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We consider the DOA estimation problem by using adaptive nulling antenna technology and bias-compensated adaptive filtering framework. The adaptive nulling antenna array consists of one reference antenna element and multiple auxiliary antenna elements, which forms nulls in certain directions. By defining the reciprocal of array pattern as the spatial spectrum, the DOA estimation problem is considered as the peak searching for it. In this paper, we present an efficient bias-compensated adaptive filtering algorithm for DOA estimation based on the adaptive nulling array. First of all, a new noise-free mean square error (NF-MSE) cost function associated with the adaptive nulling array is provided. Secondly, the proposed algorithm, called variable step-size bias-compensated least mean square (VSS-BC-LMS) algorithm, can be derived by minimizing the NF-MSE. Due to the bias compensation, the bias of the estimated weight vector caused by noises is canceled. More importantly, in order to resist the unsatisfactory DOA estimation performance when using the fixed step-size, a variable step size scheme is introduced. And then, its mean stability, steady state mean square performance and computational complexity are discussed. Additionally, we also explore a noise variance estimation method for calculating the bias term. Simulation results indicate that the proposed algorithm achieves an improved DOA estimation performance compared with other adaptive methods. [ABSTRACT FROM AUTHOR]

Cooperative vehicles are better able to detect the environment and self-localize than a single vehicle. Cooperative vehicles can quickly cover the entire environment by communicating and cooperating with each other and can also reduce localization and mapping error by merging the cooperative vehicle information from observation and navigation. In this paper, we propose a novel algorithm for an effective solution of navigation and mapping for cooperative vehicles in an unknown environment. We present an improved centralized and collaborative monocular simultaneous localization and mapping (CCM-SLAM) approach. The proposed algorithm can accurately compute the transformation matrix for cooperative vehicle maps and reduce the communication delay, data loss among vehicles and decrease the bandwidth demand. The quaternion and credibility similarity transformation (QC-Sim(3)) method we proposed is used to accurately merge the matched maps and accomplish loop closures. The sending messages at variable frequencies (SMVF) method we proposed and an improved detection and resending lost data (I-DRLD) method we proposed can improve the accuracy of pose estimation. SMVF solves the time-delay problem by sending messages to the vehicles at flexible frequencies while I-DRLD detects and resends the lost data. We also adopt Intra-frame Feature Compression (IFC) to decrease the bandwidth demand in the process of the transmitting data. The experiments demonstrate the superiority of our proposed algorithm compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern. [ABSTRACT FROM AUTHOR]

Machine-to-Machine (M2M) communications plays a key role in the evolution of the Internet of Things (IoT). Cellular networks are one of the main technologies to support the deployment of M2M communications. However, the characteristics and Quality-of-Service (QoS) requirements of M2M traffic are different from those of conventional Human-to-Human (H2H) traffic, that makes radio resource allocation a complex task. In this paper, a two-phase optimal resource allocation algorithm is proposed for H2H/M2M coexistence. The first phase performs joint power-resource allocation in order to satisfy the QoS requirements of H2H traffic while considering the delay constraints of delay-sensitive M2M traffic. Then, the second phase focuses on meeting the QoS requirements of M2M traffic. Simulation results are presented which show that the proposed algorithm is able to balance the demands of M2M and H2H traffic, meet their diverse QoS requirements, and ensure fairness for delay-tolerant M2M traffic. [ABSTRACT FROM AUTHOR]

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. However, directly transmitting data for centralized learning will cause long communication latency owing to the limited communication resource and may incur severe privacy issue as well. To address these issues, we consider the federated edge learning (FEEL) system in this paper and develop an importance-aware joint data selection and resource allocation algorithm to maximize the learning efficiency. Aiming at selecting important data for local training, we first analyze the relation between loss decay and gradient norm, which indicates that larger gradient norm generally leads to faster learning speed. Based on this, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Furthermore, an optimal algorithm with low computational complexity is developed to obtain the optimal end-to-end latency in one training period. We show that the sample size should be set to its upper limit in order to maximize the learning performance. Finally, we conduct extensive experiments on three popular convolutional neural network (CNN) models. The results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms. [ABSTRACT FROM AUTHOR]

To be differentiated from the literature, in this paper, we aim to alleviate the impact of pilot contamination (PC) on the massive MIMO systems by cooperatively scheduling users (rather than just assigning) among neighboring cells to share the limited orthogonal pilots. To this end, we develop the multi-cell cooperative scheduling (MCCS) algorithm together with the cooperative scheduling indexes (COSIs) for maximizing the data rate, maximizing the Jain's fairness index and reaching a better tradeoff in-between. For convenience, they are denoted by the COSIs for the CMDR, CMMF and CPF schedulers, respectively. However, its high computational complexity may somehow resists its from practical applications. Thus, a low-complexity cooperative proportional fairness (LC-CPF) algorithm is designed to well approach the MCCS algorithm using the CPF COSI; and at the meantime, the order of computational complexity can be significantly reduced. Moreover, its ability to reach a better tradeoff can still be maintained under the impact of high spectrum-sharing interference. In addition, it is interesting to find that using the proposed cooperative scheduling methods, the open-loop power control mechanism is no longer required for compensating the differences of received signal quality between users. [ABSTRACT FROM AUTHOR]

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [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]

In this paper, we propose an optimal random beamforming technique in mmWave MIMO-NOMA transmission systems. In our model, we consider a NOMA system with a user pairing scheme that works based on the distance between the cellular base station (BS) and the active mobile users. The aim of this is to reduce the system overall overhead for massive connectivity and achieve the desired Quality of Service (QoS). We present an optimization problem with the aims to find the optimal random beamforming coefficients by minimizing the outage probability. We analyze the convexity of the outage probability for three user pairing schemes and prove that the proposed optimal random beamforming is convex in high signal-to-noise ratio (SNR). We utilize the gradient-based algorithm namely the gradient descent algorithm to find optimal random beamforming coefficients. Our simulation results demonstrate that the proposed transmission schemes reduce the outage probability and improve network performance compared to previous works. [ABSTRACT FROM AUTHOR]

Aerial Base Stations (ABSs) promise resilient and perpetual connectivity after unexpected events such as natural disasters. However, the deployment and scheduling of ABSs introduce several algorithmic challenges. In particular, on-demand communication can change over time and be hard to accurately predict, so it needs to be handled in an online manner, accounting also for battery consumption constraints. This paper presents AirNet, an efficient software-based solution to operate ABSs which meet these requirements. AirNet is based on an efficient placement algorithm for ABSs which maximizes the number of covered users, and a scheduler which navigates and recharges ABSs in an energy-aware manner. To this end, we propose an energy-aware deployment algorithm and use an energy model to analyze the power consumption and thereby, improve the flight endurance. In addition, we evaluate a novel scheduling mechanism that efficiently manages the ABSs’ operations. Our simulations indicate that our approach can significantly improve the flight endurance and user coverage compared to a recent state-of-the-art approach. [ABSTRACT FROM AUTHOR]

Multibeam satellite communication plays an important role in future integrated satellite-terrestrial communication systems. However, the large area covered by the satellite beams makes itself vulnerable to be wiretapped, especially for the cases that eavesdroppers are located near the legitimate receivers. In this paper, cooperative jamming from both satellite and full-duplex receivers are investigated where we aim to minimize the power consumed by a multibeam satellite system with secrecy outage probability constraints. To tackle this problem, an algorithm of joint beamforming and power allocation via alternating optimization (JBFPA-AO) is proposed. Specifically, a feasible solution is first obtained by transforming the original non-convex problem into a semi-definitive programming (SDP) problem with relaxed constraints. And then, alternating optimization is adopted to find better solutions iteratively. Convergence of the proposed JBFPA-AO is proved, and simulation results show its effectiveness to reduce the power utilized by the satellite. [ABSTRACT FROM AUTHOR]

In this paper, weighted sum energy efficiency (WSEE) in uplink and downlink of a multi-user non-orthogonal multiple access (NOMA) system is considered. we adopt a more realistic power consumption model where signal processing power is modeled as a linear function of transmit power. Rather than the well-known network-centric global energy efficiency (GEE) metric, which is a pseudo-concave (PC) function, the weighted sum energy efficiency metric is considered, which is not PC in general. To find the optimum user power allocation Dinkelbach-like algorithm is adopted, by which each individual fractional EE function is converted to a parametric function where under some conditions on the weights falls into a class of convex optimization problems and it is solved in the dual domain. The dual variables are updated using a sub-gradient and cutting plane-based algorithm, which here ellipsoid method is used. Since the optimum solution restricts user weights, a low complexity suboptimum algorithm that does not consider any condition on user's weights is proposed. The problem is non-convex in general; hence, epigraph form followed by successive convex approximation (SCA) is used to deal with that problem. Results demonstrate that with the user-oriented metric, one can provide different priorities to users, and by choosing proper weights entail fairness among users. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been deemed a promising technology to achieve high spectral efficiency. This paper investigates transmit power optimization for a multi-user multiple-input single-output (MISO) NOMA downlink system endowed with energy harvesting and physical layer security. More specifically, the system is designed to satisfy the data-link security requirements of its legitimate users while charging energy harvesting devices. To achieve that goal, we investigate a joint design on beamforming vectors and artificial noise (AN) covariance matrix. Then, an iterative difference-of-two-convex-function (D.C.) algorithm is proposed by applying the successive convex approximation (SCA) approach. To further reduce the computational complexity, a sub-optimal alternating D.C. (ADC) algorithm is devised by decoupling beamforming and mapping the AN to the null space of legitimate users. In the ADC algorithm, optimal null space and beamforming vectors are alternately solved by convex programming. Furthermore, the convergence of the proposed D.C. and ADC algorithms is derived. Extensive simulation results show that the two proposed AN-aided algorithms significantly reduce the transmit power cost over OMA and NOMA scheme without AN, especially when there are more eavesdroppers. [ABSTRACT FROM AUTHOR]

In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground users. To maximize the users’ sum-rate, we formulate a optimization problem, in which the UAV deployment position and the power control are jointly optimized subject to the transmission power constraints and the quality of service (QoS) constraints. However, this problem is a mixed integer non-convex optimization problem and thus it is difficult to solve directly. Therefore, we first transform the non-convex optimization problem into a convex one based on the first-order approximation technique and the penalty function method. Then, a successive convex approximate (SCA) technique based iterative algorithm as well as its initialization scheme are developed to find a suboptimal solution to the original problem. Afterwards, a low-complexity approximate algorithm is proposed to reduce the high computational complexity of the iterative algorithm. Numerical results show that our proposed iterative algorithm and low-complexity approximate algorithm can achieve a similar performance, and they can efficiently improve the sum-rate compared with OMA scheme and conventional NOMA scheme. [ABSTRACT FROM AUTHOR]

In this paper, a calibration of multiple-input multiple-output (MIMO) channel sounders with internal crosstalk is considered.The objective is to minimize the number of back-to-back (B2B) connections required to estimate the transmitter (Tx) and receiver (Rx) response matrices that convey the information about linear distortion and internal crosstalk. A signal and system model is developed for the B2B measurements, where only some pairs of the Tx and Rx ports of the sounder are utilized among all pairs of the ports. Using the measurement model, a least-square estimation problem is then formulated and converted in the frequency domain to weighted rank-one approximation problems. The notion of system identifiability of a MIMO channel sounder is introduced and some optimal sets of B2B connections are proposed. Given a proposed optimal set of B2B connections, the alternate convex search (ACS) algorithm with a proper initialization is also proposed to solve the weighted rank-one approximation problems. Finally, it is shown how to calibrate field measurement data by using the estimated response matrices. Numerical results show that, only after a couple of iterations, the ACS algorithm with the proposed initialization achieves a comparable identification and calibration performance to the conventional method that requires the B2B connections of all port pairs. [ABSTRACT FROM AUTHOR]

The fast proliferation of Internet of Things (IoT) exacerbates the current spectrum shortage. As a promising solution, licensed spectrum sharing has received great attentions recently. However, due to the unpredictable activities of incumbent users, the availability of the shared spectrum is uncertain, making the quality of service (QoS) provisioning based on it a difficult task, especially under multi-hop scenarios. In this paper, considering the energy-efficiency issue, we propose an end-to-end (E2E) QoS guaranteed green traffic off-loading scheme over the uncertain shared spectrums in multi-hop networks. Specifically, we formulate it into a two-step problem, an off-loaded traffic maximization (OTM) problem and an energy-efficiency improvement (EEI) problem. The former one is to determine the admitted sessions. To achieve the E2E QoS guarantee under the uncertain spectrum supply, we develop a concept of equivalent link capacity, and construct a risk-averse joint chance constraint. Since the formulated problem is intractable, we first develop a data-driven conservative transformation to make the joint chance constraint a set of deterministic individual ones. Then, we design a convex-hull-based and a substitution-based linearization approach to make all the constraints tractable. For the second step, the EEI problem with the same constraints as the OTM problem is designed to refine the resource scheduling for the admitted sessions to optimize the energy-efficiency. To deal with the fractional-form energy-efficiency objective, a Dinkelbach method based iterative algorithm is further developed. Numerical results have shown the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Recent developments and advancements in wireless networks have brought about new challenges including the backhaul bottleneck and content download latency. The paper focuses on an enterprise wireless network consisting of various candidate caching nodes to serve users’ requests such as Femto Access Points (FAPs) and Mobile Users (MUs), supported by Device-to-Device (D2D) communications. Due to the fact that user's mobility is one of the inherent features of massive wireless networks, where FAPs operate in an open access mode, the small transmission range of FAPs leads to triggering frequent handovers. Taking the above challenges into account, we propose a novel Mobility-Aware Femtocaching scheme based on Handover (MAFH). To implement a real wireless network, a random walk model as the mobility pattern of users is utilized within the proposed scheme. In this regard, the best caching node is selected by considering the Received Signal Strength Indicator (RSSI) value and the velocity of users as decision criteria to extend the time interval between two consecutive handovers and increase the battery life of devices. The effectiveness of the proposed MAFH algorithm is evaluated in terms of the cache hit ratio, transferred byte volume, connecting time, content delivery time, the number of handovers, and the energy consumption of clients. Based on the simulation results, the proposed MAFH framework illustrates significant performance improvements in companion to its counterparts across all the aforementioned aspects. [ABSTRACT FROM AUTHOR]

In this paper, we propose a trustworthy service provisioning scheme for Safety-as-a-Service (Safe-aaS) infrastructure in IoT-based intelligent transport systems. Typically, a Safe-aaS infrastructure provides customized safety-related decisions dynamically to multiple end-users using the concept of decision virtualization. We consider road transportation as the application environment of Safe-aaS to generate trustworthy decisions. On the other hand, the efficiency and accuracy of the decisions generated depend on the security, privacy, and trustworthiness of the participating sensor nodes and the route through which data transit. We propose a trust evaluation model to compute the trustworthiness of the data generated from these nodes. Further, we consider direct and indirect trust mechanisms for each of the sensor nodes and update their trust measures at regular intervals of time. Based on these measures, we evaluate the trust of each data item sourced from the network. We formulate an integer linear programming (ILP) model to select the optimal data for decision-making, while alleviating the effects of illegitimate sensor nodes. Further, we show that the formulated ILP problem is NP-hard and use a dynamic programming approach to solve the problem. Experimental results show that our proposed trust evaluation model exhibits more than 8% attack detection rate and 13% reduction in false attack detection rate in a network with 50% malicious nodes, compared to the benchmark schemes. The proposed trustworthy data selection algorithm outperforms different existing greedy approaches. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communications are becoming a promising technology to support ultra-reliable low-latency communications (URLLC). This paper incorporates contention-based access scheme into radio resource management (RRM), which can not only deal with the sporadic packet arrivals for URLLC-oriented D2D communications, but also avoid wasting time on sending requests and waiting for feedback. However, it cannot be ignored that the collisions may occur to yield transmission failure, especially when two or more users access the same resource block (RB). Thus, we should pay more attention to optimizing users’ RB selection to foster strengths and offset weaknesses of introducing contention-based access scheme, and thereby improve the transmission reliability within the latency budget. Specifically, we formulate the contention-based RRM as an optimization problem which aims to maximize the sum successful transmission probability (STP) of all D2D pairs under the strict latency constraint. Due to its intractability, this optimization problem is transformed into a mixed strategy non-cooperative game, which is proved to be an exact potential game with the sum STP as its potential function. Accordingly, the existence of pure strategy Nash equilibrium (NE) can be guaranteed, and importantly, the optimal integral solution to the optimization problem constitutes a pure strategy NE of the game. Moreover, we propose a distributed RB selection algorithm based on sequential spatial adaptive play (S-SAP), which can converge to the optimal pure strategy NE of the proposed game. The numerical results verify the convergence and optimality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and time-delay sensitive computing task, multi-access edge computing (MEC) has been proposed. However, due to the differences among the performance of UTDs, and the resource limitation of MEC servers, the joint optimization between the offloading decisions of UTDs and the allocation of resources in network is still a focus of the research. This paper studies the joint computation offloading and resource allocation strategy in multi-user and multi-server scenarios. Firstly, we formulate the joint optimization problem of computation offloading and resource allocation as a mixed integer nonlinear programming (MINP) problem to minimize the energy consumption of UTDs, by constraining the offloading decision, channel selection, power allocation and resource allocation. Secondly, we propose a two-stage heuristic optimization algorithm based on genetic algorithms, which divides the joint optimization problem of computation offloading and resource allocation in two stages. Based on the coupling relationship between the offloading decision and the resource allocation scheme, we iteratively update the solution of the problem, and finally obtain the stable convergence solution of the optimization problem. Finally, the proposed algorithm is compared with other classical methods to prove the effectiveness. [ABSTRACT FROM AUTHOR]

Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a data-driven FDD method, namely deep slow feature analysis and belief rule base method (DSFA-BRB), for the running gears of high-speed trains. The method uses two kinds of statistics to perform fault detection on the multi-dimensional data of the running gears. In addition, the characteristics of more accurate data are extracted, which greatly reduces the complexity of constructing a diagnostic and quantitative model. Further, by constructing a BRB model combining expert knowledge and data, it is possible to avoid misjudgment caused by data incompleteness. Compared with the traditional methods, the DSFA-BRB algorithm has better performance in reducing fault alarm probability. Finally, the validity of the algorithm is verified by the actual running gears system. [ABSTRACT FROM AUTHOR]

In this paper, we study an UAV base station (UBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple UBSs, and multiple ground terminals. We assume that the MBS transmits to the UBSs and the terminals in the licensed band while the UBSs use a separate unlicensed band (e.g., Wi-Fi) to transmit to the terminals. For the utilization of the UBSs, we propose a cooperative decode–forward (DF) protocol in which multiple UBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the UBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing UBSs. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive sliding mode (ASM) controller for improving vehicle maneuverability and lateral stability of steer-by-wire (SBW) vehicles. The proposed stability control algorithm is composed of the following three remarkable characteristics: 1) As the vehicle sideslip angle which is very useful in vehicle stability control is often unavailable or unmeasurable for practical applications, a sliding mode-based state observer is proposed to online estimate the actual sideslip angle via the yaw rate and the lateral acceleration measurements. 2) ASM lateral stability controller, as an upper level controller, is designed to calculate the corrected steering angle for driving both the yaw rate and the sideslip angle to achieve their desired values in the presence of modelling uncertainties and external disturbances. Moreover, an adaptive law is incorporated in the control law to estimate the switching gain such that the complex uncertainty bound information can be avoided. 3) The desired steering angle is realized by a lower steering controller via ASM for an SBW system. The hardware-in-the-loop simulation results demonstrate the excellent stability control performance of the proposed control for different steering maneuvers. [ABSTRACT FROM AUTHOR]

Clutch fill control in clutch-to-clutch shift process is an essential technique to ensure smooth and precise synchronization of clutches. The filling control methods proposed in previous works are difficult to apply to production vehicles because of huge calibration effort and numerical issues of the system model. Therefore, this paper focuses on the development of clutch fill control logic considering practical issues at the production vehicle level. In order to increase the usability, a simplified reduced model that describes the filling phase is designed. The proposed model is used to construct a model-based feed-forward controller, which is not affected by a time delay in the hydraulic system. Furthermore, an adaptation algorithm of a key model parameter is proposed to cope with the model changes due to the disturbances and uncertainties of the plant. In addition, a new volume indicator is proposed to observe the status of the filling phase. Based on the proposed controller and the indicator, tracking performance of the filling phase is guaranteed without over-fill or under-fill. As a result, the proposed control logic is applied to a production type transmission control unit in a test vehicle and its performance is experimentally verified through various scenarios. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communication are two important technologies considered for the fifth generation (5G) of mobile networks. In this paper, we propose a machine-to-machine (M2M) communication underlaying heterogeneous network (HetNet) with mmWave-NOMA transmission, where small cell users (SCUs) and machine type communication (MTC) devices share the same resource block (RB) to increase the network capacity. We formulate an optimization problem to select efficient users and allocate transmission powers to SCUs and MTC devices. The object of optimization is to maximize the energy efficiency (EE) subject to the minimum spectral efficiency (SE) requirements and maximum transmission powers. We present a low computational complexity sub-optimum solution based on the non-cooperative game. An iterative algorithm with fast convergence speed and a reduced complexity search-based algorithm are respectively presented to solve the power control and user selection problems. The comparison between the proposed solution and optimum solution for different number of transmit antennas indicate that there is a small gap between SEs and EEs of the proposed and optimum solutions. Further, the results of user selection scheme demonstrate that the location of strong user affects the performance. We also derive analytical expressions for the outage probability which confirm Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Human mobility prediction techniques are instrumental for many important applications including service management and city planning. Previous work looks at the inherent patterns of a user's historical trajectories to predict his/her future location. Such method suffers when only a small number of historical locations are available, which is a main challenge for mobility prediction on sparse trajectory datasets. In this paper, we propose a multi-task learning based algorithm to predict users’ mobility by learning the mobility behaviors of different users at the same time and exploiting the similarities among them to overcome the sparse issue and improve the prediction performance. Specifically, we use a Bayesian mixture model to describe users’ spatio-temporal mobility patterns, where we introduce a novel von Mises distribution to model the temporal distribution of users’ mobility to better preserve its continuity across time. Then, we use the hierarchical Dirichlet process to perform joint prediction of all users’ mobility. This model allows us to leverage similar mobility patterns among users to improve the prediction performance for users with sparse trajectory data. We conduct extensive evaluations using data collected by a cellular network operator and mobile applications. Our evaluations show that our algorithms achieve prediction accuracy of 53.9% on average and 73.2% for users with high-quality mobility data, with the performance gap over 8.7% on average compared with the state-of-the-art algorithms, demonstrating the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the problem that the polyphase signal may not satisfy the orthogonality requirement in the condition of limited bandwidth is investigated. The use of waveforms that lack orthogonality leads to possible harmful effects in multiple-input and multiple-output (MIMO) radar and distributed coherent aperture radar (DCAR). For the purpose of enhancing the orthogonality of the signal, a multi-pulse joint transmission strategy of polyphase signal and the corresponding echo signal processing algorithm based on sliding window are proposed. We further analyze the relationship between the degradation of detection performance and the step size of the sliding window. In addition, the Doppler tolerance of the signal is also derived. Finally, the enhancement of the orthogonality by using the multi-pulse joint processing strategy and the relationship between Doppler tolerance and coherent time are verified via numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we design the UAV trajectory to minimize the total energy consumption while satisfying the requested timeout (RT) requirement and energy budget, which is accomplished via jointly optimizing the path and UAV's velocities along subsequent hops. The corresponding optimization problem is difficult to solve due to its non-convexity and combinatorial nature. To overcome this difficulty, we solve the original problem via two consecutive steps. Firstly, we propose two algorithms, namely heuristic search, and dynamic programming (DP) to obtain a feasible set of paths without violating the GU's RT requirements based on the traveling salesman problem with time window (TSPTW). Then, they are compared with exhaustive search and traveling salesman problem (TSP) used as reference methods. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm exhibits poorer performance with low complexity. As a result, the DP is proposed as a practical trade-off between the exhaustive and heuristic algorithms. Specifically, the DP algorithm results in near-optimal performance at a much lower complexity. Secondly, for given feasible paths, we propose an energy minimization problem via a joint optimization of the UAV's velocities along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. The results show that the DP-based algorithm approaches the exhaustive search's performance with a significantly reduced complexity. It is also shown that the proposed solutions outperform the state-of-the-art benchmarks in terms of both energy consumption and outage performance. [ABSTRACT FROM AUTHOR]

Due to the capability of simultaneously delivering the same information to multiple users, the multicast transmission has been widely applied over the satellite networks. Furthermore, more and more information needs to ensure the transmission delay within a predefined bound. However, the satellite networks possess the time-varying characteristics and limited energy, which causes challenges for the low delay multicast design. In this paper, we investigate the energy-efficient delay-bounded (EEDB) multicast problem to accomplish the multicast transmission within the given delay bound, but to consume energy as less as possible. Specially, with the help of the time-expanded graph (TEG), we construct the multicast time-expanded graph (MTEG) through introducing some auxiliary vertices and arcs to accurately depict the across-time multicast transmission and the “wireless multicast advantage”. Then, we formulate the EEDB multicast problem as one combinatorial optimization and propose an iterative heuristic algorithm to achieve the suboptimal multicast scheme. Simulation results are also presented to verify our proposed algorithm. [ABSTRACT FROM AUTHOR]

Channel gain based path selection has been recently proposed to simplify the network design of a cluster-based cross-layer multihop vehicular communications. In vehicular network systems, end-to-end packet delay increases with the growing number of vehicles associated in that path. In delay-sensitive application, the information exchange must be successfully carried out very quickly to ensure the quality of infotainment services. In the case of excessive packet delay, some important information either are needed to be enqueued before reaching the destination or are lost. The packet loss makes it impractical to deploy the network in practice. In this paper, a new delay-sensitive buffer-aided relay selection is proposed using the channel-based greedy scheduling algorithm (CGSA) to overcome the limitations imposed by processing delay. The proposed scheme significantly minimizes average delay by selecting a set of alternative paths based on individual node buffer status. This scheme has less packet delay than existing Max-link scheme with very close system performances, making it an ideal candidate for delay-sensitive applications. Finally, both analytical and simulation results are provided to demonstrate the performance improvement of the proposed algorithm compared with the existing relaying schemes. [ABSTRACT FROM AUTHOR]

With the dense deployment of millimeter wave (mmWave) front ends and popularization of bandwidth-intensive applications, shared backhaul in fiber-wireless (FiWi) networks is still facing a bandwidth crunch. To alleviate the backhaul pressure, in this paper, caching capability is enabled at the edge of FiWi networks, i.e., optical network unit access points (ONU-APs). On the other hand, as both power budget and backhaul bandwidth in FiWi networks are constrained, it is challenging to properly leverage power for caching and that for wireless transmission to achieve superior system performance. As caching has a significant impact on resource allocation, we reconsider performance optimization and analysis in cache-enabled FiWi networks. Firstly, in the cache-enabled FiWi network with mmWave, we formulate the joint power allocation and caching problem, with the goal to maximize the downlink throughput. A two-stage algorithm is then proposed to solve the problem. Secondly, to investigate the theoretical capacity of the cache-enabled FiWi network with mmWave, we derive an upper bound of the downlink throughput by analyzing properties of the average rate of wireless links. Particularly, we show that appropriate power allocation for wireless transmission and caching at ONU-APs is essential to achieve higher throughput. The numerical and simulation results validate our theoretical analysis and demonstrate the proposed algorithm can approach the analytical upper bound. [ABSTRACT FROM AUTHOR]

Place recognition and user positioning methods are becoming trending topics among the research community thanks to the need of providing cutting-edge solutions to enable innovative and efficient Location-Based Services (LBS) and applications. However, the mobile framework suffers from typical constraints on energy and computational capabilities due to the inherent characteristics of wireless mobile devices. For this reason, in order to be suitable for deploying in such framework, a place recognition algorithm should be thrifty in terms of resource consumption. In this paper we consider six different WiFi-based outdoor positioning approaches and we analytically derive an upper-bound for their computational burden in terms of number of FLoating-point OPerations (FLOPs). [ABSTRACT FROM AUTHOR]

The vehicular edge computing is considered an enabling technology for intelligent and connected vehicles since the optimization of communication and computing on edge has a significant impact on driving safety and efficiency. In this paper, with the road traffic assignment to “proactively” reshape the spatiotemporal distribution of resource demands, we investigate the joint service migration and mobility optimization problem for vehicular edge computing. The goal is to meet the service delay requirements of vehicular edge computing with minimum migration cost and travel time. As service migration and mobility optimization are coupled, the joint scheduling problem suffers from the curse of dimensionality, which cannot be solved in real time by centralized algorithms. To this end, a multi-agent deep reinforcement learning (MADRL) algorithm is proposed to maximize the composite utility of communication, computing, and route planning in a distributed way. In the MADRL algorithm, a two-branch convolution based deep Q-network is constructed to coordinate migration action and routing action. Extensive experimental results show that the proposed algorithm is scalable and substantially reduces service delay, migration cost and travel time as compared with the existing baselines. [ABSTRACT FROM AUTHOR]

This paper proposes the integration of non-orthogonal multiple access (NOMA) and broadcasting with buffer-aided (BA) full-duplex (FD) relaying to improve the spectral efficiency of a single-source two-user network. Furthermore, opportunistic relay selection is employed, activating one or two FD relays to support the end-to-end communication. The network performance is improved through buffers, allowing to switch to half-duplex (HD) transmission/reception when FD operation is not feasible. A hybrid BA FD/HD NOMA relay selection algorithm is proposed, herein called $\mathop {\mathrm{BAHyNOMA}}$ , orchestrating the three aforementioned techniques, towards a simple yet efficient and robust relay selection. $\mathop {\mathrm{BAHyNOMA}}$ is evaluated in terms of outage probability, average sum-rate and average delay, and it is observed that the average sum-rate can be doubled, while maintaining low latency. [ABSTRACT FROM AUTHOR]

At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and simultaneously face many security threats. The trust management mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in UWSNs. Therefore, this paper proposes a new trust evaluation and update mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust evidences are first collected including data-based, link-based and node-based trust evidences. Then, the collected trust evidences are used to train the C4.5 decision tree. In addition, the reward and penalty factors are defined to update trust based on the sliding time window. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional ones in a dynamic network environment in terms of malicious node detection and energy consumption. [ABSTRACT FROM AUTHOR]

As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising topics. Especially, how to coordinate the traffic light controllers of multiple intersections is a key challenge for multi-agent reinforcement learning (MARL). Most existing MARL studies are based on traditional $Q$ -learning, but unstable environment leads to poor learning in the complicated and dynamic traffic scenarios. In this paper, we propose a novel multi-agent recurrent deep deterministic policy gradient (MARDDPG) algorithm based on deep deterministic policy gradient (DDPG) algorithm for traffic light control (TLC) in vehiclar networks. Specifically, the centralized learning in each critic network enables each agent to estimate the policies of other agents in the decision-making process and each agent can coordinate with each other, alleviating the problem of poor learning performance caused by environmental instability. The decentralized execution enables each agent to make decisions independently. We share parameters in actor networks to speed up the training process and reduce the memory footprint. The addition of LSTM is beneficial to alleviate the instability of the environment caused by partial observable state. We utilize surveillance cameras and vehicular networks to collect status information for each intersection. Unlike previous work, we have not only considered the vehicle but also considered the pedestrians waiting to pass through the intersection. Moreover, we also set different priorities for buses and ordinary vehicles. The experimental results in a vehicular network show that our method can run stably in various scenarios and coordinate multiple intersections, which significantly reduces vehicle congestion and pedestrian congestion. [ABSTRACT FROM AUTHOR]

Underwater gliders are being increasingly used for data collection, and the development of methods for optimizing their routes has become a topic of active research. With this aim in mind, in this paper, a complete-coverage path-planning obstacle-avoidance (CCPP-OA) algorithm that ensures avoidance for underwater gliders in sea areas with thermoclines is proposed. First, the entire sea area with the thermocline layer is stratified based on the underwater communication radii of the gliders. Next, the glide angles and initial navigation points of the gliders are determined based on their communication radii at each level to construct the complete-coverage path. Finally, by combining the ant colony algorithm and the determined initial navigation points, the complete-coverage path with obstacle avoidance is planned for the gliders. Simulation results show that the proposed CCPP-OA algorithm enables complete coverage of the entire sea area. Furthermore, the length of the planned path is shorter and the amount of energy consumed is less than that of other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid beamforming design algorithm in multi-user massive multiple-input-multiple-output millimeter-wave channels. In the proposed structure, we employ variable radio frequency attenuators at each phase shifter which flexibly controls the transmit signal amplitude to achieve the performance of fully digital (FD) precoders. We formulate a power loss minimization problem which can be easily solved via the alternating optimization process. Simulation results demonstrate that the proposed architecture achieves the same performance as the FD scheme with the reduced number of radio frequency chains and the proposed scheme exhibits a 37.4% power gain over conventional methods. [ABSTRACT FROM AUTHOR]

Ethical decision-making during inevitable crashes, especially when humans involved, has become a big and sensitive roadblock for future mass adoption of autonomous vehicles. Towards addressing this challenge, this paper proposes a predictive control framework for ethical decision-making in autonomous driving using rational ethics. For flexibly implementing of ethical rules, the Lexicographic Optimization-based model predictive controller (LO-MPC) has been designed, in which obstacles and constraints are prioritized. Simulation environment is set up in PreScan, with different edge cases. The results show that the proposed LO-MPC approach has the capability to deal with the ethical decision-making during inevitable crashes by avoiding the obstacles with the assumed priority orders compared with traditional decision-making algorithm. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has become a promising technology for real-time communications. Mobile devices can reduce the energy consumption and prolong the lifetime significantly via offloading the computing tasks to the MEC server. Moreover, physical layer security techniques can ensure the secure transmission of the offloading data. This paper investigates a MEC system that consists of an access point, multiple mobile devices and a malicious eavesdropper. The tasks allocation, local central processor's frequency, offloading power, and offloading timeslots are optimized jointly to minimize the total energy consumption of the system. A difference of convex algorithm based scheme is proposed to solve the joint optimization problem. Moreover, a Karush Kuhn Tucker conditions based algorithm is also proposed to reduce the computational complexity. Numerical results show that the proposed algorithms are very effective. Moreover, the power consumption for secure offloading decreases with the increase of the distance between the mobile devices and the eavesdropper. [ABSTRACT FROM AUTHOR]

Wireless virtual reality (VR) is predicted to become a killer application in 5G and beyond, which provides an immersive experience and revolutionizes the way people communicate. It is well-known that rendering is the key performance bottleneck in wireless VR systems, especially for VR games. However, real-time rendering and data correlation are ignored by most researchers. In this paper, we propose an adaptive VR framework that enables high-quality wireless VR in future mmWave-enabled wireless networks with mobile edge computing (MEC), where real-time VR rendering tasks can be offloaded to MEC servers adaptively and the caching capability of MEC servers enables further performance improvement. First, we formulate the addressed problem to maximize the quality of experience (QoE) of the users, where association, adaptive offloading mode selection, and caching policy are jointly optimized. Considering the high complexity of the addressed problem, we then propose a distributed learning approach consisting of an offline training phase and an online running phase, which maintains scalability and adaptation capability. The offline phase is based on deep reinforcement learning (DRL) while the latter utilizes game theory. At last, simulation results show the superiority of the proposed algorithm over the other baseline algorithms in terms of QoE utility values, latency, and convergence time. [ABSTRACT FROM AUTHOR]

Ultra-reliable and low-latency communications (URLLC) partakes a major role in 5 G networks for mission-critical applications. Sparse vector coding (SVC) appears as a strong candidate for future URLLC networks by enabling superior performance in terms of bit error rate (BER). SVC exploits the virtual digital domain (VDD) and compressed sensing (CS) algorithms to encode and decode its information through active symbol indices. In this paper, first, a clever encoding/decoding algorithm is proposed for the SVC scheme, which allows the use of all possible activation patterns (APs) resulting in increasing spectral efficiency. Second, a novel solution is proposed to convey additional information bits by further exploiting index modulation (IM) for the codebooks of the SVC scheme. Computer simulation results reveal that our low-complexity algorithm and novel IM solution provide not only a superior BER performance but also an increase in the number of bits conveyed by IM compared to the ordinary SVC approach. [ABSTRACT FROM AUTHOR]

Long-Term Evolution (LTE) over unlicensed spectrum extends LTE technology to the spacious unlicensed spectrum with readily available bandwidth. The provided capacity surge makes it one of the most high-profile technologies to meet the explosive growth of mobile traffic demand. Among its different variants, Licensed Assisted Access (LAA) is considered as a promising global solution attributed to its mandatory listen-before-talk (LBT) procedure. Nevertheless, although LBT effectively maintains transmission fairness between LTE and other unlicensed systems (e.g., Wi-Fi), the current LAA protocol specified in 3GPP Release 13 is far from perfect to achieve harmony coexistence. To this end, in this paper, we first develop an analytical model to evaluate the throughput performance of Category 4 (Cat 4) algorithm agreed in 3GPP release 13. Subject to the system fairness constraint, the aggregate throughput of LTE-LAA and Wi-Fi networks is maximized based on a semi branch and bound algorithm. To make the complex optimization tractable, reinforcement learning techniques are introduced to intelligently tune the contention window size for both LTE-LAA and Wi-Fi nodes. Specifically, a cooperative learning algorithm is developed assuming that the information between different systems is exchangeable. A non-cooperative version is subsequently developed to remove the previous assumption for better practicability. Extensive simulations are conducted to demonstrate the performance of the proposed learning algorithms in contrast to the analytical upper bound under various conditions. It is shown that both proposed learning algorithms can significantly improve the total throughput performance while satisfying the fairness constraints. Particularly, the proposed cooperative learning algorithm can closely approach the analytical bound. [ABSTRACT FROM AUTHOR]

Random network delay will introduce uncertainty into trajectory tracking model of the autonomous vehicle, which seriously deteriorates the vehicle's control system stability and trajectory tracking accuracy. In this paper, considering steering angle oscillation caused by random network delay, trajectory tracking system robustness and stability is analyzed and a linear uncertain time-delay system is established. Comprehensively considering control system accuracy, robustness, and computational efficiency in the rolling optimization of Model Predictive Control (MPC), Adaptive Model Predictive Control for Uncertain model (UM-AMPC) algorithm is proposed to predict control variables for the next sampling time and alleviate the target angle discontinuity. This is achieved by operating target angle signal and augmented state variables, which are received by the lower nodes during the period from the current sampling time to network delay upper bound. The hardware-in-the-loop simulation results show that the proposed algorithm can effectively guarantee system stability and tracking accuracy of the autonomous vehicle under random network delay. [ABSTRACT FROM AUTHOR]

The approach of factor-graphs (FGs) is applied in the context of power control and user pairing in Device-to-Device (D2D) communications as an effective underlay concept in wireless cellular networks. D2D communications can increase the spectral efficiency of wireless cellular networks by establishing a direct link between devices with limited help from the evolved node base stations (eNBs). A well-designed user pairing and power allocation scheme with low complexity can remarkably improve the system's performance. In this paper, a simple and distributed FG based approach is utilized for power control and user pairing implementation in an underlay cellular network with D2D communications. A max-min criterion is proposed to maximize the minimum rate of all active users in the network, including the cellular and multiple D2D co-channel links in the uplink direction. An associated message-passing (MP) algorithm is presented to distributedly solve the resultant NP-hard maximization problem, with a guaranteed convergence compared to game theoretic and Q-learning based methods. The complexity and convergence of the proposed method is analyzed and numerical results confirm that the proposed scheme outperforms alternative algorithms in terms of complexity, while keeping the sum-rate of users nearly the same as centralized counterpart methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider parallel and sequential task offloading to multiple mobile edge computing servers. The task consists of a set of inter-dependent sub-tasks, which are scheduled to servers to minimize both offloading latency and failure probability. Two algorithms are proposed to solve the scheduling problem, which are based on genetic algorithm and conflict graph models, respectively. Simulation results show that these algorithms provide performance close to the optimal solution, which is obtained through exhaustive search. Furthermore, although parallel offloading uses orthogonal channels, results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading. On the other hand, parallel offloading provides less latency. However, as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases. [ABSTRACT FROM AUTHOR]

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]

This paper addresses the formulation of power allocation for cluster based multihop vehicular relaying communications in order to improve the network quality-of-services (QoS). The cluster-to-cluster (C2C) channel is completely depend on the power gain of vehicle-to-vehicle (V2V) channel that increases the difficulties of the power allocation problem. To meet this goal, we have derived a global channel obtained by joint V2V and C2C cooperation. The most challenging aspect is however the joint V2V and C2C power allocation of the global channel due to the association of its multi-variables objective function. To solve this problem, we apply an alternative optimization technique to make the global problem into a series of sub-problems. We then apply a semi-definite programming (SDP)-based iterative gradient descent (SDP-IGD) power allocation to assign power in each relay. The SDP-IGD power allocation algorithm has been extended from Lagrange Multiplier (LM) that provides better performance than separate LM technique. The minimum mean-square error (MMSE)-decision feedback equalizer (MMSE-DFE) is designed at the receiver to improve the signal detection capability under exact channel state information (CSI) in all forward-and-backward links. Finally, simulation results confirm that our proposed solution performs significantly superior compared to other solutions found in the literature. [ABSTRACT FROM AUTHOR]

In the heterogeneous networks (HetNets), the downlink-uplink decoupled (DUDE) user association is a very promising technology that can employ different association strategies for uplink and downlink. The DUDE mode has great potential for improving system capacity and frequency efficiency. However, interference between the DUDE user equipment (DUDE UE), macro user equipment (MUE) and small user equipment (SUE) still needs to be considered and properly tackled. Therefore, this paper presents an optimization scheme of frequency allocation and power control to improve the communication quality of users. Firstly, the multiple region frequency allocation (MRFA) scheme is proposed for non-uniform user distribution to alleviate interference among users and allocate spectrum resources equally to dense users. This scheme is modelled as a sub-region partition problem with maximum sum rate, which can disperse densely distributed users to different sub-bands for transmission. Secondly, the convergent power control (CPC) algorithm is considered to improve the transmission performance of each user. It can converge the transmit power to the optimal transmit power for reducing interference among co-channel users when there is no disturbance in the network. The CPC algorithm can also make the signal-to-interference-plus-noise-ratio (SINR) of each active user always greater than the target threshold until the end of communication by dynamically adjusting the transmit power of users. Simulation results show that the joint scheme of MRFA and CPC achieves a higher system throughput and better user performance than existing frequency allocation schemes or power control schemes. [ABSTRACT FROM AUTHOR]