Block Successive Upper-bound Minimization Research Articles

Tensor Completion Using Bilayer Multimode Low-Rank Prior and Total Variation.

In this article, we propose a novel bilayer low-rankness measure and two models based on it to recover a low-rank (LR) tensor. The global low rankness of underlying tensor is first encoded by LR matrix factorizations (MFs) to the all-mode matricizations, which can exploit multiorientational spectral low rankness. Presumably, the factor matrices of all-mode decomposition are LR, since local low-rankness property exists in within-mode correlation. In the decomposed subspace, to describe the refined local LR structures of factor/subspace, a new low-rankness insight of subspace: a double nuclear norm scheme is designed to explore the so-called second-layer low rankness. By simultaneously representing the bilayer low rankness of the all modes of the underlying tensor, the proposed methods aim to model multiorientational correlations for arbitrary N -way ( N ≥ 3 ) tensors. A block successive upper-bound minimization (BSUM) algorithm is designed to solve the optimization problem. Subsequence convergence of our algorithms can be established, and the iterates generated by our algorithms converge to the coordinatewise minimizers in some mild conditions. Experiments on several types of public datasets show that our algorithm can recover a variety of LR tensors from significantly fewer samples than its counterparts.

An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, can be promisingly enhanced by reconfigurable intelligent surfaces (RIS). Under this context, we explore a system comprising a small base-station (SBS) with limited cache capacity, two users, and one RIS. The SBS transmits the contents from the cache or fetches them from the remote backhaul hub to communicate with users through directional and possibly reflective channels. In this point-to-multipoint connection, non-orthogonal multiple access (NOMA) is applied, improving the capacity of the system. To minimize the outage probability, we first propose a caching policy from entropy perspective, based on which we investigate the beamforming and power allocation problem. The issue, however, is non-convex and involves multi-dimensional optimization. To address this, we introduce an efficient block successive upper-bound minimization algorithm, grounded in Gershgorin’s circle theorem. This algorithm aims to find the globally optimal solution for power allocation and RIS beamformer, considering both the channel condition and content popularity. Numerical studies are performed to verify the effectiveness of the proposed algorithm.

A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars

Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear programming (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.

Distance-Angle Beamforming for Covert Communications via Frequency Diverse Array: Toward Two-Dimensional Covertness

In this paper, we study the beamforming schemes via the novel frequency diverse array (FDA) on enhancing covert communications performance. We first consider the ideal scenario where the channel state information (CSI) is perfectly known at the transmitter. Then we characterize the key role of minimizing the correlation of the communication and detection channels in boosting the covertness of the system, which also provides a theoretical design principle for FDA-specific carrier frequency scheduling. By exploiting the optimization framework block successive upper bound minimization (BSUM), we propose a frequency scheduling method which leads to a two-phase beamforming scheme to facilitate the covert transmission. Subsequently, we extend the scenario to the more practical one with partial CSI. By leveraging the convex hull, we manage to transform the formulated semi-infinite programming problem to an equivalent semi-definite one which can be solved optimally. In addition, we present a process to construct the optimal beamforming vector. To mitigate the channel correlations in this scenario, we generalize the steps of BSUM and propose an algorithm to schedule the frequencies efficiently. Afterwards, a three-phase robust beamforming scheme is summarized, which boosts the covert rate significantly. Numerical results are provided to demonstrate the superiority of the proposed schemes.

Group Sparse Space Information Network With Joint Virtual Network Function Deployment and Maximum Flow Routing Strategy

For the space information network (SIN) with network function virtualization (NFV), a large number of active nodes deployed with virtual network functions (VNFs) impose heavy coordination overhead. In this paper, we investigate the trade-off between the network maximum flow and coordination overhead under the service function chain (SFC) constraints. Specifically, we propose the group sparse joint VNFs deployment and flow routing strategy (GS-VNF-R) to strike the trade-off between the network maximum flow and coordination overhead. Although the GS-VNF-R scheme can be formulated as a convex problem, for a large-scale SIN, solving the GS-VNF-R problem by traditional convex optimizations imposes a heavy computation burden. In order to reduce the time complexity, we propose a novel optimal low-complexity block-successive upper-bound minimization method of multipliers based group sparse (BSUM-M-GS) algorithm, which can converge to the global optimal with much less complexity. Simulation results show that for some scenarios, 60% of active nodes can be saved by using the proposed GS-VNF-R scheme without any performance loss compared to the full cooperation scheme, which results in significant cooperation overhead reduction. Moreover, simulation results demonstrate that our proposed BSUM-M-GS algorithm can significantly reduce the complexity to the extent of 7 orders of magnitude for some scenarios.

An Off-Grid Iterative Reweighted Approach to One-Bit Direction of Arrival Estimation

This paper addresses the problem of direction of arrival (DOA) estimation only relying on the sign bits of the array signal. A new optimization model is formulated to realize the simultaneous estimation of one-bit DOAs and source number. Specifically, the proposed model jointly optimizes the DOA vector and row sparse signal matrix under an improved maximum posteriori probability criterion. The resulting problem is quite challenging due to the non-convexity of DOA vector and the integral form in the model. By utilizing the framework of block successive upper-bound minimization, an off-grid iterative reweighted algorithm is devised to transform the original problem into a series of tractable subproblems. Numerical results are presented to confirm the superiority of our method.

Multimodal Core Tensor Factorization and its Applications to Low-Rank Tensor Completion

Low-rank tensor completion has been widely used in computer vision and machine learning. This paper develops a novel multimodal core tensor factorization (MCTF) method combined with a tensor low-rankness measure and a better nonconvex relaxation form of this measure (NC-MCTF). The proposed models encode low-rank insights for general tensors provided by Tucker and T-SVD and thus are expected to simultaneously model spectral low-rankness in multiple orientations and accurately restore the data of intrinsic low-rank structure based on few observed entries. Furthermore, we study the MCTF and NC-MCTF regularization minimization problem and design an effective block successive upper-bound minimization (BSUM) algorithm to solve them. Theoretically, we prove that the iterates generated by the proposed models converge to the set of coordinatewise minimizers. This efficient solver can extend MCTF to various tasks such as tensor completion. A series of experiments including hyperspectral image (HSI), video and MRI completion confirm the superior performance of the proposed method.

Jamming the Relay-Assisted Multi-User Wireless Communication System: A Zero-Sum Game Approach

Recently, various wireless Internet-of-Things devices and unmanned aerial vehicles have been frequently used to facilitate daily life. On the other hand, they can also pose serious threats to public security if maliciously used. A countermeasure against these threats is to transmit jamming signals to break the communication links between attacker and wireless devices. However, this is not easy since modern multi-device (multi-user) wireless communication systems are smart in avoiding jamming. Moreover, a relay is widely used to improve the jamming resistance. To successfully jam those malicious devices, we consider a jamming and anti-jamming zero-sum game, in which the attacker tries to maximize the sum-rate of a relay-assisted wireless system and the jammer tries to minimize it. Finding the equilibrium is challenging due to its non-convexity and complex structure. We address this problem in two cases. Specifically, in the single-device (single-user) case, we show that putting the whole jamming power to either relay or device achieves the Nash equilibrium. In the multi-device case, an efficient method is developed, which transforms this game into a min-max optimization problem and decouples it into two sub-problems by the hybrid block successive approximation method. The resultant sub-problems are solved by the multi-block alternating direction method of multipliers and the block successive upper-bound minimization method of multipliers, respectively. Then, a stationary point of the original problem can be iteratively achieved. Numerical results demonstrate that the two proposed jamming methods outperform many traditional methods.

Matrix Factorization With Framelet and Saliency Priors for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection aims to separate sparse anomalies from low-rank background components. A variety of detectors have been proposed to identify anomalies, but most of them tend to emphasize characterizing backgrounds with multiple types of prior knowledge and limited information on anomaly components. To tackle these issues, this paper simultaneously focuses on two components and proposes a matrix factorization method with framelet and saliency priors to handle the anomaly detection problem. We first employ a framelet to characterize nonnegative background representation coefficients, as they can jointly maintain sparsity and piecewise smoothness after framelet decomposition. We then exploit saliency prior knowledge to measure each pixel’s potential to be an anomaly. Finally, we incorporate the pure pixel index with Reed-Xiaoli’s method to possess representative dictionary atoms. We solve the optimization problem by using a block successive upper-bound minimization framework with guaranteed convergence. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed method outperforms some state-of-the-art anomaly detection methods.

Joint Computational and Wireless Resource Allocation in Multicell Collaborative Fog Computing Networks

In 6G and future networks, joint optimization of communication and computational resources lays the foundation for various delay-sensitive intelligent IoT services in the fog computing architecture. In this paper, we present a multi-device collaborative computing architecture in the cell association environment to accelerate the processing procedure of data generated by smart IoT devices. In this scenario, a two-tier task scheduling scheme and an uplink and downlink power allocation factor are jointly optimized to reduce the data processing delay and improve fairness among different users, which is in nature a hard problem due to a series of non-convex constraints. To make the problem tractable, the problem is transformed into a smooth non-convex problem with the introduction of auxiliary variables and then decoupled into two subproblems based on the data transmission and processing procedure. Thereafter, different methods such as Successive Convex Approximation (SCA) and Block Successive Upperbound Minimization (BSUM) are employed to reconstruct several upper-bound convex optimization subproblems. Besides, a fast 0–1 binary offloading scheme is proposed based on the original algorithm. Finally, the simulation results depict the effectiveness of the proposed algorithms in detail, and the scalability of the system is also examined.

Resource Reservation in Backhaul and Radio Access Network With Uncertain User Demands

Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multi-path routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker (KKT) solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to run the applications with high computation demand and stringent delay requirement in beyond 5G era. In this work, we study a multi-UAV-assisted two-stage MEC system in which UAVs provide the computing and relaying services to the mobile devices. Due to the limited computing resources, each UAV executes only a portion of the offloaded tasks from its associated MDs in the first stage. Hence, in the second stage, each UAV relays the portions of the tasks to the terrestrial base station (TBS) which has rich computing resources enough to handle all the tasks relayed to it. In this regard, we formulate a joint task offloading, communication and computation resource allocation problem to minimize the energy consumption of MDs and UAVs by considering the limited resources of UAVs and the tolerable latency of the tasks. The formulated problem is a mixed-integer non-convex problem which is NP hard. To solve the formulated optimization problem, we apply the Block Successive Upper-bound Minimization (BSUM) method which guarantees to obtain the stationary points of the non-convex objective function. Finally, the extensive evaluation results are conducted to show the superior performance of our proposed framework.

Resource Allocation Optimization for Secure Multidevice Wirelessly Powered Backscatter Communication With Artificial Noise

Wirelessly powered backscatter communications (WPBC) is an emerging technology for providing continuous energy and ultra-low power communications. Despite some progress in WPBC systems, resource allocation for multiple devices towards secure backscatter communications (BC) and efficient-energy harvesting (EH) requests a deep-insight investigation. In this paper, we consider a WPBC system in which a full-duplex access point (AP) transmits multi-sinewave signals to power backscatter devices (BDs) and injects artificial noise (AN) to secure their backscatter transmissions. To maximize the minimum harvested energy and ensure fairness and security of all BDs, we formulate an optimization problem by jointly considering the backscatter time, power splitting ratio between multi-sinewave and AN, and signal power allocation. For a single-BD system, we characterize the achievable secrecy rate-energy region with a non-linear energy harvester and propose two algorithms to solve an energy maximization problem. We then analyze the effect of multi-sinewave and AN signals on BD’s secrecy rate and harvested energy through simulations and proof-of-concept experiments. For a multi-BD system, we propose an iterative algorithm by leveraging block successive upper-bound minimization (BSUM) techniques to solve the non-convex problem of fair resource allocation and show its convergence and complexity. Numerical results show the proposed algorithm achieves optimal and equitable harvested energy for all BDs with satisfying the security constraint.

Exploiting Amplitude Control in Intelligent Reflecting Surface Aided Wireless Communication With Imperfect CSI

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the promising gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided multiuser communications with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of the IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the achievable rate of each user is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoders, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Next, for the single-user case, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors; while for the multiuser case, a new penalty dual decomposition (PDD)-based algorithm is proposed to maximize the users' weighted sum-rate. Finally, simulation results are presented to validate the effectiveness of our proposed algorithms as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase-shift control) on the system performance under imperfect CSI.

Block-Term Tensor Decomposition: Model Selection and Computation

The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of \emph{blocks} of rank higher than one, a scenario encountered in numerous and diverse applications. Its uniqueness and approximation have thus been thoroughly studied. Nevertheless, the challenging problem of estimating the BTD model structure, namely the number of block terms and their individual ranks, has only recently started to attract significant attention. In this paper, a novel method of BTD model selection and computation is proposed, based on the idea of imposing column sparsity \emph{jointly} on the factors and in a \emph{hierarchical} manner and estimating the ranks as the numbers of factor columns of non-negligible magnitude. Following a block successive upper bound minimization (BSUM) approach for the proposed optimization problem is shown to result in an alternating hierarchical iteratively reweighted least squares (HIRLS) algorithm, which is fast converging and enjoys high computational efficiency, as it relies in its iterations on small-sized sub-problems with closed-form solutions. Simulation results for both synthetic examples and a hyper-spectral image de-noising application are reported, which demonstrate the superiority of the proposed scheme over the state-of-the-art in terms of success rate in rank estimation as well as computation time and rate of convergence.

Spectrally compatible aperiodic sequence set design with low cross- and auto-correlation PSL

It is known that unimodular sequence set with low cross- and auto-correlation peak sidelobe level (C/A-PSL) can improve the detection ability of the signal-of-interest in the presence of multipath propagation and/or clutters, while that with specified spectral power null/notch in certain frequency bands can prevent mutual interference of the systems that are operating in the same frequency bands. However, it is exceedingly difficult to design such a unimodular sequence set because nonsmooth quadratic objective function as well as nonlinear unimodular and spectral constraints are involved. In this paper, under the unimodular and spectral constraints, we formulate the sequence set design problem based on minimum C/A-PSL. Then, we apply block successive upper-bound minimization to solve the corresponding NP-hard problem and use the relation of the sequence auto-correlation and its fast Fourier transform (FFT) and inverse FFT to devise an efficient algorithm. Numerical examples show that the proposed method can effectively produce spectrally constrained sequence set with low C/A-PSL.

Joint Computational Offloading and Data-Content Caching in NOMA-MEC Networks

Multi-access edge computing (MEC) can improve the users’ computational capacity and battery life by moving computing services to the network edge. In addition, data-content caching on a MEC server improves the user quality of experience and decreases the backhaul network congestion. Moreover, non-orthogonal multiple access (NOMA) has recently been implemented to increase network throughput and capacity. Combining these techniques can improve the user performance and benefit the network. This paper investigates a combined computational offloading and data-content caching problem for NOMA-MEC systems. The aim was to achieve the minimum total completion latency of all users by jointly optimizing the offloading decision, caching strategy, computational resource, and power allocation. This satisfies the constraints within the scope of the potential violation for energy consumption, offloading decision, and the computation and storage capacity of the MEC server. The formulated problem is a mixed-integer non-linear programming and a non-convex problem. To solve this challenging problem, a block successive upper-bound minimization method was implemented to obtain efficient solutions. Numerous simulation results were presented to demonstrate the convergence and efficacy of the proposed algorithm. Compared with other schemes of all-offloading, local-only, and equal resources, our proposed algorithm can approximately reduce the total completion latency by 17.68%, 26.02%, and 70.98%.

Enhanced Nonconvex Low-Rank Approximation of Tensor Multi-Modes for Tensor Completion

Higher-order low-rank tensor arises in many data processing applications and has attracted great interests. Inspired by low-rank approximation theory, researchers have proposed a series of effective tensor completion methods. However, most of these methods directly consider the global low-rankness of underlying tensors, which is not sufficient for a low sampling rate; in addition, the single nuclear norm or its relaxation is usually adopted to approximate the rank function, which would lead to suboptimal solution deviated from the original one. To alleviate the above problems, in this paper, we propose a novel low-rank approximation of tensor multi-modes (LRATM), in which a double nonconvex L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</sub> norm is designed to represent the underlying joint-manifold drawn from the factorization factors of each mode in the underlying tensor. A block successive upper-bound minimization method-based algorithm is designed to efficiently solve the proposed model, and it can be demonstrated that our numerical scheme converges to the coordinate-wise minimizers. Numerical results on three types of public multi-dimensional datasets have tested and shown that our algorithm can recover a variety of low-rank tensors with significantly fewer samples than the compared methods.

Low-RCS and Wideband Reflectarray Antenna With High Radiation Efficiency

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the potential gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided multiuser systems with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the achievable rate of each user is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoders, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Then, for the single-user case, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors; while for the multiuser case, a new penalty dual decomposition (PDD)-based algorithm is proposed to maximize the users' weighted sum-rate. Simulation results are presented to validate the effectiveness of our proposed algorithms as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase shift) on the system performance under imperfect CSI.

Waveform Design for Collocated MIMO Radar With High-Mix-Low-Resolution ADCs

Adopting low-resolution analog-to-digital converters (ADCs) for receive antennas of a multiple-input multiple-output (MIMO) system can remarkably reduce the hardware cost, circuit power consumption as well as amount of data to be transferred from RF components and the baseband-processing unit. However, an obvious performance loss is also expected. Towards this end, in this work we introduce a new architecture which contains both high- and low-resolution ADCs (named as high-mix-low-resolution ADCs) for collocated MIMO radar, and the associated problem of waveform design is discussed in detail. Specifically, the problem jointly optimizes the waveform, ADC switch vector and receive filter, so as to maximize the signal-to-interference-plus-quantization-error-and-noise ratio (SIQENR). To tackle the resultant nonconvex problem, an alternating optimization method (AOM) is devised. To be more specific, the ADC switch vector and waveform matrix are optimized with the block successive upper-bound minimization (BSUM) framework based on the Dinkelback method. Furthermore, a continuous and concave function is introduced to approximate the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm to achieve a binary solution of the ADC switch vector. It is shown that the optimal solutions to the ADCs switch vector and waveform matrix can be efficiently attained by using the alternating direction method of multipliers (ADMM) approach and Karush-Kuhn-Tucher (KKT) conditions, respectively. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.