Successive Approximation Technique Research Articles

Rate-Splitting Multiple Access and Dynamic User Clustering for Sum-Rate Maximization in Multiple RISs-Aided Uplink mmWave System

In this paper, a reconfigurable intelligent surfaces (RISs)-aided millimeter wave (mmWave) uplink (UL) rate-splitting multiple access (RSMA) system is investigated which targets to achieve better rate performance and enhanced coverage capability for multiple users. The considered UL RSMA model splits the rate for each user by dividing their message into multiple parts and hence exploits all the necessary degrees of freedom to achieve maximum capacity region and high user fairness. In particular, we focus on the sum-rate maximization for considered UL RSMA system subject to joint optimization of power allocation to the UL users and beamforming design, i.e., active receive beamforming at the base-station (BS) and passive beamforming at multiple RISs. To efficiently mitigate high inter-node interference in multi-user scenario, we first provided a low-complex user pairing scheme based on k-means clustering and then develop an effective low-cost alternating optimization framework to solve the joint optimization problem sub-optimally by decoupling the problem into different sub-problems of power allocation and beamforming design. Specifically, the sub-problems of power allocation and beamforming design are solved using successive convex approximation, Riemannian manifold and fractional programming techniques. Later, the unified solution based on block coordinate descent (BCD) algorithm is proposed. Extensive numerical simulations validate that the user-clustering effectively significantly improves the performance gain and the considered RSMA system outperforms the conventional multiple schemes in terms rate and user-fairness. Also, the exploitation of spatial correlation among each RIS elements i.e., non-diagonal phase-matrices at each RIS achieve better performance that conventional diagonal phase-matrices setting.

A remark on the existence of entire large positive radial solutions to nonlinear differential equations and systems

This paper establishes the existence of entire large positive radial solutions for a class of nonlinear equations and systems. The method employed is based on a successive approximation technique. Our results improve some existing works recently published.

Two‐timescale optimization approach for coordinated multi‐point design in unmanned aerial vehicle‐assisted cellular networks

AbstractThanks to their mobility, flexibility, and adaptive altitude, unmanned aerial vehicles (UAVs) have been admitted for several applications in wireless communication systems. In particular, UAVs can function as aerial base stations (BSs) to increase cellular network capacity and provide persistent coverage. Due to the line‐of‐sight channels between UAVs and user equipments (UEs), strong co‐channel interference may be generated in the network. In this article, a joint transmission coordinated multi‐point technique is considered in the downlink cellular network to effectively manage the interference brought by UAVs. A two‐timescale optimization approach is proposed to prevent exchanging absurd messages and channel information between the transmission points. The objective of the proposed method is to maximize the sum rate of the network subject to the constraints on UEs' quality of service as well as per‐ground BS and per‐UAV total transmitted power. The proposed approach is decomposed into long‐term and short‐term problems. The long‐term problem aims to jointly optimize the UEs' clusters, the UAVs' locations, and the initial transmit beamforming vectors based on large‐scale channel gain. This problem is a mixed‐integer nonlinear programming optimization problem, which is nondeterministic polynomial hard. Meanwhile, the short‐term problem requires obtaining the transmit beamforming vectors according to the overall channel vector. This problem is also a non‐convex optimization problem. To address these challenges, two algorithms are proposed based on difference of convex functions programming and successive convex approximation technique to deal with these two problems. Extensive simulations show that the performance of the proposed approach outperforms the comparable algorithms.

3D trajectory design and resource management for UAV-assisted multi-user two-way transmission system

With the advantages of mobility and high spectrum efficiency, the unmanned aerial vehicle (UAV) enabled two-way relay technology has practical significance in improving the network's throughput. This paper proposes a UAV-enabled two-way relaying framework, in which a UAV assists the exchange of information between multiple pairs of ground users (GUs) based on the physical layer network coding. We consider the probabilistic line-of-sight (LoS) links of the UAV-GU channels, and formulate an optimization problem with the goal of maximizing the overall rate of the two-way relaying network, by jointly optimizing the time-slots pairing, power allocation, and three-dimensional (3D) trajectory of the UAV. To address the formulated non-convex problem, we first propose an efficient offline algorithm that decomposes the problem into four low-complex subproblems, which are optimized iteratively by using the successive convex approximation (SCA) technique and the coordinate descent (BCD) method. To fully utilize the instantaneous channel state information (CSI) of the two-way relaying network during the flight of the UAV, we also propose a hybrid offline-online algorithm to further improve the performance of the network on the basis of the offline algorithm. The simulation results demonstrate that the proposed hybrid offline-online algorithm improves the network's rate by about 20 percent compared to the offline algorithm, and validate that the 3D UAV trajectory optimization is superior to boost the overall rate compared to the traditional two-dimensional (2D) UAV trajectory optimization.

Joint Optimization Method of Spectrum Resource for UAV Swarm Information Transmission

For the problems brought by malicious interference in the unmanned aerial vehicle (UAV) swarm network, we establish a cluster-based UAV swarm information transmission model. We mainly consider four aspects: cluster head selection, channel allocation, power allocation and UAV position. In order to improve the backhaul information rate of UAV swarm, we propose a joint optimization method of spectrum resource with the goal of maximizing the sum throughput of the cluster head UAV. We decompose the original mixed integer nonlinear programming (MINLP) problem into multiple sub-problems based on the block coordinate descent (BCD) technique, and then solve them based on convex optimization and successive convex approximation (SCA) technique. Simulation results show that the proposed method can obtain the spectrum resource optimization strategy of UAV swarm information transmission, reduce the impact of malicious interference, and effectively improve the backhaul information rate of UAV swarm.

Resource allocation for UAV-assisted backscatter communication

As a promising new technology for green communication, backscatter communication has attracted wide attention in academics and industry. This paper studies the resource allocation problem for an unmanned aerial vehicle (UAV)-assisted backscatter communication network. The UAV acts as an airborne mobile base station and broadcasts signals to the ground backscatter devices (BDs), which transmit the data signals to the backscatter receiver (BR) in a backscattered manner. Specifically, the ground-based BDs communicate with the BR using a dynamic protocol based on time division multiple access. Considering the fairness among BDs, we aim to investigate maximizing the minimum (max–min) rate of the proposed network by jointly optimizing backscatter device scheduling, reflection coefficient, UAV’s power control, and UAV’s trajectory. The optimization problem is a non-convex problem, which is challenging to obtain the optimal solution. Therefore, we propose an efficient iterative algorithm to decompose the optimization problem into four subproblems by the block coordinate descent method. The variables are alternatively optimized by the interior point method and successive convex approximation techniques in each iteration. Finally, simulation results show that the max–min rate of the system obtained by the proposed scheme outperforms other benchmark schemes.

Joint resource allocation, user clustering and 3-D location optimization in multi-UAV-enabled mobile edge computing

This paper studies a multi-unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where UAVs are equipped with a powerful MEC server to provide computation services for ground users. Under NOMA protocol and partial offloading mode, we aim to jointly optimize the NOMA user clustering, sub-carrier assignment, transmit power of users, CPU frequency, and 3-D location of UAVs to maximize the sum computation bits of all users. Since the formulated problem is a mixed-integer non-linear optimization, we propose a three-stage algorithm to address the four sub-problems iteratively. First, two efficient NOMA user clustering algorithms are proposed to divide the users into several NOMA clusters. Second, a heuristic sub-carrier assignment algorithm is presented based on the channel gain and interfering users. Third, by adopting the successive convex approximation (SCA) technique and Lagrange dual method, the closed-form expressions of the transmit power and CPU frequency of users are derived. Forth, a convex optimization problem is obtained to optimize the 3-D location of UAVs. The numerical evaluations illustrate that the proposed system outperforms the benchmark schemes in terms of sum computation bits performance.

Quality-driven video streaming for ultra-dense OFDMA heterogeneous networks

The explosive growth of mobile video traffic has brought enormous traffic pressure to the existing bandwidth-limited cellular network. Ultra-dense heterogeneous networks (HetNets) are deemed as one of the most promising 5G technologies for increasing the cellular network capacity. In this paper, we study quality-driven multiuser video streaming algorithm for two-tier orthogonal frequency division multiple access (OFDMA) HetNets. Specifically, we formulate a stochastic optimization problem for the quality-driven multiuser video streaming in two-tier OFDMA HetNets to maximize the average perceived video quality of small cell users while meeting the average data rate demand of macrocell users. We then decompose the original stochastic problem into a sequence of per-slot deterministic subproblems of adaptive bitrate selection and radio resource management which have no requirement of prior channel knowledge by employing Lyapunov optimization technique. We accordingly propose an online quality-driven video streaming algorithm. In this algorithm, we derive the optimal bitrate decision for the adaptive bitrate selection subproblem, and design an iterative scheduling algorithm of joint subchannel assignment and power control by utilizing alternating optimization and successive convex approximation techniques to solve the radio management subproblem. Extensive simulation results validate the high efficiency of the proposed quality-driven video streaming algorithm in OFDMA HetNets.

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confirm that the proposed solution significantly outperforms three benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.

Energy-Efficient UAV Communications: A Generalized Propulsion Energy Consumption Model

This letter proposes a generalised propulsion energy consumption model (PECM) for rotary-wing unmanned aerial vehicles (UAVs) under the consideration of the practical thrust-to-weight ratio (TWR) with respect to the velocity, acceleration and direction change of the UAVs. To verify the effectiveness of the proposed PECM, we consider a UAV-enabled communication system, where a rotary-wing UAV serves multiple ground users as an aerial base station. We aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing the user scheduling and UAV trajectory variables. However, the formulated problem is a non-convex fractional integer programming problem, which is challenging to obtain its optimal solution. To tackle this problem, we propose an efficient iterative algorithm by decomposing the original problem into two sub-problems to obtain a suboptimal solution based on the successive convex approximation technique. Simulation results show that the optimized UAV trajectory by applying the proposed PECM are smoother and the corresponding EE has significant improvement as compared to other benchmark schemes.

Energy efficient MIMO–NOMA aided IoT network in B5G communications

To accelerate future intelligent wireless systems , we designed an energy-efficient Massive multiple-input-multiple-output (MIMO)- non-orthogonal multiple access (NOMA) aided internet of things (IoT) network in this paper to support the massive number of distributed users and IoT devices with seamless data transfer and maintain connectivity between them. Massive MIMO has been identified as a suitable technology to implement the energy efficient IoT network in beyond 5G (B5G) communications due to its distinct characteristics with large number of antennas. However, to provide fast data transfer and maintain hyper connectivity between the IoT devices in B5G communications will bring the challenge of energy deficiency. Hence, we considered a massive MIMO–NOMA aided IoT network considering imperfect channel state information and practical power consumption at the transmitter. The far users of the base stations are selected to investigate the power consumption and quality of service. Then, calculate the power consumption which is non-convex function and non-deterministic polynomial problem. To solve the above problem, fractional programming properties are applied which converted polynomial problem into the difference of convex function . And then we employed the successive convex approximation technique to represent the non-convex to convex function. Effective iterative based branch and the reduced bound process are utilized to solve the problem. Numerical results observe that our implemented approach surpasses previous standard algorithms on the basis of convergence, energy-efficiency and user fairness.

A Robust IRS-Aided Wireless Information Surveillance Design With Bounded Channel Errors

This letter studies an intelligent reflecting surface (IRS)-aided wireless information surveillance system, in which the IRS is leveraged to help the legitimate receiver (LR) intercept the information transmitted from a suspicious transmitter (ST) to a suspicious user (SU). In particular, we consider the challenging scenario where all involved channels are not perfectly known at the LR. We investigate the joint design of the receiver beamformer at the LR and the phase shifts at the IRS for maximization of the worst-case information monitoring rate (WCIMR) by considering the deterministically bounded errors of channel state information (CSI). To solve the resulting non-convex optimization problem, a robust algorithm is proposed based on the general S-procedure, the general sign-definiteness lemma, the penalty-based and successive convex approximation (SCA) techniques. Simulation results demonstrate the effectiveness of the proposed algorithm and also show that the IRS can significantly improve the WCIMR compared to conventional architectures without IRS.

Energy minimization for UAV communication systems assisted with multiple IRSs

Unmanned aerial vehicles (UAVs) have steadily gained attention to overcome the harsh propagation loss and blockage issue of millimeter-wave communication. However, UAV communication systems suffer from energy consumption, which limits the flying time of UAVs. In this paper, we propose several UAV energy consumption minimization techniques through the aid of multiple intelligent reflecting surfaces (IRSs). In specific, we introduce a tractable model to effectively capture the characteristics of multiple IRSs and multiple user equipments (UEs). Then, we derive a closed form expression for the UE achievable rate, resulting in tractable optimization problems. Accordingly, we effectively solve the optimization problems by adopting the successive convex approximation technique. To compensate for the high complexity of the optimization problems, we propose a low complexity algorithm that has marginal performance loss. In the numerical results, we show that the proposed algorithms can save UAV energy consumption significantly compared to the benchmark with no IRSs, justifying that exploiting the IRSs is indeed favorable to UAV energy consumption minimization.

Service Caching Based Aerial Cooperative Computing and Resource Allocation in Multi-UAV Enabled MEC Systems

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

Cellular-Connected Multi-UAV MEC Networks: An Online Stochastic Optimization Approach

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.

Trajectory and Transmit Power Optimization for IRS-Assisted UAV Communication Under Malicious Jamming

In this letter, we investigate an unmanned aerial vehicle (UAV) communication system, where an intelligent reflecting surface (IRS) is deployed to assist in the transmission from a ground node (GN) to the UAV in the presence of a jammer. We aim to maximize the average rate of the UAV communication by jointly optimizing the GN's transmit power, the IRS's passive beamforming and the UAV's trajectory. However, the formulated problem is difficult to solve due to the non-convex objective function and the coupled optimization variables. Thus, to tackle it, we propose an alternating optimization (AO) based algorithm by exploiting the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Simulation results show that the proposed algorithm can significantly improve the average rate compared with the benchmark algorithms. Moreover, it also shows that when the jamming power is large and the number of IRS elements is relatively small, deploying the IRS near the jammer outperforms deploying it near the GN, and vice versa.

Energy-Efficient UAV-Aided Ocean Monitoring Networks: Joint Resource Allocation and Trajectory Design

The Internet of Underwater Things (IoUT) plays a key role in maritime monitoring systems, but energy-efficient data-uploading has been a challenging task owing to energy-constrained and expensive facilities, such as buoys and underwater sensors. In this article, we present an energy-efficient data collection scheme for unmanned aerial vehicle (UAV)-aided ocean monitoring networks (OMNs), where underwater acoustic and aerial radio frequency (RF) links are considered collaboratively. Our goal is to maximize energy efficiency (EE) of the entire OMN by jointly optimizing the transmit power of buoys and sensors, scheduling their transmissions, as well as designing the UAV's trajectory; the objective function is constrained by minimum throughput thresholds, power consumption budgets, and the UAV's kinematic conditions. Furthermore, we introduce a tradeoff between the energy consumption of buoys and sensors to bridge the gap between acoustic and RF links. The formulated problem is decomposed into three subproblems and they are solved alternatively. In each iteration, we leverage Dinkelbach's method and successive convex approximation (SCA) technique to tackle the fractional program (FP) and transform an original subproblem into a convex form, respectively. Extensive simulations confirm the convergence of our proposed scheme, reveal the influence of the tradeoff on EE, and show that our scheme outweighs other benchmarks in different scenarios.

Resource Allocation and Beamforming Optimization for IRS-Assisted OFDMA Uplink in C-RAN

This letter studies the resource allocation and passive beamforming for the cloud radio access network (C-RAN) with orthogonal frequency division multiple access (OFDMA) assisted by intelligent reflecting surface (IRS). Specifically, we consider the uplink transmission where remote radio heads (RRHs) quantize the received signals from the users by independent point-to-point compression and transmit the quantization bits to the baseband unit (BBU) pool through capacity-limited fronthaul links. We jointly optimize the resource allocation and passive beamforming to maximize the uplink sum-rate, using alternating approach and successive convex approximation (SCA) technique. Numerical results verify the performance gain brought by IRS and validate the effectiveness of the proposed algorithm.

Cooperative beamforming design for double-IRS-assisted MISO communication system

Intelligent reflecting surface (IRS) has arisen as a promising technology to reconfigure the wireless propagation environment cost-effectively. Most of the existing works on IRS focused on the passive beamforming (PB) optimization and performance enhancement without considering the multiple inter-IRS links cooperation that did not reveal the full preponderance of the multi-IRS-assisted reconfigurable communication system. In this work, we investigate a double-IRS-assisted multiple input single output (MISO) downlink communication system with the active beamforming (AB) and the cooperative PB design in the absence of direct link. Taking both the double-reflection links and the single-reflection links into account, the AB at the base station (BS) and the cooperative PB at two IRSs are jointly optimized to maximize the weight sum rate (WSR) under the constraint of the transmit power. To tackle the problem, we propose the double-IRS-assisted fractional programming block coordinate descent (D-FPBCD) method to find the sub-optimal solution with low complexity. We first reconstruct the original issue as a tractable one by the closed-form fractional programming (FP) approach, then, the prox-linear block coordinate descent (BCD) and successive convex approximation (SCA) techniques are used to find the sub-optimal solution. Finally, simulation results demonstrate the effectiveness of the proposed double-IRS-assisted wireless communication scheme.

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.