Showing papers in "IEEE Communications Letters in 2018"
TL;DR: A closed-form low-complexity solution with joint trajectory design and power control is proposed to solve the outage probability of an unmanned aerial vehicle (UAV) relay network, where the UAV works as an amplify-and-forward relay.
Abstract: In this letter, we consider an unmanned aerial vehicle (UAV) relay network, where the UAV works as an amplify-and-forward relay. We optimize the trajectory of UAV, the transmit power of UAV, and the mobile device by minimizing the outage probability of this relay network. The analytical expression of outage probability is derived first. A closed-form low-complexity solution with joint trajectory design and power control is proposed to solve this non-convex problem. Simulation results show that the outage probability of the proposed solution is significantly lower than that of the fixed power relay and circle trajectory for the UAV relay.
428 citations
TL;DR: The results demonstrate that inter-SF collisions are indeed an issue in LoRa networks and, thus, allocating higher SFs to users far from the gateway might not necessarily improve their link capacity, in case of congested networks.
Abstract: In this letter, we focus on the evaluation of link-level performance of LoRa technology, in the usual network scenario with a central gateway and high-density deployment of end-devices. LoRa technology achieves wide coverage areas, low power consumption and robustness to interference thanks to a chirp spread-spectrum modulation, in which chirps modulated with different spreading factors (SFs) are quasi-orthogonal. We focus on the performance analysis of a single receiver in presence of collisions. First, we analyze LoRa modulation numerically and show that collisions between packets modulated with different SFs can indeed cause packet loss if the interference power received is strong enough. Second, we validate such findings in experiments based on commercial devices and software-defined radios. Contradicting the common belief that SFs can be considered orthogonal, our results demonstrate that inter-SF collisions are indeed an issue in LoRa networks and, thus, allocating higher SFs to users far from the gateway might not necessarily improve their link capacity, in case of congested networks.
297 citations
TL;DR: Numerical results show that different reliability measures have slightly different optimum altitudes and that decode-and-forward is better than amplify- and-forward.
Abstract: Unmanned aerial vehicles (UAVs) as aerial base stations or relays are becoming increasingly important in communications. In this letter, the optimum placement of a relaying UAV for maximum reliability is studied. The total power loss, the overall outage, and the overall bit error rate are derived as reliability measures. The optimum altitude is investigated for both static and mobile UAVs. Numerical results show that different reliability measures have slightly different optimum altitudes and that decode-and-forward is better than amplify-and-forward.
291 citations
TL;DR: Through simulations, it is shown that the DPC can achieve almost the same or even higher SE and EE than a conventional power control scheme, with a much lower computation time.
Abstract: In this letter, deep power control (DPC), which is the first transmit power control framework based on a convolutional neural network (CNN), is proposed. In DPC, the transmit power control strategy to maximize either spectral efficiency (SE) or energy efficiency (EE) is learned by means of a CNN. While conventional power control schemes require a considerable number of computations, in DPC, the transmit power of users can be determined using far fewer computations enabling real-time processing. We also propose a form of DPC that can be performed in a distributed manner with local channel state information, allowing the signaling overhead to be greatly reduced. Through simulations, we show that the DPC can achieve almost the same or even higher SE and EE than a conventional power control scheme, with a much lower computation time.
219 citations
TL;DR: This work jointly optimize the UAV’s flying altitude and antenna beamwidth for throughput optimization in three fundamental multiuser communication models, namely, UAV-enabled downlink multicasting, downlink broadcasting, and uplink multiple access.
Abstract: We study multiuser communication systems enabled by an unmanned aerial vehicle (UAV) that is equipped with a directional antenna of adjustable beamwidth. We propose a fly-hover-and-communicate protocol, where the ground terminals are partitioned into disjoint clusters that are sequentially served by the UAV as it hovers above the corresponding cluster centers. We jointly optimize the UAV’s flying altitude and antenna beamwidth for throughput optimization in three fundamental multiuser communication models, namely, UAV-enabled downlink multicasting, downlink broadcasting, and uplink multiple access. Results show that the optimal UAV altitude and antenna beamwidth critically depend on the communication model considered.
215 citations
TL;DR: This letter proposes a novel PAPR reduction scheme, known as P APR reducing network (PRNet), based on the autoencoder architecture of deep learning, where the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique.
Abstract: High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.
183 citations
TL;DR: A deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder.
Abstract: Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.
180 citations
TL;DR: In this article, a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications is proposed.
Abstract: In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage.
180 citations
TL;DR: The problems of UAV node placement and communication resource allocation are investigated jointly for a UAV relaying system for the first time and the global optimal solution is achieved.
Abstract: Utilizing unmanned aerial vehicle (UAV) as the relay is an effective technical solution for the wireless communication between ground terminals faraway or obstructed. In this letter, the problems of UAV node placement and communication resource allocation are investigated jointly for a UAV relaying system for the first time. Multiple communication pairs on the ground, with one rotary-wing UAV serving as relay, are considered. Transmission power, bandwidth, transmission rate, and UAV’s position are optimized jointly to maximize the system throughput. An optimization problem is formulated, which is non-convex. The global optimal solution is achieved by transforming the formulated problem to be a monotonic optimization problem.
165 citations
TL;DR: Numerical results validate the theoretical analysis and demonstrate the superior performance of NOMA in reducing transmission latency, and obtain the near-optimal power allocation coefficients and blocklength to ensure certain reliability.
Abstract: This letter investigates the performance of non-orthogonal multiple access (NOMA) in short-packet communications. We aim to answer a fundamental question–for given reliability requirements of users: how much physical-layer transmission latency can NOMA reduce when compared with orthogonal multiple access in the finite blocklength regime? We derive closed-form expressions for the block error rates of users in NOMA. Further, we obtain the near-optimal power allocation coefficients and blocklength to ensure certain reliability. Numerical results validate our theoretical analysis and demonstrate the superior performance of NOMA in reducing transmission latency.
158 citations
TL;DR: This letter devotes itself to citywide cellular traffic prediction and proposes a deep learning approach to model the nonlinear dynamics of wireless traffic by treating traffic data as images, both the spatial and temporal dependence of cell traffic are well captured utilizing densely connected convolutional neural networks.
Abstract: With accurate traffic prediction, future cellular networks can make self-management and embrace intelligent and efficient automation. This letter devotes itself to citywide cellular traffic prediction and proposes a deep learning approach to model the nonlinear dynamics of wireless traffic. By treating traffic data as images, both the spatial and temporal dependence of cell traffic are well captured utilizing densely connected convolutional neural networks. A parametric matrix based fusion scheme is further put forward to learn influence degrees of the spatial and temporal dependence. Experimental results show that the prediction performance in terms of root mean square error can be significantly improved compared with those existing algorithms. The prediction accuracy is also validated by using the data sets of Telecom Italia.
TL;DR: This letter analyzes LoRa systems for increasing average system packet success probability (PSP) under unslotted ALOHA random access protocol and formulate an optimization problem for maximizing the average system PSP to propose a sub-optimal SF allocation scheme to each traffic.
Abstract: Along with enhanced mobile broadband and ultra-reliable low latency communication, massive connectivity has been one of the key requirements for enabling technologies of 5G. For IoT, low power consumption and wide area coverage for end devices (ED) are important figures of merit, for which LoRa, SigFox and Narrow Band-IoT are dominant technologies. In this letter, we analyze LoRa systems for increasing average system packet success probability (PSP) under unslotted ALOHA random access protocol. The lower bound for average system PSP is derived by stochastic geometry. And it is shown that the average system PSP can be maximized by properly allocating spreading factor (SF) to each traffic, which also maximizes connectivity of EDs. We formulate an optimization problem for maximizing the average system PSP to propose a sub-optimal SF allocation scheme to each traffic. Analysis on PSP is validated through simulations, and comparison with existing schemes reveals that our proposed scheme achieves the highest PSP and so the maximum connectivity.
TL;DR: This letter reinterpret PDA from a new perspective, i.e., the strong edge coloring of bipartite graph (bigraph), and proves that, a PDA is equivalent to a strong edge colored bigraph.
Abstract: The technique of coded caching proposed by Madddah-Ali and Niesen is a promising approach to alleviate the load of networks during peak-traffic times. Recently, placement delivery array (PDA) was presented to characterize both the placement and delivery phase in a single array for the centralized coded caching algorithm. In this letter, we reinterpret PDA from a new perspective, i.e., the strong edge coloring of bipartite graph (bigraph). We prove that, a PDA is equivalent to a strong edge colored bigraph. Thus, we can construct a class of PDAs from existing structures in bigraphs. The class subsumes the scheme proposed by Maddah-Ali et al. and a more general class of PDAs proposed by Shangguan et al. as special cases.
TL;DR: A deep-learning-based SEI approach that uses the features of the received steady-state signals to identify specific emitters using the compressed bispectrum, which outperforms other existing schemes in the literature.
Abstract: Specific emitter identification (SEI) is a technique that distinguishes between unique emitters using the external feature measurements from their transmit signals, primarily radio frequency fingerprints. The SEI has been widely adopted for military and civilian spectrum management applications. We propose a deep-learning-based SEI approach that uses the features of the received steady-state signals. In particular, the bispectrum of the received signal is calculated as a unique feature. Then, we use a supervised dimensionality reduction method to significantly reduce the dimensions of the bispectrum. Finally, a convolutional neural network is adopted to identify specific emitters using the compressed bispectrum. This approach essentially extracts overall feature information hidden in the original signals, which can then be used to improve identification performance. Results from both the simulations and the software radio experiments are provided. A signal acquisition system is designed to collect steady-state signals from multiple universal software radio peripherals. Both the simulations and the experiments validate our conclusion that the proposed approach outperforms other existing schemes in the literature.
TL;DR: Close-form approximations for LoRa BER performance are derived for both additive white Gaussian noise and Rayleigh fading channels, confirming accuracy of the derived approximation is confirmed by comparisons against numerical results.
Abstract: LoRa has been demonstrated as a front runner when it comes to evolving low-power wide area networks. At its core is LoRa modulation which depicts a patented chirp spread-spectrum modulation technique that supports energy-efficient and reliable long-range communication. The underlying bit error rate (BER) performance of LoRa modulation has not been yet rigorously analyzed in the literature. In this letter, closed-form approximations for LoRa BER performance are derived for both additive white Gaussian noise and Rayleigh fading channels. Accuracy of the derived approximations is confirmed by comparisons against numerical results.
TL;DR: The proposed BPL decoder provides the best performance of plain polar codes under iterative decoding known so far, and it is shown that a different selection strategy of frozen bit positions can further enhance the error-rate performance of the proposed decoder.
Abstract: We propose a belief propagation list (BPL) decoder with comparable performance to the successive cancellation list (SCL) decoder of polar codes, which already achieves the maximum likelihood (ML) bound of polar codes for sufficiently large list size $L$ . The proposed decoder is composed of multiple parallel independent belief propagation (BP) decoders based on differently permuted polar code factor graphs. A list of possible transmitted codewords is generated and the one closest to the received vector, in terms of Euclidean distance, is picked. To the best of our knowledge, the proposed BPL decoder provides the best performance of plain polar codes under iterative decoding known so far. The proposed algorithm does not require any changes in the polar code structure itself, rendering the BPL into an alternative to the SCL decoder, equipped with a soft output capability enabling, e.g., iterative detection and decoding to further improve performance. Further benefits are the lower decoding latency than the SCL decoder and the possibility of high throughput implementations. Additionally, we show that a different selection strategy of frozen bit positions can further enhance the error-rate performance of the proposed decoder.
TL;DR: In this paper, a recursive convolutional neural network was designed to cope with the challenge of infinite state of spectrum waterfall, and an anti-jamming deep reinforcement learning algorithm was proposed to obtain the optimal antijamming strategies.
Abstract: This letter investigates the problem of anti-jamming communications in a dynamic and intelligent jamming environment through machine learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the temporal and spectral information, i.e., the spectrum waterfall, directly. First, to cope with the challenge of infinite state of spectrum waterfall, a recursive convolutional neural network is designed. Then, an anti-jamming deep reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the proposed approach. The proposed algorithm does not need to model the jamming patterns, and naturally has the ability to explore the unknown environment, which implies that it can be widely used for combating dynamic and intelligent jamming.
TL;DR: This letter investigates a power-efficient unmanned aerial vehicle (UAV)-based wireless sensor network, where the UAV is used as a flying base station to communicate with the sensor nodes (SNs) with flexible movement path by jointly optimizing the SNs-UAV scheduling scheme, power allocation strategy, and flight trajectory of the Uav.
Abstract: This letter investigates a power-efficient unmanned aerial vehicle (UAV)-based wireless sensor network, where the UAV is used as a flying base station to communicate with the sensor nodes (SNs) with flexible movement path. We aim at minimizing the total power consumption of the UAV with a guarantee of the required transmission rate of SNs by jointly optimizing the SNs-UAV scheduling scheme, power allocation strategy, and flight trajectory of the UAV. Due to the non-convex and mixed-integer nature of the original problem, block coordinate decent and successive convex optimization techniques are employed to decompose it into two sub-problems, which leads to an efficient iterative algorithm. Numerical results show the performance gain of our proposed scheme.
TL;DR: By utilizing stochastic perturbation techniques, it is shown that the proposed method can train a deep learning-based communication system in real channel without any assumption on channel models.
Abstract: Recent developments in applying deep learning techniques to train end-to-end communication systems have shown great promise in improving the overall performance of the system. However, most of the current methods for applying deep learning to train physical-layer characteristics assume the availability of the explicit channel model. Training a neural network requires the availability of the functional form all the layers in the network to calculate gradients for optimization. The unavailability of gradients in a physical channel forced previous works to adopt simulation-based strategies to train the network and then fine tune only the receiver part with the actual channel. In this letter, we present a practical method to train an end-to-end communication system without relying on explicit channel models. By utilizing stochastic perturbation techniques, we show that the proposed method can train a deep learning-based communication system in real channel without any assumption on channel models.
TL;DR: Numerical results show that the proposed algorithm can achieve good performance in terms of uplink sum power saving and an iterative algorithm is proposed with low complexity to obtain a suboptimal solution.
Abstract: This letter investigates an uplink power control problem for unmanned aerial vehicles (UAVs)-assisted wireless communications. We jointly optimize the UAV’s flying altitude, antenna beamwidth, UAV’s location, and ground terminals’ allocated bandwidth, and transmit power to minimize the sum uplink power subject to the minimal rate demand. An iterative algorithm is proposed with low complexity to obtain a suboptimal solution. Numerical results show that the proposed algorithm can achieve good performance in terms of uplink sum power saving.
TL;DR: A resource allocation strategy based on a deep neural network (DNN) is proposed for multi-channel cognitive radio networks, where the secondary user (SU) opportunistically utilizes channels without causing excessive interference to the primary user (PU).
Abstract: In this letter, a resource allocation strategy based on a deep neural network (DNN) is proposed for multi-channel cognitive radio networks, where the secondary user (SU) opportunistically utilizes channels without causing excessive interference to the primary user (PU). In the proposed scheme, the allocation of transmit power in each channel for SUs is found by utilizing the newly proposed DNN model, which separately determines the overall transmit power of individual SUs and the proportion of transmit power allocated to each channel. Both the spectral efficiency (SE) of the SU and the amount of interference caused to the PU are considered in the training of the DNN model, such that the interference caused to the PUs can be properly regulated while the SE of the SU is improved. Through simulations, we show that our scheme enables a high SE of the SU to be achieved while the interference caused to the PU can be maintained at less than the threshold.
TL;DR: A deep reinforcement learning-based framework (DRLF) is proposed for DRA problems in MBS systems and a novel image-like tensor reformulation on the system environments is adopted to extract traffic spatial and temporal features.
Abstract: Dynamic resource allocation (DRA) is the key technology to improve the network performance in resource-limited multibeam satellite (MBS) systems. The aim is to find a policy that maximizes the expected long-term resource utilization. Existing iterative metaheuristics DRA optimization algorithms are not practical due to the high computational complexity. To solve the problem of unknown dynamics and prohibitive computation, a deep reinforcement learning-based framework (DRLF) is proposed for DRA problems in MBS systems. A novel image-like tensor reformulation on the system environments is adopted to extract traffic spatial and temporal features. A use case of dynamic channel allocation in DRLF is simulated and shows the effectiveness of the proposed DRLF in time-varying scenarios.
TL;DR: A blind channel estimator based on the expectation maximization algorithm to acquire the modulus values of channel parameters is proposed and the ranges of the initial values of the suggested estimator are obtained and the modified Bayesian Cramér–Rao bound is derived.
Abstract: The availability of perfect channel state information is assumed in current ambient-backscatter studies. However, the channel estimation problem for ambient backscatter is radically different from that for traditional wireless systems, where it is common to transmit training (pilot) symbols for this purpose. In this letter, we thus propose a blind channel estimator based on the expectation maximization algorithm to acquire the modulus values of channel parameters. We also obtain the ranges of the initial values of the suggested estimator and derive the modified Bayesian Cramer–Rao bound of the proposed estimator. Finally, simulation results are provided to corroborate our theoretical studies.
TL;DR: The 3-D geometry channel model is formulated as a combination of the UAV movement state information and the channel gain information, where the former can be obtained by the sensor fusion of the flight control system, while the latter can be estimated through the pilot transmission.
Abstract: Unmanned aerial vehicle (UAV) communications could offer flexible scheduling, improved reliability, enhanced capacity over much wider range, and has become a key part of the space-air-ground integrated network. In this letter, we consider a communication system in millimeter wave band, where UAV serves as an airborne base station with multiple antennas, and propose a new flight control system-based channel tracking method. Specifically, the 3-D geometry channel model is formulated as a combination of the UAV movement state information and the channel gain information, where the former can be obtained by the sensor fusion of the flight control system, while the latter can be estimated through the pilot transmission. Simulation results are provided to verify the effectiveness of the proposed method.
TL;DR: A generalized nested array with two flexible co-prime factors for enlarging the inter-element spacing of two concatenated uniform linear subarrays with the same number of degrees of freedom as (super) nested array but with reduced mutual coupling is proposed.
Abstract: In this letter, we propose a generalized nested array (GNA) with two flexible co-prime factors for enlarging the inter-element spacing of two concatenated uniform linear subarrays. It is shown that both the prototype nested array and generalized co-prime array can be interpreted as special cases. The closed-form expressions for the range of consecutive lags and the number of unique lags are derived for any given factors. After optimization, GNA has the same number of degrees of freedom as (super) nested array but with reduced mutual coupling. Numerical simulations prove the superiority of proposed configuration using compressed sensing algorithm.
TL;DR: A deep learning-based pilot assignment scheme for a massive multiple-input multiple-output (massive MIMO) system that utilizes a large number of antennas for multiple users that improves the performance in cellular networks with severe pilot contamination by learning the relationship between pilot assignment and the users’ location pattern.
Abstract: This letter proposes a deep learning-based pilot assignment scheme (DL-PAS) for a massive multiple-input multiple-output (massive MIMO) system that utilizes a large number of antennas for multiple users. The proposed DL-PAS improves the performance in cellular networks with severe pilot contamination by learning the relationship between pilot assignment and the users’ location pattern. In this letter, we design a novel supervised learning method, where input features and output labels are users’ locations in all cells and pilot assignments, respectively. Specifically, pretrained optimal pilot assignments with given users’ locations are provided through an exhaustive search method as the training data. Then, the proposed DL-PAS provides a near-optimal pilot assignment from the produced inferred function by analyzing the training data. We implement the proposed scheme using a commercial deep multilayer perceptron system. Simulation-based experiments show that the proposed scheme achieves almost 99.38% theoretical upper-bound performance with low complexity, requiring only 0.92-ms computational time.
TL;DR: This letter proposes a new technique to transform a TDOA model into a TOA model and develops a semidefinite programming method with new constraints for effective NLOS error mitigation in TDOA systems.
Abstract: Non-line-of-sight (NLOS) error mitigation for the time-of-arrival (TOA) localization has been extensively studied, but these methods cannot be directly applied for the time-difference-of-arrival (TDOA) systems. Recent work has applied convex optimization for NLOS error mitigation in TDOA systems. Issues remain unsolved with this technique include the convex hull problem, reference-anchor selection problem, and difficulties dealing with a wide range of NLOS-caused ranging errors. This letter proposes a new technique to transform a TDOA model into a TOA model and develops a semidefinite programming method with new constraints for effective NLOS error mitigation in TDOA systems. Major advantages of this method include: 1) it resolves the issues such as convex hull and reference-anchor selection issues that existing schemes are facing; 2) it does not require any a priori information about NLOS links or NLOS error statistics; and 3) it achieves a better performance than existing convex optimization schemes, which is verified in both simulation and real experiments.
TL;DR: A channel learning scheme using the deep autoencoder, which learns the channel state information (CSI) at the energy transmitter based on the harvested energy feedback from the energy receiver, in the sense of minimizing the mean square error of the channel estimation.
Abstract: We propose a deep-learning-based channel estimation technique for wireless energy transfer Specifically, we develop a channel learning scheme using the deep autoencoder, which learns the channel state information (CSI) at the energy transmitter based on the harvested energy feedback from the energy receiver, in the sense of minimizing the mean square error (mse) of the channel estimation Numerical results demonstrate that the proposed scheme learns the CSI very well and significantly outperforms the conventional scheme in terms of the channel estimation mse as well as the harvested energy
TL;DR: Simulation results show that the proposed network-based Slow HTTP DDoS attack defense method successfully protects Web servers against Slow HTTPDDoS attacks.
Abstract: A Slow HTTP distributed denial of service (DDoS) attack causes a Web server to be unavailable, but it is difficult to detect in a network, because its traffic patterns are similar to those of legitimate clients. In this letter, we propose a network-based Slow HTTP DDoS attack defense method, which is assisted by a software-defined network that can detect and mitigate Slow HTTP DDoS attacks in the network. Simulation results show that the proposed Slow HTTP DDoS attack defense method successfully protects Web servers against Slow HTTP DDoS attacks.
TL;DR: This letter proposes a low-complexity iterative algorithm to maximize the energy efficiency of the D2D pair while guaranteeing the quality of service of CUs, and results validate the superiority of the proposed scheme over the existing schemes.
Abstract: This letter investigates the resource allocation problem in device-to-device (D2D) communications underlaying a nonorthogonal multiple access based cellular network, where both cellular users (CUs) and D2D users harvest energy from the hybrid access point in the downlink and transmit information in the uplink. We propose a low-complexity iterative algorithm to maximize the energy efficiency of the D2D pair while guaranteeing the quality of service of CUs. In each iteration, by analyzing the Karush–Kuhn–Tucker conditions, the globally optimal solution can be derived in the closed form despite the nonconvexity. Simulation results validate the superiority of the proposed scheme over the existing schemes.