To address the waste of spectrum resources caused by the separate operation of integrated sensing and communication (ISAC) and mobile edge computing (MEC), the integrated sensing, communication, and computing (ISCC) paradigm has been proposed. However ISCC systems are still facing serious channel fading and obstacle occlusion problems, which are expected to be solved by the emerging simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In this paper, we propose a STAR-RIS-assisted ISCC framework, where the computational tasks from users are offloaded to a base station (BS) for processing with the assistance of a STAR-RIS, and the results are subsequently downloaded back to the corresponding users. Notably, target sensing operation is executed concurrently throughout the process of result downloading. To enhance communication efficiency, the non-orthogonal multiple access (NOMA) scheme is incorporated. The number of offloading bits of users is maximized by optimizing the uplink-downlink resource of the network, including the received beamforming vector, active beamforming vector, time slot, phase shift of STAR-RIS, and computational resource. In order to solve the highly complex non-convex problem, we employ the block coordinate descent (BCD) framework to decompose the proposed problem into three subproblems. The convex-concave procedure (CCCP) method is used to deal with the nonconvex constraints. For the rank-1 constraints, the semidefinite relaxation (SDR) approach and the penalty method are invoked. The numerical results show that: 1) the application of STAR-RIS significantly improves the offloading capability of the network, 2) the NOMA scheme significantly outperforms the orthogonal multiple access (OMA) scheme, and 3) comparing with other benchmark schemes, the proposed algorithm significantly improves the overall network performance.

This paper explores cargo delivery strategies and path planning for unmanned aerial vehicle (UAV) assigned with kinematically-constrained multi-user pickup and delivery operations. Specifically, the UAV is programmed to depart from a warehouse to complete user-generated orders and then delivery packages to designated user locations. To ensure the safety and efficiency of UAV operations, it is essential that the cargo UAV maintain a reliable connection with ground base stations (GBSs) throughout the entire flight missions. Our aim is to simultaneously enhance both the cargo delivery efficiency and energy efficiency of the system by jointly optimizing the delivery sequence and flight trajectories of the UAV. Initially, we create an urban radio map, which serves as the foundation for implementing a simulated particle swarm optimization (SPSO) algorithm. This approach efficiently determines the optimal sequence of UAV visits to the warehouse and user locations. Subsequently, using these sequences, we apply a hybrid grid search algorithm (HGSA) to meticulously plan the trajectory between pickup and delivery locations according to our objective function. Simulations comparing traditional traveling salesman problem (TSP) models and our proposed time and energy-constrained TSP with kinematic constraints (TETSPKC) show that our approach offers significant optimization advantages.

In this paper, we propose an unmanned aerial vehicle (UAV) assisted integrated sensing and communication (ISAC) system for the Internet of Things (IoT), where the UAV beams simultaneously sense the status information of multiple IoT sensing nodes and transmit the sensing data to a data center. The main objective is to enable the UAV to perform sensing using radar beams and then transmit the collected information to the data center via communication beams. To evaluate the sensing performance of the ISAC system, we introduce radar mutual information as a key metric from an information-theoretic perspective. Considering the mutual interference between sensing and communication as well as the communication rate limitations, we aim to maximize radar mutual information by optimizing the UAV flight trajectory and transmit beamforming. To address this problem, we propose a joint optimization algorithm that employs semi-finite and concave programming techniques to maintain rank-1 constraints with low complexity. Numerical results demonstrate the effectiveness of the proposed algorithm in achieving the maximum radar estimation rate, thereby validating the rationality and feasibility of the proposed design approach.

In this paper, we consider a cargo autonomous aerial vehicle (AAV)-aided multi-parcel pick-up and delivery network, where the communication ability of the AAV is provided by the ground base stations (GBSs). For such a system setup, our goal is to optimize the trajectory of the cargo AAV while minimizing the combined impact of total energy consumption and total outage time. Simultaneously, we aim to maximize overall user satisfaction throughout the entire flight duration. More specifically, we propose a pick-up and delivery of AAV (PDU) framework to address this problem and this framework consists of two parts. First, a simulated annealing (SA) algorithm is used to obtain the pick-up and delivery (P&D) order of parcels. On the basis of obtaining the P&D order through SA, we further use deep reinforcement learning (DRL) to optimize the flight trajectory of the AAV to ensure the expected communication quality between the AAV and GBSs. To verify the effectiveness of our proposed algorithms, we design three baseline strategies for comparison, and also investigate the effect of using the PDU framework with different weights. Finally, numerical results show that the performance of PDU strategy is improved by about 5%-30% compared with other strategies in solving the performance tradeoff of AAV energy consumption, communication quality, and user satisfaction.

Cargo Unmanned Aerial Vehicles (UAVs) have been increasingly utilized in short-distance logistics scenarios within urban environments. This paper delves into the path planning problem for multi-user pickup and delivery operations of UAVs. The UAVs embark from a depot, execute the pickup and delivery tasks, and subsequently return to the depot, all while maintaining a reliable connection with the ground base station (GBS) throughout their flight. The energy consumption of the UAVs is intricately tied to the sequence of pickup and delivery, as well as the weight of the transported goods. To minimize the maximum energy consumption of the UAVs and optimize their flight trajectories, this paper focuses on optimizing the access sequence. Firstly, we construct a radio map of the target area, enabling us to assess communication reliability. Subsequently, we introduce an enhanced Dijkstra’s algorithm to compute the shortest paths between any two access points that ensure reliable communication. Then, we devise a chromosome structure tailored for a hybrid genetic algorithm (HGA), specifically addressing the multi-UAV pickup and delivery problem. Leveraging the constructed distance matrix and the designed chromosome structure, we apply the HGA to solve the problem of minimizing the maximum energy consumption during multi-UAV pickup and delivery operations. Finally, we validate the effectiveness of our proposed method through simulation results.

In this paper, we propose an inspection system based on a cellular-connected unmanned aerial vehicle (UAV), where UAV departs from the starting point and flies to the inspection points for patrol inspection. The purpose of our work is to minimize the total inspection service time of the UAV, while ensuring that the total communication outage time throughout the flight is less than a certain threshold. The total inspection service time encompasses the cumulative time spent by the UAV traveling to each inspection point. To address the non-convex problem, we propose a hybrid algorithm that combines deep reinforcement learning (DRL) with simulated annealing (SA). Initially, we employ DRL to determine the trajectory between any two points within the defined scenario, followed by the utilization of SA to derive the optimal inspection sequence. The numerical results show that the total inspection service time obtained by our proposed algorithm is always lower than the benchmark algorithm, and the effect is better when the threshold is smaller, about 10% lower than the benchmark algorithm.

In this paper, we consider an unmanned aerial vehicle (UAV)-based patrol inspection scenario, where the cellular-connected UAV traverses multiple pre-determined waypoints for data collection, and then offloads computation task to the ground base stations (GBSs). This paper aims to minimize the sum of the total energy consumption by jointly optimizing the task completion time, communication scheduling, computation resource allocation, and UAV's trajectory. First, we decompose the original problem into two trackable subproblems: 1) design the optimal traverse order among cruise points; and 2) determine the optimal transmission strategy between two consecutive cruise points. Then, by involving the communication rate performance and the topology construct among the GBSs and the cruise points, a novel weighted factor of the edge is proposed to design the traverse order, which can be compatible with the light and heavy task offloading scenarios. The successive convex approximation (SCA) technique and block coordinate descent (BCD) framework are adopted to optimize the UAV's trajectory and the wireless resource allocation. The numerical results finally indicate that our proposed transmission strategy solution decreases the total energy consumption in various scenarios and outperforms other benchmark schemes.

In this paper, we consider a multi-user oriented UAV cargo delivery system, cellular-connected UAV fly to all users within the distribution range successively from the starting point to deliver goods. The UAV needs to complete its mission quickly and maintain good communication with ground base stations (GBSs). To satisfied the above requirements, we propose a three-step approach. Firstly, the influence of cargo weight on UAV energy consumption is considered, we propose a weight change travel salesman problem (WCTSP) to desgin initial trajectory. Secondly, the entire flight trajectory is divided into a series of sub-trajectories base on the obtained initial trajectory. Finally, deep reinforcement learning (DRL) is adopted to optimize all the subtrajectives. By setting reasonable neural network parameters and reward function, the optimal trajectory under the current standard can be obtained after the neural network is trained continuously until it converges. This paper aims to minimize the weighted sum of total energy consumption and total outage time by jointly optimizing cargo distribution scheduling, communication scheduling and UAV flight strategy. The simulation results demonstrate the effectiveness of our proposed trajectory optimization scheme.

This paper studies a mobile edge computing (MEC) framework for cellular-connected multiple unmanned aerial vehicles (UAVs), where the UAVs compute the tasks locally or offload them to ground base stations (GBSs). Considering the time-varying characteristics of the task arriving, we formulate a stochastic problem to minimize the system's average weighted sum energy consumption, by optimizing UAV-GBS association, communication and computation resource allocation, and UAVs' trajectories. We apply Lyapunov approach to convert the stochastic problem into a deterministic problem that is then solved by invoking Lagrange duality method and successive convex approximation technique, based on which an online joint optimization algorithm is proposed. Moreover, we design a velocity-triggered penalty term (VTPT) to reduce the UAVs' energy. Numerical results validate the effectiveness of the designed VTPT, and also demonstrate that our proposed algorithm not only decreases the energy consumption but also maintains the task queue stability.

Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max–min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV’s trajectory. We develop a concave–convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max–min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement.

In this paper, we consider a mobile edge computing (MEC) network where multiple cellular-connected unmanned aerial vehicles (UAVs) can offload their computation tasks to multiple ground base stations (GBSs). In practice, the UAVs are generally unable to master stochastic information of task arrival and channel changes in advance, which may cause a severe issue in terms of energy consumption. Therefore, we formulate a stochastic optimization problem with the goal of minimizing the average weighted sum energy consumption, by jointly optimizing UAV-GBS associations, communication and computation resource allocation, and three-dimensional (3D) UAV trajectories, during which a velocity-triggered penalty term (VTPT) is designed to suppress a large amount of the energy consumption of the UAVs. To handle the stochastic problem, we propose an online resource allocation and trajectory optimization algorithm with outer and inner structures. The outer structure transforms the original problem to a deterministic one by applying the Lyapunov-based optimization framework. The inner structure solves the obtained deterministic problem via the Lagrange duality method and the successive convex approximation technique, based on the block coordinate descent framework. Numerical results demonstrate that: 1) VTPT dramatically decreases the UAVs’ energy consumption, and 2) the proposed algorithm not only reduces the energy consumption but also ensures the computation queue stability compared with other benchmark schemes.

With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV’s mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV’s trajectory, based on the Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with an MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things in a finite period. In this article, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling, and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

This paper proposes a new transmission strategy for device‐to‐device (D2D) multicast cooperative communication systems based on Simultaneous Wireless Information and Power Transfer (SWIPT) technology. The transmission block is divided into two slots. In the first slot, the source user transmits the information and energy to the help user by SWIPT. In the second slot, the help user uses the cellular spectrum and forwards the information to multiple receivers by using harvested energy. In this paper, we aim to maximize the total system rate, and to tackle the problem, we propose a two‐step scheme: In the first step, the resource allocation problem is solved by linear programming. In the second step, the power‐splitting coefficient value is obtained by taking the benefit of help user into account. Numerical results show that the proposed strategy not only effectively improves the overall throughput and spectrum efficiency but also motivates the cooperation.

In this study, energy cooperation schemes are considered in wireless cooperative networks; in these networks multiple pairs of users communicate with each other, assisted by an energy harvesting relay that gathers energy from the received signal by applying a power splitting scheme and forwards the received signal by using the harvested energy. This study is focused on the energy cooperation strategies for the relay to distribute the harvested energy between the multiple user pairs in amplify‐and‐forward and in decode‐and‐forward modes. Specifically, optimal solutions without energy cooperation at the relay node are first proposed and then the authors formulate the energy cooperation optimisation problem. This optimisation problem is non‐convex. They propose an iterative energy cooperation solution to maximise the system throughput by assigning the proper harvested energy to each user pair for both types of forwarding models. They show that with the proposed method, the update in each iteration consists of a group of convex problems with a continuous parameter. Moreover, they derive the optimal solution to these convex problems in closed‐form and it is shown that the solution can converge to a local optimum. Simulation results demonstrate that the proposed algorithm outperforms the traditional non‐cooperation method.

Future wireless multimedia services require a quality of service guarantee and ubiquitous high data rate for all users. In this paper a scheduling algorithm is proposed for multimedia services in relay-based cellular networks. The decode-and-forwarding relaying protocol with transparent frame structure is used. The scheduling priority takes into account the packet delay and maximum tolerance packet loss probability for both direct-link users and relay-link users. Since the relay-link users have two hops, the priority factor of the first hop of relay-link users is defined as the average priority factors of relay-link users in the second hop, and a match factor is proposed considering the capacity match between the first hop link and the second hop link for each relay. Simulation results show that the proposed algorithm achieves overwhelming significant performance gain on the average packet delay and packet loss rate, although suffering some throughput and fairness reduction.

This paper considers using wideband slope as the measure to compare the energy efficiency between different transmission schemes for wideband cooperative wireless relay networks. Two different scenarios are considered (relay with unlimited power supply and relay with limited power supply) and two different relay strategies (amplify and forward, decode and forward) with direct transmission being used as a benchmark. The condition for getting optimal energy efficiency is obtained using theoretical analysis and simulation. The results show that (i) the source-relay distance is the most important factor influencing the energy efficiency of the whole system and (ii) the conditions for optimum energy efficiency depend on the type of relay mode.

An adaptive distributed precoding scheme is proposed for coordinated multi-point transmission systems. The precoding vector used by coordinated base station (BS) is determined by adaptive gradient iteration according to the perturbation vector and adjustment factor. The user equipment only feeds one quantized adjustment factor back to each coordinated BS. The adjustment factor is selected based on the precoding vector of the coordinated BS in the previous frame and the perturbation vector predefined in this frame. The proposed scheme takes advantage of the spatial non-correlation and temporal correlation of the distributed MIMO channel. The design of the perturbation vector set is given. Simulation results show that the proposed scheme has a good trade-off between system performance and the system control feedback overhead.

In coordinated multi-point transmission (CoMP) systems, the optimal remote radio unit (RRU) location is analyzed theoretically and a RRU location design scheme for energy efficiency in practical scenarios is given. An average minimum access distance criterion is given for RRU location optimization. By minimizing the average distance between users and RRU, the optimal RRU distribution can be obtained when users are located uniformly in the cell. Taking into account the fact that user distribution will not be completely uniform in a practical environment, the k-means clustering algorithm is used to get the optimized RRU deployment in a practical user distribution. Simulation results show that the uplink transmission power can be greatly reduced with the RRU optimized location design in both the uniform and non-uniform user distribution.

The power allocation problem with co-channel interference is studied in multi-cell OFDMA cellular relay systems. The multi-cell non-cooperative power allocation game model is given in the first time subslot for base stations (BSs) and in the second time subslot for relay stations (RSs) respectively. In all the cells, each transmitter (BS or RS) controls the power allocation on each subchannel to maximize its own utility in a distributed way. In the first time subslot, a match factor is defined for considering the mismatch between the first hop link data rate and the second hop link data rate and the pricing factor for the RS will be adjusted adaptively by this match factor. As a Nash equilibrium is used to determine the solution of this problem, the existence and uniqueness of the Nash equilibrium are studied. The simulation results of system throughput and transmission power of BS and RS is given with different pricing factors. Compared with equal power allocation, the system throughput is improved and transmission power is reduced.

A load-based relay selection algorithm that improves the fairness of resource allocation is proposed for relay-based OFDMA systems. This algorithm takes into account the traffic load condition (bandwidth requirement) of relay link users and direct link users. The transmission mode (direct transmission mode or relay transmission mode) of each user will be adjusted based on the user average data rate; this will make the long-term average data rate of relay link users and direct link users equal so that user fairness is enhanced. The simulation results show that the proposed algorithm will improve the user fairness compared with a popular traditional relay selection algorithm, for both uniform distribution and hot-spot scenarios.

In this paper the fairness of the subchannel allocation problem for relay based OFDMA systems is studied. The proportional fairness algorithm is extended to a two-hop scenario to ensure that both direct-link users and relay-link users are fairly allocated capacity; this approach is called the "two-hop proportional fairness scheduling algorithm". The fairness allocation problem is considered both in the first time subslot between direct-link users and relay stations, and the second time subslot among relay-link users. Simulation results show that this algorithm achieves an overwhelming performance on the long-term fairness of the user data rate, although suffering some throughput reduction.