Successive Convex Approximation Research Articles

Optimization for UAV-assisted simultaneous transmission and reception communications in the existence of malicious jammers

In this paper, we study an unmanned aerial vehicle (UAV)-assisted communication system, where the UAV is dispatched to implement simultaneous transmission and reception (STR) in the existence of multiple malicious jammers. Two schemes are investigated, namely frequency band-division-duplex (FDD) and time-fraction (TF). Based on the FDD scheme, the UAV can transmit information by using the portion of the bandwidth and receive information within the remaining portion of the bandwidth simultaneously. To perform the STR within the whole bandwidth, the TF-based scheme is considered by using a fraction of a time slot for the downlink, while the remaining fraction of the time slot is allocated for the uplink. We aim to maximize the worst-case throughput by optimizing the UAV three-dimensional (3D) trajectory and resource allocation for each scheme. The optimization problem is non-convex and thus computationally intractable. To handle the nonlinear problem, we use the block coordinate decomposition method to disaggregate the optimization problem into four subproblems and adopt the successive convex approximation technique to tackle non-convex problems. The simulation results demonstrate the performance of the TF-based scheme over the benchmark schemes.

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

Rate Optimization Based on Successive Convex Approximation Algorithm in the Self-powered Visible Light Communication and Positioning System

Rate Optimization Based on Successive Convex Approximation Algorithm in the Self-powered Visible Light Communication and Positioning System

Massive MIMO for Serving Federated Learning and Non-Federated Learning Users

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a promising learning framework for beyond 5G wireless networks. It is anticipated that future wireless networks will jointly serve both FL and downlink non-FL user groups in the same time-frequency resource. While in the downlink of each FL iteration, both groups simultaneously receive data from the base station in the same time-frequency resource, the uplink of each FL iteration requires bidirectional communication to support uplink transmission for FL users and downlink transmission for non-FL users. To overcome this challenge, we present half-duplex (HD) and full-duplex (FD) communication schemes to serve both groups. More specifically, we adopt the massive multiple-input multiple-output technology and aim to maximize the minimum effective rate of non-FL users under a quality of service (QoS) latency constraint for FL users. Since the formulated problem is nonconvex, we propose a power control algorithm based on successive convex approximation to find a stationary solution. Numerical results show that the proposed solutions perform significantly better than the considered baselines schemes. Moreover, the FD-based scheme outperforms the HD-based counterpart in scenarios where the self-interference is small or moderate and/or the size of FL model updates is large.

Joint Beamforming and Phase Shifts Design for RIS-Aided Multi-User Full-Duplex Systems in Smart Cities.

Full-duplex (FD) and reconfigurable intelligent surface (RIS) are potential technologies for achieving wireless communication effectively. Therefore, in theory, the RIS-aided FD system is supposed to enhance spectral efficiency significantly for the ubiquitous Internet of Things devices in smart cities. However, this technology additionally induces the loop-interference (LI) of RIS on the residual self-interference (SI) of the FD base station, especially in complicated urban outdoor environments, which will somewhat counterbalance the performance benefit. Inspired by this, we first establish an objective and constraints considering the residual SI and LI in two typical urban outdoor scenarios. Then, we decompose the original problem into two subproblems according to the variable types and jointly design the beamforming matrices and phase shifts vector methods. Specifically, we propose a successive convex approximation algorithm and a soft actor-critic deep reinforcement learning-related scheme to solve the subproblems alternately. To prove the effectiveness of our proposal, we introduce benchmarks of RIS phase shifts design for comparison. The simulation results show that the performance of the low-complexity proposed algorithm is only slightly lower than the exhaustive search method and outperforms the fixed-point iteration scheme. Moreover, the proposal in scenario two is more outstanding, demonstrating the application predominance in urban outdoor environments.

Physical layer security‐oriented energy‐efficient resource allocation and trajectory design for UAV jammer over 5G millimeter wave cognitive relay system

AbstractThis work focuses on the optimal trajectory of a rotary‐wing unmanned aerial vehicle (UAV) jammer and the system power allocation with the objective of maximizing the total secure energy efficiency (EE) of a slotted relay‐assisted millimeter wave (mm‐Wave) cognitive radio system. The UAV flies from starting location to final one in a round of communication mission that is overheard by a malicious eavesdropper. The network terminals are equipped with multiple‐input multiple‐output (MIMO) antenna array. This work first develops a practical approximation propagation model by addressing three key challenges: path loss, small‐scale Nakagami‐m fading channel gains, and transceiver antenna beamforming gains. By using the approximate model, the upper and lower bounds of the ergodic rate of each communication link are derived. Second, with the objective of maximizing secure EE, the optimal UAV trajectory and system resource allocation framework is established by considering the constraints of interference at primary receiver (PR), information‐causality relationship, rotary‐wing UAV propulsion energy and so forth. The optimal framework is solved with a tractable CVX tool by using the derived upper and lower bounds and successive convex approximation. Finally, an iterative optimizing algorithm is established by using Dinkelbach's method. In the simulation, the work presents the comparison of UAV trajectories, secure rates, and secure EEs of three schemes, that is, the proposed EE scheme, the non‐EE scheme where only the optimal secure rate is considered, and the EE scheme with the consideration of all communication‐related energy consumption. The simulations show that the EE schemes achieve smoother UAV trajectory than non‐EE, but the non‐EE scheme has higher secure rate. The impact of communication energy consumption is negligible. In addition, the effect of MIMO antenna array and mm‐Wave on system performance is exploited.

Energy‐efficiency optimization and control for electric vehicle platooning with regenerating braking

AbstractIt is a critical problem to improve energy efficiency for electric vehicle platooning systems. Moreover, different from internal combustion engine vehicles, the electric engine has higher efficiency, and further regenerating braking is widely used to recycle part of the energy in the electric vehicle when it is braking. What is more, if vehicles take a formation to drive, they can save more energy. Combining all the favorable factors, this paper presents a two‐layer energy‐efficiency optimization strategy for electric vehicle platooning. The upper layer presents an optimization method to find the optimal velocities and distances between vehicles under different road conditions during the cruise status of the electric vehicle platooning. Due to the nonconvex cost function and considering regenerative braking, the optimization problem is addressed by the dynamic programming method combined with the successive convex approximation method. Further, the lower layer presents a real‐time Model Predictive Control (MPC) strategy, and it directly introduces the battery pack state of charge consumption as the input, which not only finishes the control mission but also consumes minimal energy. Finally, simulation results are provided to verify the effectiveness and advantages of the proposed methods.

Max–Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.

Coalitional Game Based Resource Allocation in D2D-Enabled V2V Communication

The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

A UAV-Assisted Secure Communication System by Jointly Optimizing Transmit Power and Trajectory in the Internet of Things

Secure communication technology has attracted more and more attention to improve the security of the Internet of Things (IoT). In this article, we discuss the maximum secrecy capacity problem for a mobile unmanned aerial vehicle (UAV) assisted secure communication in IoT, where the UAV relays the covert information from the transmitter (Alice) to the legal receiver (Bob) without detection by an eavesdropper (Eve) though sharing the spectrum with the ground device-to-device (D2D) communications. Our design aims to maximize the sum secrecy capacity Bob received by optimizing the transmit power and the UAV’s trajectory jointly while guaranteeing that the normal communication behavior between ground D2D pairs is not affected. Therefore, we propose a jointly optimizing the transmit power and UAV trajectory algorithm (JOTUTA) based on the block coordinate descent (BCD) method. Specifically, we first transform the non-convex problem of transmit power based on a fixed UAV’s trajectory into a convex problem by D.C. (difference of two convex functions) programming. Secondly, we solve the problem of UAV’s trajectory optimization with given transmit power by the successive convex approximation (SCA) method. Experiments results show the effectiveness of JOTUTA and the advantages of improving the secrecy capacity are verified compared with existing work.

Marine IoT Systems With Space–Air–Sea Integrated Networks: Hybrid LEO and UAV Edge Computing

Marine Internet of Things (IoT) systems have grown substantially with the development of non-terrestrial networks (NTN) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance and sea transportation. Due to the unpredictable climate changes and the extreme channel conditions of maritime networks, however, it is challenging to efficiently and reliably collect and compute a huge amount of maritime data. In this paper, we propose a hybrid low-Earth orbit (LEO) and unmanned aerial vehicle (UAV) edge computing method in space-air-sea integrated networks for marine IoT systems. Specifically, two types of edge servers mounted on UAVs and LEO satellites are endowed with computational capabilities for the real-time utilization of a sizable data collected from ocean IoT sensors. Our system aims at minimizing the total energy consumption of the battery-constrained UAV by jointly optimizing the bit allocation of communication and computation along with the UAV path planning under latency, energy budget and operational constraints. For availability and practicality, the proposed methods are developed for three different cases according to the accessibility of the LEO satellite“, Always On”, “Always Off” and “Intermediate Disconnected”, by leveraging successive convex approximation (SCA) strategies. Via numerical results, we verify that significant energy savings can be accrued for all cases of LEO accessibility by means of joint optimization of bit allocation and UAV path planning compared to partial optimization schemes that design for only the bit allocation or trajectory of the UAV.

Joint Service Caching and Computing Resource Allocation for Edge Computing-Enabled Networks

In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)’ service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs’ service cache size, and an EC servers’ computing capability on the SSP.

Reliability-Oriented Resource Allocation for Wireless Powered Short Packet Communications With Multiple WPT Sources

We study a multi-source wireless power transfer (WPT) enabled network supporting multi-sensor transmissions. Activated by the energy harvesting from multiple WPT sources, the sensors transmit short packets to a destination with finite blocklength (FBL) codes. This work <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">for the first time</i> characterizes the FBL reliability for such multi-source WPT enabled network and accordingly provides reliability-oriented resource allocation designs, while a practical nonlinear EH model (including the effects of mutual interference among multiple RF signals) is considered. For the scenario with a fixed frame structure, we aim to maximize the FBL reliability via optimally allocating the transmit power among the multiple WPT sources. In particular, we investigate the relationship between the overall error probability and the transmit power of multiple WPT sources, based on which a power allocation problem is formulated. To solve the formulated non-convex problem, we first introduce auxiliary variables to make the problem analytically tractable, based on which an iterative algorithm is proposed while applying successive convex approximation (SCA) technique to the non-convex components of the problem. Then, we extend our design into a dynamic frame structure scenario, i.e., the blocklength allocated for WPT phase and short-packet transmission phase are adjustable, which introduces more flexibility and new challenges. In particular, we provide a joint power and blocklength allocation design maximizing the overall reliability under total power and blocklength constraints. A problem with high-dimension variables is formulated, which suffers from the complex and non-convex relationship among system reliability, multiple source power and blocklength. To tackle the difficulties, auxiliary variables introduction, multiple variable substitutions along with SCA technique utilization are exploited to reformulate and efficiently solve the problem. Finally, through numerical results, we validate our analytical model and evaluate the system performance, where a set of guidelines for practical system design are concluded.

Airborne Internet: An Ultradense LEO Networks Empowered Satellite-to-Aircraft Access and Resource Management Approach

With the rapid development of low earth orbit (LEO) satellite technologies and constellation projects (like Starlink, OneWeb), the ongoing ultra-dense LEO satellite networks can provide an efficient solution to ubiquitous connection of aircraft. It is urgent to design the ultra-dense LEO network enabled satellite-to-aircraft communication for in-cabin users with high-speed Internet connectivity requirements. In this paper, we investigate the resource scheduling of ultra-dense LEO network enabled satellite-to-aircraft communication and the in-cabin communication inside aircraft in civil aviation. First, we adopt the Lyapunov optimization framework to tackle the high dynamic and fast mobility of satellites and aircraft. For the satellite-to-aircraft access and subchannel assignment, we propose a dynamic access and subchannel allocation algorithm based on matching theory, which can converge to a stable matching within a finite iterations. To guarantee customized needs of in-cabin users, we propose a power control algorithm based on the successive convex approximation technology to optimize the power allocation of satellites on each subchannel and the transmit power of aircraft for each in-cabin user, which is proven to obtain a saddle point or a local minimum with low complexity. Extensive simulations have been carried out to validate the effectiveness of the proposed resource allocation method.

Joint Trajectory Design and Resource Allocation for Secure Air–Ground Integrated IoT Networks

We investigate in this paper joint trajectory design and resource allocation for secure air-ground integrated Internet of Things (IoT) networks with unmanned aerial vehicle (UAV) jamming and device to device (D2D) enhancement. By jointly optimizing ground user scheduling, UAV flight trajectory and transmit power, we are able to maximize the minimum system secrecy rate of UAV and D2D communications. The formulated optimization problems of the two typical network scenarios, i.e., with and without UAV jamming, are challenging to be solved due to the corresponding non-smooth and non-concave objective functions. Therefore, we propose alternating iterative algorithms to solve the problems by employing the successive convex approximation and block coordinate descent methods. Extensive results indicate that the proposed joint optimization schemes can effectively improve the secrecy communication performance under different spatial distributions of ground users.

Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)–Assisted Vehicular Networks

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

Hybrid/Reflective Beamforming for IRS-Assisted Dual-Function Radar-Communication System

This paper investigates the joint design problem of the hybrid/reflective beamforming for the dual-function radar-communication (DRC) system with the help of an intelligent reconfigurable surface (IRS). The degrees of freedom for beamformer design are the analog and digital beamformers at the base station (BS), and the reflective beamformer at the IRS. To begin with, we formulate the beamformer design problem as a weighted sum-rate (WSR) maximization problem subject to the transmit power and similarity between the designed beamformer and the reference one with desired beampattern property. Then, utilizing equivalent reformulation of the preceding design problem, we conceive an alternating direction sequential programming (ADSP) based algorithm that iteratively updates the following low-complexity subproblems: 1) digital beamformer at the BS through the Powell-Hestenes-Rockafellar (PHR) method; 2) analog beamformer at the BS using the riemannian conjugate gradient method; and 3) reflective beamformer at the IRS by the successive convex approximation (SCA) technique. The convergence is ensured by analytically offering the non-decreasing and boundary of the WSR value. Finally, numerical results verify the effectiveness of the proposed algorithm, which achieves superior WSR performance while ensuring the radar requirement.

Energy-Efficiency Optimization for Mutual-Coupling-Aware Wireless Communication System Based on RIS-Enhanced SWIPT

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this paper, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several sub-problems and solved alternatively due to its nonconvexity. Firstly, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then an alternative optimization (AO) framework and semi-definite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach’s algorithm are applied to solve each sub-problem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and thus solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions.

Performance Improvement in UAV Communication Systems With Uncertain Solar Energy Supply

In this paper, we study a problem of improving the communication performance in a solar-assisted UAV (unmanned aerial vehicle) communication system (SA-UCS) where UAVs equipped with solar panels are deployed to provide communication services to ground users in a target area. Previous works have ignored the inherent uncertainty in solar energy supply caused by naturally unpredictable weather conditions and UAV vibrations, consequently degrading communication performance in SA-UCSs. To bridge the gap, we propose to explore the extreme value theory to tackle uncertain solar energy supply. Specifically, we formulate a throughput maximization problem that is subject to transmission power, link capacity, UAV position, and minimum residual energy. To handle the uncertainty in solar energy supply, we propose to find the GEV (generalized extreme value) distribution of the minimum amount of harvested solar energy, in order to bound the solar energy supply in extreme cases. To solve the formulated problem, which is in general non-convex, we develop an iterative optimization method that first decomposes the originally formulated problem into two sub-problems being solved alternately at each iteration through employing the successive convex approximation (SCA) technique. We perform extensive simulations using real-world data from the National Solar Radiation Database NSRD18. The simulation results corroborate the throughput enhancement with guaranteed energy supply available for UAV communications. Additionally, the results also show the fast convergence achieved by the developed iterative method.

Secrecy Energy Efficiency Maximization for Secure Unmanned-Aerial-Vehicle-Assisted Simultaneous Wireless Information and Power Transfer Systems

Unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) systems have recently gained significant attraction in internet-of-things (IoT) applications that have limited or no infrastructure. Specifically, the free mobility of UAVs in three-dimensional (3D) space allows us good-quality channel links, thereby enhancing the communication environment and improving performance in terms of achievable rates, latency, and energy efficiency. Meanwhile, IoT devices can extend their battery life by harvesting the energy following the SWIPT protocol, which leads to an increase in the overall system lifespan. In this paper, we propose a secure UAV-assisted SWIPT system designed to optimize the secrecy energy efficiency (SEE) of a ground network, wherein a base station (BS) transmits confidential messages to an energy-constrained device in the presence of a passive eavesdropper. Here, we employ a UAV acting as a helper node to improve the SEE of the system and to aid in the energy harvesting (EH) of the battery-limited ground device following the SWIPT protocol. To this end, we formulate the SEE maximization problem by jointly optimizing the transmit powers of the BS and UAV, the power-splitting ratio for EH operations, and the UAV’s flight path. The solution is obtained via a proposed algorithm that leverages successive convex approximation (SCA) and Dinkelbach’s method. Through simulations, we corroborate the feasibility and effectiveness of the proposed algorithm compared to conventional partial optimization approaches.