Search

The age of information (AoI) is a key performance metric for data freshness in real-time systems, measuring the time elapsed between status updates received at a remote destination. This paper addresses the analysis of both the AoI and the delay for a system demanding timely status updates, consisting of an internet of things (IoT) community, and a multi-core edge computing node (EN). The IoT devices perform channel contention according to a slotted aloha non-orthogonal multiple access scheme with two transmission power levels to access the EN. The objective of the paper is the dynamic selection of both the EN computation capabilities and the suitable access probability for the two power levels, aiming at maximizing the time-average system utility, while guaranteeing the queues stability. The device end-to-end delay is analyzed by resorting to the martingale theoretical approximation under the condition that the Lyapunov optimization framework is performed on a time slot basis, hence leading to a novel instantaneous mixed integer problem formulation. Furthermore, the generalized benders decomposition method is employed to provide a suboptimal solution for the formulated problem. Finally, the accuracy of the proposed framework is validated by comparing the obtained analytical predictions with simulation results. [ABSTRACT FROM AUTHOR]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

Drones and drone swarms, characterized by their low price and ease of deployment, are being utilized to launch assaults, and presenting a great threat to the public and homeland security in recent years. Countering drones or drone swarms is thus of great significance. Some effective counter approaches for a small group of drones have been studied. However, these approaches may be insufficient to counter a large-scale drone swarm due to the lack of efficient time-saving detecting technologies. Towards this end, this paper proposes a fast counter approach to deprive the drone swarm of its coordination and clustering capabilities in a short time by splitting the drone swarm into several unconnected components. To achieve efficient splitting, two efficient algorithms for searching critical nodes are proposed, namely, the genetic algorithm and the particle swarm optimization algorithm. Extensive simulation results are presented to validate the superior performances of the proposed two algorithms for splitting the drone swarms in different formations. The results show that the drone swarms lose their coordination ability as they are forced to split into multiple components with a constraint of group size. The high accuracy and efficiency of the proposed algorithms are also verified by a series of comparative experiments. [ABSTRACT FROM AUTHOR]

The air-ground cooperative emergency networks can assist with the rapid reconstruction of communication in the disaster area, where unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are deployed as base stations. The trajectory optimization of emergency base stations is of vital importance to the communication performance, which is related to the timeliness and effectiveness of rescue. In this paper, federated multi-agent deep deterministic policy gradient (F-MADDPG) based trajectory optimization algorithm is proposed to maximize the average spectrum efficiency. Specifically, the property of MADDPG is inherited to jointly control of multiple vehicles and federated averaging (FA) is utilized to eliminate the isolation of data to accelerate the convergence. Distributed F-MADDPG (DF-MADDPG) is further designed to reduce the communication overhead with a distributed architecture. The simulation results indicate that the proposed F-MADDPG and DF-MADDPG based algorithms significantly outperform the existing trajectory optimization algorithms, in terms of the average spectrum efficiency and the speed of convergence. [ABSTRACT FROM AUTHOR]

This paper proposes a new reentry trajectory generation method, which mainly comprises two parts: reentry profile design and trajectory optimization. Different from conventional methods that design angle-of-attack (AOA) and bank-angle profiles, a height-range profile is proposed in this paper. Using this profile, the AOA can be directly derived and the quasi-equilibrium glide condition is easily satisfied. In addition, the bank angle is analytically derived with the heading error, with only one reversal. Finally, the original problem is transformed into a parameter optimization problem and solved by an improved particle swarm optimization (PSO) method. In PSO, a comparison principle is proposed to tackle constraints and three modifications are applied to the swarm evolution to enhance the searching capacity. To conquer disturbances, an online profile updating strategy is developed based on the nominal profile, so complicated work of designing a trajectory tracker is avoided. Simulation validates the effectiveness of the proposed method and demonstrates that the improved PSO is comparable to the pseudospectral method and is better than the basic PSO. [ABSTRACT FROM AUTHOR]

We all have encountered unsatisfactory video viewing experience with mobile devices in hot spot areas where a crowd of people are demanding video streaming simultaneously. One possible solution to relieve the problem is to use the unmanned aerial vehicle (UAV) as a relay to improve the quality-of-experience (QoE). In this paper, we focus on the allocation of bandwidth and transmission power for multiuser video streaming in UAV relay networks. Our goal is to maximize the total long-term QoE of users, which are determined by the freezing time and the bit rate of video. We employ the Lyapunov optimization theory to transform the original problem with long-term average form into the drift-plus-penalty problem, and propose a decentralized allocation scheme using alternative ascending clock auction to derive the optimal solution. Simulations are conducted to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of designing an efficient spectrum-allocation mechanism in heterogeneous multicell coordinated cognitive radio networks (CRNs) under time-varying channel availabilities and traffic demands across the different cells. The problem is modeled as a utility proportional fair maximization problem subject to traffic demand, spectrum heterogeneity, and exclusive spectrum sharing constraints. Specifically, the optimization problem is formulated as a binary linear programming (BLP) problem. Due to its integrality properties, the BLP is anticipated to be NP-hard. However, this paper proves that the optimal solution of the formulated BLP can be found in polynomial time using linear programming (LP) because of its total unimodular constraint matrix. The proposed solution is a spectrum-aware allocation design that ensures a fair spectrum sharing among cells based on their current traffic loads while considering the unique characteristics of their radio-frequency (RF) environment. Simulation results reveal that the proposed spectrum-aware proportional weighted fair allocation leads to improved system performance while effectively dealing with spectrum heterogeneity and time-varying traffic demands. The results also indicate that the proposed spectrum-allocation design is quite robust to traffic estimation and spectrum sensing errors. [ABSTRACT FROM AUTHOR]

In this paper, we study an uplink network configuration in a two-tier macro–femto heterogeneous system with hybrid access control. We consider a system where one macro base station (MBS) and a cluster of adjacent femto base stations (FBSs) together serve a number of mobile users. In this system, base stations and users make decisions in various network configuration processes with different optimization objectives. Such decision-making processesz are usually correlated, and an efficient mechanism is needed to coordinate the decision makers. In this paper, we propose a five-stage network configuration mechanism where access control, resource allocation, and power management are sequentially performed at the base stations and users, respectively. We show that this mechanism provides incentive for the FBSs to operate at the hybrid access mode. We model the configuration mechanism as a multistage decision-making process and formulate a multilevel optimization problem. We analyze the problem in a bottom-up manner and propose efficient algorithms to solve the optimization problem in each level sequentially. Simulation results show that the proposed network configuration mechanism achieves a higher system utility than configuration mechanisms with topology-based hybrid access or closed access. [ABSTRACT FROM AUTHOR]

In this paper, we consider an underwater linear network, where an autonomous underwater vehicle (AUV) gathers data from a set of underwater devices. The AUV monitors a set of physical processes, where the status of each process can be sensed by one or more devices and each device is capable of sensing one or more processes. The AUV needs to maintain freshness of its information status about the monitored processes. To quantify the freshness of the information at the AUV, we consider the concept of the age of information (AoI), which represents the amount of time elapsed since the most recently delivered update information was generated. A framework is proposed to optimize the AUV's linear movement trajectory and scheduling of process status updates with the objective of minimizing the normalized weighted sum of the average AoI of the monitored physical processes. The formulated optimization problem is a non-convex mixed integer problem, which cannot be solved by the standard optimization techniques. We develop a solution approach based on the technique of deep reinforcement learning (DRL). Specifically, we leverage an actor-critic DRL approach to find the optimum locations and stopping time of the data gathering points. Simulation results illustrate that the proposed framework maintains robustness under different scenarios and provides better performance when compared with baseline and $K$ -means clustering approaches. [ABSTRACT FROM AUTHOR]

This paper considers cognitive multiuser two-way relay networks (MU-TRNs) where multiple secondary users (SUs) concurrently exchange their data with other SUs with an aid of a two-way relay in licensed primary networks. In this paper, we particularly focus on linear relay filter designs that effectively minimize the mean square error of SUs while providing a certain level of quality of service for the primary network. To this end, we first propose an iterative algorithm that identifies a solution based on an alternating optimization technique. Then, a closed-form relay filter design is presented, which employs a high signal-to-noise ratio approximation to reduce the computational complexity. Through the simulation results, we confirm that the proposed closed-form relay filter design outperforms conventional approaches and provides the performance close to the proposed iterative algorithm with much reduced complexity. [ABSTRACT FROM PUBLISHER]

With the proliferation of sensors, such as accelerometers, in mobile devices, activity and motion tracking has become a viable technology to understand and create an engaging user experience. This paper proposes a fast adaptation and learning scheme of activity tracking policies when user statistics are unknown a priori, varying with time, and inconsistent for different users. In our stochastic optimization, user activities are required to be synchronized with a backend under a cellular data limit to avoid overcharges from cellular operators. The mobile device is charged intermittently using wireless or wired charging for receiving the required energy for transmission and sensing operations. First, we propose an activity tracking policy by formulating a stochastic optimization as a constrained Markov decision process (CMDP). Second, we prove that the optimal policy of the CMDP has a threshold structure using a Lagrangian relaxation approach and the submodularity concept. We accordingly present a fast Q-learning algorithm by considering the policy structure to improve the convergence speed over that of conventional Q-learning. Finally, simulation examples are presented to support the theoretical findings of this paper. [ABSTRACT FROM PUBLISHER]

In cooperative cognitive radio networks (CRNs), multiple secondary base stations are managed by a central unit (CU) via finite-capacity backhaul links, which performs joint encoding and precoding of the messages intended for the secondary users (SUs). In this paper, the limitation of backhaul links is handled with the implementation of compression strategies, by which precoding is followed at the CU. In particular, focusing on underlay cooperative CRNs, we propose the joint optimal design of precoding and backhaul quantization with the aim of maximizing the sum throughput of the secondary network under limited backhaul capacity, transmit power, and interference power constraints. Furthermore, both perfect and imperfect channel state information (CSI) available at the CU are considered. In the imperfect-CSI case, the uncertainty of the channels is explicitly accounted for via a robust or worst-case optimization formulation. The proposed algorithmic solutions leverage rank relaxation and majorization-minimization programming, whose performances are evaluated via numerical results. [ABSTRACT FROM PUBLISHER]

This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and mobile devices are enabled with device-to-device (D2D) communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station (BS) or by connecting through another UE that acts as an access point (AP) and relays the traffic from a cellular BS. This paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision-making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multicell scenarios, in which the optimum solution cannot be easily known a priori, the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power while exhibiting much lower computational requirements. [ABSTRACT FROM PUBLISHER]

The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme. [ABSTRACT FROM AUTHOR]

In this paper, a general two-way relay (TWR) transmission method is proposed under per-antenna power constraints by combining space-time line codes (STLCs) with transmit power allocation for two source nodes. We introduce a general STLC-based encoding scheme for a decode-and-forward TWR and derive the detection signal-to-interference-plus-noise ratio (SINR) values at two source nodes. An optimal encoder structure with power allocation is proposed in terms of maximizing the minimum SINRs, and it is verified that the optimal encoder is identical to the superposition of two conventional STLCs. An iterative method is proposed to find the optimal power allocation for detection at two source nodes. Moreover, a low-complexity suboptimal encoder is proposed for practical implementation. Numerical simulations present that the proposed STLC-based transmission method outperforms a conventional eigen-beamforming scheme with nulling and an STLC-based scheme with equal power allocation, in terms of the average bit error rate of source nodes, regardless of the distribution of source nodes, the number of TWR antennas, and the transmit power. [ABSTRACT FROM AUTHOR]

Terahertz (THz) frequency band has become one of the popular researches for the short-range broadband, high-rate data communication, reliability, and strong anti-interference ability, which can realize better performance for dense vehicular network scenario. In this paper, we introduce a cache based dense vehicular networks with THz frequency link and focus on the resource allocation optimization problem of subband and power allocation by maximizing energy efficiency (EE). In order to reduce the computational complexity, the original optimization problem is formulated as a mean-field game (MFG) problem, where each cache access point (cAP) can solve its local EE optimization problem by Dinkelbach-style method and Lagrangian dual method. Different access cells interact with MFG through mean subband power. Then a global MFG based EE optimization algorithm is proposed to associate the local strategy of each cell, which guarantee the globality of the optimized solutions. The numerical results suggest the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search. [ABSTRACT FROM AUTHOR]

Resource allocation is an important strategy to optimize system performance in multiuser cooperative orthogonal frequency-division multiple-access (OFDMA) systems. In this paper, we jointly consider three different transmission modes in cooperative OFDMA systems, i.e., direct transmission (DT) mode, half-duplex (HD) relay cooperative transmission (HDRCT) mode, and full-duplex (FD) relay transmission (FDRT) mode. The joint optimization problem of transmission mode selection, subcarrier assignment, relay selection, subcarrier pairing, and power allocation (PA) is investigated. In this paper, we transform the binary assignment problem into a maximum-weighted bipartite matching problem, which can be solved by the classical Hungarian method. Based on the dual method, we solve the joint PA and binary assignment problem iteratively. Specifically, since the direct link is considered interference in the FD relay transmission mode, the PA problem in FD relay transmission mode is nontrivial. Thus, we provide a novel hierarchical dual method to solve the PA problem in FD relay transmission mode. In addition, in HDRCT mode, the joint transmission of both the source and relay is taken into account, and we provide a simple and insightful PA scheme. Simulation results show that the proposed algorithms can significantly enhance the overall system throughput, compared with previous works. [ABSTRACT FROM AUTHOR]

Generally, resource allocation for multicarrier cooperation communication networks includes subcarrier and power allocation; however, it is difficult to solve because of the 0-1 integer programming of subcarrier allocation, which makes the problem nonconvex. This paper focuses on solving the nonconvex problems and provides a general solution to the resource allocation for relay-enhanced multicarrier systems. An established scenario, namely, a decode-and-forward (DF) relay-assisted orthogonal frequency-division multiplexing (OFDM) system, is considered, and we formulate the resource allocation as a joint subcarrier pairing, assignment, and power allocation problem, in which heterogeneous users' data rate requirements are also considered. To make the expression of the proposed optimization approaches for the original maximization problem clear, a four-step methodology is given. First, we transform the original nonconvex problem into a standard convex problem by imposing a new constraint of subcarrier allocation index, regardless of the convexity of the objective function. Furthermore, we prove that the optimum resource allocation algorithm (ORAA) for the dual problem of the transformed optimization problem is equivalent to the optimization of the original function. Subsequently, the optimum solution could be obtained by the golden section search method and the iterative resource allocation algorithm. Finally, a suboptimal resource allocation algorithm (SRAA) that solves the primal problem in an asymptotic manner is proposed. Simulation results illustrate that our proposed SRAA achieves comparable performance to the ORAA with satisfied complexity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cycle-based sizing optimization for diesel-engine two-cell selective catalytic reduction (SCR) systems. In modeling the SCR system, it is generally assumed that the states inside the catalyst can are homogeneous to simplify the modeling work. However, the actual states are not homogeneous, and the modeling error becomes large if the catalyst length is long. To reduce the modeling error, the states are assumed piecewise homogeneous, i.e., the catalyst is sliced into multicells and the states for each cell are assumed homogeneous. In this paper, we focus on the two-cell SCR systems where the two cells are connected in series and where the chemical reaction rates are assumed identical for the two cells. Considering the main chemical reactions and assuming that each cell is a continuous stirred tank reactor, a nonlinear system model with six states is obtained. To simultaneously reduce the tailpipe \mboxNOx and ammonia slip, it is quite interesting to study the optimal control of the urea dosing such that the \mboxNOx emissions can be maximally converted and where the ammonia slip is constrained. A dynamic programming (DP) algorithm that has a tolerable computational load is proposed. Since the whole catalyst is sliced into two cells, the next motivation is how to divide the catalyst such that the \mboxNOx conversion efficiency can be maximized while meeting the tailpipe ammonia constraint. The best sizing ratio of the SCR system in our application for the US06 test cycle is found to be 60%–40%. [ABSTRACT FROM PUBLISHER]

In this paper, a three-node wireless powered communication system is studied, where a power receiver harvests energy from a wireless power transmitter via wireless power transfer in the downlink and then executes information transfer in the uplink. According to this simple harvest-then-transmit protocol, the power receiver cannot harvest energy and send information simultaneously. Thus, it is necessary to investigate the tradeoff between wireless power transfer and information transfer to obtain good performance. The goal of this paper is to achieve the maximum throughput by balancing the time duration between the wireless power transfer phase and the information transfer phase while satisfying the energy causality constraint, the time duration constraint, and the quality-of-service constraint (i.e., the symbol error rate is lower than a target value). By solving the optimization problem, an optimal time allocation can be obtained. Simulation results demonstrate the effectiveness of the solution. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic resource allocation (RA) problem in cooperative OFDMA systems to maximize the average utility of all mobile stations (MSs) under different services. We propose a dynamic optimization framework for RA by considering three dynamic situations: time-varying fading channel, MSs' change of states, and relay stations (RSs)' change of states. Moreover, a dynamic RA algorithm based on discrete particle swarm optimization (DPSO) is proposed. The correlation between the adjacent frames is exploited to improve the performance of the dynamic RA algorithm. Simulation results show that the proposed dynamic algorithm achieves better performance at linear complexity, compared with the existing algorithms under different dynamic environments, while guaranteeing fairness to a proper level. [ABSTRACT FROM AUTHOR]

To support ultra-reliable and low-latency communication (URLLC) in vehicular networks, the virtual cells, where multiple access points (APs) cooperatively serve one mobile node, have been proposed to reduce the end-to-end latency in the downlink. The latency can be further reduced by eliminating the need for retransmission and feedback control, i.e., open-loop communications. However, it is difficult to achieve a high reliability of the uplink via virtual cells, because of multiple access interference and collisions from other virtual cells nearby. In this paper, we formulate the proactive radio resource allocation in the open-loop uplink of vehicular networks as a stochastic optimization problem with the objective to maximize the uplink reliability while ensuring the network stability. The optimal resource allocation policies are obtained solving the optimization problem using the Lyapunov optimization technique in a distributed manner. To reduce the computational and to improve the challenges of the Lyapunov optimization, we propose a virtual resource slicing algorithm that maps radio resource units to virtual resource blocks. Simulation results exhibit that both ultra-low latency and ultra high reliability are guaranteed in the open-loop uplink of vehicular networks. Based on the theoretical performance analysis and simulations, the uplink radio access procedure is summarized for the URLLC in vehicular networks. [ABSTRACT FROM AUTHOR]

We consider end-to-end live video streaming in mobile edge networks with fluctuating but predictable throughput. We employ scalable video coding (SVC) to adapt the bitrate of each frame to the throughput evolution in a timely manner. When streaming SVC-encoded live video, the video source should dynamically derive and configure a set of coding parameters for each upcoming chunk, which consists of a number of consecutive frames. The coding parameters quantify the throughput evolution of an upcoming chunk by providing a few bitrate options for the frames within this chunk to encode with. Unlike the majority of existing works that simply treat the coding parameters as given (i.e., taking them for granted), in this paper, we derive the coding parameters from a comprehensive mathematical modeling and optimization perspective. Our approach consists of an optimization problem that captures the fundamental trade-off in determining the coding parameters and a customized search algorithm based on the analysis of the optimization problem's solution structure. Numerical results produced by the NS-3 network simulator show that our approach outperforms the state-of-the-art benchmarks. [ABSTRACT FROM AUTHOR]

In this correspondence paper, an underlay cognitive radio multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs’ individual interference constraint. The optimization problem formulated for this is proved to be nonquasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme. [ABSTRACT FROM AUTHOR]

Reduction of fuel consumption is an indispensable part of automotive industry in recent years. This induces several developments of hybrid vehicles with different structures. This paper deals with reliable and robust energy management strategy for a hybrid hydraulic-electric intelligent vehicle. The main objective of this paper is the development of a suboptimal control strategy based on fuzzy logic and neural network for minimizing total energy consumption while ensuring a better battery life. For this purpose, fuzzy supervisory fault management, which can detect and compensate the battery faults, regulates all of the possible vehicle's operation modes. Then, control strategy based on fuzzy logic controller (FLC) is developed. The FLC membership function parameters are tuned by employing neural network to manage power distribution between electric motor and internal combustion engine (ICE). Control strategy is switched between optimized FLCs to enhance the suboptimal power split between the different energy sources and manage the ICE to work always in the vicinity of its optimal condition. Finally, a robust fuzzy tuning controllers are investigated to give a good torque set point tracking. Simulation results, while using TruckMaker/MATLAB software, confirm that the proposed approach leads to suboptimal energy consumption of the vehicle for any unknown driving cycles and compensate battery faults effects. [ABSTRACT FROM PUBLISHER]

In this paper, we study the difference between two major strategies of cooperative driving at nonsignalized intersections: namely the “ad hoc negotiation-based” strategy and the “planning-based” strategy. The fundamental divide of these two strategies lies in how to determine the passing order of vehicles at intersections. The “ad hoc negotiation-based” strategy makes vehicles roughly follow first-come-first-served order but allows some adjustments. This leads to a local optimal solution in many situations. The “planning-based” strategy aims to find a global optimal passing order of vehicles. However, constrained by the planning complexity and time requirement, we often stop at a local optimal solution, too. We carry out a series of simulations to compare the solutions found by two strategies, under different traffic scenarios. Results indicate the performance of a strategy mainly depends on the passing order of vehicles that it finds. Although there exist several trajectory planning algorithms associating with the solving process of passing orders, their differences are negligible. Moreover, if the traffic demand is very low, the performance difference between two strategies is small. When the traffic demand becomes high, the “planning-based” strategy yields significantly better performance since it finds better passing orders. These findings are important to cooperative driving study. [ABSTRACT FROM PUBLISHER]

This paper presents a model predictive control-based obstacle avoidance algorithm for autonomous ground vehicles at high speed in unstructured environments. The novelty of the algorithm is its capability to control the vehicle to avoid obstacles at high speed taking into account dynamical safety constraints through a simultaneous optimization of reference speed and steering angle without a priori knowledge about the environment and without a reference trajectory to follow. Previous work in this specific context optimized only the steering command. In this paper, obstacles are detected using a planar light detection and ranging sensor. A multi-phase optimal control problem is then formulated to simultaneously optimize the reference speed and steering angle within the detection range. Vehicle acceleration capability as a function of speed, as well as stability and handling concerns such as preventing wheel lift-off, are included as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Simulation results show that the proposed algorithm is capable of safely exploiting the dynamic limits of the vehicle while navigating the vehicle through sensed obstacles of different sizes and numbers. It is also shown that the proposed variable speed formulation can significantly improve performance by allowing navigation of obstacle fields that would otherwise not be cleared with steering control alone. [ABSTRACT FROM PUBLISHER]

Machine-type communications (MTC) is a key technology for Internet-of-Things (IoT) services in 5G mobile communications and beyond. An essential design problem for an MTC network is the efficient and scalable data collection from low-power machine-type communication devices (MTCDs). This paper uses unmanned aerial vehicles (UAVs) to facilitate data collection from a clustered MTC network on the ground. The notion of artificial energy map (AEM) is introduced as a novel modeling technique for energy efficiency analysis, which is critical to the subject of investigation here considering the limited energy of battery-powered MTCDs and UAVs. The proposed design framework first determines the number of MTCD clusters $L_\mathrm{{opt}}$ according to a certain criterion. A greedy learning clustering (GLC) algorithm is then employed to divide the MTCDs into $L_\mathrm{{opt}}$ clusters. For each MTCD cluster, an AEM is constructed, and the optimal UAV hovering strategy within the cluster can be obtained accordingly. Finally, the UAV stations travel across the clusters and collect data from each cluster while hovering above it. This AEM-based modeling technique leads to a solution that can effectively improve the energy efficiency (EE) of UAV-enabled data collection. However, the MTCD clustering strategy, UAV hovering strategy, and UAV flying strategy all have impacts on the overall energy efficiency, which results in a coupled optimization problem that is difficult to solve. The GLC-AEM method is proposed to decouple the original EE optimization problem into sub-problems that can be handled easily by standard optimization techniques. Simulation results show that the GLC-AEM algorithm can be applied to UAV-enabled data collection scenarios with single and multiple UAV stations, and it can improve the overall EE effectively. Besides, the GLC-AEM algorithm shows good scalability and consistent performance in clustered MTC networks. The more MTCDs, the higher the achieved EE. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) and millimeter wave (mmWave) communications are two major technologies to enhance the spectrum efficiency (SE). Considering that the SE improvement in the CRNs is limited due to the interference temperature imposed on the primary user (PU), and the severe path loss and high directivity in mmWave communications make it vulnerable to blockage events, we introduce an intelligent reflecting surface (IRS) into mmWave CRNs. Due to the estimation mismatch and the passivity of Eavesdroppers (Eves), perfect channel state information (CSI) of wiretap links is challenging to obtain, which promotes our research on robust secure beamforming (BF) design in the IRS-assisted mmWave CRNs. This paper considers the collaborate scenario of Eves, which allows us to investigate the BF design in the harsh eavesdropping environment. Specifically, by using a uniform linear array (ULA) at the cognitive base station (CBS) and a uniform planar array (UPA) at the IRS, and supposing that imperfect CSIs of angle-of-departures for wiretap links are known, we formulate a constrained problem to maximize the worst-case achievable secrecy rate (ASR) of the secondary user (SU) by jointly designing the transmit BF at the CBS and reflect BF at the IRS. To solve the non-convex problem with coupled variables, an efficient alternating optimization algorithm is proposed. As for the transmit BF at the CBS, we propose a heuristic robust transmit BF algorithm to attain the BF vectors analytically. As for the reflect BF at the IRS, by means of an auxiliary variable, we transform the non-convex problem into a semi-definite programming problem with rank-1 constraint, which is handled with the help of an iterative penalty function, and then obtain the optimal reflect BF through CVX. Finally, simulation results indicate that the ASR performance of our proposed algorithm has a small gap with that of the optimal solution with perfect CSI compared with the other benchmarks. [ABSTRACT FROM AUTHOR]

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the vehicles’ position state information (PSI), and communicates with the vehicles based on PSI. The objective of this paper is to minimize the maximum communication delay among all vehicles by considering the estimation accuracy constraint of the vehicles’ PSI and the transmit power constraint of RSU. By leveraging convex optimization theory, two iterative power allocation algorithms are proposed with different complexities and applicable scenarios. Simulation results indicate that the proposed power allocation algorithm converges and can significantly reduce the maximum transmit delay among vehicles compared with other schemes. [ABSTRACT FROM AUTHOR]

This paper utilizes a reconfigurable intelligent surface (RIS) to enhance the anti-jamming performance of wireless communications, due to its powerful capability of constructing smart and reconfigurable radio environment. In order to establish the practical interactions between the base station (BS) and the jammer, a Bayesian Stackelberg game is formulated, where the BS is the leader and the jammer acts as the follower. Specifically, with the help of a RIS-assisted transmitter, the BS attempts to reliably convey information to users with maximum utilities, whereas the smart jammer tries to interfere the signal reception of users with desired energy efficiency (EE) threshold. Since the BS and the jammer are not cooperative parties, the practical assumption that neither side can obtain the other's strategies is adopted in the proposed game, and the angular information based imperfect channel state information (CSI) is also considered. After tackling the practical assumption by using Cauchy-Schwarz inequality and the imperfect angular information by using the discretization method, the closed-form solution of both sides can be obtained via the duality optimization theory, which constitutes the unique Stackelberg equilibrium (SE). Numerical results demonstrate the superiority and validity of our proposed robust schemes over the existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, we study an unmanned aerial vehicle (UAV) enabled video streaming system, where a rotary-wing UAV is dispatched as a mobile base station to serve multiple ground users (GUs) with dynamic adaptive streaming over HTTP (DASH). By introducing the Quality of Experience (QoE) utility model, we investigate the joint design of transmit power and bandwidth allocation as well as UAV trajectory for maximization of minimum utility among all GUs, subject to information-causality constraints for video playing and UAV total energy budget, which consists of both communication related energy and UAV propulsion energy. The formulated problem is non-convex and difficult to be solved directly. An efficient algorithm is proposed to obtain the suboptimal solution by applying the successive convex approximation and alternating optimization techniques. Simulation results show that the proposed solution can achieve significant gains in terms of QoE utility over benchmark schemes for energy-efficient video streaming. [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]

The paper considers a cloud-based Content Delivery Network (CDN) with Device-to-Device (D2D) and Unmanned Aerial Vehicle (UAV) enabled caching. The network is managed by the operator that offers content transfer services to the set of Content Providers (CPs). A subscriber of any CP can receive its content either by first, cellular content transfer via a terrestrial Base Station (BS) or a UAV, or by second, D2D content transfer from another subscriber of this CP. Each CP aims to maximize its expected payoff defined as a reciprocal of the weighted sum of the expected content transfer cost and delay for its subscribers. The operator's objective is to maximize its long-term average revenue and stabilize the queuing system which represents the content transfer services. To model the interactions between the operator and the CPs, a novel framework is proposed that combines: first, cooperative game that enables the CPs to form coalitions in which all subscribers can exchange content via D2D links, thereby reducing content transfer costs and delays; second, Lyapunov optimization based dynamic channel and UAVs’ activity allocation policy of the operator. Through analytical and numerical evaluations, it is proven that if the operator and each CP are rational, the network reaches a state where all the CPs are in the stable coalitional structure, whereas, the dynamic policy of the operator is optimal with the trade-off in the total queue backlog. [ABSTRACT FROM AUTHOR]

The coexistence of multiple types of users in vehicular networks results in diversified service requirements and multiscale content differences, which poses new challenges to reliable content transmission. This paper proposes a hierarchical coded caching framework coupled with coded caching, vehicle-to-everything communications, and non-orthogonal multiple access techniques. We quantify the user request hit ratio under different communication modes and study the hit ratio maximization problem under the constraint of limited communication and caching resources. We propose a two-layer matching based optimization algorithm, including a user association layer in distributed content sharing and a user grouping layer in centralized content requesting. Simulation results verify that the proposed scheme can achieve a balance between hit ratio and transmission time, and meet the requirements of the content sharing scenario with multiscale content files and scalable user density. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication. [ABSTRACT FROM AUTHOR]

Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at the both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial secure offloading system is proposed in the presence of multiple ground eavesdroppers. Specifically, the IOS is adopted to prevent information leakage, improve legitimate reception quality and expand the security deployment range of unmanned aerial vehicles (UAVs). To maximize the secrecy energy efficiency (SEE) of the considered system, a non-convex resource allocation problem is formulated by allocating computing frequency, determining offloading strategy, controlling transmit power, designing phase shifts, and deploying UAV locations. The formulated problem is non-trivial and challenging to be solved directly due to the highly coupled variables. To tackle this issue, the alternating optimization technique is invoked to decouple the original one into five subproblems, and then the computing and communication settings are alternatively optimized using a low complexity iterative algorithm. Finally, simulation results demonstrate that: i) In terms of the SEE performance, our IOS-enhanced aerial secure offloading system significantly outperforms the benchmark schemes; ii) Compared with reflecting-only surfaces, IOSs can take full advantage of the flexible deployment ability of UAVs, such that their secure offloading space can be considerably expanded. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies for low-power communication. In this paper, we consider a NOMA backscatter system, where signals from two backscatter devices are multiplexed on a frequency resource block using NOMA in each time slot. Our objective is to maximize the average energy efficiency by optimizing backscatter device pairing, reflection coefficients of backscatter devices, and the transmit power of the reader. We formulate the average energy efficiency maximization problem subject to the minimum circuit power and the minimum data rate requirements of the backscatter devices, and the transmit power constraint of the reader. The formulated problem is nonconvex. To obtain a suboptimal solution for this problem, we use alternating optimization technique and decompose the problem into two subproblems. The subproblems are solved by using fractional programming, Dinkelbach’s algorithm, and successive convex approximation method. Simulation results show that our proposed algorithm converges quickly to a suboptimal solution. Our proposed algorithm outperforms several baseline algorithms, including the genetic algorithm, fixed device pairing scheme, conventional device pairing scheme, maximum transmit power allocation scheme, and random reflection coefficient selection scheme, in terms of the average energy efficiency. The optimality gap of our proposed algorithm is investigated by comparing with the optimal scheme, in which the optimal device pairing is obtained based on exhaustive search. [ABSTRACT FROM AUTHOR]

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR. [ABSTRACT FROM AUTHOR]

The around view monitor system (AVM) has been widely used in passenger vehicles. However, for articulated engineering vehicles (AEV), AVM is difficult to directly apply due to the long body and the articulation angle. In this paper, a flexible technique for variable-angle around view monitor system (VAVM) towards the AEV is proposed, which includes static calibration and dynamic execution. In the static calibration stage, the camera poses in the vehicle coordinate system with the joint of AEV as the coordinate origin can be calibrated using irregularly placed multiple checkerboards without knowing their relative position. This flexible calibration approach makes the variable-angle mapping from the fisheye image to the bird’s eye view image can be directly obtained by updating the camera poses on the tractor according to the articulation angle when the AEV turns. In the dynamic execution stage, an optimization algorithm based on minimizing the photometric error is designed to reduce the cumulative error of the angle sensor. Finally, all the bird’s eye view images are mosaiced and fused to a variable-angle around view image (VAVI). The experimental results show that the VAVM can output globally balanced and seamless VAVIs under different angles and visual environment conditions. Furthermore, the practicability of VAVM is also demonstrated by a real AEV. [ABSTRACT FROM AUTHOR]

In this paper, we first develop an analytical framework for the total energy consumption of an amplify-and-forward (AF) multihop network employing M-ary quadrature amplitude modulation (MQAM) while satisfying an average bit error rate (BER) requirement at the destination over Rayleigh fading channels. Based on this framework, we then establish the conditions under which a dual-hop relay network is always more energy efficient when compared with a reference single-hop network. Moreover, the impact of the relay's location and the energy resource allocation between the relay and the source on the possible energy savings is also analyzed. Since a realistic power consumption model is considered, it is shown that there exists an optimal number of relays for the linear multihop relay network and that the maximum energy efficiency (EE) is obtained through the joint optimization of the number of relays and the constellation size. Considering this fact, we first formulate the optimization problem, and then, this problem is transformed into a convex form by relaxing constraints to be defined over the real numbers (instead of taking only discrete values). The new closed-form optimal solution that involves only elementary functions is derived for this relaxed problem. It is also demonstrated that the optimized multihop network has the potential not only to reduce the total energy consumption but to satisfy the peak-power constraint simultaneously at the expense of more delay as well. Finally, our analysis shows that significant energy savings are possible through joint optimization not only for delay-tolerant networks but for delay-limited networks as well, and up to about 55% energy savings are achievable over the optimized single-hop system. [ABSTRACT FROM PUBLISHER]

Multicast downlink transmission in a multicell network with multiple users is investigated. Max–min beamforming (MB) enables a fair distribution of the signal-to-interference-plus-noise ratio (SINR) among all users in a network for given power constraints at the base stations (BSs) of the network. The multicast MB problem (MBP) is proven to be NP-hard and nonconvex in general. However, the MBP has an equivalent quasi-convex (QC) form and can be optimally solved with an efficient algorithm for special instances, depending on the structure of the available channel state information (CSI). This paper derives the equivalent QC form of the MBP for the practically relevant scenario of long-term CSI in the form of Hermitian positive semi-definite Toeplitz (HPST) matrices and per-antenna array power constraints. The optimization problem is then given by a convex feasibility check problem with finite autocorrelation sequences (FASs) as optimization variables. Using FASs, the MBP can be expressed as a QC fractional program (FP). Based on the theory of QC programming, this paper proposes a fast root-finding algorithm with superlinear convergence. [ABSTRACT FROM PUBLISHER]

This paper studies wireless charging of lithium-ion batteries for electric vehicles. The charging profile mandates a constant-current (CC) charging for a discharged battery until the battery voltage reaches the cutoff voltage at rated power. The charging continues at the cutoff voltage with a constant-voltage (CV) charging at a power level down to 3% of the rated power in order to fully charge the battery. An inductive-power-transfer (IPT) converter should be designed with minimal number of stages to achieve high efficiency. However, high efficiency for such a wide load range is difficult to achieve. Moreover, the efficiency-to-load relationship is distinctly different for CC and CV charging operations, posing difficulties for the single-stage design. This paper describes the design of a single-stage IPT converter that complies with the battery charging profile and, at the same time, achieves optimal efficiency. Design optimization includes soft switching for the entire battery load range, efficiency optimization for CC and CV modes of operation, and system efficiency optimization for the whole battery charging profile. Measured results of two experimental IPT battery chargers are presented for illustration and verification. [ABSTRACT FROM PUBLISHER]

Device-to-device (D2D) communications have recently attracted much attention for the potential capability to improve spectral efficiency (SE) underlaying the existing heterogeneous networks (HetNets). Due to no sophisticated control, D2D-worked user equipments (DUEs) themselves cannot resist eavesdropping or security attacks. It is urgent to maximize the secure capacity for both cellular users and DUEs. This paper formulates the radio resource-allocation problem to maximize the secure capacity of DUEs for D2D communication underlaying HetNets, which consist of high-power nodes (HPNs) and low-power nodes (LPNs). The optimization objective function with transmit bit rate and power constraints, which is nonconvex and hard to directly derive, is first transformed into a matrix form. Then, the equivalent convex form of the optimization problem is derived according to Perron–Frobenius theory. A heuristic iterative algorithm based on the proximal theory is proposed to solve this equivalent convex problem through evaluating the proximal operator of the Lagrange function. Numerical results show that the proposed radio resource-allocation solution significantly improves the secure capacity with a fast convergence speed. [ABSTRACT FROM PUBLISHER]

This paper studies the resource-allocation problem for a heterogeneous network (HetNet) in which the spectrum owned by a macrocell operator (MCO) can be shared by both unlicensed users (UUs) and licensed users (LUs). We formulate a novel hierarchical game theoretic framework to jointly optimize the transmit powers and subband allocations of the UUs and the pricing strategies of the MCO. In our framework, an overlapping coalition formation (OCF) game has been introduced to model the cooperative behaviors of the UUs. We then integrate this OCF game into a Stackelberg game-based hierarchical framework. We prove that the core of our proposed OCF game is nonempty and introduce an optimal subband-allocation scheme for UUs. A simple distributed algorithm is proposed for UUs to autonomously form an optimal coalition formation (CF) structure. The Stackelberg equilibrium (SE) of the proposed hierarchical game is derived, and its uniqueness and optimality are proved. A distributed joint optimization algorithm is also proposed to approach the SE of the game with limited information exchanges between the MCO and the UUs. [ABSTRACT FROM PUBLISHER]

Distributed phased Multiple-Input Multiple-Output (phased-MIMO) radar system consisting of separated subarrays possesses a high angle resolution but with sharing high sidelobes. This paper therefore focuses on the design of a virtual low-sidelobe beampattern in distributed phased-MIMO radar. An iterative framework with respect to subarray layout and receive weighting coefficients is proposed to minimize beampattern Peak Sidelobe Level (PSL) accounting for practical position constraints as well as the mainlobe level restriction. In each iteration, an efficient cyclic optimization algorithm based on the Coordinate Descent (CD) framework is developed to seek for the subarray layout through splitting high dimensional layout optimization problem into multiple one-dimensional optimization problems, and the SemiDefinite Relaxation (SDR) technique and rank one approximation are explored to design weighting coefficients. Numerical simulations are provided to assess the performance of the proposed algorithms in term of the achieved beampattern under different constraints and parameter settings. [ABSTRACT FROM AUTHOR]

This paper investigates a wireless federated learning (FL) network with limited communication bandwidth, where multiple mobile clients train their individual models with the help of one central server. We consider the practical communication scenarios, where the clients should complete the local computation and model upload within a defined latency. By jointly exploiting the dynamic characteristics of wireless channels and computational capability at the clients, we optimize the federated learning network by maximizing the number of active clients under the constraints of both latency and bandwidth. Specifically, we propose two bandwidth allocation (BA) schemes, where scheme I is based on the instantaneous channel state information (CSI), while scheme II employs the particle swarm optimization (PSO) method, based on the statistical CSI. Simulation results on the test accuracy and convergence rate are finally provided to demonstrate the advantages of the proposed optimization schemes for the considered FL network. [ABSTRACT FROM AUTHOR]