Joint Iterative Optimization Research Articles

Analysis and Design of Enhanced Distributed Fountain Codes in Multiple Access Networks with Cooperative Relay

Distributed fountain coding plays an important role in rateless code research. The reliability and effectiveness of these coding schemes are increasingly challenged with the growing applications. In this paper, a novel multiple-access network with cooperative relay is presented, and a novel enhanced distributed fountain coding scheme for this network is proposed. The overall degree distributions are derived, and the asymptotic decoding performance is analyzed theoretically by employing the And-Or tree method. On this basis, a design method using joint iterative optimization algorithms is proposed to optimize the degree distributions of the sources and relays. Simulation results show that the proposed enhanced distributed fountain codes outperform the existing generalized distributed fountain codes (GDFC) and have a good performance on both lossless and lossy channels. It reveals that the proposed codes can provide unequal error protection (UEP) property for different sources by introducing the extra cooperative relay. The performance improvement is not restricted to the sources connected to the cooperative relay but applies to all sources. With the additional relay, the proposed codes are able to overcome the effects of bad channel conditions caused by terrain, obstacles, and so on, to avoid communication interruptions and improve the reliability of the network.

Joint Communication and Trajectory Optimization for Multi-UAV Enabled Mobile Internet of Vehicles

Due to its flexibility and high maneuverability, Unmanned Aerial Vehicle (UAV) is able to quickly provide wireless connections to the ground vehicles in mobile environment. In this paper, a multi-UAV enabled mobile Internet of Vehicles (IoV) model is proposed, where the UAVs track to serve the mobile vehicles and send downlink information to the vehicles during the flight time. Considering the constraints of anti-collision and communication interference between the UAVs, the system throughput is maximized by jointly optimizing vehicle communication scheduling, UAV power allocation and UAV trajectory. The formulated non-convex optimization problem is separated into three subproblems, including communication scheduling optimization, power allocation optimization and UAV trajectory optimization, which can be solved by successive convex approximation (SCA). A joint iterative optimization algorithm of the three subproblems is put forward to get the optimal solution. Then, a fairness optimization problem is proposed to guarantee the fair communications for each vehicle. The numerical results reveal the excellent performance of the multi-UAV enabled mobile IoV by joint communication and trajectory optimization.

Extraction and Mitigation of Radio Frequency Interference Artifacts Based on Time-Series Sentinel-1 SAR Data

Radio frequency interference (RFI) is a critical issue for accurate remote sensing by synthetic aperture radar (SAR). Existing literature mainly detects and mitigates RFI in the raw data domain, which is generally not accessible to the end-user. In this article, a novel RFI extraction and mitigation scheme in the image domain is proposed using multitemporal analysis of SAR images. By exploiting the coupling correlation and complementary information among the time-series images, the background landscape could be modeled as relatively stationary with the low-rank property. Meanwhile, the radiometric artifacts corresponding to RFI could be well extracted and characterized by the sparse components. Extraction and mitigation of RFI signatures could be achieved simultaneously via a joint iterative optimization process. Experimental results on typical real-measured Sentinel-1 datasets acquired in different regional areas with various RFI types demonstrate the validity of the proposed method.

Latency-aware secure transmission in MEC-assisted HSR communication systems

Mobile edge computing can provide short-range cloud computing capability for the mobile users, which is considered to be a promising technology in 5G communication. The mobile users offload some computing tasks to the edge server through the wireless backhaul link, which can reduce the energy consumption and the time latency. Meanwhile, due to the open characteristics of the wireless channel, the offloading tasks through the backhaul link may face the risk of eavesdropping. Therefore, the secure transmission based on physical layer security for the offloading tasks to the edge server is considered. The optimization problem of minimizing the energy consumption for the vehicular stations (VSs) in mobile edge computing-assisted high-speed railway communication system is studied in this paper. The energy consumption of the mobile users is generated by executing the local computing task and by transmitting the partial offloading task to the edge server. In this paper, a novel joint iterative optimization algorithm is proposed. By jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency. Numerical simulation results verify the effectiveness of the proposed algorithm.

Power Optimization for Aerial Intelligent Reflecting Surface-Aided Cell-Free Massive MIMO-Based Wireless Sensor Network

Intelligent reflecting surfaces (IRSs) have significant advantages in enhancing the coverage and reducing the deployment cost of wireless networks. This paper studies an aerial IRS- (AIRS-) enhanced cell-free massive multiple-input multiple-output- (MIMO-) based wireless sensor network (WSN) in which multiple access points (APs) serve several sensor users (SUs). Direct links between the APs and SUs are blocked due to occlusion by tall buildings. Hence, we deploy an AIRS to improve the communication quality of the SUs. Our goal is to minimize the total transmit power of all APs under a given minimum signal-to-interference-plus-noise ratio (SINR) requirement. We propose a joint iterative optimization algorithm by designing an active beamforming mechanism at each AP and a passive beamforming mechanism at the AIRS to solve this problem. Simulation results illustrate the good performance of the proposed method.

Joint Iterative Optimization-Based Low-Complexity Adaptive Hybrid Beamforming for Massive MU-MIMO Systems

This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications.

A Novel Multi-Feature Joint Learning Ensemble Framework for Multi-Label Facial Expression Recognition

The facial expression is one of the most common ways to reflect human emotions. And understand different classes of facial expressions is an important method in analyzing human perceived and affective states. In the past few decades, facial expression analysis (FEA) has been extensively studied. It illustrates few of the facial expressions are exactly individual of the predefined affective states but are blends of several basic expressions. Some researchers have realized that facial expression recognition can be treated as a multi-label task, but they are still troubled by the inaccurate recognition of multi-label expressions. To overcome this challenge, a novel multi-feature joint learning ensemble framework, called MF-JLE framework, is proposed. The proposed framework combines global features with several different local key features to consider the multiple labels of expressions embodied in many facial action units. The ensemble learning is introduced into the framework, combines the global module and the local module on the loss, and carries out the joint iterative optimization. The ensemble of the whole framework improves the accuracy of multi-label recognition of different modules as weak classifiers. In addition, the traditional multi-classifier cross-entropy loss has been replaced by the binary cross-entropy loss for a better ensemble. The proposed framework is evaluated on the real-world affective faces (RAF-ML) dataset. The experimental results show that the proposed model is better than other methods in both quantitative and qualitative aspects, whether compared with traditional shallow learning methods or recent deep learning methods.

Cascaded suppression method based on joint iterative optimization for airborne passive radar

To overcome the severe space-time adaptive processing (STAP) performance degradation caused by array gain/phase (GP) errors and undesirable random range sidelobe couplings, a novel cascaded suppression method based on joint iterative optimization is proposed for airborne passive radar. The proposed cascaded method (CM) exploits the sparse nature of these undesirable sidelobe couplings, reformulates the sparsity recovery problem as a joint optimization problem of the spatio-temporal profile and array GP errors, and achieves sidelobe couplings suppression before STAP. A range of simulations is conducted to test the proposed method.

Optimization-based simultaneous alignment and reconstruction in multi-element tomography.

As x-ray microscopy is pushed into the nanoscale with the advent of more bright and coherent x-ray sources, associated improvement in spatial resolution becomes highly vulnerable to geometrical errors and uncertainties during data collection. We address a form of error in tomography experiments, namely, the drift between projections during the tomographic scan. Our proposed method can simultaneously recover the drift, while tomographically reconstructing the specimen based on a joint iterative optimization scheme. This approach utilizes the correlation provided from different view angles and different signals. While generally applicable, we demonstrate our method on x-ray fluorescence tomography from a tissue specimen and compare the reconstruction quality with conventional methods.

Extended reduced‐rank joint estimation of direction of arrival with mutual coupling for coherent signals

AbstractThis paper proposes an extended method for direction‐of‐arrival (DOA) estimation with unknown mutual coupling for coherent signals. The proposed method employs the forward/backward spatial smoothing to decorrelate the coherent signals in the process of rank reduction with the joint iterative subspace optimization approach. Then, the autocalibration of mutual coupling is performed based on the Capon's minimum variance criterion. Performance of the proposed method is compared with the state of the art algorithms in terms of DOA root‐mean‐square error and in terms of probability of successful estimation, where successful estimation is the case when the average of absolute DOA estimation error is within 2.5°. It is shown that the proposed method has a similar performance as the state of the art autocalibration methods while offering a lower computational complexity.

Adaptive Reduced-Rank Beamforming Method Based on Knowledge-Aided Joint Iterative Optimization

In this letter, a reduced-rank beamforming method is presented based on knowledge-aided joint iterative optimization. The proposed adaptive reduced-rank processing is realized by joint optimization for the reduced-dimension matrix and beamforming weight vector. Meanwhile, recursively updating the prior covariance matrix using the spatial spectrum reconstruction technology and the weighted processing improve the estimation precision of the array covariance matrix. The simulation results show that the proposed method is robust to the dimension of the reduced-dimension matrix and significantly improves the output signal-to-interference-plus-noise ratio (SINR) of the adaptive beamformer under the condition of few samples.

发输电联合优化的区域电网日内滚动节能调度策略及社会效益

电力工业节能减排是国家“十二五”能源发展战略的重要目标，也是新一轮电改中放开发用电计划必须兼顾的关键因素，进一步挖掘电力生产全过程节能潜力是未来电网面临的挑战性课题。本文针对某区域电网交直流并联运行的特有方式，提出以日内滚动的跨省交易实现高、低能耗能源互补，以交直流潮流优化分配实现输电运行经济性最优，最终实现日内滚动节能调度的发输全过程优化。为了适应大电网快速优化决策的需要，本文进一步设计了发输电联合迭代优化计算流程，并对日内滚动节能调度的经济、节能效益进行了测算，揭示了实施发输电联合优化的日内滚动节能调度的巨大效益空间。 Energy-saving in electricity industry is regarded as one of important strategic targets in “Twelfth Five-Year Plan” for energy. And it is the key issue for deregulation of generation schedule in revo-lution of power market. CSG is facing a challenging topic of mining potential for the whole process of real electric energy production. The intraday rolling optimization strategy under the circumstance of AC/DC hybrid operation mode of regional grid is discussed. This paper proposed a new strategy about high energy-consumption and clean energy replacement as well as optimizing transmission distribution in AC/DC channels. A Joint iterative optimization process was proposed to solute generation and transmission models with a target of minimizing real-time energy consumption increment. The assessment of economic benefits and energy-saving profit based on practical data proved huge potential of intraday rolling energy-saving scheduling using joint optimization of generation and transmission.

Adaptive Widely Linear Reduced-Rank Beamforming Based on Joint Iterative Optimization

We propose a reduced-rank beamformer based on the rank- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$D$</tex></formula> Joint Iterative Optimization (JIO) of the modified Widely Linear Constrained Minimum Variance (WLCMV) problem for non-circular signals. The novel WLCMV-JIO scheme takes advantage of both the Widely Linear (WL) processing and the reduced-rank concept, outperforming its linear counterpart as well as the full-rank WL beamformer. We develop an augmented recursive least squares algorithm and present an improved structured version with a much more efficient implementation. It is shown that the improved adaptive scheme achieves the best convergence performance among all the considered methods with a low computational complexity.

Direction finding algorithms based on joint iterative subspace optimization

In this paper, a reduced-rank scheme with joint iterative optimization is presented for direction of arrival estimation. A rank-reduction matrix and an auxiliary reduced-rank parameter vector are jointly optimized to calculate the output power with respect to each scanning angle. Subspace algorithms to estimate the rank-reduction matrix and the auxiliary vector are proposed. Simulations are performed to show that the proposed algorithms achieve an enhanced performance over existing algorithms in the studied scenarios.

Adaptive Linear Minimum BER Reduced-Rank Interference Suppression Algorithms Based on Joint and Iterative Optimization of Filters

In this letter, we propose a novel adaptive reduced-rank strategy based on joint iterative optimization (JIO) of filters according to the minimization of the bit error rate (BER) cost function. The proposed optimization technique adjusts the weights of a subspace projection matrix and a reduced-rank filter jointly. We develop stochastic gradient (SG) algorithms for their adaptive implementation and introduce a novel automatic rank selection method based on the BER criterion. Simulation results for direct-sequence code-division-multiple-access (DSCDMA) systems show that the proposed adaptive algorithms significantly outperform the existing schemes.

Low-Complexity Reduced-Rank Linear Interference Suppression Based on Set-Membership Joint Iterative Optimization for DS-CDMA Systems

This paper presents and analyzes a novel low-complexity reduced-rank linear interference suppression technique for direct-sequence code-division multiple access (DS-CDMA) systems based on the set-membership joint iterative optimization of receive parameters. Set-membership filtering is applied to the design and adaptation of the dimensionality-reducing projection matrix and the reduced-rank interference suppression filter. The specification of error bounds on the projection matrix and reduced-rank filter lead to the formation of two constraint sets from which estimates of the adaptive structures are selected at each time instant. The result is a low-complexity sparsely updating reduced-rank technique that does not require eigendecomposition or subspace tracking procedures. We develop least squares and stochastic gradient-type algorithms and a low-complexity rank-selection algorithm and also devise a time-varying adaptive error bound implementation. We present a stability and mean-square-error convergence analysis of the proposed algorithms along with a study of their complexity. The proposed schemes are applied to interference suppression in the uplink of a multiuser spread-spectrum DS-CDMA system, and the results confirm the validity of the analysis and the effective operation of the schemes. Performance comparisons are given against existing reduced-rank and full-rank algorithms, which act to highlight the improvements obtained by the proposed technique and algorithms.

Blind Reduced-Rank Adaptive Receivers for DS-UWB Systems Based on Joint Iterative Optimization and the Constrained Constant Modulus Criterion

A novel linear blind adaptive receiver based on joint iterative optimization (JIO) and the constrained-constant-modulus design criterion is proposed for interference suppression in direct-sequence ultrawideband systems. The proposed blind receiver consists of the following two parts: 1) a transformation matrix that performs dimensionality reduction and 2) a reduced-rank filter that produces the output. In the proposed receiver, the transformation matrix and the reduced-rank filter are jointly and iteratively updated to minimize the constant-modulus cost function subject to a constraint. Adaptive implementations for the JIO receiver are developed using the normalized stochastic gradient (NSG) and recursive least squares (RLS) algorithms. To obtain a low-complexity scheme, the columns of the transformation matrix with the RLS algorithm are individually updated. Blind channel estimation algorithms for both versions (i.e., NSG and RLS) are implemented. Assuming perfect timing, the JIO receiver only requires the spreading code of the desired user and the received data. Simulation results show that both versions of the proposed JIO receivers have excellent performance in terms of suppressing the intersymbol interference (ISI) and multiple-access interference (MAI) with low complexity.

Adaptive Reduced-Rank Equalization Algorithms Based on Alternating Optimization Design Techniques for MIMO Systems

This paper presents a novel adaptive reduced-rank multiple-input-multiple-output (MIMO) equalization scheme and algorithms based on alternating optimization design techniques for MIMO spatial multiplexing systems. The proposed reduced-rank equalization structure consists of a joint iterative optimization of the following two equalization stages: 1) a transformation matrix that performs dimensionality reduction and 2) a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced-rank architecture is incorporated into an equalization structure that allows both decision feedback and linear schemes to mitigate the interantenna (IAI) and intersymbol interference (ISI). We develop alternating least squares (LS) expressions for the design of the transformation matrix and the reduced-rank estimator along with computationally efficient alternating recursive least squares (RLS) adaptive estimation algorithms. We then present an algorithm that automatically adjusts the model order of the proposed scheme. An analysis of the LS algorithms is carried out along with sufficient conditions for convergence and a proof of convergence of the proposed algorithms to the reduced-rank Wiener filter. Simulations show that the proposed equalization algorithms outperform the existing reduced- and full- algorithms while requiring a comparable computational cost.

Reduced-Rank STAP Algorithms using Joint Iterative Optimization of Filters

We develop a reduced-rank space-time adaptive processing (STAP) method based on joint iterative optimization of filters (JOINT) for airborne radar applications. The proposed method consists of a bank of full-rank adaptive filters, which forms the projection matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe the proposed method for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. Adaptive algorithms including the stochastic gradient (SG), the recursive least square (RLS), and their hybrid algorithms are derived for the efficient implementation of the JOINT STAP method. The computational complexity analysis of the proposed algorithms is shown in terms of the number of multiplications and additions per snapshot. Furthermore, the convexity analysis of the proposed method is carried out. Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.

Adaptive Reduced-Rank Constrained Constant Modulus Algorithms Based on Joint Iterative Optimization of Filters for Beamforming

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The novel scheme is designed according to the constant modulus (CM) criterion subject to different constraints. The proposed scheme consists of a bank of full-rank adaptive filters that forms the transformation matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters to estimate the desired signal. We describe the proposed scheme for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. For each structure, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithms for its adaptive implementation. The Gram-Schmidt (GS) technique is applied to the adaptive algorithms for reformulating the transformation matrix and improving the performance. An automatic rank selection technique is developed and employed to determine the most adequate rank for the derived algorithms. A detailed complexity study and a convexity analysis are carried out. Simulation results show that the proposed algorithms outperform the existing full-rank and reduced-rank methods in convergence and tracking performance.