Successive Approximation Technique Research Articles

QoE-Aware Video Delivery in Multimedia IoT Network with Multiple Eavesdroppers

How to deal with the increasing video traffic and diverse service demands while ensuring the security of transmission is an open issue in the multimedia Internet of Things (IoT). This paper addresses this issue and studies a secure delivery scheme under a multicast scenario in the presence of multiple eavesdroppers where small base stations (SBSs) can send videos to users cooperatively. Aiming at potential eavesdroppers, a channel model including artificial noise is introduced to reduce the harm of illegal data acquisition. A network quality of experience (QoE) optimization problem is first formulated to account for video quality and delivery delay. In order to solve the nonconvex problem, the successive convex approximation (SCA) technique is applied to optimize multicast group beamforming, reduce the possibility of multicast video eavesdropping, and select video quality where a heuristic scheme is proposed to maximize the network QoE. The effectiveness of the proposed scheme is finally validated by extensive simulations in terms of algorithm convergence performance and network QoE-enhanced performance.

Full Duplex Cooperative Rate Splitting Multiple Access for a MISO Broadcast Channel With Two Users

In this letter, we investigate the performance of a full-duplex (FD) cooperative rate splitting multiple access (C-RSMA) scheme in a downlink multiple-input single-output (MISO) system consisting of one base station (BS) that serves two users simultaneously. With the objective of maximizing the minimum achievable rate, we jointly optimize the BS precoding vectors, the common-stream split and the device-to-device transmit power subject to the power budget of both the BS and the relay device. As the entire problem is non-convex, we propose a low complexity algorithm based on the successive convex approximation (SCA) technique. Simulation results demonstrated that the FD C-RSMA can achieve a higher minimum achievable rate than some baseline schemes, including the FD cooperative non-orthogonal multiple access (FD C-NOMA), the half-duplex (HD) C-RSMA, RSMA and NOMA.

Network Energy-Efficiency Maximization in UAV-Enabled Air–Ground-Integrated Deployment

The unmanned aerial vehicle (UAV) technique combined with the cellular network is a promising and reliable technique to provide a low-cost, flexible, and easy-to-expandable network. In this article, we propose a UAV-assisted cooperative transmission network (UCTN), where UAVs and the base station (BS) transmit data jointly through the cooodination of the software-defined network (SDN). As the UAV has limited on-board energy, we aim to maximize the network energy efficiency (EE) by jointly optimizing the UAV–user association, UAV location, BS resource allocation, and the load allocation between the two systems. The formulated problem is a mixed-integer nonconvex optimization problem, which is intractable. To solve it, alternative optimization (AO) and Dinkelbach’s method are adopted to recast the original problem into a tractable form. After that, the corresponding problem is divided into three subproblems and can be solved iteratively. An energy-constrained coalition game is proposed to solve the UAV–user association problem, and the UAV hovering point is carefully designed to balance the coalition serving time. Furthermore, the successive convex approximation (SCA) technique is adopted to optimize the BS transmit power and bandwidth allocation. Numerical experiments demonstrate that the proposed algorithm achieves significant performance gains over other benchmark schemes.

Energy-Efficient NOMA Multicasting System for Beyond 5G Cellular V2X Communications With Imperfect CSI

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.

Smart Grid Enabled Computation Offloading and Resource Allocation for SWIPT-Based MEC System

In this brief, we consider a wireless powered mobile edge computing (MEC) system, where the base station (BS) is integrated with a MEC server to support computation offloading. Meanwhile, the BS is equipped with renewable energy resource and can transfer energy to the wireless devices (WDs) and trade energy with the grid. The objective is to minimize the total energy cost of the operator under the offloading delay constraint by jointly optimizing the BS’s spot-market energy demand and transmit power as well as users’ offloading data size and transmit power. Both the perfect and imperfect channel state information (CSI) are considered. For the former, we propose a low computational-complexity algorithm for solving the formulated problem. For the latter, the non-convex problem is transformed into a convex one by using the successive convex approximation technique, and then an iterative algorithm is proposed. Simulation results verify the effectiveness of our proposed algorithms.

Energy Efficient Beamforming Optimization for Integrated Sensing and Communication

This letter investigates the optimization of beamforming design in a system with integrated sensing and communication (ISAC), where the base station (BS) sends signals for simultaneous multiuser communication and radar sensing. We aim at maximizing the energy efficiency (EE) of the multiuser communication while guaranteeing the sensing requirement in terms of individual radar beampattern gains. The problem is a complicated nonconvex fractional program which is challenging to be solved. By appropriately reformulating the problem and then applying the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR), we propose an iterative algorithm to address this problem. In theory, we prove that the introduced relaxation of the SDR is rigorously tight. Numerical results validate the effectiveness of the proposed algorithm.

Joint Trajectory and Communication Design for IRS-Assisted UAV Networks

Intelligent reflecting surface (IRS) is a promising technology for future communication systems because of its ability to intelligently configure the wireless environment. In this letter, an IRS is equipped on an unmanned aerial vehicle (UAV) as a passive relay system, in which the UAV serves as a flexible airborne platform. Our aim is to maximize the minimum average transmission rate by jointly optimizing the communication scheduling, phase shift of the IRS, and trajectory of the UAV. The coupling of the optimization variables and non-convexity make this problem hard to solve directly. Therefore, we divide the problem into three subproblems and apply the successive convex approximation (SCA) technique to solve this problem iteratively. Numerical results demonstrate that the minimum transmission rate can be greatly improved by adopting the proposed iterative algorithm, and also confirm the effectiveness of the algorithm.

Energy-Effective Offloading Scheme in UAV-Assisted C-RAN System

In this article, we aim to minimize the total power of all the Internet of Things Devices (IoTDs) by jointly optimizing user association, computational capacity, transmit power, and the location of unmanned aerial vehicles (UAVs) in an UAV-assisted cloud radio access network (C-RAN). In order to solve this nonconvex problem, we propose an effective algorithm by solving four subproblems iteratively. For the user association and the computational capacity subproblems, the nonconvex constraints are relaxed and the optimal solutions are obtained. For the transmit power control and the location planning subproblems, the successive convex approximation (SCA) technique is used to transform the nonconvex constraints into convex ones. Moreover, to obtain the suboptimal solutions, slack variables are also introduced to deal with the feasibility-check problems. The simulation results demonstrate that the proposed algorithm can greatly reduce the total power consumption of IoTDs.

Number and Operation Time Minimization for Multi-UAV-Enabled Data Collection System With Time Windows

In this article, we investigate multiple unmanned aerial vehicles (UAVs)-enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time-constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated, considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer nonconvex problem, we decompose the problem into two subproblems: 1) UAV trajectory and 2) hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed the MACO-based algorithm, is given by leveraging the MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm.

Aerogravity-assist capture into the three-body system: A preliminary design

Aero-gravity assist (AGA) is a low-cost maneuver for an interplanetary vehicle since it can potentially replace thrust with aerodynamic force to save fuel consumption. In this study, an AGA-maneuver capture scheme to deliver a vehicle into a three-body system is presented, and a successive approximation-based method is proposed to optimize atmospheric trajectories. Based on the analysis of the aerodynamic deceleration process, the AGA capture maneuver is first formulated as a coplanar atmospheric flight process with fixed time and strict endpoint-state constraints. Then, the lossless convexification and successive approximation techniques are applied to construct the solving process of iteratively optimizing the AGA trajectories. Finally, the relaxation strategy to the strict terminal state constraints and the heavily penalized-function method are developed to avoid the artificial infeasibility of the problem and improve the convergence. Numerical simulations, demonstrated by the AGA-capture into Sun-Mars L2 Halo orbits and Sun-Mars backward stable orbits, show the effectiveness and reliability of the proposed method. It is the first time that the AGA-maneuver is combined with the dynamic property of the three-body system, and the proposed method can potentially enrich the possibility of the low-energy interplanetary transfer.

Securing the UAV-Aided Non-Orthogonal Downlink in the Face of Colluding Eavesdroppers

Non-orthogonal unmanned aerial vehicle (UAV)-aided secure downlink transmissions are investigated. A single-antenna UAV serves multiple ground users in the face of multiple colluding eavesdroppers (EVs) and only imperfect location information of the EVs is available. Specially, a power splitting based secure non-orthogonal downlink transmission scheme is considered, where the transmit power is divided into two parts for transmitting confidential information and artificial noise. Explicitly, we maximize the minimum average secrecy rate among all the users by optimizing the UAV trajectory, the transmit power and the power splitting ratio. In order to tackle this non-convex optimization problem, we propose an iterative algorithm based on the block coordinate descent and successive convex approximation techniques. Numerical results demonstrate the superiority of our proposed algorithm.

RIS‐assisted secure UAV communications with resource allocation and cooperative jamming

Unmanned aerial vehicles (UAVs) are widely used in wireless communication networks due to their rapid deployment and high mobility. However, in practical scenarios, the existence of obstacles and eavesdroppers will seriously interfere with the communication quality of the UAV network and produce a security risk. Thus, this paper combines reconfigurable intelligent surface (RIS) technology with UAVs to build a secure UAV communication network. Normally, a rotary-wing UAV (labeled as UAV-S) acting as a base station sends information signals to a legitimate user on the ground with RIS equipment. However, there is a passive eavesdropper on the ground who can steal the information. Therefore, a friendly UAV jammer (labeled as UAV-J) with a fixed location is introduced to send jamming signals to confuse the eavesdropper. The goal of this paper is to maximize the average secrecy rate of the communication network by jointly optimizing the flight trajectory, transmit power of the UAV-S and UAV-J, and phase shifter of the RIS. Since the constructed problem is highly nonconvex, an alternating optimization algorithm based on successive convex approximation techniques is proposed to solve the problem. Simulation results show that the proposed algorithm can achieve a higher secrecy rate in comparison with other schemes.

Throughput Maximization for Backscatter- and Cache-Assisted Wireless Powered UAV Technology

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source’s signals to the destination. Whereas the source S can act as a wireless charging station or a base station to supply power or transmit information to the UB using the dynamic time splitting (DTS) method. The UAV utilizes its harvested energy for backscattering (i.e., passive communication) and transmit information (i.e., active communication) to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the DTS ratio and the UB’s trajectory with caching capability at the UB. The formulation is troublesome to solve since it is a non-convex problem. To find solutions, we decompose the original problem into two sub-problems, whereas we first optimize the DTS ratio for a given UB’s trajectory and the UB’s trajectory optimization for a given DTS ratio. By using the KKT conditions, a closed-form expression for the optimal value of the DTS ratio is obtained, greatly reducing the computation time. Moreover, the solution of the second sub-problem can be acquired by adopting the successive convex approximation (SCA) technique. Consequently, an efficient alternating algorithm is proposed by leveraging the block coordinate descent (BCD) method. To show the advantages of the proposed BCD-based algorithm, we also provide the solution of the original problem applying the inner approximation (IA) method. Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes.

Terahertz Meets Untrusted UAV-Relaying: Minimum Secrecy Energy Efficiency Maximization via Trajectory and Communication Co-Design

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR's inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR's trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks.

Throughput Maximization for Active Intelligent Reflecting Surface-Aided Wireless Powered Communications

This letter considers an active intelligent reflecting surface (IRS)-aided wireless powered communication network (WPCN), where devices first harvest energy and then transmit information to a hybrid access point (HAP). Different from the existing works on passive IRS-aided WPCNs, this is the first work that introduces the active IRS in WPCNs. To guarantee fairness, the problem is formulated as an amplifying power-limited weighted sum throughput (WST) maximization problem, which is solved by successive convex approximation technique and fractional programming alternatively. To balance the performance and complexity tradeoff, three beamforming setups are considered at the active IRS, namely user-adaptive IRS beamforming, uplink-adaptive IRS beamforming, and static IRS beamforming. Numerical results demonstrate the significant superiority of employing active IRS in WPCNs and the benefits of dynamic IRS beamforming. Specifically, it is found that compared to the passive IRS, the active IRS not only improves the WST greatly, but also is more energy-efficient and can significantly extend the transmission coverage. Moreover, different from the symmetric deployment strategy of passive IRS, it is more preferable to deploy the active IRS near the devices.

Energy efficiency maximization for buffer-aided multi-UAV relaying communications

This paper studies a multiple unmanned aerial vehicle (UAV) relaying communication system, where multiple UAV relays assist the blocked communication between a group of ground users (GUs) and a base station (BS). Since the UAVs only have limited-energy in practice, our design aims to maximize the energy efficiency (EE) through jointly designing the communication scheduling, the transmit power allocation, as well as UAV trajectory under the buffer constraint over a given flight period. Actually, the formulated fractional optimization problem is difficult to be solved in general because of non-convexity. To resolve this difficulty, an efficient iterative algorithm is proposed based on the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, as well as the Dinkel-bach's algorithm. Specifically, the optimization variables of the formulated problem are divided into three blocks and we alternately optimize each block of the variables over iteration. Numerical results verify the convergence of the proposed iterative algorithm and show that the proposed designs achieve significant EE gain, which outperform other benchmark schemes.

RIS-Assisted Joint Transmission in a Two-Cell Downlink NOMA Cellular System

This paper investigates the integration of reconfigurable intelligent surface (RIS) with downlink non-orthogonal-multiple-access (NOMA) in a multi-user two-cell network assisted by the joint-transmission coordinated multipoint (JT-CoMP). Specifically, the RIS is deployed at the edge of two adjacent cells to assist the JT-CoMP from these two cells to multiple far NOMA users located at their edges. Under this setup, we jointly optimize the power allocation (PA) coefficients at the base stations (BSs), the user clustering (UC) policy, and the phase-shift (PS) matrix of the RIS with the objective of maximizing the network sum-rate subject to a target quality-of-service, defined in terms of the minimum required data rate at each cellular user, and the successive interference cancellation (SIC) constraints. The formulated problem ends to be a non-convex mixed-integer non-linear program that is difficult to be solved in a straightforward manner. To alleviate this issue, and with the aid of alternating optimization (AO), the original optimization problem is decomposed into two sub-problems, a joint PA and UC sub-problem and a PS sub-problem, that are solved in an alternating way. For the first sub-problem, we invoke the bi-level optimization approach to decouple the PA sub-problem from the UC sub-problem. For the PA sub-problem, closed-form expressions for the optimal PA coefficients are derived. On the other hand, the UC problem is projected to multiple 2-dimensional assignment problems, each of which is solved using the Hungarian method. Finally, the PS sub-problem is formulated as a difference-of-convex problem and an efficient solution is obtained using the successive convex approximation technique. The numerical results reveal that the network sum-rate of the proposed RIS-assisted CoMP NOMA networks outperforms the conventional CoMP NOMA scheme without the assistance of the RIS, the RIS-assisted CoMP orthogonal multiple access (OMA) scheme, and RIS-assisted NOMA scheme, especially for low transmit power from the BSs.

Hybrid Uplink and Downlink Transmissions for Full-Duplex UAV Communication With RIS

This letter proposes a reconfigurable intelligent surface (RIS)-aided full-duplex unmanned aerial vehicle (FD-UAV) communication system. The FD-UAV performs its mission to communicate with an uplink (UL) user and a downlink (DL) user simultaneously over the same time-frequency dimension. The RIS is deployed to enhance the signal strength to the UL and DL users while suppressing the co-channel interference (CCI) from the user side. We aim to maximize the weighted sum rate of UL and DL transmissions by jointly optimizing the transmit power of the UL user, RIS’s phase-shift, and UAV trajectory. Although the formulated problem is difficult to solve due to its joint non-convexity, we propose an efficient algorithm based on the successive convex approximation and fractional programming techniques to solve the problem suboptimally. Simulation results show that the proposed RIS-aided communication system can achieve a significant sum rate gain over various bechmark schemes.

Joint trajectory and communications design for high air-interface-resource efficiency

Applying aerial devices to collect data from terrestrial sensors is a research hotspot in recent years. In this paper, we consider a novel system model that terrestrial sensors’ data are first collected by low-altitude UAVs and then transferred to a relatively-high-altitude central processor. We formulate the model in such an air-ground integrated system as a non-convex optimization problem of maximizing the air-interface-resource efficiency (AIRE) by jointly optimizing the trajectories and communication schedules. By using techniques of block coordinate descent (BCD) and successive convex approximation (SCA), the proposed problem is relaxed and transformed into three subproblems, which can be solved by iteration. Simulation results show the optimized trajectories and communication schedules, and the designed schemes perform better than the benchmarks.

Joint uplink and downlink scheduling and UAV trajectory design in the presence of multiple unfriendly jammers and eavesdroppers

In this paper, we investigate secure uplink and downlink communications between an unmanned aerial vehicle (UAV) and multiple user equipments (UEs) in the presence of multiple ground-based eavesdroppers (EVs) and unfriendly jammers. In order to guarantee the secure uplink and downlink transmissions, we consider a novel secure transmission scheme, which involves a power splitting based downlink transmission and scheduling of uplink and downlink transmission. Explicitly, we aim to maximize the average secrecy rate (ASR) by optimizing the UAV trajectory, the transmit power of the UAV and UEs, and scheduling of uplink and downlink transmission. Although the formulated problem is nonconvex, we propose an efficient solution by jointly applying the techniques of block coordinate descent and successive convex approximation. Numerical results show that the proposed scheme achieves a better ASR than the benchmark schemes.