This paper investigates energy-minimal data collection from multiple low-power ground devices (GDs) adopting a rotary-wing unmanned aerial vehicle (UAV). Unlike most prior works that assume flexible trajectories, the UAV in our study adheres to a fixed or predetermined flight path, due to practical requirements from e.g. patrol and inspection missions. To improve communication performance and prolong GDs’ operational lifetime, the UAV employs a directional antenna for wirelessly transferring energy to the GDs before collecting their data. We jointly optimize the UAV’s flight speeds, hovering locations, and radio resource allocation along the predefined flight path to minimize the UAV’s total energy consumption. For acyclic flight paths, we show that the UAV’s propulsion energy consumption is a strictly convex function of flight speed. However, the fixed path imposes a stringent nonconvex constraint, complicating the optimization. To overcome this challenge, we decompose the problem into two layers and solve it by proposing a novel monotonic optimization method in polar coordinates, referred to aspolar polyblock approximation. This method guarantees a globally optimal solution under mild conditions. Additionally, we propose a low-complexity suboptimal algorithm to balance system performance and computational efficiency. Simulation results show that both the proposed optimal and suboptimal algorithms can effectively mitigate the limitation of a fixed flight path, resulting in significant reductions in the UAV’s energy consumption during data collection, with more energy savings achieved as antenna directivity increases.

In this paper, we consider multiple multi-functional unmanned aerial vehicles (UAVs) with sensing, communication, and computation capabilities to simultaneously perform sensing and provide computing services to ground devices (GDs). The UAVs process both, the sensed data and the tasks offloaded by the GDs, onboard. To enable a scalable decentralized design, we formulate the task offloading problem as a matching game. In each time slot, each GD proposes to a UAV for task offloading, aiming to maximize its utility, defined as the time saved through offloading compared to local computing. Each UAV accepts to serve a subset of proposing GDs while handling its own sensing task as well. To balance resource allocation between offloaded and sensing tasks, each UAV aims to maximize its utility defined as a weighted sum of the total time saved for GDs minus the extra time incurred in processing its own sensing data when sharing partial computing resources with GDs compared to utilizing the entire computing resources for its sensing task. However, in practice, GDs may lack prior information about their channel conditions, the UAVs' communication and computing resources, and the task offloading strategies of other GDs, therefore, solving the task offloading matching game is challenging. To tackle this, we propose a novel gradient-based Multi-Armed-Bandit (GMAB) algorithm for task offloading, enabling GDs to learn and coordinate their offloading strategies in a decentralized manner. Simulation results show that the proposed GMAB algorithm outperforms several baseline task offloading schemes in terms of improving both, GD-side and UAV-side utility, by up to 34.1 % and 37.2 %, respectively.

This paper investigates UAV-aided wireless communications with multiple users while utilizing a rotatable dipole array at the UAV. We jointly optimize user scheduling, array steering, and beamforming to minimize the UAV's mission completion time, i.e., the time required to deliver a specified data volume from the UAV to each user. To tackle the resulting nonconvex mixed-integer nonlinear program (MINLP) problem, we identify a hidden convexity in the optimization of continuous variables for given values of the discrete variables. Leveraging this result, we reformulate the original problem as a multi-stage dynamic programming (DP) problem and characterize its optimal solution via the Bellman optimality equation. We further propose a novel low-complexity one-step lookahead rollout (OSLR) algorithm based on approximate DP and semidefinite programming (SDP) jointly to optimize the discrete and continuous variables, respectively. Simulation results demonstrate that our proposed algorithm achieves significant reductions in the UAV's mission completion time compared to two baseline schemes, even when only a small number of dipoles are deployed at the UAV.

In this paper, we investigate the efficient compression, transmission, and processing of high-volume sensor data collected from physical systems (PSs) to enable timely and accurate digital twin (DT) synchronization over resource-limited wireless networks. The sensors distributed in the PSs compress their sensed data prior to transmission to a base station (BS) for DT updating. However, due to the lack of a centralized decision-making unit, each sensor independently selects its own compression ratio to balance between its transmission time and compression overhead. To coordinate the compression across the sensors and ensure efficient DT updating globally, we formulate the problem as a Stackelberg game, where the BS acts as the leader for allocating communication/computing resources, while the sensors act as followers to optimize their data compression. By deriving each sensor’s best response (BR), we further propose a low-complexity iterative algorithm to compute the Stackelberg equilibrium. Simulation results show that incorporating data compression significantly reduces DT synchronization time. Furthermore, the proposed algorithm achieves near-optimal performance, closely matching the centralized joint optimization scheme with almost no price of anarchy.

Hybrid digital-analog precoding is a pivotal transmission technique to balance communication performance and hardware costs associated with radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multipleoutput (MIMO). However, most existing designs utilize Shannon rate and assume an infinite blocklength, which is impractical for emerging finite blocklength (FBL) applications, such as massive machine-type communications. To fill in this gap, this paper investigates hybrid precoding optimization in the FBL regime. The aim is to maximize the weighted sumrate (WSR), while fulfilling the transmit power budget at the base station (BS) and users' minimum rate requirements. The formulated optimization problem is highly challenging to solve, particularly due to the complex and nonconcave FBL rate function and the intricate coupling between analog and digital precoders. To tackle these issues, we propose a computationally efficient solution based on the penalty dual decomposition (PDD) method, which is guaranteed to converge to the Karush-KuhnTucker (KKT) solutions under mild conditions. Simulation results demonstrate that our proposed hybrid precoding design significantly outperforms several baseline schemes, especially those ignoring the impact of blocklength and adopting Shannon rate as the performance metric.

Hybrid digital-analog precoding is essential for balancing communication performance, energy efficiency, and hardware costs in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most existing designs rely on the Shannon capacity and assume infinite blocklengths, which are impractical for emerging applications, such as massive machine-type communications, operating with finite blocklength (FBL). To address this gap, this paper pioneers a novel hybrid precoding design for mmWave massive MIMO in the FBL regime. We meticulously optimize hybrid precoding based on both the weighted sum-rate (WSR) and the max-min fairness (MMF) criteria, while fulfilling the transmit power budget and users’ minimum rate requirements. Both continuous and discrete phase shifters are considered for analog precoding. The formulated optimization problems are highly challenging to solve due to the nonconvex objective functions and nonconvex constraints. These challenges are further intensified by the nonconcave FBL rate function and the intricate coupling between analog and digital precoders. By proposing novel problem transformation and decomposition techniques, we reformulate the original complex problems into forms solvable with the penalty dual decomposition (PDD) method. We then develop two efficient iterative algorithms with parallel, and even closed-form variable updates, and guaranteed convergence to solve the WSR and MMF optimization problems, applicable to both continuous and discrete phase shifters. Simulation results show that our proposed hybrid precoding designs significantly outperform several baseline schemes, especially those adopting the Shannon capacity and infinite blocklength. Additionally, our proposed optimization algorithms enable hybrid precoding exploiting discrete phase shifters with limited quantization resolution (e.g., 3-bit) to closely match the performance of fully digital precoding in FBL scenarios.

In this paper, we consider bistatic integrated sensing and communication (ISAC) enabled by an unmanned aerial vehicle (UAV) and a ground sensing receiver. The UAV employs a rotatable array of patch antennas to communicate with multiple ground users and simultaneously probe multiple targets, while using the same transmit signals. To minimize the UAV’s load and transceiver complexity, the sensing receiver collects and processes echoes of the probing signals for e.g. detecting the activities of the targets. We jointly optimize transmit beamforming and array steering at the UAV to maximize the minimum received signal-to-interference-plus-noise ratio (SINR) for all targets while ensuring quality-of-service (QoS) for each communication user. Given the highly nonconvex nature of the formulated problem and the difficulty in obtaining its optimal solution, we propose a low-complexity suboptimal algorithm based on proximal block coordinate descent (BCD). This algorithm can exploit the underlying structure of the problem by decomposing it into several convex and manifold optimization subproblems, which are then alternately solved using off-the-shelf solvers. Simulation results demonstrate that by jointly optimizing transmit beamforming and array steering, the rotatble patch antenna array significantly enhances the sensing performance of UAV-aided bistatic ISAC while guaranteeing communication QoS, even in scenarios with limited number of transmit antennas and undesirable line-of-sight (LoS) interference from the ISAC transmitter.

In this paper, we investigate unmanned aerial vehicle (UAV)-aided integrated sensing and communication (ISAC) by employing a uniform linear array (ULA) of patch antennas onboard the UAV. The three-dimensional (3D) directional gain pattern of the patch antennas and the array beamforming are jointly exploited to facilitate efficient ISAC signal transmission for sensing multiple targets and communicating with multiple users. Assuming the positions of the targets and users are known, we jointly optimize the beamforming and 3D array-steering of the patch antenna array to maximize the sum of transmit beam-pattern gains towards the targets while guaranteeing quality-of-service (QoS) for each communication user. The formulated optimization problem is nonconvex and generally intractable. Exploiting the special structures underlying the problem, we propose a low-complexity iterative algorithm based on proximal block coordinate descent (BCD) to decompose the problem into several convex and manifold optimization subproblems and iteratively solve them. Simulation results verify the benefits of joint beamforming and 3D steering optimization for UAV-aided ISAC using patch antenna array, particularly when the communication QoS requirements are stringent.

Digital twin (DT) applications usually need to be synchronized in real time with the state of the physical system (PS). This process includes both synchronizing the data collected by sensors in the PS to a server and updating the DT at the server in adaptation to the dynamics of the PS. However, communicating with power- and rate-limited sensors and processing high-volume sensed data with limited computing resources present significant challenges for DT synchronization. In this paper, we tackle both bottlenecks by proposing a joint communication and computing design for DT synchronization. In particular, we exploit buffering at an aerial cluster head of the sensors and enable buffer-aided (BA) relaying to increase the communication throughput of the sensors during data synchronization. Moreover, we adopt a novel stream computing scheme, which allows for DT updating in parallel with data synchronization, to accelerate DT synchronization. To maximize the performance of the proposed approach, we jointly optimize the trajectory of the aerial relay and the communication and computing resource allocation for minimizing the DT synchronization time. The formulated problem is a mixed-integer nonconvex problem, which is generally intractable. To tackle this challenge, we propose a low-complexity two-layer iterative suboptimal algorithm. Our simulation results show that the DT synchronization time can be significantly reduced by up to 43.8%, through stream computing and the joint optimization of the relay's trajectory and the communication/computing resource allocation.

Unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communication presents a promising solution for high data-rate wireless applications in urban environments. However, due to limited energy supply and communication capability, UAVs can only provide temporary communication services. This challenge motivates the exploration of three-dimensional (3D) integrated aerial and ground mmWave communications utilizing the multi-connectivity (MC) technique. By leveraging both ground and aerial mmWave links over licensed and unlicensed mmWave spectrums, respectively, the MC technique can effectively exploit spatial and frequency diversity to enhance the connectivity and reliability of UAV-assisted mmWave communications. We develop a unified framework based on stochastic geometry and Markov chain to analyze the coverage and outage probabilities of the 3D integrated aerial/ground mmWave networks. Furthermore, we show that an optimal UAV flight altitude for maximizing the coverage probability of UAV communication exists and derive it in a closed-form expression. Simulation results demonstrate that UAVs can maintain reliable mmWave connections even when connections from terrestrial mmWave base stations (BSs) are obstructed by buildings, underscoring the benefits of MC in enhancing the robustness of 3D integrated aerial and ground mmWave networks.

Federated learning (FL) over wireless networks offers a promising approach to enable decentralized machine learning among massive mobile edge nodes while ensuring privacy in training data. However, the convergence speed of FL is limited by the straggler effect, which arises from heterogeneous nodes, wireless fading channels, and non-independently and identically distributed (non-IID) training data. In this paper, we consider an adaptive semi-asynchronous FL to mitigate the straggler effect, by dynamically selecting subsets of nodes over time to synchronize the global model. We jointly optimize the node scheduling and computing/communication resource allocation to minimize the completion time required for convergence of the adaptive semi-asynchronous FL. Leveraging the convergence condition of semi-asynchronous FL, we further propose a greedy heuristic policy for node scheduling while tackling the remaining computing/communication resource allocation problem by exploiting a hidden convexity. Simulation results on open datasets demonstrate that, compared with existing FL algorithms, our proposed adaptive semi-asynchronous algorithm can significantly lower the latency of FL convergence.

In this paper, we consider unmanned aerial vehicle (UAV)-aided downlink communication using a rotatable array of directional antennas such as half-wavelength dipoles. The antenna array is mounted onboard the UAV using a gimbal and can be flexibly rotated in the three-dimensional (3D) space. As such, the directional gain pattern of dipoles and the array beamforming can be both best exploited to facilitate efficient information transmission to multiple low-priority (or secondary) users while mitigating co-channel interference for another high-priority (or primary) user coexisting with but not served by the UAV. Assuming that the direction of the high-priority user is known, we jointly optimize the electrical beamforming and mechanical steering of the rotatable dipole array for maximizing the weighted sum-rate achievable by the low-priority users while minimizing the interference power radiated in the direction of the high-priority user. The formulated optimization problem is nonconvex and generally intractable. Exploiting its special problem structure, we decompose the problem into several convex and manifold optimization subproblems and further propose a low-complexity iterative algorithm based on proximal block coordinate descent for solution. Our simulation results verify the convergence of the proposed algorithm. Moreover, compared with systems employing non-rotatable and rotatable arrays of isotropic antennas, the rotatable dipole array can flexibly adjust the gain patterns in different azimuth and elevation angles to increase the communication throughput by up to 300% and 77%, respectively.

Massive multiple-input multiple-output (MIMO) multicasting is a promising approach for simultaneously delivering common messages to multiple users in next-generation wireless networks. However, existing studies have exclusively focused on multicast beamforming designs based on the Shannon capacity, assuming the infinite blocklength (IBL) for transmission. This assumption may lead to strictly suboptimal designs for practical multicast transmissions with finite blocklength (FBL), especially in ultra-reliable low-latency communications. In this paper, we explore the beamforming design for massive MIMO multi-group multicasting in the FBL regime. Our study considers both the max-min fairness and the weighted sum rate criteria for a comprehensive treatment. Due to the non-concave FBL rate function, the resulting optimization problems are known to be notoriously hard. We characterize the necessary and sufficient condition for the non-negative FBL rate to be a concave function of the received signal-to-interference-plus-noise ratio (SINR). Considering a finite number of transmit antennas, we propose low-complexity majorization-minimization (MM) type algorithms, which update variables in either closed or semi-closed form, to achieve locally optimal solutions of the formulated optimization problems. We further show that, as the number of transmit antennas becomes large, the optimal beamformer of each group aligns asymptotically with a linear combination of the channel vectors of that group of users, where the optimal normalized combining coefficients are derived in closed form. Subsequently, we obtain the globally optimal multicast beamformers by optimizing the power allocation using low-complexity iterative algorithms. Simulation results show that the proposed schemes outperform several existing methods, especially those employing the Shannon capacity as the performance metric. Moreover, the proposed algorithms exhibit complexities that only slightly grow with the number of transmit antennas and they can notably reduce the computation time by up to two orders of magnitude over the benchmarks, making them highly beneficial for massive MIMO applications.

In this paper, we consider employing an unmanned aerial vehicle (UAV) equipped with an onboard radar transceiver to localize a ground target at an unknown position. Exploiting the UAV's mobility, we aim to gather line-of-sight (LoS) range measurements from favorable waypoints and improve the ensuing multi-lateration process while estimating the target's location. To this end, we introduce a novel localization error metric, characterized geometrically by the radius of a defined confidence region where the target resides at a predetermined confidence level. Additionally, we investigate robust dynamic optimization of the UAV's trajectory to minimize the defined localization error metric online, utilizing sequentially available but delayed range estimates. The formulated optimization problem belongs to a convex-nonconcave minimax problem, which is generally intractable. To solve this problem, we further propose two iterative online algorithms based on semidefinite programming (SDP) relaxation and alternating/sequential convex optimization techniques. Simulation results show that the proposed online schemes outperform several benchmarks, either in the final localization accuracy or in the rate of decreasing the localization error.

This paper considers a buffer-aided unmanned aerial vehicle (UAV) serving as an aerial relay for communication between a base station (BS) and multiple ground users (GUs). Thanks to its flexible mobility, the UAV can achieve high-rate communications with the GUs/BS by buffering the communication data and exploiting the favorable channel conditions on its flight trajectory for transmission and reception. However, the size of the buffer is limited in practice, which may severely restrict the throughput gains enabled by buffering. Whether it is beneficial to consider buffering at UAVs with a small buffer is an open research problem, which is tackled in this paper. Assuming a finite buffer mounted at the UAV, we consider joint optimization of the resource allocation, data buffering, and trajectory planning for minimizing the UAV's completion time required for delivering a given data volume from each GU to the BS, where the resource allocation contains power and bandwidth allocation. The formulated optimization problem is a mixed-integer nonconvex program, which is generally intractable. To solve this problem, we propose a novel low-complexity two-layer iterative suboptimal algorithm based on bisection search and penalty successive convex approximation (PSCA). Note that minimizing the completion time in turn maximizes the average throughput, i.e., the amount of data delivered from the GUs to the BS per unit of time. Simulation results show that the buffer with sufficiently large size can increase the UAV's average throughput by up to 123.8% compared to without buffering. Moreover, with our proposed scheme, 63.2% of the throughput gains can already be achieved using only a small buffer.

Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable. For optimal use of a limited number of UAVs, we propose providing both DTN and a cellular base station on each UAV. Here, DTN is used for large amounts of low-priority data, while capacity-constrained cell coverage remains reserved for emergency calls or command and control. We optimize cell coverage via a novel optimal transport-based formulation using alternating minimization, while for data ferrying we periodically deliver data between dynamic clusters by solving quadratic assignment problems. In our evaluation, we consider different scenarios with varying mobility models and a wide range of flight patterns. Overall, we tractably achieve optimal cell coverage under quality-of-service costs with DTN-based data ferrying, enabling large-scale deployment of UAV swarms for crisis communication.

Fiber-to-the-room (FTTR) wireless local area networks (WLANs) are a promising sixth-generation (6G) technology for extreme broadband low-latency indoor wireless communications. With dense deployment of access points (APs), namely optical network units (ONUs), and efficient spatial frequency reuse across the ONUs, FTTR WLANs enable the mobile devices to flexibly access any ONU in its communication range and reduce the collisions during data packet transmissions. However, FTTR WLANs share a passive optical network (PON) for time division multiplexing (TDM) based backhauling, which may incur long delays for scheduling packet transmissions over the PON. In this paper, the full-link age of information (FL-AoI) is proposed as a new performance metric to analyze the timeliness of indoor communications in FTTR WLANs, taking into account both the carrier sense multiple access with collision avoidance (CSMA/CA) based wireless transmission and the TDM based packet scheduling over the PON. The FL-AoI of FTTR WLANs is analyzed using stochastic geometry and stretched exponential path-loss (SEPL) based indoor wireless channel model. We show that rather than accessing the nearest ONUs, the FTTR WLAN also enable the mobile devices to access further ONUs to reduce the average FL-AoI. Meanwhile, there exists an optimal transmission distance to achieve minimal average FL-AoI in the FTTR WLAN, whose value depends on the deployment density of ONUs.

This paper considers optimal resource allocation for data collection from multiple ground devices (GDs) using a rotary-wing unmanned aerial vehicle (UAV). The UAV’s flight path, i.e., the sequence of moving positions, is given a priori due to requirements of e.g. patrol and inspection missions, whereas the UAV’s trajectory, i.e., the path and time schedule of movement, remains dependent on its hovering positions and flying speeds along the path. To improve the spectral and energy efficiency of the GDs, the UAV employs a directional antenna and performs wireless power transfer (WPT) to the GDs before collecting data from them. We jointly optimize the UAV’s flying speeds, hovering locations, and radio resource allocation (including time, bandwidth and transmit power) for minimization of the total energy consumption of the UAV required for completing data collection along the flying path. We show that given any flight path, the propulsion energy consumption of the UAV is a convex function of the flight speeds. However, due to the highly directive transmission, communication and flight of the UAV become strongly coupled and complicates the problem, e.g. the selection of the UAV’s hovering points will affect both the order of serving the GDs and the antenna gain of the UAV. Moreover, nonconvexity in the flight path constraints further obscures an efficient solution to the resource allocation problem. To tackle these challenges, we propose an iterative algorithm based on the branch-and-bound (BnB) method, which can obtain the globally optimal solution when the flight path coincides with the boundary of a convex set. Simulation results show that compared with several baseline algorithms, the proposed algorithm can significantly lower the energy consumption of the UAV during data collection.

This paper presents a stochastic geometry framework for modeling and evaluating the connectivity of uplink transmission in a large-scale intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) communication network, where the uplink user equipments (UEs) attempt to communicate with the nearest base stations (BSs) either without or with the help of an IRS. We propose a novel elliptical geometry model, which can effectively capture the impact of IRS location and orientation, as well as incident/reflection angle on mmWave signal propagation, while, at the same time, significantly simplifying the analysis of the system performance. Employing the elliptical geometry model, the approximate reflection probability of IRS as well as its upper and lower bounds are derived in closed form. Based on these results, we further analyze the successful connection probability of uplink UEs for IRS-assisted mmWave cellular networks. Our results show that compared with conventional direct UE-to-BS communication without IRS, indirect communication with the aid of IRS exhibits a slower decaying in the connection probability as the communication distance increases, as the latter can significantly increase the connection probability for cell-edge UEs. Moreover, for mmWave BSs with small receiving power thresholds, the deployment of IRS can effectively mitigate the impact of blockages to improve mmWave signal propagation.

In this paper, we consider optimal resource allocation for unmanned aerial vehicle (UAV)-assisted delay-sensitive communications, where a UAV flies to deliver time-critical messages to multiple ground users (GUs) as soon as possible. However, the GUs' locations cannot be perfectly known at the UAV, which may jeopardize the timeliness of message delivery to the GUs. To tackle this challenge, we consider a disk-based fixed-rate transmission scheme at the UAV, which can exploit the mobility of the UAV to facilitate timely communications despite uncertain user locations. Consequently, the system performance hinges on the UAV's flight trajectory and the scheduling of GUs, which are further optimized using a cost minimization approach. Thereby, a general class of delay-aware cost functions, referred to as the cost of delivery delay (CoDD), is defined taking into account the diverse delay-sensitivity requirements of the GUs, and we jointly optimize the user scheduling and the UAV's trajectory for minimization of the sum CoDD of all GUs incurred before the UAV's mission completes. The formulated optimization problem is a nonconvex mixed-integer nonlinear program. Exploiting the underlying structure of this problem, we further propose two novel low-complexity solutions based on approximate dynamic programming (DP). Simulation results show that the proposed schemes can flexibly adjust the UAV's flight trajectory and resource allocation according to the GUs' individual delivery delays, delay tolerance, and location uncertainty, which translates into significantly lower sum CoDD for the GUs than several benchmark schemes.

The blockchain is a promising technology to enhance the security and resilience of industrial Internet of Things (IIoT) networks. However, generating blockchain for the IIoT devices usually consumes excessive energy which may not be affordable for battery-powered IIoT devices. To address this problem, in this paper, we consider an unmanned aerial vehicle (UAV) assisted private blockchain-based IIoT system. Thereby, a UAV mounted with computing processor is deployed as a multi-access edge computing platform, which is responsible for collecting data from the IIoT devices, generating blocks based on the collected data, and broadcasting the blocks to the IIoT devices. To minimize the energy consumption of the UAV, joint optimization of the central processing unit (CPU) frequencies for data computation and block generation, the amount of offloaded IIoT data, the bandwidth allocation, and the trajectory of the UAV is formulated as a nonconvex optimization problem and solved via a successive convex approximation (SCA) algorithm. Simulation results show that, compared with several baseline schemes, the proposed scheme can significantly lower the energy consumption required for the blockchain generation in IIoT networks.

Aerial base stations (BSs) attached to unmanned aerial vehicles (UAVs) constitute a new paradigm for next-generation cellular communications. However, the flight range and communication capacity of aerial BSs are usually limited due to the UAVs' size, weight, and power (SWAP) constraints. To address this challenge, in this paper, we consider dynamic cooperative transmission among multiple aerial BSs for power-efficient aerial communications. Thereby, a central controller intelligently selects the aerial BSs navigating in the air for cooperation. Consequently, the large virtual array of moving antennas formed by the cooperating aerial BSs can be exploited for low-power information transmission and navigation, taking into account the channel conditions, energy availability, and user demands. Considering both the fronthauling and the data transmission links, we jointly optimize the trajectories, cooperation decisions, and transmit beamformers of the aerial BSs for minimization of the weighted sum of the power consumptions required by all BSs. Since obtaining the global optimal solution of the formulated problem is difficult, we propose a low-complexity iterative algorithm that can efficiently find a Karush-Kuhn-Tucker (KKT) solution to the problem. Simulation results show that, compared with several baseline schemes, dynamic multi-UAV cooperation can significantly reduce the communication and navigation powers of the UAVs to overcome the SWAP limitations, while requiring only a small increase of the transmit power over the fronthauling links.

Solar-powered unmanned aerial vehicles (SUAVs) are a promising solution to increase the flight time of unmanned aerial vehicles (UAVs) in the sky, reducing human interventions for battery charging. Exploiting networked SUAVs for providing long-duration wireless communication cannot only improve the signal transmission reliability but realize energy autonomy. To reap these benefits, in this article, we propose an efficient energy and radio resource management framework based on intelligent power cognition at the SUAVs. Thereby, power-cognitive SUAVs can learn the environment including the spatial distributions of solar energy density, the channel state evolution, and the traffic patterns of wireless communication applications in adaption to the environment changes. These SUAVs intelligently adjust the energy harvesting, information transmission, and flight trajectory to improve the utilization of solar energy for two primary goals: staying aloft over a long time period and achieving high communication performance. We adopt reinforcement learning to compute the optimal decisions for maximization of the total system throughput within the lifetime of the SUAV. Simulation results show that the proposed power cognition scheme can simultaneously improve the communication throughput and the harvested energy for SUAVs.

In this paper, we propose a novel joint caching and massive multiple-input multiple-output (MIMO) transmission scheme, referred to as cache-aided massive MIMO, for advanced downlink cellular communications. In addition to reaping the conventional advantages of caching and massive MIMO, the proposed scheme also exploits the side information provided by cached files for interference cancellation at the receivers. This interference cancellation increases the degrees of freedom available for precoding design. In addition, the power freed by the cache-enabled offloading can benefit the transmissions to the users requesting non-cached files. The resulting performance gains are not possible if caching and massive MIMO are designed separately. We analyze the performance of cache-aided massive MIMO for cache-dependent maximum-ratio transmission (MRT), zero-forcing (ZF) precoding, and regularized zero-forcing (RZF) precoding. Lower bounds on the ergodic achievable rates are derived in closed form for MRT and ZF precoding. The ergodic achievable rate of RZF precoding is obtained for the case when the numbers of transmit antennas and users are large but their ratio is fixed. Compared to conventional massive MIMO, the proposed cache-aided massive MIMO scheme achieves a significantly higher ergodic rate especially when the number of users approaches the number of transmit antennas.

In a content-delivery network, files’ popularity and users’ requests change fast. Conventional caching schemes, e.g., caching (re)placement once per day during the off-peak hours, may not capture the up-to-date popularity. In this case, the contents in caches have to be regularly updated to prevent information becoming outdated, and at the same time users’ requested files must be delivered. These two tasks are challenging in practical heavy-traffic and multi-user scenarios when the network resources are limited. In this paper, we apply non-orthogonal multiple access (NOMA) to facilitate concurrent caching replacement and content delivery in downlink transmission. We formulate a resource allocation problem to investigate how to efficiently push proactive files to the cache at the small base station and deliver the requested files to users. The resource-allocation problem is formulated as a mixed-integer exponential conic optimization problem. To enable a computationally-efficient optimal solution with finite convergence, we develop an iterative algorithm based on polyhedral outer approximation, where a polyhedral relaxation subproblem and a convex subproblem are constructed and iteratively solved to tighten the lower and upper bounds for the optimum, respectively. The numerical results demonstrate significant performance gains of the NOMA-enabled data transmission scheme in power and resource savings compared to the baseline scheme.

In this paper, we propose a novel joint caching and non-orthogonal multiple access (NOMA) scheme to facilitate advanced downlink transmission for next generation cellular networks. In addition to reaping the conventional advantages of caching and NOMA transmission, the proposed cache-aided NOMA scheme also exploits cached data for interference cancellation which is not possible with separate caching and NOMA transmission designs. Furthermore, as caching can help to reduce the residual interference power, several decoding orders are feasible at the receivers, and these decoding orders can be flexibly selected for performance optimization. We characterize the achievable rate region of cache-aided NOMA and investigate its benefits for minimizing the time required to complete video file delivery. Our simulation results reveal that, compared to several baseline schemes, the proposed cache-aided NOMA scheme significantly expands the achievable rate region for downlink transmission, which translates into substantially reduced file delivery times.

In this paper, we propose a novel wireless caching scheme to enhance the physical layer security of video streaming in cellular networks with limited backhaul capacity. By proactively sharing video data across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative joint transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. Assuming imperfect channel state information (CSI) at the transmitters, we formulate a two-stage non-convex mixed-integer robust optimization problem for minimizing the total transmit power while providing the quality of service and guaranteeing communication secrecy during video delivery, where the caching and the cooperative transmission policy are optimized in an offline video caching stage and an online video delivery stage, respectively. Although the formulated optimization problem turns out to be NP-hard, low-complexity polynomial-time algorithms, whose solutions are globally optimal under certain conditions, are proposed for cache training and video delivery control. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design. Simulation results confirm that the proposed caching scheme achieves simultaneously a low secrecy outage probability and a high power efficiency. Furthermore, due to the proposed robust optimization, the performance loss caused by imperfect CSI knowledge can be significantly reduced when the cache capacity becomes large.

This paper studies secure video streaming in cache-enabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted. Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding of video files to enable secure cooperative multiple-input multiple-output transmission and, at the same time, exploit the cache memory of both the trusted and untrusted BSs for improving the system performance. Considering imperfect channel state information at the transmitters, we formulate a two-timescale non-convex mixed-integer robust optimization problem to minimize the total transmit power required for guaranteeing the quality of service and secrecy during video streaming. We develop an iterative algorithm based on a modified generalized Benders decomposition to solve the problem optimally, where the caching and the cooperative transmission policies are determined via offline (long-timescale) and online (short-timescale) optimization, respectively. Furthermore, inspired by the optimal algorithm, a low-complexity suboptimal algorithm based on a greedy heuristic is proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to several baseline schemes.

This paper studies secure video streaming in cache-enabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted. Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding (SVC) of video files to enable secure cooperative multiple-input multiple-output (MIMO) transmission and exploit the cache memory of all BSs for improving system performance. The caching and delivery design is formulated as a non-convex mixed-integer optimization problem to minimize the total BS transmit power required for secure video streaming. We develop an algorithm based on the modified generalized Benders decomposition (GBD) to solve the problem optimally. Inspired by the optimal algorithm, a low-complexity suboptimal algorithm is also proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to three baseline schemes.

This paper evaluates the energy efficiency of uplink transmission in heterogeneous cellular networks (HetNets), where fractional power control (FPC) is applied at user equipments (TIEs) subject to a maximum transmit power constraint. We first consider an arbitrary deterministic HetNet and characterize the properties of energy efficiency for TIEs in different path loss regimes, or different access regions. By introducing the notion of transfer path loss, we reveal that, for TIE whose path loss is below the transfer path loss, its energy efficiency highly depends on the value of power control coefficient adopted by FPC. In contrast, for TIE with path loss above the transfer path loss, the uplink energy efficiency asymptotically decreases inversely with path loss, independent of the adopted power control coefficient. Based on these properties, we characterize the optimal power control coefficients for maximizing the energy efficiency of FPC in different access regions. Next, we extend the analysis to stochastic HetNets where TIEs and BSs are distributed as independent Poisson point processes, and investigate the distribution of transmit power for uplink TIEs. Moreover, the probability of truncation outage due to constrained maximal transmit power, as well as the average energy efficiency of TIEs are analytically derived as functions of the BS and TIE densities, power control coefficient, and receiver threshold. Simulation results validate the analytical results, show the consistency between deterministic and stochastic analyses, and suggest suitable power control coefficient for achieving energy efficient uplink transmission by FPC in HetNets.

This paper investigates the cross-layer optimization of fast video delivery and caching for minimization of the overall video delivery time in a two-hop relaying network. The half-duplex relay nodes are equipped with both a cache and a buffer which facilitate joint scheduling of fetching and delivery to exploit the channel diversity for improving the overall delivery performance. The fast delivery control is formulated as a two-stage functional non-convex optimization problem. By exploiting the underlying convex and quasi-convex structures, the problem can be solved exactly and efficiently by the developed algorithm. Simulation results show that significant caching and buffering gains can be achieved with the proposed framework, which translates into a reduction of the overall video delivery time. Besides, a trade-off between caching and buffering gains is unveiled.

Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated.

In this paper, we study power allocation for secure communication in a multiuser multiple-input single-output (MIS-O) downlink system with simultaneous wireless information and power transfer. The receivers are able to harvest energy from the radio frequency when they are idle. We propose a multi-objective optimization problem for power allocation algorithm design which incorporates two conflicting system objectives: total transmit power minimization and energy harvesting efficiency maximization. The proposed problem formulation takes into account a quality of service (QoS) requirement for the system secrecy capacity. Our designs advocate the dual use of artificial noise in providing secure communication and facilitating efficient energy harvesting. The multi-objective optimization problem is non-convex and is solved by a semidefinite programming (SDP) relaxation approach which results in an approximate of solution. A sufficient condition for the global optimal solution is revealed and the accuracy of the approximation is examined. To strike a balance between computational complexity and system performance, we propose two suboptimal power allocation schemes. Numerical results not only demonstrate the excellent performance of the proposed suboptimal schemes compared to baseline schemes, but also unveil an interesting trade-off between energy harvesting efficiency and total transmit power.

Energy efficiency has gained its significance when service providers' operational costs burden with the rapidly growing data traffic demand in cellular networks. In this paper, we propose an energy efficiency model for Poisson-Voronoi tessellation (PVT) cellular networks considering spatial distributions of traffic load and power consumption. The spatial distributions of traffic load and power consumption are derived for a typical PVT cell, and can be directly extended to the whole PVT cellular network based on the Palm theory. Furthermore, the energy efficiency of PVT cellular networks is evaluated by taking into account traffic load characteristics, wireless channel effects and interference. Both numerical and Monte Carlo simulations are conducted to evaluate the performance of the energy efficiency model in PVT cellular networks. These simulation results demonstrate that there exist maximal limits for energy efficiency in PVT cellular networks for given wireless channel conditions and user intensity in a cell.

The sleeping strategy has become popular to reduce power consumption of base stations (BSs) by shutting down underutilized BSs in the management of green cellular networks. In this paper, we propose a novel solution for an energy efficient use of cellular networks, based on traffic load balancing. By modeling the power consumption for BSs connected to uniformly distributed users, the relationship between the optimal number of active (or shut down) BSs and the traffic load is then derived through the power ratio, which is the ratio between dynamic and fixed power part of BS power consumption. Both analytical and simulation results demonstrate that, in order to achieve significant energy savings, less BSs should be turned on at low traffic load while more BSs turned on at high traffic load.

How to predict the self-similar network traffic with high burstiness is a great challenge for network management. The covariation orthogonal prediction could effectively capture the burstiness in the network traffic, and the artificial neural network prediction could adapt the network traffic change by self-learning. To improve the prediction accuracy, we propose a new hybrid network traffic prediction method based on the combination of the covariation orthogonal prediction and the artificial neural network prediction. Through empirical study, the accuracy of the new prediction method can be effectively improved seen from the mean and the prediction error.

Abstract In this paper, we analyze the impacts of different frame types on the self‐similarity and burstiness characteristics of the aggregated frame traffic in a real 802.11 wireless local area network (WLAN). We find that the impacts of different frame types are related to the mean frame sizes and the proportions of specified frame types in the aggregated frame traffic. Furthermore, we propose an analytical model to capture the relationship of self‐similarity characteristics between the aggregated frame traffic and different frame types. These new results provide an insight of frame traffic characteristics and some practical guidelines for developing new efficient algorithms to improve the common medium utilization and system throughput performance. Copyright © 2009 John Wiley & Sons, Ltd.