Block Coordinate Descent Method Research Articles

Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting With D2D Communications

The cellular network coexisting with device-to-device (D2D) communications has been studied extensively. Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are promising technologies for the evolution of 5G, 6G and beyond. Besides, sparse code multiple access (SCMA) is considered suitable for next-generation wireless network in code-domain NOMA. In this paper, we consider the RIS-aided uplink SCMA cellular network simultaneously with D2D users. We formulate the optimization problem which aims to maximize the cellular sum-rate by jointly designing D2D users’ resource block (RB) association, the transmitted power for both cellular users and D2D users, and the phase shifts at the RIS. The power limitation and users’ communication requirements are considered. The problem is non-convex, and it is challenging to solve it directly. To handle this optimization problem, we propose an efficient iterative algorithm based on block coordinate descent (BCD) method. The original problem is decoupled into three subproblems to solve separately. Simulation results demonstrate that the proposed scheme can significantly improve the sum-rate performance over various schemes.

Reconfigurable Intelligent Surface Based Uplink MU-MIMO Symbiotic Radio System

In this paper, we investigate a novel uplink reconfigurable intelligent surface (RIS) based multi-user multi-input multi-output symbiotic radio system. It indicates that each RIS, as an Internet-of-Things (IoT) device, enhances the primary transmission from a nearby user to the base station (BS), and simultaneously transmits its own information to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary communications from the users to the BS. We consider both the case of perfect and imperfect channel state information (CSI), and design the active beamforming at the BS and the passive beamforming at the RISs jointly to maximize the weighted sum-rate of both the primary and IoT transmissions. For the perfect CSI case, we propose an algorithm based on the block coordinate descent (BCD) method to solve the problem. We also propose another algorithm with a similar framework to reduce the computational complexity. For the imperfect CSI case, an algorithm based on BCD and the online successive convex approximation technique is proposed. Simulation results show that the proposed system achieves significant performance gain over a number of baseline schemes for both the perfect and imperfect CSI cases. Furthermore, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.

Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors.

Goal: Inferring autonomous nervous system (ANS) activity is a challenging issue and has critical applications in stress regulation. Sweat secretions caused by ANS activity influence the electrical conductance of the skin. Therefore, the variations in skin conductance (SC) measurements reflect the sudomotor nerve activity (SMNA) and can be used to infer the underlying ANS activity. These variations are strongly correlated with emotional arousal as well as thermoregulation. However, accurately recovering ANS activity and the corresponding state-space system from a single channel signal is difficult due to artifacts introduced by measurement noise. To minimize the impact of noise on inferring ANS activity, we utilize multiple channels of SC data. Methods: We model skin conductance using a second-order differential equation incorporating a time-shifted sparse impulse train input in combination with independent cubic basis spline functions. Finally, we develop a block coordinate descent method for SC signal decomposition by employing a generalized cross-validation sparse recovery approach while including physiological priors. Results: We analyze the experimental data to validate the performance of the proposed algorithm. We demonstrate its capacity to recover the ANS activations, the underlying physiological system parameters, and both tonic and phasic components. Finally, we present an overview of the algorithm's comparative performance under varying conditions and configurations to substantiate its ability to accurately model ANS activity. Our results show that our algorithm performs better in terms of multiple metrics like noise performance, AUC score, the goodness of fit of reconstructed signal, and lower missing impulses compared with the single channel decomposition approach. Conclusion: In this study, we highlight the challenges and benefits of concurrent decomposition and deconvolution of multichannel SC signals.

Trustworthiness of Process Monitoring in IIoT Based on Self-Weighted Dictionary Learning

Process monitoring, a typical application of industrial Internet of Things (IIOT), is crucial to ensure the reliable operation of the industrial system. In practice, due to the harsh environment and unreliable sensors and actuators, it is often difficult for IIoT to collect enough tagged and highly reliable data, which further degrades the process monitoring performance and makes the monitoring results not trustworthy. In order to reduce the negative impact of these unreliable factors, a self-weighted dictionary learning process monitoring method is proposed. In particular, a label propagation classifier is implemented from the labeled data to unlabeled data to obtain a credible label prediction. Subsequently, considering the interference of low-quality data and label information, we reweight the classification loss and label-consistency constraints to enhance the trustworthiness of feature extraction. Finally, a novel iterative optimization algorithm that combines the block coordinate descent method with the alternating direction multiplier method is developed to ensure the convergence speed of the learned classifier and dictionary. Extensive experiments indicate that the proposed method can guarantee the trustworthiness of the process monitoring results.

Maximizing the energy efficiency in intelligent reflecting surface‐aided unmanned aerial vehicle data collection system in presence of malicious jammers

AbstractIn this article, intelligent reflecting surface (IRS) aided an unmanned aerial vehicle (UAV) data collection system is investigated, where an IRS is deployed to assist in the communication from a cluster of Internet‐of‐Things (IoT) devices to a UAV in the presence of a jammer. We aim to improve the energy efficiency (EE) via the joint design of UAV trajectory, IRS passive beamforming, device power allocation and communication scheduling. However, the formulated nonlinear fractional programming problem is challenging to solve due to its nonconvexity and coupled variables. To overcome the difficulty, we propose an alternating optimization based algorithm to solve it sub‐optimally by leveraging the Dinkelbach's algorithm, successive convex approximation (SCA) technique, and block coordinate descent (BCD) method. Extensive simulation results show that the proposed design can significantly improve the anti‐jamming performance. In particular, for the remote jammer case, the proposed design can largely shorten the flight path and thus decrease the energy consumption via the signal enhancement; while for the local jammer case, which is deemed highly challenging in conventional systems without IRS since the retreating away strategy becomes ineffective, our proposed design even achieves a higher performance gain owing to the efficient jamming signal mitigation.

Robust Trajectory and Resource Optimization in UAV-Enabled IoT Networks under Probabilistic LoS Channel in Presence of Jammers

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer's approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices' transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S-procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms.

Energy-Efficient Power Allocation for Multiuser Distributed MISO Systems With Beamforming and Imperfect CSI

In this article, we study the energy-efficient power allocation (PA) for multiuser distributed multiple-input-single-output (D-MISO) system with beamforming (BF) and imperfect channel state information (CSI). Given a target bit error rate, the system energy efficiency (EE) with imperfect CSI is deduced. Subject to the power budget constraints, a constrained optimization problem is first formulated to maximize EE of single-user D-MISO. The optimal BF is shown to be aligned with the channel direction that renders the joint optimization problem to be a PA problem. A near-optimal PA scheme is proposed to tackle this problem by using the concave–convex procedure method and fractional programming (FP) and block coordinate descent (BCD) method, and it can provide nearly optimal performance close to that of optimal exhaustive search scheme with less complexity. On this basis, a suboptimal PA scheme based on the FP and BCD method is developed to further decrease the complexity and maintain similar performance to near-optimal scheme, and it is suitable for the system with more distributed antennas. With these results, two PA schemes are, respectively, proposed for multiuser D-MISO with imperfect CSI to maximize EE. The first scheme can obtain better performance with relatively higher complexity, and the second scheme can gain the performance close to the first scheme with lower complexity, and only smaller performance loss is found. Interestingly, these two schemes have the same optimal performance for perfect CSI, and include the existing ones under perfect CSI as special cases. Simulation results verify the effectiveness of the developed schemes.

Securing Transmission for UAV Swarm-Enabled Communication Network

Deploying unmanned aerial vehicles (UAVs) in communication networks could achieve desirable degrees of freedom, but resulting from the high line-of-sight link attributes, security threats faced by UAV communications are serious. Taking that into consideration, we focus on a UAV swarm-enabled communication network in this article, where a fixed-wing UAV acts as a legitimate user and a neighboring UAV eavesdropper attempts to eavesdrop the confidential information forwarded by rotary-wing UAV relays. In particular, we jointly optimize transmit power on the base station and UAV relays, power allocation coefficient and beamforming on UAV relays, and trajectory of the fixed-wing UAV to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is utilized to solve it. Specifically, the overall optimizing problem is divided into four subproblems in which the transmit power, power allocation coefficient, beamforming vector, and the trajectory of user are optimized, respectively. By employing the successive convex approximate method, subproblems are transformed to convex problems which are numerically tractable. Numerical simulations validate the effectiveness of the proposed algorithm and show that compared with benchmark schemes, the considered transmission scheme with artificial noise and optimized network parameters can improve secrecy rate by about 13.8%.

Resource Allocation for Networked Telemetry System of Mega LEO Satellite Constellations

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and geostationary earth orbit (GEO)-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems, which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system and approximates the upper bound performance.

Joint Wireless Charging and Data Collection for UAV-Enabled Internet of Things Network

Internet of Things (IoT) devices usually lack perpetual power supply since they are generally deployed at remote areas with limited battery capacities. Moreover, the computing power of the IoT devices is usually limited and the generated data need to be offloaded to a more powerful computing server for further processing. In this article, we study a novel unmanned aerial vehicle (UAV)-enabled IoT network, where the UAV delivers energy to the ground IoT devices by employing wireless power transfer (WPT) in the downlink and collects data in the uplink. In our work, we try to minimize the total energy consumption of the UAV by determining the trajectory and charging power of the UAV, the resource allocation, and the transmission scheme subject to task collection and resource budget requirements. To make the formulated problem more tractable, we decompose the primal problem into three subproblems (i.e., the transmission association problem, the trajectory design problem, and the resource allocation problem) and utilize the block coordinate descent (BCD) method to solve them alternately. Since the trajectory design problem is still highly nonconvex, we further transform it into a convex one by leveraging the successive convex approximation (SCA) technique. In the simulation, we provide extensive numerical results to corroborate the effectiveness of our proposed algorithm.

Multi-UAV Trajectory Control, Resource Allocation, and NOMA User Pairing for Uplink Energy Minimization

In this work, we study the joint optimization of multiple unmanned aerial vehicles (UAVs)’ trajectories, power allocation, user-UAV association, and user pairing for UAV-assisted wireless networks employing the nonorthogonal multiple access (NOMA) for uplink communications. The design aims to minimize the total energy consumption of ground users while guaranteeing to successfully transmit their required amount of data to the UAV-mounted base stations. The underlying problem is a mixed-integer nonlinear program (MINLP), which is difficult to solve optimally. To tackle this problem, we derive the optimal power allocation as a function of other variables, which is used to transform the optimization problem into an equivalent form. We then propose an iterative algorithm to solve the resulting optimization problem by using the block coordinate descent (BCD) method where three subproblems are solved in each iteration and this process is repeated until convergence. Specifically, given the UAVs’ trajectories and data rates, we solve the NOMA user pairing, and user-UAV association subproblem optimally by exploiting its special structure. Then, we describe how to optimize the users’ data rates and tackle the UAV trajectory optimization in the second and third subproblems, respectively, by using the successive convex approximation (SCA) method. Numerical results show that our proposed algorithm can provide efficient active-inactive schedules (by setting user’s transmit powers to zero), and lower energy consumption compared to an existing baseline, and an OMA-based resource allocation and UAV-trajectory optimization strategy.

Active Reconfigurable Intelligent Surface for Mobile Edge Computing

This letter investigates an active reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system. Compared with passive RIS, the active RIS is equipped with active reflective amplifier, which can effectively circumvent the “double path loss” attenuation. We propose a joint computing and communication design to minimize the maximum computational latency (MCL), subject to both the phase shift constraints and the edge computing capability constraints. Specifically, the original problem is decoupled into four subproblems, and then the block coordinate descent (BCD) method and the successive convex approximation (SCA) method are applied to alternately optimize the subproblems. The simulation results show that with the same power budget, the performance gain achieved by the active RIS is much larger than that by the passive RIS.

FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things

Recently, the Internet of Medical Things (IoMT) could offload healthcare services to 5G edge computing for low latency. However, some existing works assumed altruistic patients will sacrifice quality of service for the global optimum. For priority-aware and deadline-sensitive healthcare, this sufficient and simplified assumption will undermine the engagement enthusiasm, i.e., unfairness. To address this issue, we propose a long-term proportional fairness-driven 5G edge healthcare, i.e., FairHealth. First, we establish a long-term Nash bargaining game to model the service offloading, considering the stochastic demand and dynamic environment. We then design a Lyapunov-based proportional-fairness resource scheduling algorithm, which decouples the long-term fairness problem into single-slot subproblems, realizing a tradeoff between service stability and fairness. Moreover, we propose a block-coordinate descent method to iteratively solve nonconvex fair subproblems. Simulation results show that our scheme can improve 74.44% of the fairness index (i.e., Nash product), compared with the classic global time-optimal scheme.

NOMA Enhanced Hybrid RIS-UAV-Assisted Full-Duplex Communication System With Imperfect SIC and CSI

In this work, we consider a hybrid aerial full-duplex (FD) relaying protocol consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in the decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of non-orthogonal multiple access (NOMA) in such networks and focus on both the performance analysis and design optimization of the considered RIS-NOMA network under imperfect channel state information (CSI) and successive interference cancellation (SIC) at each user, and residual-self interference (RSI) at UAV. We first formulate the sum rate maximization problem and adopt the block coordinate descent method to deal with the non-convex nature of the problem. Thereafter, we propose an algorithm based on the Riemannian conjugate gradient method to get the optimal phase shifts at the RIS, an iterative algorithm to obtain the optimal UAV/RIS position and the exhaustive method to obtain the optimum power allocation coefficients. Next, with obtained optimal position, phase shift and power coefficients, we further analyze the performance of the network and derive the closed-form expressions of outage probability, achievable throughput and ergodic capacity. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions and demonstrate the superiority of NOMA over OMA.

Resource allocation for UAV-assisted backscatter communication

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

Power Allocation for Energy-Efficient Optimization in IoT-Based Distributed Antenna System With Imperfect Channel State Information

This article investigates energy efficiency (EE) maximization for an Internet of Things-based (IoT) distributed antenna system (DAS) with imperfect channel state information (CSI) subject to maximum transmit power and minimum data rate constraints. First, we derive achievable rates and corresponding EE expressions of the system for single and multiple IoT devices. Then, we formulate the EE maximization problem in the single-device IoT-DAS with imperfect CSI, and it has a special form of the difference of concave functions. To solve this problem, we develop a near-optimal power allocation (PA) scheme by using the concave–convex procedure (CCCP) and block coordinate descent (BCD) method. It can achieve an EE performance similar to that of the optimal exhaustive search scheme, but with lower complexity. Based on this result, we present a suboptimal PA scheme with a single-layer iteration to decrease the complexity and maintain a performance similar to that of the near-optimal scheme. Using the results above, the EE optimization for IoT-DAS with imperfect CSI for multiple devices is addressed. A near-optimal PA scheme based on the fractional programming (FP) theory as well as the CCCP and BCD methods for solving the optimization problem is presented. Also, a suboptimal PA scheme based on the FP theory and BCD method is developed to further decrease the complexity. Interestingly, these two schemes have the same performance for perfect CSI. Simulation results show the validity of the developed schemes and their superior performances over existing methods.

The SparSpec algorithm and the application to the detection of spatial periodicities in tokamaks: from a 1D to a 2D analysis

A well-known, previously only 1D, algorithm using the Sparse Representation of Signals and an iterative Block Coordinate Descent method (the SparSpec-1D algorithm) has been further developed and tested in a 2D spatial domain to obtain the toroidal and poloidal periodicities of magnetic fluctuations in a tokamak. The tests are performed essentially using simulated data, because we know what the answer must be, and therefore it is straightforward to verify the accuracy of the algorithm. Two more examples using actual data from the JET and TCV tokamaks are considered to test the algorithm in real-life experiments; a further example using simulated data constructed from nominal test cases for the forthcoming ITER tokamak is also considered. The CPU run-time and the precision of the SparSpec-2D algorithm are studied as function of different analysis parameters. The stability of the algorithm is also tested via the introduction of random errors in the input signal. We find that the spatial-2D version of the baseline SparSpec-1D algorithm accurately finds the modes in the 2D toroidal and poloidal space, provided the set of magnetic sensors used for the analysis do not have a (quasi-)ignorable coordinate. The number of probes and their position are the key parameters that must be optimized for finding correct solutions. The main difficulty, as for the baseline SparSpec-1D algorithm, lies in dealing correctly with the intrinsic measurement uncertainties associated to the input magnetic fluctuation data, particularly the phase error, and this has been already separately reported in a companion work. However, the required CPU run-time for SparSpec-2D is significantly longer than that needed for 2 × SparSpec-1D, and thus SparSpec-2D is effectively suitable for use only when the 2 × 1D analyses cannot provide accurate results, which is the case when the set of measurements does not have an ignorable coordinate.

IRS Empowered UAV Wireless Communication With Resource Allocation, Reflecting Design and Trajectory Optimization

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.

Optimal Power Allocation for Integrated Visible Light Positioning and Communication System With a Single LED-Lamp

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver’s location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.

Joint Antenna and Device Scheduling in Full-Duplex MIMO Wireless-Powered Communication Networks

In this article, we study a joint antenna and Internet of Things device (ID) scheduling problem in the full-duplex (FD) multiple-input–multiple-output (MIMO) wireless-powered communication network (WPCN) over time-varying fading channels. We first formulate an optimization problem to maximize the average sum rate of IDs while satisfying their minimum average data rate requirements by jointly scheduling ID selection for uplink data transmission, antenna switching, and beamforming. To deal with the problem, we propose a scheduling algorithm based on Lagrangian duality and the stochastic optimization theory. The proposed scheduling algorithm necessitates solving per-time-slot problems, each of which aims at maximizing the weighted sum of the selected ID’s uplink data rate and the nonselected IDs’ harvested power from the downlink by jointly optimizing ID selection, antenna switching between uplink and downlink, and beamforming at that time slot. To solve the per-time-slot problem, we develop a joint ID selection, antenna switching, and beamforming (Joint-IAB) algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Through simulation, we demonstrate that our scheduling algorithm with the proposed Joint-IAB algorithm provides better performance than the other scheduling algorithms while well satisfying the given minimum average data rate requirements of IDs.