In mobile edge computing (MEC) networks, the limited computational capabilities of edge devices present challenges in achieving energy-efficient and high-accuracy inference, highlighting the critical role of cooperative inference with an edge server. This study proposes a cooperative inference framework in which edge devices employ dual confidence thresholds to filter ambiguous images, which are stored in task buffer queues, and offloaded to the edge server for more accurate inferences. We conduct a numerical analysis of the inference accuracy and energy consumption for the proposed cooperative inference method and formulate a joint optimization problem to determine the optimal confidence thresholds, offloading ratio, transmit power, and transmission/reception time, aiming to minimize energy consumption while ensuring accuracy and queue stability. We employ a Lyapunov-based optimization approach to convert this optimization problem into a time-independent real-time decision problem. Subsequently, we decompose it into tractable subproblems and propose an iterative algorithm based on the block coordinate descent method to derive suboptimal variables efficiently. The experimental results reveal a trade-off between inference accuracy and cooperation penalty depending on the confidence thresholds. In particular, optimizing these thresholds in conjunction with radio resources significantly reduces energy consumption and queue backlog while maintaining the required accuracy in diverse MEC environments, compared with the conventional inference methods.

In this paper, we propose a novel deep learning (DL)-based framework for three-dimensional (3-D) trajectory design and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks to maximize the minimum average spectral efficiency (SE) among moving ground nodes (GNs). The proposed framework incorporates an obstacle avoidance algorithm for 3-D trajectory planning and employs a practical blockage-aware channel model that accounts for the blockage effects on air-to-ground links caused by obstacles, while also accounting for the stochastic movement of GNs. To this end, we develop a new mathematical approximation based on a point cloud method to efficiently determine whether the channel between the UAV and the GN is blocked by obstacles and whether the trajectory of the UAV intersects with obstacles. Subsequently, we introduce a DL framework that integrates deep neural network (DNN) structures designed to solve the formulated problem with an unsupervised learning-based training methodology. This approach enables efficient modeling of 3-D trajectory and resource allocation while also facilitating the effective training of the DNN without the need for labeled data. Through performance evaluations, we demonstrate that the proposed scheme accurately accounts for the location-dependent channel blockage effects caused by obstacles and successfully avoids potential UAV collisions with obstacles. Furthermore, the proposed scheme outperforms baseline schemes in achieving the minimum average SE by jointly optimizing the 3-D trajectory and resource allocation while maintaining low computation times for real-time operation.

This study investigates the joint optimization of trajectory and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless-powered two-way communication (WPTWC) under probabilistic line-of-sight (LoS) channel models. In this communication protocol, the UAV transmits signals over wireless links while the ground nodes (GNs) simultaneously receive information and harvest energy based on either a power splitting (PS) or time switching (TS) policy. Each GN then uses the harvested energy to transmit collected data back to the UAV. Due to the nature of downlink broadcast transmission in UAV-enabled WPTWC, where GNs at different locations receive signals simultaneously, both LoS and non-LoS components significantly influence the channel characteristics. To capture these location-dependent effects accurately, we reformulate both components into equivalent convex forms, which enhances analytical tractability. Subsequently, we jointly optimize the time allocation, the three-dimensional UAV trajectory, the transmit powers of the UAV and GNs, and the energy harvesting ratio of the GNs to maximize the minimum uplink spectral efficiency (SE) of the GNs while satisfying the downlink SE requirements for all GNs. To tackle the nonconvexity of the optimization problem, we first decompose it into four subproblems and then transform each subproblem into a convex problem with respect to its corresponding optimization variable via successive convex approximation and advanced optimization methods. Thereafter, we propose a low-complexity algorithm that employs the block coordinate descent method to iteratively find the optimal solution for each convex subproblem. Simulations reveal distinct trajectory and resource allocation behaviors under the PS and TS policies, influenced by the downlink SE requirement. Furthermore, by adaptively optimizing the UAV trajectory and radio resource allocation based on the network conditions, the proposed scheme significantly improves the minimum uplink SE of the GNs relative to baseline schemes.

This study explores interference coordination for space-air-ground integrated networks under probabilistic line-of-sight (LoS) channel models, where the LoS probability of a wireless channel is statistically modeled based on the elevation angle between an unmanned aerial vehicle (UAV) and a ground node (GN). Unlike conventional approaches that simplify the model by neglecting either LoS or non-LoS (NLoS) channel components, we jointly optimize user scheduling, transmit power, and three-dimensional trajectory while fully accounting for both components. The objective is to maximize the minimum average spectral efficiency (SE) among GNs while ensuring the required SE for earth stations (ESs) served by satellites. Given the nonconvexity of the optimization problem, we decompose it into four subproblems and apply a successive convex approximation to make each subproblem convex with respect to the relevant optimization variable. Subsequently, we propose a low-complexity algorithm based on the block coordinate descent method to iteratively determine the optimal solution for each convex subproblem. Extensive simulations under various scenarios confirm that the UAV optimizes its horizontal and vertical trajectories to increase the LoS probability of the signal channel and the NLoS probability of the interference channel. This allows the UAV to serve GNs with high SE while reducing interference to the ESs. The results also demonstrate that the proposed scheme outperforms baseline schemes in terms of average SE by jointly optimizing the three-dimensional trajectory and communication resources.

In this paper, we explore the rigorous mathematical modeling of an unmanned aerial vehicle (UAV)-enabled parcel delivery to optimize a three-dimensional (3D) trajectory and pickup/drop-off strategy. Taking into account practical considerations including the avoidance of no-fly zones (NFZs) and the weight restrictions of the UAV, our goal is to jointly optimize the pickup and drop-off indicators, lengths of time slots, and horizontal and vertical trajectories, with the objective of minimizing the weighted-sum of completion time and energy consumption. To address the nonconvexity of the formulated problem, which involves mixed-integer nonlinear programming, we first apply a successive convex approximation to transform the nonconvex problem into a convex one for optimization variables. Moreover, we utilize a penalty convex-concave procedure to maintain the binary nature of integer variables. Finally, for the relaxed convex problem, we propose a low-complexity algorithm that derives the suboptimal UAV strategy iteratively. The simulation results demonstrate the effectiveness of the proposed strategy in establishing 3D trajectories for specific objectives and completely avoiding NFZs while maintaining the binary nature of the pickup and drop-off indicators. Furthermore, the comparative study provides insight into the trade-offs between time-minimization and energy-minimization strategies, offering the flexibility to choose the most suitable approach based on the specific service requirements and objectives.

This study challenges the conventional view that wireless signal blockages are solely detrimental by exploring how non-line-of-sight (NLoS) channels can reduce co-channel interference and improve network performance. By leveraging the mobility of unmanned aerial vehicles (UAVs), this research examines the joint optimization of communication resources and UAV trajectories in multi-UAV-enabled wireless networks to maximize the minimum spectral efficiency (SE) among ground nodes (GNs) by coordinating co-channel interference. We first propose a new analytical model to identify potential signal blockages caused by multiple buildings and a novel building avoidance method that ensures safe and efficient UAV operations. To solve the problem formulated as non-convex mixed-integer nonlinear programming, we employ various optimization techniques: We first decompose the original problem into multiple convex subproblems for each optimization variable using quadratic transform and successive convex approximation. The penalty convex-concave procedure is applied to maintain the binary nature of scheduling indicators. To efficiently address signal blockage and building avoidance constraints, the separating hyperplane theorem is applied along with the approximation of the indicator function. Finally, we utilize block coordinate descent algorithm to iteratively solve the convex subproblems in sequence. The simulation results confirm that UAVs optimize their trajectories to establish LoS channels for transmitting desired signals to scheduled GNs while forming NLoS channels to mitigate interference for others. In this way, the network performance compared to baseline schemes is significantly enhanced. Furthermore, under the proposed building avoidance constraint, UAVs maintain continuous trajectories without violating building boundaries.

In this paper, we propose a new cooperative infer-ence method between the end device and the edge server for intelligent surveillance services. In this method, the end device with a small neural network (NN) model operates with dual confidence thresholds to filter ambiguous input images, which are then forwarded to the edge server and reevaluated by a large NN model. We numerically analyze the performance of the proposed method in terms of accuracy and end-to-end latency, taking into account the confidence scores derived from both positive images and negative images that induce false alarms. Subsequently, we identify the optimal confidence thresholds for both the end device and the edge server to minimize the end-to-end latency while ensuring the required accuracy. The simulation and analysis results show that a tradeoff exists between accuracy and latency according to the confidence thresholds, and the selection of optimal confidence thresholds significantly reduces the latency while satisfying the required accuracy. Accordingly, the proposed method achieves higher accuracy than the device-only inference and lower latency than the server-only inference. This highlights the importance of employing cooperative inference with optimal confidence thresholds in surveillance systems to avoid the fate of ‘The Boy Who Cried Wolf.’

To overcome the limitations of standalone inference that relies on either an edge device or a server, this study proposes a new cooperative inference method between the edge device and the edge server for intelligent surveillance services. In this method, the edge device equipped with a small neural network (NN) model operates with dual confidence thresholds to filter ambiguous input images, which are then forwarded to the edge server and reevaluated by a large NN model. We numerically analyze the performance of the proposed method in terms of inference accuracy and end-to-end latency, taking into account the distribution of confidence scores resulting from positive images as well as negative images that may induce false alarms. Subsequently, we formulate an optimization problem to minimize the end-to-end latency while ensuring the required accuracy, and propose a greedy search algorithm to find the optimal confidence thresholds with low complexity in a nonconvex problem. We also present an operational framework to utilize the proposed cooperative inference method in a practical on-site environment. The simulation and analysis results show that a tradeoff exists between accuracy and latency according to the confidence thresholds, and the selection of optimal confidence thresholds significantly reduces the latency while satisfying the required accuracy. Therefore, the proposed cooperative inference achieves higher accuracy than the device-only inference and much lower latency than the server-only inference across various system parameters. This verifies the importance of optimizing confidence thresholds when applying a cooperative inference method to mobile edge networks.

This study investigates an interference coordination for multiple unmanned aerial vehicle (UAV)-enabled communications under a probabilistic Line-of-Sight (LoS) channel model. Given that the LoS probability of a wireless channel is determined by the elevation angle between the UAV and ground node (GN), we jointly optimize the 3-D trajectory, user scheduling, and transmit power of the UAVs to maximize the minimum average spectral efficiency (SE) among GNs. We first present an effective lower bound for the channel-state-dependent SE to handle its complex form that inevitably arises from the probabilistic LoS channel model. To solve the nonconvex optimization problem under consideration, we also divide the original problem into four subproblems and apply a successive convex approximation to make each subproblem convex for a relevant optimization variable. We then propose an iterative algorithm based on a block coordinate descent method to efficiently find the optimal solution for each convex subproblem. The results of extensive simulation show that to mitigate interference with other UAV networks, each UAV optimizes its horizontal and vertical trajectories, along with its radio resources, to establish LoS for desired channels and Non-LoS for interference channels. The proposed scheme is also verified to be superior to conventional schemes in terms of average SE by effectively adjusting co-channel interference between different UAV networks.

Accurate and real-time three-dimensional (3D) pose estimation is challenging in resource-constrained and dynamic environments owing to its high computational complexity. To address this issue, this study proposes a novel cooperative inference method for real-time 3D human pose estimation in mobile edge computing (MEC) networks. In the proposed method, multiple end devices equipped with lightweight inference models employ dual confidence thresholds to filter ambiguous images. Only the filtered images are offloaded to an edge server with a more powerful inference model for re-evaluation, thereby improving the estimation accuracy under computational and communication constraints. We numerically analyze the performance of the proposed inference method in terms of the inference accuracy and end-to-end delay and formulate a joint optimization problem to derive the optimal confidence thresholds and transmission time for each device, with the objective of minimizing the mean per-joint position error (MPJPE) while satisfying the required end-to-end delay constraint. To solve this problem, we demonstrate that minimizing the MPJPE is equivalent to maximizing the sum of the inference accuracies for all devices, decompose the problem into manageable subproblems, and present a low-complexity optimization algorithm to obtain a near-optimal solution. The experimental results show that a trade-off exists between the MPJPE and end-to-end delay depending on the confidence thresholds. Furthermore, the results confirm that the proposed cooperative inference method achieves a significant reduction in the MPJPE through the optimal selection of confidence thresholds and transmission times, while consistently satisfying the end-to-end delay requirement in various MEC environments.

This study re-examines non-line-of-sight (NLoS) channels, proposing a novel approach that leverages three-dimensional (3D) building blockage to mitigate interference signals, thereby enhancing space-air-ground integrated network performance. Unmanned aerial vehicles (UAVs) benefit from mobility, allowing the adaptable formation of line-of-sight (LoS) and NLoS channels by considering building blockage. Accordingly, a mathematical model is presented to determine whether the interference channels from the UAV to satellite nodes (SNs) are blocked by buildings. We then formulate a joint optimization problem involving scheduling, transmit power, and trajectory to maximize the minimum throughput of ground nodes (GNs), ensuring the minimum required throughput for the SNs. We employ various optimization techniques to solve the formulated nonconvex problem and find that this approach requires significant computational complexity and its performance is sensitive to initialization. To address these challenges, we propose an integrated approach, combining an unsupervised learning-based deep learning (DL) framework for determining initial values with subsequent refinement through optimization. Simulation results provide useful insights into employing building blockage for interference mitigation. Notably, the UAV avoids direct access to GNs in areas that can form the LoS interference channels to the SNs and stays in NLoS areas to serve all GNs, preventing severe interference with the SNs. The integrated approach exhibits superior performance with a much faster convergence time compared to the optimization approach by improving the strategy inferred by our DL model with optimization methods.

In this study, we consider a new cooperative unmanned aerial vehicle (UAV)–unmanned ground vehicle (UGV) delivery framework, where the UAV picks up parcels and the UGV drops off the parcels it is carrying to share the workload associated with picking up and delivering parcels. Because of the limited load capacity of the UAV, it can unload the parcels onto the UGV or at the destination. We mathematically formulate this system model and aim to optimize the horizontal and vertical trajectories of the two vehicles, as well as the binary indicators representing pickup, drop-off, and cooperation, to minimize the total mission completion time. To handle the nonconvexity of the formulated problem, we utilize a successive convex approximation technique to transform nonconvex constraints into convex sets. Additionally, we apply a penalty convex–concave procedure to relax the binary indicators to achieve continuous values for optimizations while preserving their binary characteristics. Finally, we propose a cooperative algorithm to iteratively derive suboptimal solutions from the relaxed convex problem. The simulation results demonstrate that the proposed scheme effectively optimizes both path planning and the cooperation strategy, enabling the UAV to drop off the parcels it carries onto the UGV at the optimal location. This approach significantly reduces delivery time and outperforms the baseline schemes in various environments.

In this study, we investigate an optimal load balancing of cooperative parcel pickup between an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). By considering practical aspects, such as the movement characteristics of each vehicle and the avoidance of no-fly zones for the UAV, we aim to optimize the three-dimensional trajectories and pickup strategies of the two vehicles to identify the shortest route to minimize pickup completion time. To deal with the nonconvex optimization problem, we employ a successive convex approximation to convert the original problem into a convex form for optimization variables, and we also use a penalty convex-concave procedure to retain the binary natures of the control parameters that are required to design the pickup strategy. We also propose a two-stage iterative algorithm based on interior-point methods to solve the relaxed convex problem to find suboptimal solutions. The simulation results confirm that the proposed scheme can successfully allow the UAV and UGV to follow the shortest effective path and thus improve pickup completion time through load balancing, while outperforming the baseline schemes under various scenarios.

In multibeam satellites, transmit power is a limited resource shared among beams, and allocating higher power to certain beams may cause more interference with others. Moreover, beam placement is the issue of determining the center position of each beam and potentially leads to interbeam interference depending on the locations of ground nodes. Therefore, in this study, we investigate the joint optimization problem of beam placement and transmit power to maximize the sum spectral efficiency in multibeam low-Earth-orbit satellite communication systems, taking into account the interbeam interference and user distribution. We solve the optimization problem using the gradient ascent method and quadratic transform, and then propose an optimization-based algorithm that iteratively searches for the beam center positions and transmit power levels. To reduce the complexity of this iterative algorithm, we present a deep neural network (DNN) architecture and a training method for approximating optimal solutions, and propose a deep-learning (DL)-based algorithm that quickly infers the optimal values using the pretrained DNN. Simulation results show that the two proposed algorithms have a clear tradeoff in performance between the spectral efficiency and the computation time. In particular, the DL-based algorithm achieves 3% lower spectral efficiency than the optimization-based algorithm; however, the computation time can be significantly reduced. Furthermore, both schemes achieve at least 10% higher spectral efficiency than the benchmark schemes without joint optimization by optimally adjusting both the beam center position and transmit power according to the node distribution and satellite environments.

This study considers an unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) system in which the UAV broadcasts wireless signals to ground nodes (GNs) that receive information and harvest energy simultaneously using a policy of power splitting (PS) or time switching (TS). While taking into account the throughput and fairness of GNs, we investigate a joint optimization of the trajectory, transmit power of the UAV, and the energy harvesting (EH) ratio of the GNs to maximize the sum of the logarithmic average throughput of the GNs while ensuring the average harvested energy requirement for each GN in terms of both linear and nonlinear EH models. We employ a successive convex approximation method to address the nonconvex nature of this problem, which stems from incorporating a nonlinear EH model. This approach allows us to approximate the problem as convex for each control parameter. Thereafter, we propose a respective iterative algorithm based on the block coordinate descent method to identify the optimal solution for each convex problem under each PS or TS policy. Extensive simulations confirm that the proposed method improves average throughput and fairness index while satisfying the EH constraint by effectively optimizing the three control parameters, thereby achieving a near-optimal performance that is superior to existing baseline methods. Our results also reveal significant differences in the UAV trajectory and resource allocation patterns between linear and nonlinear EH models under the PS and TS policies. Furthermore, we explore the practical aspects of EH by comparing both EH models, such as the limitations of the linear EH model in satisfying the EH requirement in real-world SWIPT environments. The findings underscore the importance of considering nonlinear EH models in practical UAV-assisted SWIPT environments.

This paper considers a multiple unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In this WPCN, UAVs broadcast radio frequency (RF) signals to facilitate a wireless power transfer (WPT) during the downlink phase, and ground nodes (GNs) harvest energy from these RF signals and transmit data to their respective UAVs in the uplink phase. To maximize the minimum uplink throughput of GNs, we jointly optimize the scheduling, transmit power of GNs, and trajectory of UAVs, while satisfying the energy neutrality of GNs and the mobility constraints of UAVs. To solve this non-convex optimization problem, we apply a successive convex approximation to divide the original problem into subproblems and make each of them convex for each optimization variable. Subsequently, we propose an iterative algorithm based on a block coordinate descent technique and efficiently find the optimal solution for each convex subproblem. The simulation result reveals that resource allocation and the trajectory of UAVs are strongly influenced by the interference level within the network. Furthermore, the result verifies that the proposed optimization approach significantly outperforms existing baseline schemes by properly coordinating co-channel and cross-link interferences between distinct UAV networks.

In this paper, we explore a joint optimization of unmanned aerial vehicle (UAV) trajectory and communication resources with complete avoidance of no-fly-zones (NFZs). In particular, we introduce a new constraint that allows the UAV to perfectly avoid NFZs throughout the entire continuous trajectory with rigorous mathematical proof. Under the proposed constraint on NFZs, we aim to optimize the scheduling, transmit power, length of the time slot, and trajectory of the UAV to maximize the minimum throughput among ground nodes without violating NFZs. To find the optimal UAV strategy from the non-convex optimization problem formulated here, we use various optimization techniques, such as quadratic transform, successive convex approximation, and the block coordinate descent algorithm. Simulation results confirm that the proposed constraint prevents NFZs from being violated over the entire trajectory in any scenario. Furthermore, the proposed scheme shows significantly higher throughput than the baseline scheme using the traditional NFZ constraint by achieving a zero outage probability due to NFZ violations.

In this paper, we investigate robust trajectory design and resource allocation in unmanned aerial vehicle (UAV) enabled wireless networks to maximize the minimum average spectral efficiency (SE) among mobile nodes (MNs) on the ground while coping with uncertainties in trajectory. Our work specifically addresses practical challenges encountered during trajectory planning, namely: 1) the mobility of MNs, causing changes in their locations over time; 2) the positioning error of UAV, leading to deviations from its planned trajectory; and 3) the presence of no-fly zones (NFZs), which must be avoided during UAV flight. Taking these practical aspects into account, we propose a deep learning (DL) framework that integrates a deep neural network (DNN) structure with an unsupervised learning-based training methodology. The former enables efficient modeling of UAV trajectory and resource allocation, while the latter allows effective training of DNNs without labeled data. Through performance evaluations, we demonstrate that the proposed DL-based scheme outperforms the comparative baseline schemes in terms of the minimum average SE by optimizing trajectory and resource allocation with low computation time. Furthermore, we validate the robustness of our proposed scheme against the uncertainty associated with positioning errors.

In this paper, we investigate the optimal resource allocation in unmanned aerial vehicle (UAV)-assisted wireless-powered two-way communications. The communication process considered here consists of two steps. First, the UAV transmits a control signal over wireless links while ground terminals (GTs) receive information and harvest energy simultaneously, with each GT then using the harvested energy to send data to the UAV. We aim to maximize the minimum uplink throughput among GTs while ensuring the minimum requirement of the downlink throughput for each GT by optimizing the time allocation, the transmit power and the trajectory of the UAV along with the energy harvesting ratio of GTs. First, we propose an effective optimization-based approach to address the non-convexity of the formulated problem, which is difficult to solve. Specifically, we apply a successive convex optimization technique to approximate the convex problem for each optimization variable and find the optimal resource management strategy through a block coordinate descent algorithm. To reduce the high computational complexity of the optimization-based approach, we also develop a deep learning (DL)-based approach consisting of an efficient deep neural network framework and a novel training methodology. Simulation results confirm that the proposed schemes show significant performance improvements over existing baseline schemes. We also confirm that the DL-based scheme achieves performance comparable to the optimization-based scheme with a much shorter computation time.

In this study, we explore a three-dimensional trajectory and pick-up design of an unmanned aerial vehicle (UAV) for parcel delivery. In particular, we consider the real-world scenario in which a weight-restricted UAV cannot pick up all parcels within a single route; therefore the parcel delivery must be divided into multiple trajectories while avoiding no-fly zones. We formulate this problem mathematically as the minimization of total delivery time, which jointly optimizes the pick-up indicators, the lengths of the time slots, and the horizontal and vertical trajectories. To address the non-convexity of the formulated mixed-integer nonlinear programming, we employ a successive convex approximation to convert the problem into a convex form concerning optimization variables and utilize a penalty convex-concave procedure to preserve the binary characteristics of the pick-up indicators. Subsequently, we propose an iterative algorithm based on a block decent algorithm to efficiently identify the optimal solution by solving the relaxed convex problem. To address the problem of high computational complexity associated with the optimization-based algorithm, we also present an unsupervised deep learning (DL)-based heuristic algorithm. The simulation results confirm that the proposed schemes achieve considerably shorter delivery times than the baseline schemes in various scenarios. Furthermore, the DL-based scheme requires about 10% longer delivery time than the optimization-based scheme, but it can approximate the UAV strategy with substantially reduced computation time.

This paper presents a novel framework that combines an optimization-based approach with a deep learning (DL)-based approach to devise a cooperative strategy for unmanned aerial vehicles (UAVs) in wireless-based secure communications by leveraging the strengths of both approaches and addressing their respective limitations. We first formulate a joint optimization problem to maximize the minimum achievable secrecy rate while guaranteeing the minimum harvested energy requirement for each ground node by optimizing scheduling, transmit power, and trajectory. To address the difficulty of solving the formulated non-convex mixed-integer nonlinear programming problem, optimization techniques, such as continuous convex approximation and the block coordinate descent algorithm, are used to efficiently find feasible solutions. To tackle the challenges posed by the high computational complexity and initialization sensitivity of the optimization-based approach, we also propose an unsupervised learning-based deep neural network (DNN) structure with a specialized loss function tailored to our goals that allows the DNN to effectively approximate the optimal strategies for UAVs. Finally, we design a pioneering method that integrates the strengths of the two aforementioned approaches, in which the output of the trained DNN serves as the initial values of the optimization variables, and subsequently, optimization techniques are applied to fine-tune these optimization variables, leading to further performance improvements. Through intensive simulations, we confirm that the integrated scheme provides superior performance without constraint violation compared to the DL-based scheme, while guaranteeing faster convergence than the optimization-based scheme.

We study cooperative communication with an active reconfigurable intelligent surface (RIS) for the$K$-user rank-deficient multiple-input multiple-output (MIMO) interference channel. Specifically, we assume that transmitters and receivers use$M$antennas each, and the channel matrix between each transmitter and each receiver has rank$L_{2}$whereas the channel matrix between each transmitter or each receiver and the RIS has rank$L_{1}$, where$L_{1}, L_{2}\leq M$. In the absence of the RIS, the multiplexing gain from MIMO is severely limited when$L_{2}$is small. We develop a novel cooperative transmission technique utilizing the RIS to overcome the channel rank deficiency and manage inter-user interference, and the key idea is to neutralize interfering links using signals reflected from the RIS or to reflect incident waves at the RIS to increase the rank of effective channel matrices, depending on the system configuration parameters. We analyze the achievable sum degrees of freedom (DoF) and sum rate, and derive an upper bound on the sum DoF, which is tight under certain conditions. The results show that using an active RIS can significantly improve both the sum rate and sum DoF compared to the network without the RIS, especially when$L_{1}$is small and/or$L_{2}$is large.

In this paper, we present a realistic and novel protocol for two-way communication in multiple-input-single-output (MISO) wireless-powered interference channels, namely, a simultaneous wireless information and power transfer (SWIPT) then wireless information transfer (WIT) protocol. In the protocol considered, transmitters first perform SWIPT in a forward link (FL) and receivers and then execute WIT using the harvested energy in a backward link (BL). Given that the operation of SWIPT in the FL affects the performance of WIT in the BL, our aim is to find a resource allocation strategy that maximizes the sum spectral efficiency (SE) of the FL and BL, which requires the joint optimization of the transmit beamforming vector, transmit power, and energy harvesting (EH) ratio in the FL and the receive beamforming vector in the BL. To deal with the non-convexity of the optimization problem, a deep learning (DL) framework is devised, in which the optimal resource allocation strategy is approximated by a well-designed deep neural network (DNN) model consisting of six independent DNN modules where the sigmoid and softmax functions are jointly utilized to properly model each control parameter. Furthermore, a two-stage training method is proposed where the DNN model is initialized using suboptimal solutions that are found using a low complexity algorithm in a supervised manner before it is fine-tuned using the main training based on unsupervised learning. Through intensive simulations performed in various environments, we confirm that the proposed method improves the training performance of the DNN model while reducing the training overhead. As a result, the proposed DL-based resource allocation achieves a near-optimal performance in terms of the sum SE with a low computation time.

In this article, we consider wireless-powered two-way communication in an$N$-user interference channel with imperfect channel state information (CSI). In the system considered, the receivers harvest energy and receive information simultaneously from data signals sent by transmitters using a time switching (TS) policy, before transmitting response signals back to the transmitters in a subsequent phase using the harvested energy. We aim to find the resource allocation that allows the transmit power and TS ratio to be determined jointly to maximize the sum rate of the response links while guaranteeing a predetermined rate requirement for each data link, even in the presence of errors in the estimated CSI. To deal with the nonconvexity of our optimization problem, we first introduce a gradient algorithm with a barrier function that finds suboptimal solutions heuristically. Moreover, to overcome the limitations of the gradient algorithm, e.g., its high computational complexity and vulnerability to channel error, we devise a robust strategy for resource allocation based on deep learning, in which artificially distorted CSI is fed into the deep neural network (DNN) during training to compensate for the incompleteness of the derived solutions caused by channel error. The performances of the considered schemes are examined through simulations, in which the proposed DNN scheme achieves a near-optimal performance with respect to the sum rate of the response links and outage probability under imperfect CSI, which validates its usefulness and robustness.

In this article, we consider a practical scenario for secure wireless-powered communication in the presence of imperfect channel state information (CSI) with simultaneous energy harvesting, in which it is required to keep information secret from an untrusted energy receiver allowed only to harvest energy from the transmitted signals. We aim to find the robust transmit power control (TPC) strategy to maximize the secrecy rate whilst ensuring the spectral efficiency of transceiver pairs and the amount of energy harvested by the energy receiver, even when the CSI is inaccurate. To deal with the nonconvexity of the formulated optimization problem, we first derive a suboptimal form of TPC in an iterative manner by adopting dual methods. In order to overcome the drawbacks of the conventional optimization-based approach regarding the suboptimality of performance and requiring long computation time, we devise a deep learning (DL)-assisted TPC as an alternative means of deriving the TPC. In the considered DL-assisted TPC, a deep neural network (DNN) is trained to compensate for the distortion caused by channel errors in an unsupervised manner. More specifically, artificially distorted CSI, which reflects the difference between actual and estimated CSI, is fed into the DNN during training and used to update the weights and biases of the proposed DNN using a bounded loss function, which allows a robust TPC strategy to be approximated by the DNN. Simulation results reveal the robustness of the proposed DL-assisted TPC against channel errors, such that it achieves a near-optimal performance with a lower computation time, even when the CSI is incorrect.

To extend the lifetime of a multihop network by addressing energy shortages in wireless nodes, we apply wireless energy transfer (WET) technology to the multihop transmission. Considering a linear multihop topology, we establish a system model for a WET-enabled multihop transmission and formulate an optimization problem that obtains the optimal WET time of each node to maximize the lifetime of multihop networks. To solve this problem, we adopt a flocking model inspired by the similarity between flocking behaviors and WET-enabled multihop transmissions. Applying the underlying principles of the flocking model, we propose a bioinspired cooperative WET (BiCoWET) algorithm, in which each node adjusts its own WET time to equalize the lifetime of all nodes in a distributed manner. Theoretical analysis verifies that the proposed BiCoWET algorithm achieves optimality with exponential convergence. The intensive simulation shows that the proposed BiCoWET outperforms the conventional multihop transmission methods without WET and maximizes the network lifetime by equalizing the lifetime of all nodes.

To extend the lifetime of multihop communication suffering from energy shortages in wireless nodes, we apply the concept of wireless energy sharing (WES) to bidirectional linear multihop transmission and formulate an optimization problem to determine the amount of energy shared in each node that maximizes the lifetime of the multihop path. To solve this problem analytically, we first verify that the lifetimes of multihop nodes have the solidarity property and reveal that the lifetime of the multihop path is maximized when the lifetimes of all the constituting nodes become equal. Based on this property, we then convert the considered optimization problem to a tractable linear programming (LP) problem and obtain the optimal amount of energy shared in each node by solving this LP problem in a centralized manner. Considering the control overhead and complexity in this centralized WES, we also propose a distributed WES operation in which each node autonomously determines the amount of energy shared by matching its lifetime with its neighbors' lifetime without a central coordinator. Thereafter, we prove that the proposed distributed WES algorithm always guarantees convergence, and numerically analyze the control overhead in both centralized and distributed WESs. Intensive simulations in various environments demonstrate that the proposed WES algorithm maximizes the lifetime of the multihop path by equalizing the lifetimes of all the nodes and, thus, increasing the path lifetime almost twice as much as that of the typical one-way wireless energy transfer. Moreover, distributed WES achieves a near-optimal performance and exhibits a smaller control overhead than centralized WES as the number of hops increases.

To solve security vulnerability and energy scarcity problems in relay, we propose two secure relaying protocols, power splitting-based relaying (PSR) and time switching-based relaying (TSR), in a wireless-powered relay network with destination-assisted cooperative jamming. In these protocols, the relay adaptively controls the amount of energy harvested from the received signals using PS or TS policy, considering information leakage to the eavesdropper. We first prove the convexity of the secrecy outage probability with respect to the PS ratio ($\rho $) and TS ratio ($\alpha $), and then derive the closed-form expressions of the optimal$\rho $and$\alpha $for minimizing secrecy outage under the signal-to-noise ratio (SNR) assumption. Numerical results reveal that the proposed PSR and TSR protocols using the derived$\rho $and$\alpha $can achieve near-optimal performance in terms of secrecy outage. It is observed that the optimal$\rho $and$\alpha $do not depend on the eavesdropping channels in a high SNR regime such that the near-optimal secrecy outage can be achieved practically without knowledge of the eavesdropper location. Furthermore, intensive simulations reveal that it is advantageous to allocate more power to energy harvesting for PSR, whereas more time to signal processing for TSR to minimize secrecy outage.

In this paper, we consider a wireless-powered two-way communication, calledtransmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.

Low-power wide-area (LPWA) networks have been proposed to satisfy the features of massive machine-type communication (mMTC) of Internet-of-Things (IoT) networks with the large number of end devices, such as low data rates, high network scalability, wide area coverage, and delay tolerance with very low energy cost. The LoRa wide-area network (LoRaWAN) is one of the leading technologies among LPWA networks (LPWANs) and supports three types of medium access control (MAC) options: Class A, Class B, and Class C, each of which are used to address different needs of various applications. Specifically, Class B is designed to reduce downlink frame transmission delay while the end device maintains a relatively low energy consumption. In this article, we propose an analytical model of LoRaWAN Class B mode, focusing on the delay, the data throughput, and the energy consumption for downlink frame transmission using the M/G/1 queueing model. Based on the analytical model, a cost function considering both the average waiting time of the frame in the gateway and the average energy consumption of an end device is proposed. Using the cost function, we derive the optimal number of ping slots which maximizes the value of the cost function. Results show the tradeoff relation between the waiting time of the frame in the gateway and the energy consumption of an end device, and it is verified that the optimal number of ping slots increases as the traffic density increases.

In this paper, we propose a resource management method based on deep learning, which controls both the transmit power and the power splitting ratio to maximize the sum rate with low computational complexity in D2D networks with energy harvesting requirements. The introduction of the energy harvesting requirements to D2D networks makes it hard to design an effective resource management solution since the treatment of interference signals should be completely different from the conventional resource management focusing only on the rate maximization. To deal with drawbacks of the conventional deep learning-based approach, we propose a new training algorithm suitable for our resource management problem. Numerical simulations show that the proposed learning-based method outperforms the benchmark methods, which are derived from some relevant works, in most situations and achieves performances comparable to an exhaustive search in terms of the sum rate and energy outage probability. Although the conventional optimization-based method is derived to achieve the asymptotic optimal performance for a large network, the proposed deep learning method is shown to achieve almost the same performance with much lower computational complexity. Furthermore, simulation results offer new insights to the impact of the energy harvesting requirements on the behaviour of the optimal resource management.

We investigate the effect of outdated channel state information (CSI) on the secrecy performance of wireless-powered untrusted relay networks, in which the relay is a potential eavesdropper. To keep the information secret from this untrusted relay, the destination sends a jamming signal to the relay when the source transmits an information signal. At the same time, the relay harvests energy from the radio-frequency power, and forwards the received signals to the destination using this harvested energy. To determine the proportions of energy harvesting and information processing, the relay makes use of relaying based on power splitting or time switching policy. Although the destination tries to remove the jamming signal from the relaying signal, it cannot cancel out the jamming signal perfectly due to imperfect channel reciprocity caused by the outdated CSI; this residual jamming signal therefore has a negative impact on secrecy performance. In this scenario, we derive the closed-form expressions for the outage probability and average secrecy rate, and find the jamming power ratio, power splitting ratio, and time switching ratio to optimize these secrecy performance metrics. The numerical results demonstrate the accuracy of our analysis, and show that the proposed secure relaying protocols achieve a near-optimal secrecy performance, as well as outperforming the conventional scheme without the jamming power control.

This paper investigates a secure communication via an untrusted relay with channel estimation error, in which a destination-assisted jamming strategy is adopted to prevent the relay from recovering a source signal. In the presence of channel estimation error in the relay-to-destination link, a destination cannot decode the source signal as well as eliminate a jamming signal perfectly, which causes a serious degradation in secrecy performance. Therefore, it is required to propose an effective strategy to determine jamming power level for maximizing secrecy rate. Under the assumption of high transmit signal-to-noise ratio, we derive the closed-form expression of a suboptimal jamming power ratio and propose an adaptive jamming power allocation which can be operated in practical systems. Numerical results confirm that the proposed scheme outperforms the conventional scheme in terms of secrecy rate.

This paper considers a two-way relay network, in which two sources exchange data through a relay and a cooperative jammer transmits an artificial noise (AN) while a number of nearby eavesdroppers overhear to recover data from both sources. The relay harvests energy from the two source signals and the AN, and then, uses this harvested energy to forward the received signals to the two sources. Each source eliminates its own signal from the relaying signal by self-cancellation and then decodes the data signal received from the other source. For this wireless-powered two-way relay system, we propose two secure relay protocols based on power splitting and time switching techniques. The two protocols are power splitting-based two-way relaying (PS-TWR) and time switching-based two-way relaying (TS-TWR), in which the relay, respectively, controls the power splitting ratio (p) and time switching ratio (α), in order to achieve a balance between the data receiving and the energy harvesting. The optimal values of p and α for each protocol are found analytically to maximize the minimum guaranteed secrecy capacity (CSmin) considering multiple eavesdroppers in high signal-to-noise ratio environments. Numerical results show that both the PS-TWR and TS-TWR protocols using the optimized values of p and α achieve the near-optimal CSminno matter how many eavesdroppers exist anywhere. Comparisons of the two protocols in various scenarios also show that PS-TWR achieves better CSminthan TS-TWR because PS-TWR inherently has a shorter vulnerable time for eavesdropping than TS-TWR.

A two-hop relay network is considered, in which an eavesdropper can overhear the relaying signal. To prevent the eavesdropper from decoding this signal, a destination transmits a jamming noise while a source transmits the data signal to the relay. At the same time, the relay can harvest energy from both the source signal and the jamming noise, and use this harvested energy to forward the received signal to the destination. In such a wireless-powered relay system with cooperative jamming, we propose two adaptive relaying schemes based on power splitting and time switching techniques. In the proposed power splitting-based relaying (PSR) and time switching-based relaying (TSR) schemes, the relay controls the power splitting ratio (p) and time switching ratio (α), respectively, in order to achieve a balance between signal processing and energy harvesting. We find analytically the optimal values of p and α in each scheme to maximize the secrecy capacity under the assumption of high signal-to-noise ratio (SNR). Interestingly, although the eavesdropper's channel state information (CSI) is used in the derivation of the optimal control parameters (p and α), they are shown not to be affected by the eavesdropper's CSI in a high SNR regime. This implies that the proposed schemes can be effective even for practical environments where there is no eavesdropper's CSI. Furthermore, simulation results show that they well coincide with the exact solutions in practical environments even though the closed-form solutions are obtained with a high SNR assumption. Moreover, the comparisons of PSR and TSR in various scenarios show that the two relaying schemes have complementary performances depending on the network conditions. Specifically, PSR achieves greater secrecy capacity than TSR when the channel condition is unfavorable to the eavesdropper for wiretapping.

A cooperative cognitive radio for satellite networks is considered, in which the primary network is a satellite network and the secondary network is a cellular network. Due to the lack of multipath in a satellite environment, the channel matrices of the satellite network are assumed to be rank-deficient, which implies that the capacity cannot be increased in proportion to the number of antennas. To overcome the rank deficiency, we propose a novel cooperative transmission strategy where the base station or mobile users in the cellular network both help the communication of the satellite network and transmit and receive their own streams. Not only does the secondary network carefully adjust the number of transmitted streams to avoid causing interference to the primary network beyond a certain threshold; it also provides alternative signal paths for the primary network, thereby effectively increasing the channel ranks of the primary network. We obtain both the achievable sum degrees of freedom (DoFs) and the sum rate under the proposed scheme, and we also derive upper bounds on the sum DoF. Using the analytical and numerical analysis, we show that our scheme significantly improves the overall system throughput compared with the satellite network alone, without cognitive access.

It was recently shown that full-duplex (FD) operation at a base station (BS) can provide up to twice as many degrees of freedom (DoF) as a conventional half-duplex (HD) cellular network in the absence of self-interference. In practice, however, self-interference in an FD BS may not be eliminated completely due to imperfect cancellation, and it is not yet known whether FD operation can improve the sum DoF of cellular networks in the presence of residual self-interference. In this paper, we provide a complete characterization of the sum DoF in an FD-BS cellular network with self-interference, where the rank of the self-interference matrix is arbitrary. Specifically, we propose a self-interference cancellation scheme that maximizes the sum DoF, and we prove its optimality by deriving the matching upper bound. Our results show that even in the presence of residual self-interference, an FD-BS network can still outperform a conventional HD-BS network, and furthermore, the sum DoF coincides with that of an FD-BS network with no self-interference under certain conditions. We also derive an achievable sum rate under ergodic phase fading, showing that not only a sum-DoF gain but also a sum-rate gain can be obtained over the entire signal-to-noise ratio range even if residual self-interference exists.

Recently, self-healing has been actively investigated for mitigating an unforeseen network failure. In particular, to enable self-healing operations in indoor wireless communications systems, an autonomous mechanism to resolve unforeseen network failure problems should be considered. Therefore, we here address the issue of autonomous self-healing, in which continuous connectivity can be provided to users by resolving unexpected network failures. To overcome this problem, we propose a low-complexity resource allocation algorithm based on an optimization approach with reduced message passing. In the proposed algorithm, normal base stations perform subchannel and power allocations with a minimum amount of information sharing (NH(M-1) to provide reliable service to users in faulty cells autonomously. We also show that the proposed algorithm converges to a unique fixed point in the low-interference region by using a contraction mapping technique. Through simulation results, we demonstrate that the proposed algorithm achieves good performances with respect to the average cell capacity, user fairness, and outage probability while reducing the message passing overhead and computational complexity.

For the purpose of automating network management, self-organizing network (SON) technology is currently being investigated. There are three important issues in SON: self-configuration, self-optimization, and self-healing. This paper focuses on self-healing, in order to resolve the problem of unexpected network faults and improve network throughput simultaneously. To deal with this problem, we propose a healing channel selection and a cooperative beamforming-based iterative resource allocation algorithms. We utilize the cooperative beamforming in the healing channel based on phase pre-adjustment, and this cooperative beamforming can be performed without power cooperation between distributed nodes. Moreover, we derive the sub-optimality and convergence of the proposed algorithm in weak interference condition by using contraction mapping. Finally, simulation results demonstrate that the proposed algorithm improves average cell capacity and user fairness while repairing network failure effectively.

In this paper, we focus on the network lifetime maximization in multiband cognitive radio systems. To deal with this problem, we formulate the optimization problem of an adaptive subchannel allocation and find an optimal solution. To reduce the computational complexity of the optimal solution, we also propose a heuristic algorithm. We use intensive simulations to verify the effectiveness of the proposed algorithm with respect to network lifetime and normalized throughput.

Sensors are embedded in dense mediums, such as walls of building and underground, for various applications. However, traditional wireless communication using electromagnetic (EM) waves does not operate well in this embedded sensor networks because the EM waves are attenuated severely in new propagation mediums including rock, soil, and water. Magnetic induction (MI) communication is rising as a promising technique for embedded sensor networks since the magnetic field experiences little attenuation in dense mediums. In this paper, we investigate the capacity maximization of MI communication. First, in strongly coupled scenario, we find the splitting coupling point from an equivalent circuit model to investigate frequency splitting problem. In loosely coupled scenario, optimal quality factor for maximizing the capacity is derived from the channel model of MI communication. Finally, we show the consistency of our analytic results and the effectiveness of the proposed algorithm through numerical results.

The main objectives of a self-organizing network (SON) technology are autonomous network deployment, network performance optimization and real-time adaptation to environmental changes. There are three functionalities in SON, such as self-configuration, self-optimization and self-healing. In this paper, we focus on the self-healing issue with respect to abrupt network faults. In order to solve this problem, we design a healing channel (HC) and propose a collaborative resource allocation (CRA) algorithm based on a modified iterative water-filling (MIWF) algorithm. Through intensive simulations, we show that CRA efficiently supports users in disabled femtocell base stations (FBS) with the little degradation in system capacity.

In this paper, we focus on the guarantee of inter-cell fairness in OFDM femtocell systems. To solve this problem, we formulate an optimization problem to maximize the summation of logarithmic cell capacity. Based on analytic results, we propose an enhanced modified iterative water-filling algorithm which improves inter-cell fairness compared to a modified iterative water-filling algorithm. The key feature of the proposed algorithm is that each cell performs power allocation to minimize the interference that it causes to a heavy traffic cell to ensure the inter-cell fairness. We verify the effectiveness of the proposed algorithm with respect to fairness improvement and the little degradation of sum capacity by intensive simulations.

We propose a fault-management channel (FMC) to address the problem of indoor-RAS (radio access station) faults and analyze it by using an optimization method. To reduce the complexity of the optimization problem, we also propose a suboptimal algorithm that is based on the FMC: an equal power allocation algorithm (EPA). Our main contributions are as follows: 1) proposing a solution for indoor-RAS faults; and 2) presenting an efficient suboptimal algorithm (EPA) for the improvement of fairness. Through intensive simulations, we evaluate the performance of our proposed algorithm with respect to fairness, spectral efficiency, and a new proposed metric that considers spectral efficiency and fairness together.