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Vehicle-to-everything (V2X) communications face a great challenge in delivering not only the low-latency and ultra-reliable safety-related services but also the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. This paper focuses on the robust resource management of V2X communications with the consideration of channel uncertainties. First, we formulate a transmit power minimization problem, whilst guaranteeing the different quality-of-service (QoS) requirements. To achieve the robustness of QoS provisions against channel uncertainties, a statistical leaning approach is developed to learn the uncertainties from the data samples of the random channel coefficients as a convex ellipsoid set, which is also called high-probability-region (HPR). Then, the highly intractable power minimization problem is converted into a second-order cone program by the robust optimization approach. Afterwards, we propose a joint set partitioning and reconstruction mechanism to further reduce the total transmit power by pruning the rough HPR into a more precise uncertainty set, which leads to a trackable second-order cone program and a linear program. Finally, we prove that the network performance can be effectively enhanced by the improvement mechanism. Simulation results verify the effectiveness of the robust resource allocation approaches over the non-robust one.

Channel estimation in vehicle-to-everything (V2X) communications is a challenging issue due to the fast time-varying and non-stationary characteristics of wireless channel. To grasp the complicated variations of channel with limited number of pilots in the IEEE 802.11p systems, data pilot-aided (DPA) channel estimation has been widely studied. However, the error propagation in the DPA procedure, caused by the noise and the channel variation within adjacent symbols, limits the performance seriously. In this letter, we propose a deep learning based channel estimation scheme, which exploits a long short-term memory network followed by a multilayer perceptron network to solve the error propagation issue. Simulation results show that the proposed scheme outperforms currently widely-used DPA schemes for the IEEE 802.11p-based V2X communications.

This paper proposes a deep learning (DL) resource allocation framework to achieve the harmonious coexistence between the transceiver pairs (TPs) and the Wi-Fi users in LTE-U networks. The noncon- vex resource allocation is considered as a constrained learning problem and the deep neural network (DNN) is employed to approximate the optimal resource allocation decisions through unsupervised manner. A parallel DNN framework is proposed to deal with the two optimization variables in this problem, where one is the licensed power allocation unit and the other is the unlicensed time fraction occupied unit. Besides, to guarantee the feasibility of the proposed algorithm, the Lagrange dual method is used to relax the constraints into the DNN training process. Then, the dual variable and the DNN parameter are alternating update via the batch-based gradient decent method until the training process converges. Numerical results show that the proposed algorithm is feasible and has better performance than other general algorithms.

Tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ) is a challenging problem. To overcome the complicated problem, we proposed a tracker based on the multiple model probability hypothesis density (MM-PHD) filter. However, the PHD filter is only the first-order moment approximation of the multi-target Bayesian filter, and it cannot output track labels in principle. In order to improve the tracking performance, another novel tracker is proposed in this paper. To track multiple maneuvering targets, the proposed tracker is built on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. Moreover, it incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Finally, a measurement-driven adaptive track initiation is introduced to address the fixed track initiation problem of the standard MM-GLMB filter. It is demonstrated through numerical examples that the proposed tracker outperforms the existing work significantly, especially in terms of both accuracy and robustness of cardinality estimation.

To track multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we have proposed an MM-GLMB-DBZ tracker based on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. To further enhance the tracking performance, this paper combines the sensor control technique to the MM-GLMB-DBZ tracker. Macroscopically, the proposed algorithm consists of the MM-GLMB-DBZ tracker and a controller. Unlike conventional control approaches where separate prediction and update implementations are usually adopted, the proposed control algorithm constructs a systematic process flow for the joint prediction and update implementation of GLMB-like filters. Moreover, inside the core controller module, we apply the previously designed safety indicator and reward function for avoiding the DBZ, and derive the Cauchy-Schwarz divergence (CSD) compatible with the tracker. Hence, this control algorithm considers such factors as the safety of the sensor itself, the DBZ avoidance, and the improvement of the tracking accuracy. Numerical examples verify the effectiveness of the proposed control scheme, showing that it is significantly better than the random control strategy, the original MM-GLMB-DBZ tracker without the control technology, and the state-of-the-art control approach.

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead.

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named ${M}$ time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other ${M}-{1}$ time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks.

To improve the performance of tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we put forward the idea of using sensor control technique to suppress the DBZ masking problem for the first time, by utilizing the principle that the absolute Doppler of a target with respect to a sensor is affected by the target-to-sensor relative geometry and extending multi model probability hypothesis density (MM-PHD) filter for DBZ masking to the partially observable Markov decision process (POMDP) framework. First, the process flow of sensor control is systematically constructed based on our existing work. Second, in the core sensor controller module, we devise three objective functions (including a new safety indicator ensuring sensor safety, a novel reward rule for the DBZ avoidance, and the Cauchy-Schwarz divergence (CSD) compatible with the multi-maneuvering-target tracking) and a decision-making logic for the selection of control commands. Finally, the feasibility and effectiveness of the proposed control scheme are verified through numerical examples, and it is demonstrated that it is obviously superior to the random control strategy and the earlier work without using the control technology.

For a ground moving target indication (GMTI) radar, the presence of Doppler blind zone (DBZ) results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.

LTE deployed with both licensed and unlicensed bands (LTE-U) is one of the promising approaches to meet the rapidly growing data demand in wireless networks. In this paper, both throughput and fairness for the LTE-U system are maximized by a multi-objective optimization problem. Then, a log-sum-exp approximation method is developed to convert the multi-objective optimization into a single objective optimization problem. At the same time, the tradeoff between throughput and fairness is mathematically depicted by a control parameter. To tackle the obtained single objective optimization problem, a Markov chain directed algorithm is developed to convert it into a coexistence protocol design subproblem at the MAC layer and a resource allocation subproblem at the physical layer, respectively. Then, we propose adaptive exponential backoff schemes for both the LTE-U devices and the incumbent devices on the unlicensed bands. After that, a low-complexity two-iterative optimization procedure is developed to jointly allocate the licensed and unlicensed resources of the LTE-U system. The simulation results show that our proposed coexistence protocol and resource allocation can achieve fair coexistence between the LTE-U devices and the incumbent devices on the unlicensed bands, moreover it can achieve higher throughput than the non-adaptive coexistence protocol in the unlicensed bands.

Deploying a radio frequency (RF) access point (AP) to the visible light communication (VLC) system is a promising strategy to overcome the VLC’s limitations, such as limited coverage, strictly line-of-sight transmission, and mobility robustness, etc. In this paper, we focus on the energy-aware design of network selection and resource allocation for a heterogeneous network combining with RF and VLC APs. For adapting to different timescale network states and stochastic data arrival, we propose an on-line two-timescale adaptive network resource optimization (ANRO) framework by employing the Lyapunov optimization technique. At the large timescale, we first develop a closed-form solution for the subproblem of network selection for user equipment. Second, we design a cost-effective and easy-to-realize algorithm for VLC’s joint transmission scheduling and power control subproblem, which is a nonconvex optimization. While at the small timescale, we obtain the optimal solution for RF’s joint resource block and power allocation subproblem, which is proven a mixed integer nonlinear optimization. Simulation results demonstrate that the ANRO can achieve a tradeoff between network power consumption and delay. Furthermore, it not only can stabilize the network but also can significantly reduce the energy consumption compared with other existing schemes.

This paper investigates the energy-efficient traffic splitting for time-varying wireless networks, which have been configured with multiple radio access technologies (multi-RATs). A single stream of the media content is split into multiple segments, which could be transmitted over multiple RATs simultaneously so that the complementary advantages of different RATs can be exploited. To address this problem, we formulate the traffic splitting as a long-term energy efficiency (EE) maximization problem with respect to the time-varying channel state information (CSI). An equivalent transformation method is proposed to convert the long-term nonconvex EE maximization problem into a concave optimization. To reduce the computational complexity, we develop a dynamic traffic splitting (DTS) algorithm, which iterates only one time when the network state changes. Then, we use the definition of tracking error to describe the difference between the DTS and the target optimal traffic splitting solution. After that, an adaptive-compensation traffic splitting (ACTS) algorithm is proposed to offset the tracking error so as to enhance the EE performance. More specifically, we give a sufficient condition for significantly eliminating the tracking errors of the ACTS algorithm. Simulation results show that the proposed ACTS algorithm obtains the EE performance comparable with the optimal solution at the overhead of only a single iteration at each timeslot of the network state acquisition.

In heterogeneous wireless networks (HetNet), the multihomed user equipment (UE) utilizes multiple access options (AO) simultaneously and aggregate the offered resources from different AOs so as to support the same application, such as media streaming. Two important challenges of the resource optimization in this context are: 1) determining the connection results between UEs and AOs, and 2) determining the amount of radio resources that AOs should allocate to UEs. In this paper, we investigate the energy-efficient resource optimization for the HetNet with multihomed UEs. First, the energy-efficient resource optimization is formulated as an energy efficiency (EE) maximization problem. Second, the nonconcave objective function of EE maximization is converted by a fractional programming theory into a convex optimization problem. Considering that the connection results between UEs and AOs correspond to certain network topology configurations, the convex optimization problem is decomposed into a topology building problem and a resource allocation problem, which are proved as combination optimization and mixed integer nonlinear optimization, respectively. Then, the Markov approximation framework and continuity relaxation method are employed for solving the two problems, respectively. Finally, a joint topology building and resource allocation algorithm are proposed for optimizing the AOs radio resource energy efficiently. Simulation results validate the theoretical analysis of our proposed scheme.

In this paper, we investigate the resource allocation problem in time-varying heterogeneous wireless networks (HetNet) with multi-homing user equipments (UE). The stochastic optimization model is employed to maximize the network utility, which is defined as the difference between the HetNet's throughput and the total energy consumption cost. In harmony with the hierarchical architecture of HetNet, the problem of stochastic optimization of resource allocation is decomposed into two subproblems by the Lyapunov optimization theory, associated with the flow control in transport layer and the power allocation in physical (PHY) layer, respectively. For avoiding the signaling overhead, outdated dynamic in- formation, and scalability issues, the distributed resource allocation method is developed for solving the two subproblems based on the primal-dual decomposition theory. After that, the adaptive re- source allocation algorithm is developed to accommodate the time- varying wireless network only according to the current network state information, i.e. the queue state information (QSI) at radio access networks (RAN) and the channel state information (CSI) of RANs-UE links. The tradeoff between network utility and delay is derived, where the increase of delay is approximately linear in V and the increase of network utility is at the speed of 1/V with a control parameter V . Extensive simulations are presented to show the effectiveness of our proposed scheme.

In this paper, we design an adaptive multi-homing resource allocation algorithm for time-varying heterogeneous wireless networks (HetNet), where the algorithm iteration timescale is the same to the network state acquisition timescale. First, the network utility maximization is characterized by a stochastic optimization model. Second, the multi-homing resource allocation (MHRA) algorithm is developed to accommodate the dynamic wireless network states, i.e., time-varying wireless channels between the access points (AP) and mobile terminals and as well the queuing dynamics at the APs. Then, we investigate the tracking error between the MHRA algorithm output and the target optimal resource allocation solution. Based on these results, an adaptive-compensation multi-homing resource allocation (AMRA) algorithm is proposed to offset the tracking error so as to enhance the network utility. Specifically, we give a sufficient condition that the AMRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Finally, we derive a tradeoff between network utility and media transmission delay, where the increase of average delay is approximately linear in $V$ and the increase of network utility is at the speed of $1/V$ with the control parameter $V$ . Simulation results validate the theoretical analysis of our proposed scheme.

When there is the correlation between Doppler and slant range, previous literatures have presented some sequential filter algorithms. However, they are only applied to simplified fixed radar’ s local coordinate system. As a result, a Sequential converted measurement Kalman filter (SCMKF) with Doppler measurements based on the Earth centered earth fixed (ECEF) coordinate system applicable to a moving airborne platform which has time varying attitude is proposed. Firstly, the correlated Doppler and range are decorrelated using the Cholesky factorization, then the converted position measurements are obtained by a series of coordinate transformation with unchanged range component and other observations, such as azimuth and elevation angles; the corresponding error covariances are derived which are used to the Converted measurement Kalman filter (CMKF). Finally the sequential filter is implemented for changed pseudo-Doppler measurements. The proposed method is validated through Monte Carlo test compared with the performance of CMKF with just converted position measurements and traditional SCMKF with Doppler which ignores the correlation between Doppler and range noises, and the conclusion is obtained that utilizing Doppler information correctly can improve tracking performance, nevertheless, the improvement gain of filter accuracy is limited, which can provide some references for engineering application.