Successive Convex Approximation Technique Research Articles

Energy-efficient mobile edge computing assisted by layered UAVs based on convex optimization

The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption.

Robust Energy Efficiency Optimization Strategy for Emergency Communication Based on Fixed-Wing UAV

<p>The geographic location information of a user serves as the foundation for post-disaster emergency applications. However, uncertainties in the user positioning may arise due to factors such as building obstruction and damage to ground base stations. To ensure optimal communication quality, unmanned aerial vehicles (UAVs) can be implemented to maintain close proximity to the user with a minimal turning radius. However, a small turning radius can result in increased energy consumption due to propulsion requirements. To achieve an optimal balance between high communication throughput and low energy consumption, a robust energy efficiency optimization strategy is proposed based on a fixed-wing UAV, addressing the energy efficiency of emergency communication in imprecise user locations. First, the air-to-ground channel model is established, taking into account UAV propulsion energy consumption and formulating a multi-constraint problem to maximize emergency energy efficiency. Second, by incorporating the circular region method to address location uncertainty and considering the worst-case scenario, a robust problem is formulated. Finally, the worst-case scenario is addressed by utilizing the first-order Taylor approximation and successive convex approximation (SCA) technique to solve the nonconvex problem. Through simulation experiments, the proposed scheme is compared with three benchmark schemes, demonstrating its superior energy efficiency and robustness.</p> <p> </p>

Enhancing security for physical layer communication in RIS-aided MIMO-NOMA systems in the presence of an eavesdropper

This paper investigates the potential of leveraging a reconfigurable intelligent surface (RIS) in the multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) wireless communication systems. In fact, NOMA improves spectral efficiency (SE) through power domain utilization, and RIS enhances the system performance by manipulating the propagation environment. In this paper, we investigate the physical layer security (PLS) of a RIS-aided MIMO-NOMA system serving two users in the presence of an eavesdropper (EV), where the channel state information (CSI) of the EV is unknown for the BS. To address the security challenge, we utilize secrecy outage probability (SOP) and minimize it by optimizing the RIS’s phase shift matrix, the beamforming vector, and the power allocation coefficients. Due to the non-convexity of the main optimization problem, we decompose the initial problem into three subproblems, and then, we propose an iterative approach to determine the unknown parameters alternately. The first subproblem involves obtaining the optimal beamforming vector and phase shift matrix using the semidefinite relaxation (SDR) method, which introduces high complexity. As an alternative, another algorithm is proposed based on the duality of the optimization problem, offering lower complexity and better accuracy for high-dimensional optimization problems. In this algorithm, the successive convex approximation (SCA) technique is employed to achieve the optimum phase shift matrix, and the stochastic gradient (SG) approach is used to determine the beamforming vector. Furthermore, the paper establishes the optimal values of power allocation coefficients by minimizing the difference between the SOPs of the two users. Finally, the simulation results demonstrate that the proposed power allocation coefficients scheme significantly enhances system performance compared to the existing fixed power allocation coefficients scheme.

Multi-UAV-assisted Internet of Remote Things communication within satellite–aerial–terrestrial integrated network

Due to the limited transmission capabilities of terrestrial intelligent devices within the Internet of Remote Things (IoRT), this paper proposes an optimization scheme aimed at enhancing data transmission rate while ensuring communication reliability. This scheme focuses on multi-unmanned aerial vehicle (UAV)-assisted IoRT data communication within the satellite–aerial–terrestrial integrated network (SATIN), which is one of the key technologies for the sixth generation (6G) networks. To optimize the system’s data transmission rate, we introduce a multi-dimensional coverage and power optimization (CPO) algorithm, rooted in the block coordinate descent (BCD) method. This algorithm concurrently optimizes various parameters, including the number and deployment of UAVs, the correlation between IoRT devices and UAVs, and the transmission power of both devices and UAVs. To ensure comprehensive coverage of a large-scale randomly distributed array of terrestrial devices, combined with machine learning algorithm, we present the Dynamic Deployment based on K-means (DDK) algorithm. Additionally, we address the non-convexity challenge in resource allocation for transmission power through variable substitution and the successive convex approximation technique (SCA). Simulation results substantiate the remarkable efficacy of our CPO algorithm, showcasing a maximum 240% improvement in the uplink transmission rate of IoRT data compared to conventional methods.

Energy-efficient trajectory design for secure SWIPT systems assisted by UAV-IRS

Due to its controllable maneuverability, wide coverage, and low cost, unmanned aerial vehicle (UAV) has great potential in post-disaster rescue, cargo transport and emergency communication. Considering its limited onboard energy, energy-efficient UAV communication is a challenge. This research examines the security of simultaneous wireless information and power transfer (SWIPT) systems assisted by intelligent reflecting surfaces (IRS) and UAVs while considering the flight energy of rotary-wing UAVs. Specifically, an IRS is mounted on a UAV to enhance the quality of legitimate transmission, and artificial noise (AN) is introduced into the base station (BS) to reduce eavesdropping quality. The power splitting (PS) technology is adopted at ground devices (GDs) to simultaneously decode information and harvest energy. First, we jointly design the BS transmit beamforming, UAV-IRS phase shifts and trajectory/velocity as well as GDs PS ratio with the aim of maximizing the sum secrecy rate of all GDs. Then, an iterative algorithm is developed to address the formulated problem. In particular, additional variables are introduced to handle this complicated objective function, and the original problem is decoupled into multiple sub-problems, which can be solved alternately by invoking the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Finally, numerical results demonstrate that the proposed scheme exhibits a substantial performance in the security rate of SWIPT systems assisted by UAV-IRS, and its performance is improved by at least 12% compared to benchmark schemes at the flight energy budget ethr=5KJ and the number of reflecting elements Nr=25.

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.

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.

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.

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.

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.

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.

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

A Deep Reinforcement Learning Scheme for Sum Rate and Fairness Maximization Among D2D Pairs Underlaying Cellular Network With NOMA

Device-to-device (D2D) communication is an emerging technology in 5G and upcoming 6G networks due to its properties to enhanced spectral efficiency (SE), energy-efficiency (EE), and sum rate. Despite these advantages, co-channel and cross-channel interference, and ultra-massive connectivity are major issues which can deteriorate performance of any implemented solution in this environment. To address these issues, in this paper, we integrated the power domain non-orthogonal multiple access techniques (PD-NOMA) on the base station (BS). NOMA serves more than one user using the same resource block (RB) and reduces the effect of interference at CUs due to the presence of successive interference cancellation (SIC). The problem is formulated as a mixed-integer non-linear programming (MINLP) with associated resources and power constraints of the BS and DDPs with an aim to maximize the sum rate and fairness among the NOMA-enabled CUs and D2D pairs (DDPs). We firstly used the centralized deep deterministic policy gradient (DDPG) and arithmetic-geometric mean approximation (AGMA) technique to reduce cross-channel interference (CR-CI) and control the power. Then, to provide fairness to all the users, we transformed the proposed solution into distributed deep deterministic policy gradient (D3PG). Also, the successive convex approximation technique is then integrated into the D3PG to mitigate the effect of co-channel (CO-CI) interference among DDPs. The experimental results show that the proposed scheme has superior performance with respect to sum rate and fairness. Also, the results reveal that the proposed scheme has 21.05%, 34.21%, and 49.8% higher sum rate in comparison to DDPG, Deep dueling, and DQN scheduling.

Secrecy-Energy-Efficiency Maximization for UAV-Enabled Two-Way Relay Systems

In wireless communication, unmanned-aerial-vehicle (UAV) relay seems to be an available paradigm to combat path-loss and expand signal coverage. Due to the openness of wireless channel and the inconvenient recharging, transmission security and energy efficiency are the most concerned issues for UAV-enabled communication. In this paper, for an UAV-enabled two-way relay system, where two wireless terminals exchange their information assisted by a flying UAV and a malicious user tries to eavesdrop the transmitted information, the achievable secrecy energy-efficiency is formulated when the UAV speed, trajectory, allowable power and the time scheduling in the uplink and downlink transmission are considered in a comprehensive manner. To solve this non-convex problem, we decompose it into three subproblems based on the block coordinate descent (BCD) method. Then, the successive convex approximation (SCA) technique and the Dinkelbach algorithm are combined to obtain the global solution in an iterative manner. Simulation results show that the proposed schemes have higher secrecy energy-efficiency compared to the existing baseline schemes.

Sensing Fairness-Based Energy Efficiency Optimization for UAV Enabled Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is developing rapidly due to the advantages of bandwidth saving, hardware cost reduction and energy saving. This paper proposes a unmanned aerial vehicle (UAV) enabled ISAC (UAV-ISAC) system, where the UAV will sense ground users and forward sensed information to base station (BS). Radar mutual information (MI) is introduced to measure the UAV sensing performance. On the basis of sensing fairness for each user, a multi-objective resource optimization problem of maximizing both energy efficiency (EE) and minimum radar MI is studied by jointly optimizing user scheduling, transmit power and UAV trajectory. To solve the non-convex optimization problem, we decompose it into three sub-problems: user scheduling optimization, transmit power optimization, and UAV trajectory optimization. Each subproblem can be solved by successive convex approximation (SCA), fractional programming and relaxation technique. By iteratively optimizing the three sub-problems, we can obtain the suboptimal solution to original optimization problem. Simulation results show that the proposed optimization scheme can maximize the EE of UAV while ensuring sensing fairness for all users.

Energy efficiency maximization for WPT-enabled UAV-assisted emergency communication with user mobility

We investigate the energy efficient communication in the wireless power transfer (WPT)-enabled unmanned aerial vehicle (UAV)-assisted emergency communication system. Specifically, the UAV acts as a base station performs a two-phase task in each time-slot (TS). During the first phase of the TS, frequency division multiple access (FDMA) mode is used to serve multiple mobile users in the downlink. During the second phase, WPT technology is used to charge multiple mobile users on the ground. Due to the limited on-board energy of the UAV, our goal is to maximize the system energy efficiency (EE) by jointly optimizing the UAV trajectory, the information transmit power, the WPT power and the user bandwidth. However, the highly coupled nature of the optimization variables makes the nonconvex problem difficult to solve. In order to solve the problem, a low-complexity alternating iterative optimization algorithm is devised, utilizing block coordinate descent and successive convex approximation techniques. Through simulations, it is demonstrated that the proposed algorithm effectively achieves a balance between system throughput and UAV energy consumption, and can effectively improve the EE of the system compared to the benchmark schemes.

An UAV and EV based mobile edge computing system for total delay minimization

In this paper, we studied a collaborative mobile edge computing(MEC) system based on unmanned aerial vehicle(UAV) and electric vehicle(EV), in which the EV moves to the target area carrying the UAV to provide computing and offloading services to the user equipment(UE). We aimed at minimizing all the UE tasks delay by optimizing the task offloading ratio, the UAV hovering position, and the computing resource allocation of EV and UAV, respectively. The problem was formulated as a Nonlinear Programming(NLP) problem, and we decomposed it into EV related subproblem and UAV related subproblem by the Block-Coordinate Descent(BCD) method. For EV related subproblem, we obtained the optimal offloading ratio by making the computing time on the UAV equal to its offloading time, and proved the method is feasible. For the UAV related subproblem, we introduced the non-orthogonal multiple access(NOMA) and successive interference cancellation(SIC) techniques to improve the communication efficiency, and we obtained the optimal hovering position by using the successive convex approximation(SCA) technique twice to transfer this subproblem into a convex problem. Finally, an overall iterative algorithm was proposed. To verify the effectiveness of our algorithm, we compared our scheme with ‘PSO’, ‘GT’, ‘MC’ and benchmark algorithm.

Joint trajectory and CoMP clustering optimization in UAV-assisted cellular systems: a coalition formation game approach

In this paper, the flexibility of unmanned aerial vehicles (UAVs), as well as the benefits of coordinated multi-point (CoMP) transmission, are utilized for mitigating the interference in cellular networks. Specifically, the joint problem of CoMP clusters and UAVs’ trajectories is addressed for downlink transmission in a UAV-assisted cellular system. The problem is presented as a non-convex optimization problem that aims to maximize the sum rate of the ground users by taking into account the clustering, UAV mobility and backhaul capacity constraints. Since the formulated problem is known to be NP-hard, we partition it into two sub-problems. Particularly, by using coalitional game theory, the CoMP clusters are obtained with a given UAVs’ trajectories. Then, UAVs’ trajectories are optimized with given CoMP clusters using successive convex approximation technique. Based on the block coordinate descent method, the two sub-problems are solved alternatively until convergence. Numerical results are conducted and demonstrated the effectiveness of the proposed algorithm.

Residual energy maximization for NOMA-enabled UAV-assisted data collection network: trajectory optimization and resource allocation

In this paper, we concentrate on a non-orthogonal multiple access (NOMA)-enabled UAV data collection network for Internet of Things devices (IoTDs), where a unmanned aerial vehicle (UAV) is deployed as an aerial base station. During its flight period, the UAV can collect data from IoTDs and take advantage of the simultaneous wireless information and power transfer technology to charge the batteries of IoTDs. With the aid of NOMA, spectrum efficiency has been improved. We aim to prolong the lifetime of the IoT network, via jointly optimizing the UAV trajectory, the time allocation for information communication and wireless power transfer, the IoTDs’ transmit power, as well as the IoTDs’ group scheduling for NOMA. Then, we use the block coordinate decent and successive convex approximation techniques to tackle the non-convexity of the formulated problem. Numerical results show that the proposed solution increases the residual energy of the IoTDs, thus prolonging the lifetime of the network.

Energy efficiency maximization for UAV-enabled amplify-and-forward relaying via joint power and trajectory optimization

This paper investigates an optimization problem corresponding to energy efficiency maximization of an unmanned aerial vehicle (UAV)-enabled relaying system, where a fixed-wing UAV acts as an amplify-and-forward mobile relay to assist data transmission between a source node and a destination node. On the premise of satisfying the speed and acceleration constraints of the UAV, the energy efficiency (EE) of the relaying system is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay. The initial joint optimization problem is non-convex and cannot be solved directly. Therefore, the joint optimization problem is decomposed into two sub-problems which are solved by applying the successive convex approximation technique and the Dinkelbach’s algorithm. On this basis, an efficient iterative algorithm is proposed to tackle the joint optimization problem through the block coordinate descent technique. Simulation results demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm contributes to the higher EE compared with the benchmark schemes.