed over a hotspot area to help user equipments (UEs) download their traffic from the macro base station (MBS), thus improving the throughput and spectrum efficiency (SE) of the UEs. Finding the optimal 3D position of the DBS to maximize the overall SE of the UEs in the hotspot area is challenging because the 3D DBS placement and user association problems are coupled together. In this paper, we formulate the problem of jointly optimizing the 3D DBS placement and user association to maximize the overall SE in the context of drone assisted mobile networks. The spectrum efficiency aware DBS placement and user association (STAR) algorithm is designed to decompose the original problem into two subproblems, i.e., user association and DBS placement, and to iteratively solve the two subproblems until the overall SE of the hotspot area cannot be improved further. The performance of STAR is demonstrated via extensive simulations. [ABSTRACT FROM AUTHOR]

The emergence of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) is of great significance for the prospective development of Internet of Things (IoT). The additional computation capability and extensive network coverage provide energy-limited smart mobile devices (SMDs) with more opportunities to experience diverse intelligent applications. In this paper, a computation efficiency maximization problem is formulated in a multi-UAV assisted MEC system, where both computation bits and energy consumption are considered. Based on the partial computation offloading mode, user association, allocation of central processing unit (CPU) cycle frequency, power and spectrum resources, as well as trajectory scheduling of UAVs are jointly optimized. Due to the non-convexity of the problem and the coupling among variables, we propose an iterative optimization algorithm with double-loop structure to find the optimal solution. Simulation results demonstrate that the proposed algorithm can obtain higher computation efficiency than baseline schemes while guaranteeing the quality of computation service. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) has emerged as a new technology to reduce the cost of hardware deployment. It is an architecture that using virtualized functions run on the virtual machine to achieve services instead of using specific hardware. Although NFV brings more opportunities to enhance the flexibility and efficiency of the network, resource allocation problems should be well taken into consideration. In this paper, we investigate the virtual network function (VNF) resource allocation problem to minimize the network operation cost for different services. Both setting the VNF instances for each virtual machine and allocating the traffic volume in the network are considered. The problem is formulated as a mixed integer programming problem. Although it can be solved in a centralized fashion which requires a central controller to collect information from all virtual machines, it is not practical for large-scale networks. Thus, we propose a distributed iteration algorithm to achieve the optimal solution. The proposed algorithm framework is developed based on the joint Benders decomposition and alternating direction method of multipliers (ADMM), which allows us to deal with integer variables and decompose the original problem into multiple subproblems for each virtual machine. Furthermore, we describe the detail implementation of our algorithm to run on a computer cluster using the Hadoop MapReduce software framework. Finally, the simulation results indicate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Forthcoming 5G New Radio (NR) systems will be asked to handle a huge number of devices accessing or delivering “resource-hungry” and high-quality services. In view of this, the new 5G Radio Access Technology (RAT) aims to support, in next releases, Multimedia Broadcast/Multicast Service Single Frequency Network (MBSFN) to enable the simultaneous delivery of the same content to a set of users covered by different cells. According to MBSFN, all cells belonging to the same MBSFN Area are synchronized in time and the MBSFN transmission occurs over the same radio resources. In such a way, the same content flow is delivered by several cells to all the receivers in the MBSFN Area. A further means to enhance the network coverage and provide high data rate and low latency in future 5G-enabled MBSFN networks is Device-to-Device (D2D) connectivity. Along these lines, in this paper we propose a D2D-aided MBSFN Area Formation (D2D-MAF) algorithm to dynamically create MBSFN Areas with the aim to improve the system aggregate data rate while satisfying all user requests. The proposed D2D-MAF foresees that users could receive the service through either MBSFN, or D2D, or unicast transmissions. Performance evaluation results, carried out under a wide range of conditions, testify to the high effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the problem of user association, deadline scheduling and channel allocation in dense cellular networks with energy harvesting base stations. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of single channel and single base station is solved by proposing polynomial-time optimal algorithms. The case of single channel and multiple base stations is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is solved by proposing efficient heuristic algorithms. Our theoretical analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new beam allocation strategy aiming to maximize the average successful tracking probability (ASTP) of a single user time-varying millimeter-wave MIMO system. In contrast to most existing works that employ one transmitting-receiving (Tx-Rx) beam pair once only, we investigate a more general framework, where the Tx-Rx beam pairs are allowed to be used repeatedly in each beam training period to improve the received signal powers in specific directions. In the case of orthogonal Tx-Rx beam pairs, a power-based estimator is employed to track the time-varying AoA and AoD of the channel, and the resulting training beam pair sequence design problem is formulated as an integer nonlinear programming (I-NLP) problem. By dividing the feasible region into a set of subregions, the formulated I-NLP is decomposed into a series of concave sub I-NLPs, which can be solved by recursively invoking a nonlinear branch-and-bound algorithm. To reduce the computational cost, we relax the integer constraints of each sub I-NLP and obtain a low-complexity solution via solving the Karush-Kuhn-Tucker conditions of their relaxed problems. For the case when the Tx-Rx beam pairs are overlapped in the angular space, we estimate the updated AoA and AoD via an orthogonal matching pursuit (OMP) algorithm. Moreover, since no explicit expression for the ASTP exists for the OMP-based estimator, we derive a closed-form lower bound of the ASTP, based on which a favorable beam pair allocation strategy can be obtained. Numerical results demonstrate the superiority of the proposed beam allocation strategy over existing benchmarks. [ABSTRACT FROM AUTHOR]

This paper describes an optimal lane-changing strategy for intelligent vehicles under the constraints of collision avoidance in complex driving environments. The key technique is optimization in a collision-free lane-changing trajectory cluster. To achieve this cluster, a tuning factor is first derived by optimizing a cubic polynomial. Then, a feasible trajectory cluster is generated by adjusting the tuning factor in a stable handling envelope defined from vehicle dynamics limits. Furthermore, considering the motions of surrounding vehicles, a collision avoidance algorithm is employed in the feasible cluster to select the collision-free trajectory cluster. To extract the optimal trajectory from this cluster, the TOPSIS algorithm is utilized to solve a multiobjective optimization problem that is subject to lane change performance indices, i.e., trajectory following, comfort, lateral slip and lane-changing efficiency. In this way, the collision risk is eliminated, and the lane change performance is improved. Simulation results show that the strategy is able to plan suitable lane-changing trajectories while avoiding collisions in complex environments. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a UAV-assisted cooperative communication system based on simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a relay and its transmission capability is partly powered by radio signal from the source via the time-sharing mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV's decision profile, power profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into three optimization subproblems for decision profile, power profile and trajectory, and solved through alternating optimization, by which each of the subproblems is solved with the other two fixed. A binarization algorithm is further proposed to make the decision profile feasible. We show that the proposed solution outperforms not only two SWIPT-based strategies, but also a similar solution from an existing work without consideration for SWIPT. In addition, results indicate that the proposed algorithm performs efficiently in both optimality and convergence. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) and nonorthogonal multiple access (NOMA) are two enabling technologies in the 5 G network and beyond. MEC admits user equipments (UEs) running many more compute-intensive applications by providing computing capabilities at the network edge and within radio access networks, while NOMA enables multiple UEs to share the same resource block, thus leveraging considerable advantages such as greater spectral efficiency and a larger number of supported UEs. The state-of-the-art showed that the combination of NOMA and MEC can lower the energy consumption and/or overall latency; however, they mostly focused on single-carrier NOMA. In this paper, we investigate the computation offloading problem in multi-carrier NOMA enabled MEC systems and solve it from the cooperative game theory viewpoint using coalition formation game. Particularly, UEs are considered as game players and subcarriers are regarded as coalitions that can be used for computation offloading of multiple UEs. Based on the introduced coalition formation game, we develop a low-complexity algorithm with convergence guarantee to achieve the Nash-stable solution. Numerical results are provided to validate the effectiveness of the proposed coalition game based algorithm as well as its comparison with three baseline schemes. [ABSTRACT FROM AUTHOR]

With the advancement of the next generation of cellular systems, flexible mechanisms for Discontinuous Reception (DRX) are needed in order to save energy. 5G will bring heterogeneous packet sizes and traffic types, as well as an increasing need for energy efficiency. The current static DRX mechanism is inadequate to meet these needs. In this paper we exploit channel prediction to develop integer programming models. We aim to minimize the energy usage of user devices while streaming video, as well as to create extended sleep opportunities, while simultaneously preventing buffer underflows. We also develop an online algorithm to obtain an efficient solution robust to prediction errors. Our results show that using a variable DRX cycle length can reduce the energy usage by up to 60 percent and 40 percent, in the offline and online cases, respectively, compared with a static DRX configuration. Our proposed online algorithm can also reduce the number of buffer underflows by up to 97 percent compared to the offline case. Both our online and offline solutions can provide extended DRX opportunities, which is required in 5G scenarios. [ABSTRACT FROM AUTHOR]

A team of autonomous decision-making robots can be employed for some critical tasks, such as disaster detection, plant protection, and military reconnaissance. The use of such team of robots to perform well is a challenging problem. To address the challenge, a decentralized multi-robot patrolling problem in uncertain environments without prior knowledge is investigated in this paper. First, the patrolling environment is modeled as an undirected graph, and information is attached to the vertex, that follows an unknown discrete Markov chain. Each robot patrols independently within a particular area, where these areas may intersect each other. Robots coordinate their actions periodically to gather as much valuable information as possible. Then, the patrolling problem is cast as a Bayes-adaptive transition-decoupled partially observable Markov decision process formulation. However, it is intractable to solve the multi-robot patrolling problem with complex interactions. To solve the problem, a scalable decentralized online learning and planning algorithm is proposed by extending the Monte Carlo tree search method, which computes patrols that are bounded optimal. In the proposed algorithm, each local look-ahead tree has a low branching factor and the coordinated manner is not complicated. In addition, we benchmark the proposed algorithm against some state-of-the-art online solvers to empirically assess the performance. The results show that the proposed algorithm is able to achieve high performance in typical decentralized patrolling scenarios. [ABSTRACT FROM AUTHOR]