Deep reinforcement learning (DRL) has been extensively applied to Multi-Unmanned Aerial Vehicle (UAV) network (MUN) to effectively enable real-time adaptation to complex, time-varying environments. Nevertheless, most of the existing works assume a stationary user distribution (UD) or a dynamic one with predicted patterns. Such considerations may make the UD-specific strategies insufficient when a MUN is deployed in unknown environments. To this end, this paper investigates distributed user connectivity maximization problem in a MUN with generalization to arbitrary UDs. Specifically, the problem is first formulated into a time-coupled combinatorial nonlinear non-convex optimization with arbitrary underlying UDs. To make the optimization tractable, a multi-agent CNN-enhanced deep Q learning (MA-CDQL) algorithm is proposed. The algorithm integrates a ResNet-based CNN to the policy network to analyze the input UD in real time and obtain optimal decisions based on the extracted high-level UD features. To improve the learning efficiency and avoid local optimums, a heatmap algorithm is developed to transform the raw UD to a continuous density map. The map will be part of the true input to the policy network. Simulations are conducted to demonstrate the efficacy of UD heatmaps and the proposed algorithm in maximizing user connectivity as compared to K-means methods.

With the electrification of the automobile system, the overload problem is being incurred by the increased charging demands from the electric vehicles (EVs). To avoid overloading in the power grid, microgrids (MGs) can be integrated to assist the power balancing. In addition, a coordinated charging strategy among EVs can mitigate the overload problem based on spatially and temporally varying distribution of vehicle traffic in transportation. However, few works have been done on the integration of power system and transportation system in large-scale realistic EV networks. In this paper, both the power distribution and transportation systems are integrated in the high-fidelity and at-scale co-simulation models. Specifically, an LSTM-based prediction model of vehicle traffic distribution is first built and trained over realistic vehicle trace files. The predicted vehicle traffic distribution is exploited to forecast the future EV charging demand. The distribution system is then simulated to describe how the loads (e.g., controllable loads and EVs) and supplies (e.g., distributed generations and energy storages in MGs) impact a power system across the region at scale. Based on the forecast EV loads and co-simulation results from the integrated system, a spatio-temporal coordinated fast EV charging strategy is developed and executed in a distributed way to improve the reliability and resilience of the power systems. Numerical results demonstrate that our proposed strategy can improve the total EV charging performance in the power system while maintaining the power balance of the networked MGs.

Multi-agent reinforcement learning has been applied to Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) to effectively solve the problem of time-coupled sequential decision making while achieving scalability. Nevertheless, a transverse comparison on the impact of different levels of inter-agent information exchange on the learning convergence has not been well studied. In this work, we study a distributed user connectivity maximization problem in a UCN, aiming to obtain a trajectory design to optimally guide UAVs' movements in a time horizon to maximize the accumulated number of connected users. Specifically, the problem is first formulated into a time- coupled mixed-integer non-convex optimization problem. A two- stage user association policy is proposed to determine the UAV- user connectivity. A multi-agent deep Q learning algorithm is then designed to solve the optimization, featuring four different levels of information exchange and reward function design. Simulations are conducted to compare the convergence speed and total number of connected users per episode between different levels. The results show that exchanging state information with a deliberated task-specific reward function design yields the best convergence performance in both cases of stationary and dynamic user distributions.

This work studies optimal solar charging for solar-powered self-sustainable UAV communication networks, considering the day-scale time-variability of solar radiation and user service demand. The objective is to optimally trade off between the user coverage performance and the net energy loss of the network by proactively assigning UAVs to serve, charge, or land. Specifically, the studied problem is first formulated into a time-coupled mixed-integer non-convex optimization problem, and further decoupled into two sub-problems for tractability. To solve the challenge caused by time-coupling, deep reinforcement learning (DRL) algorithms are respectively designed for the two sub-problems. Particularly, a relaxation mechanism is put forward to overcome the “dimension curse” incurred by the large discrete action space in the second sub-problem. At last, simulation results demonstrate the efficacy of our designed DRL algorithms in trading off the communication performance against the net energy loss, and the impact of different parameters on the tradeoff performance.

The newly emerging intelligent reflecting surface (IRS) with large-scale passive reflecting elements has great potentials to enhance the performance of wireless-powered Internet of Things (IoT) networks, by manipulating the wireless channel. However, most of the existing works considered the ideal reflection of IRS elements with independent amplitude and phase shift. In this article, an IRS-assisted wireless-powered multiuser multi-input-multi-output network is considered, taking into account the practical coupling effect between the reflecting amplitude and the phase shift. Then, an uplink sum-rate maximization problem is investigated by jointly designing the active beamforming of multiple antennas, the passive beamforming of the IRS, and the time allocation ratio. Due to the tightly coupled optimization variables, the formulated problem is nonconvex. To effectively solve this problem, we decompose it into three subproblems, i.e., the active beamforming, the downlink passive beamforming, and the uplink passive beamforming. For the active beamforming design, access point’s optimal downlink energy beamforming matrix is proved to be rank-one, and IoT users’ optimal uplink information covariance matrices are derived in semi-closed forms. For the downlink passive beamforming design, a low-complexity algorithm based on the successive convex approximation and the penalty function method is proposed. For the uplink passive beamforming design, the multiuser problem is equivalently transformed into a virtual single-user problem, which is solved via an iterative algorithm. Numerical results show that, in comparison with algorithms without IRS, our proposed algorithm can significantly improve the uplink sum rate up to 50% when the number of passive elements is 100.

As a main use case of 5G and Beyond wireless network, the ever-increasing machine type communications (MTC) devices pose critical challenges over MTC network in recent years. It is imperative to support massive MTC devices with limited resources. To this end, Non-orthogonal multiple access (NOMA) based random access network has been deemed as a prospective candidate for MTC network. In this paper, we propose a deep reinforcement learning (RL) based approach for NOMA-based random access network with truncated channel inversion power control. Specifically, each MTC device randomly selects a pre-defined power level with a certain probability for data transmission. Devices are using channel inversion power control yet subject to the upper bound of the transmission power. Due to the stochastic feature of the channel fading and the limited transmission power, devices with different achievable power levels have been categorized as different types of devices. In order to achieve high throughput with considering the fairness between all devices, two objective functions are formulated. One is to maximize the minimum long-term expected throughput of all MTC devices, the other is to maximize the geometric mean of the long-term expected throughput for all MTC devices. A Policy based deep reinforcement learning approach is further applied to tune the transmission probabilities of each device to solve the formulated optimization problems. Extensive simulations are conducted to show the merits of our proposed approach.

Vehicular communication networks hold promise to significantly improve road safety by giving both automated vehicles and human drivers improved awareness and advanced warning to emergencies. The emergency messages are broadcasted upon emergency detection, but this does not guarantee recipients will be able to avoid collision. In this paper, we introduce a method to relate the delay tolerance of each vehicle in the network directly to the transmission range by taking into consideration the reaction time of the drivers in order to ensure each vehicle in the network can avoid a collision. The system utilizes a digital-twin system to maintain network awareness and accounts for the coexistence of automated vehicles and human driving vehicles and allows the network to minimize transmission range while effectively assuring the safety. The proposed strategy is tested in simulated road scenarios generated from measured highway traffic data. The simulation results demonstrate the efficacy of the proposed strategy through extensive evaluation of multiple traffic scenarios.

In this paper, we analytically study the performance of non-orthogonal multiple access (NOMA) transmissions in a random access network with truncated channel inversion power control. Specifically, in a slotted ALOHA network in support of NOMA transmissions, a wireless device randomly selects the transmission power with a certain probability, using channel inversion power control yet subject to the upper bound of the transmission power. Taking into consideration the stochastic nature of wireless fading channels, we first quantify two network areas such that devices in different areas have various choices of transmission powers for NOMA transmissions. An analytical model is developed to analyze the successful transmission probability and throughput of wireless devices located in different areas. Based on the analysis, two optimization problems are formulated to maximize the network throughput and the minimum throughput of wireless devices by tuning the transmission probabilities of each device. To solve the formulated combinatorial optimization problems, two heuristic algorithms are proposed. Extensive simulations are conducted to validate the analysis, and verify the efficiency of the proposed algorithm to attain the maximum network throughput and max-min fairness.

In this article, a cybertwin-assisted nonorthogonal random access (RA) system is presented, where the cybertwins of the physical devices at the access point (AP) collect the devices’ information and decide the transmission parameters on behalf of the devices to achieve the maximum system performance. Specifically, the system performance of a$p$-persistent slotted CSMA system with nonorthogonal multiple access (NOMA) is analyzed, in which wireless devices transmit data to the ensure the received signal strength at the AP side is either high power or low power with certain probabilities. We first develop an analytical framework to quantify the successful transmission probability and the sum data rate as a function of the above probabilities. Accordingly, the feasible region of the number of high-power and low-power devices to ensure successful transmission is derived. With the analysis, nonconvex optimization problems are then formulated to maximize successful transmission probability and the sum data rate, respectively. To tackle the nonconvexity, an effective and fast-convergent iterative algorithm is designed to obtain the optimal transmission probabilities for the devices. Extensive simulations are conducted to validate our analytical results and demonstrate the benefits of NOMA in RA networks.

In this article, we exploit nonorthogonal multiple access (NOMA) for simultaneous energy and information transfer in a wireless-powered Internet-of-Things (IoT) network. As double near-far problem causes severe unfairness, we propose a fairness-aware NOMA-based scheduling scheme to enhance the max-min fairness. Specifically, according to the channel conditions, we divide IoT devices into the interference and noninterference groups with relatively good and poor channel qualities, respectively. Energy transfer is concurrently scheduled with data transmissions of devices with good channels. Thus, devices can harvest more energy to achieve higher rates at the cost of reduced rates of devices with good channels due to the interfering energy signals. We then apply order statistics to theoretically analyze the achievable rates of ordered devices. Based on the analysis, devices are optimally categorized into the interference and noninterference groups to achieve the max-min fairness, i.e., the minimum rate of devices in both groups is maximized. An adaptive power allocation algorithm is also proposed to further improve the network fairness when the transmission power of the energy transmitter is controllable. Throughput-aware NOMA-based scheduling is also presented and compared with the fairness-aware NOMA-based scheduling to illustrate the performance tradeoff between the throughput and fairness. The simulation results validate that the proposed NOMA-based scheduling schemes significantly improve the fairness and throughput performance of wireless-powered IoT networks, compared with the existing solutions.

Multi-Unmanned Aerial Vehicle (UAV) control is one of the major research interests in UAV-based networks. Yet few existing works focus on how the network should optimally react when the UAV lineup and user distribution change. In this work, proactive self-regulation (PSR) of UAV-based networks is investigated when one or more UAVs are about to quit or join the network, with considering dynamic user distribution. We target at an optimal UAV trajectory control policy which proactively relocates the UAVs whenever the UAV lineupis about tochange, rather than passively dispatches the UAVsafterthe change. Specifically, a deep reinforcement learning (DRL)-based self-regulation approach is developed to maximize the accumulated user satisfaction (US) score for a certain period within which at least one UAV will quit or join the network. To handle the changed dimension of the state-action space before and after the lineup changes, the state transition is deliberately designed. To accommodate continuous state and action space, an actor-critic based DRL, i.e., deep deterministic policy gradient (DDPG), is applied with better convergence stability. To effectively promote learning exploration around the timing of lineup change, an asynchronous parallel computing (APC) learning structure is proposed. Referred to as PSR-APC, the developed approach is then extended to the case of dynamic user distribution by incorporating time as one of the agent states. Finally, numerical results are presented to demonstrate the convergence and superiority of PSR-APC over a passive reaction method, and its capability in jointly handling the dynamics of both UAV lineup and user distribution.

Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one of the major research interests in UAV based networking. Yet few existing works have focused on how the network should react around the timing when the UAV lineup is changed. In this work, we study proactive self-remedy of energy-constrained UAV networks when one or more UAVs are short of energy and about to quit for charging. We target at an energy-aware optimal UAV control policy which proactively relocates the UAVs when any UAV is about to quit the network, rather than passively dispatches the remaining UAVs after the quit. Specifically, a deep reinforcement learning (DRL)-based self remedy approach, named SREC-DRL, is proposed to maximize the accumulated user satisfaction scores for a certain period within which at least one UAV will quit the network. To handle the continuous state and action space in the problem, the state-of-the-art algorithm of the actor-critic DRL, i.e., deep deterministic policy gradient (DDPG), is applied with better convergence stability. Numerical results demonstrate that compared with the passive reaction method, the proposed SREC-DRL approach shows a 12.12% gain in accumulative user satisfaction score during the remedy period.

Non-orthogonal multiple access (NOMA) has been considered as a promising solution for improving the spectrum efficiency of next-generation wireless networks. In this paper, the performance of a p-persistent slotted ALOHA system in support of NOMA transmissions is investigated. Specifically, wireless users can choose to use high or low power for data transmissions with certain probabilities. To achieve the maximum network throughput, an analytical framework is developed to analyze the successful transmission probability of NOMA and long term average throughput of users involved in the non-orthogonal transmissions. The feasible region of the maximum number of concurrent users using high and low power to ensure successful NOMA transmissions are quantified. Based on analysis, an algorithm is proposed to find the optimal transmission probabilities for users to choose high and low power to achieve the maximum system throughput. In addition, the impact of power settings on the network performance is further investigated. Simulations are conducted to validate the analysis.

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high dynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter V and the decrease of grid power consumption cost is at the speed of 1/V. Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm.

Unmanned aerial vehicles (UAV) deployment is of pivotal importance in the promising space-air integrated localization network (SAILN), which is a typical partially controllable network and supports 3- dimensional (3D) localization. To improve the localization accuracy for specific area or user, several UAVs need to be deployed. This paper proposes an iterative UAV deployment strategy for SAILN, which can minimize the localization error by determining accurate 3D coordinate information (elevation and azimuth angles, distance) for all the supplementary UAVs. Specifically, based on the analysis of accuracy increment when a new UAV is added into SAILN, the genetic algorithm (GA) is leveraged to find its optimal geometric position in a constrained area that can maximize the accuracy increment. Then, UAVs are iteratively added until the desired number, i.e., the quantity budget of deployed UAVs, is achieved. Simulation results demonstrate that the proposed UAV deployment strategy provides considerably better localization accuracy compared with uniform angular arrays (UAA) and random deployment (RD).

In this paper, a localization-based polar construction method is proposed to directly find the set of synthetic channels for information bits given a code configuration. Taking advantage of the partial order of polar codes, only a small number of synthetic channels need to be ordered, which scales as O(N/ log23/2 N), resulting in a sublinear complexity to construct a polar code. Specifically, a practical method is put forward first to fast construct a group-based partial order diagram. A local area in the diagram with adaptive boundaries is then identified. By ordering the synthetic channels within the local area and combining selected ones with all the synthetic channels beyond the local area, the final set of synthetic channels for information bits are determined. Simulation results demonstrate how to adapt the boundary settings to different rate matching schemes and code configurations, and validate the effectiveness of the proposed method compared with the density evolution based methods.

This paper investigates quality of service (QoS) provisioning for Internet of Things (IoT) in long-term evolution advanced (LTE-A) heterogeneous networks (HetNets) with partial spectrum usage (PSU). In HetNets, the IoT users with ubiquitous mobility support or low-rate services requirement can connect with macrocells (MCells), while femtocells (FCells) with PSU mechanism can be deployed to serve the IoT users requiring high-data-rate transmissions within small coverage. Despite the great potentials of HetNets in supporting various IoT applications, the following challenges exist: 1) how to depict the unplanned random behaviors of the IoT-oriented FCells and cope with the randomness in user QoS provisioning and 2) how to model the interplay of resource allocation (RA) between MCells and FCells under PSU mechanism. In this work, the stochastic geometry (SG) theory is first exploited to statistically analyze how the unplanned random behaviors of the IoT-oriented FCells impact the user performance, considering the user QoS requirements and FCell PSU policy. Particularly, to satisfy the QoS requirements of different IoT user types, the concept of effective bandwidth (EB) is leveraged to provide the users with probabilistic QoS guarantee, and a heuristic algorithm named QA-EB algorithm is proposed to make the EB determination tractable. Then, the interplay of RA between the MCells and FCells is formulated into a two-level Stackelberg game, where the two parties try to maximize their own utilities through optimizing the macro-controlled interference price and the femto-controlled PSU policy. A backward induction method is proposed to achieve the Stackelberg equilibrium. Finally, extensive simulations are conducted to corroborate the derived SINR and ergodic throughput performance of different user types and demonstrate the Stackelberg equilibrium under varying user QoS requirements and spectrum aggregation capabilities.

In this paper, a new MAC protocol for LTE over unlicensed spectrum (LTE-U) is presented that allows friendly co-existence of LTE-U with other unlicensed wireless networks, including Wi-Fi. Specifically, in a time-slotted LTE-U system, LTE- U users can transmit continuously for a period after a successful channel reservation during the spectrum sensing period. Following each LTE transmission period, a certain duration is reserved for asynchronous Wi-Fi transmissions. By adaptively adjusting the periods of LTE transmissions, Wi-Fi transmissions, and spectrum sensing, different levels of Wi-Fi protection can be achieved. Based on the proposed MAC, an analytical model is developed to study the throughput performance of both LTE-U and Wi-Fi, considering the asynchronous transmission nature of Wi-Fi within the time-slotted MAC structure. Impacts of the protocol parameters, i.e., the periods of LTE/Wi-Fi transmissions and spectrum sensing, on the throughput performance of LTE-U and Wi-Fi are also investigated. Extensive simulation results are provided to validate the analysis.

Future small cells are expected to be energy-efficient and utilize green technologies. To this end, a promising solution is to employ automatic energy harvesting techniques, such as power splitting (PS) which harvests energy from ambient radio frequency (RF) signals in modern communication systems. In this paper, optimization on PS ratio design is investigated to maximize the average harvested energy in the context of general largescale K-tier heterogeneous cellular networks (HCNs). Specifically, coverage probabilities and average energy harvesting expressions are derived with the stochastic geometry treatment to elucidate the performance of future green networks. Then optimal fixed PS ratios for each tier are obtained under coverage performance constraints. Moreover, with receivers' position information effortlessly provided in future networks, a dynamic location-based PS ratio design (DLPS) is proposed to further enhance the energy harvesting performance. Simulation results are given to demonstrate that the average harvested energy is effectively increased by more than 30% when coverage probability requirement is greater than 0.7 by our proposed DLPS compared with the optimal fixed PS ratio while maintaining the coverage performance. Furthermore, rather than drawing the conclusions about the merits of our PS strategy, this work is to provide a tractable analytical framework for addressing the energy harvesting issues in such HCNs.

A vehicle-to-vehicle (V2V) (dis)charging strategy can provide charging plans for gridable electric vehicles (GEVs), aiming to offload the heavy power loads from the electric power system. However, designing an efficient online V2V (dis)charging strategy to achieve optimal energy utilization is still an open issue. In this paper, we propose a semi-distributed online V2V (dis)charging strategy at a swapping station based on price control. Specifically, based on the electricity price control strategy, GEVs are motivated to contribute to a V2V energy transaction due to expected high revenue for discharging GEVs and low cost for charging GEVs. The Oligopoly game and Lagrange duality optimization techniques are exploited to address the associated optimal V2V (dis)charging strategies. Simulation results are presented to demonstrate the performance of the proposed V2V (dis)charging strategy.

Green energy sources, such as solar and wind, provide an alternative solution for powering wireless networks. To maximize the utilization of green energy charged from different sources, it is desirable to allow energy trade among neighbor cells. In this paper, the local energy trade issues in a wireless mesh network powered by green energy are studied such that energy can be purchased either from neighbor cells or from electricity grid, based on the energy charging and discharging characteristics in each cell. Our objective is to determine the optimal price and quantity of energy purchase and sale for each cell such that the profits of all cells can be maximized and their energy demands can be fulfilled. To this end, the energy trading problem is formulated as a Stackelberg game. Based on the utility function, the closed-form expressions of the optimal energy quantity and price for trading are derived. Finally, an optimal scheme, namely, Optimal Profits Energy Trading (OPET), is proposed to maximize the profits of all cells. The proposed OPET can achieve the optimal solution with polynomial time complexity. Extensive simulations are conducted to verify the performance of the proposed scheme.

Carrier Aggregation is considered as a key revolution in Long Term Evolution-Advanced systems to meet the explosively increasing aspiration for high data rates. Unlike extensive simulative evaluations on CA in literature, current theoretical analysis on CA is not convincing due to lack of effective interference modeling. In this paper, we exploit the theory of stochastic geometry to provide tractable statistical interference modeling for downlink CA in LTE-A systems. Our objective is to demonstrate the benefits of CA by comparing the user performance between the legacy LTE users and LTE-A users. Specifically, we first model the distributions of base stations and users into Poisson Point Processes. Then, the user service probability and subchannel usage in each carrier are calculated for LTE and LTE-A users, respectively. The obtained probabilities are applied to derive the user SINR distribution and ergodic rates. To better clarify the impact of system/user parameters on the investigated performance, a special case is presented where the network is interference-limited and the channel fast fading is considered as Rayleigh fading. Finally, simulation results validate our analytical model and demonstrate that LTE-A users can achieve significantly better SINR and ergodic-rate performance than LTE users when the cell is not heavily loaded.

Drive-thru Internet has become a popular solution vehicular Internet access. However, the quality-of-service provisioning for high-data-rate drive-thru Internet services poses significant challenges upon medium access control (MAC) in a large-scale and highly dynamic vehicular environment. In this paper, to achieve high throughput in drive-thru Internet, we exploit the mobility interdependency between neighboring vehicles and propose a density-adaptive MAC protocol by predicting the vehicle-density dynamics. Specifically, to predict the vehicle-density fluctuations in the drive-thru Internet scenario, we leverage Navier-Stokes equations to characterize the mobility interdependence and the dynamically waving vehicle-density which can be observed from the simulated vehicle traces in VISSIM. In tune with the dynamic vehicle-density, we propose an enhanced MAC protocol to dynamically adjust the contention window (CW) setting in order to improve the throughput. Extensive simulations validate the effectiveness of the utilized mobility model, and demonstrate the efficiency of the proposed MAC protocol in improving the overall resultant system throughput.

Coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. However, designing an efficient coordinated charging strategy to route mobile EVs to fast-charging stations for globally optimal energy utilization is very challenging. In this paper, we investigate a special smart grid with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits vehicular ad-hoc networks (VANETs) to support real-time communications among road-side units (RSUs) and highly mobile EVs for collecting real-time vehicle mobility information or dispatching charging decisions. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. Specifically, the mobility-incurred travel cost for an EV is considered in two aspects: 1) the travel distance from the current position of the EV to a charging station; and 2) the transmission delay for receiving a charging decision via VANETs. The optimal mobility-aware coordinated EV charging problem is formulated as a time-coupled mixed-integer linear programming problem. By solving this problem based on Lagrange duality and branch-and-bound-based outer approximation techniques, an efficient charging strategy is obtained. To evaluate the performance of the proposed strategy, a realistic suburban scenario is developed in VISSIM to track vehicle mobility through the generated simulation traces, based on which the travel cost of each EV can be accurately calculated. Extensive simulation results demonstrate that the proposed strategy considerably outperforms the traditional EV charging strategy without VANETs on the metrics of the overall energy utilization, the average EV travel cost, and the number of successfully charged EVs.

In this paper, we analyze the user accommodation capabilities of LTE-A systems with carrier aggregation for the LTE users and LTE-A users, respectively. The adopted performance metric is equivalent capacity (EC), defined as the maximum number of users allowed in the system given the user QoS requirements. Specifically, both LTE and LTE-A users are divided into heterogeneous user classes with different QoS requirements, traffic characteristics and bandwidth weights. Two bandwidth allocation strategies are studied, i.e., the fixed-weight strategy and the cognitive-weight strategy, where the bandwidth weights of different user classes are prefixed under the former and dynamically changing with the cell load conditions under the latter. For each strategy, closed-form expressions of ECs of different user classes are derived for LTE and LTE-A users, respectively. A net-profit-maximization problem is further formulated to discuss the tradeoff among the bandwidth weights. Extensive simulations are conducted to corroborate our analytical results, and demonstrate an interesting discovery that only a slightly higher spectrum utilization of LTE-A users than LTE users can result in a significant EC gain when the user traffic is bursty. Moreover, the cognitive-weight strategy is shown to outperform considerably the fixed-weight one due to stronger adaptability to the cell load conditions.

The Long Term Evolution - Advanced (LTE-A) standard is widely accepted for the 4th generation mobile systems to satisfy the explosive growth of high-data-rate demand. Carrier Aggregation (CA) is considered as one of the most momentous techniques adopted in LTE-A standard. Many studies have been done to analyze the performance of LTE-A systems with CA in terms of average user throughput. However, the system-level capacity analysis of LTE-A systems has not been well studied. In this paper, we explore the downlink admission control process in LTE-A systems with CA to compare the capacities between LTE users and LTE-A users, based on the metric - equivalent capacity. Specifically, taking into account the user heterogeneity, the system evolution is modeled as a birth-death process for each user class based on an effective user traffic generation model. A closed-form relationship between the equivalent capacity and system bandwidth is then derived for a single-carrier LTE-A system with the help of binomial-normal approximation. The relationship is further extended to multi-carrier case for both LTE users and LTE-A users. Finally, simulation results are provided to verify our analytical ones, and demonstrate that the equivalent capacity of LTE-A users surpasses that of LTE users significantly.

Green energy is emerging as a promising alternative energy source to power network devices in next-generation wireless networks. Different from traditional energy, green energy is replenished from nature, e.g., solar and wind, and is highly dependent on the capacities and locations of the electronic devices. As such, the fundamental design criterion in the network deployment and management is shifted from energy efficiency to energy sustainability due to the sustainable nature of green energy. In this paper, we study the network resource management issues in next-generation wireless networks with sustainable energy supply. Our objective is to deploy the minimal number of green RNs, i.e., RNs powered by green energy, and optimize resource allocation to ensure full network connectivity and users' Quality of Service (QoS) requirements can be fulfilled with the harvested energy based on the cost threshold. To this end, the RN placement and sub-carrier allocation (RNP-SA) issues are jointly formulated into a mixed integer non-linear programming problem. Two low-complexity heuristic algorithms, namely RNP-SA with top-down/bottom-up algorithms (RNP-SA-t/b), are presented to solve the non-linear programming problem in different network scenarios. Extensive simulations show that the proposed algorithms provide simple yet efficient solutions and offer important guidelines on network deployment and resource management in a green radio network with sustainable energy sources.