Successive Approximation Technique Research Articles

Bistatic Backscatter Communication: Shunt Network Design

Bistatic backscatter communication is emerged as a promising technique to significantly enlarge the lifetime of Internet of Things (IoT) network due to its inherently low-power passive component. However, the effective communication range is limited to only several meters. This article studies the tag circuit shunt network, and propose three modes, namely series mode, parallel mode, and mixed mode, to adjust circuit load impedance of the tag to extend the communication range as well as address the integrated circuit (IC) power supply problem. Specifically, we formulate the bit error rate (BER) minimization problems for the three modes by changing the reflection coefficients, subject to power supply constraint. The resulting problems are shown to be nonconvex fractional optimization problems, which are hard to be solved optimally in general. We first obtain a globally optimal solution to the series mode problem by exploiting the hidden monotonic structure based on monotonic optimization theory. Subsequently, we propose a low-complexity iterative suboptimal algorithm for the three modes based on the successive convex approximation (SCA) techniques. Numerical results show that when the direct link is available, the mixed mode outperforms the parallel mode and series mode, and can adaptively adjust the reflection coefficient to satisfy the requirement of IC power supply. In contrast, when the direct link is unavailable, the series mode is the best choice in terms of IC power supply. In addition, traditional on-off keying modulation is shown to be suitable for a low IC power supply, whereas a shunt network is necessary for high of power supply. Furthermore, the performance of SCA-based method closely approaches the optimal solution while with much lower complexity.

Anti-Jamming 3D Trajectory Design for UAV-Enabled Wireless Sensor Networks Under Probabilistic LoS Channel

This paper investigates the anti-jamming three-dimensional (3D) trajectory design to safeguard the legitimate communications of unmanned aerial vehicle (UAV)-enabled wireless sensor networks (WSNs). Specifically, a UAV is dispatched to collect data over multiple ground sensors (GSs) in the presence of a malicious ground jammer. Under the practical probabilistic line-of-sight (LoS) channel model, we aim to maximize the minimum (average) expected rate among GSs over a finite flight period, by jointly optimizing the GS transmission scheduling, the UAV horizontal and vertical trajectories. To make the formulated non-convex problem tractable, we provide an achievable lower bound for the expected rate, based on which an efficient iterative algorithm is proposed to solve it suboptimally by exploiting the block coordinate descent and successive convex approximation techniques. Numerical results show the significant rate improvement of the proposed joint design and provide new insights into the anti-jamming 3D trajectory design under the probabilistic LoS model, as compared to the conventional two-dimensional (2D) UAV trajectory or under LoS channel models.

Securing Energy-Constrained UAV Communications Against Both Internal and External Eavesdropping

This letter investigates an unmanned aerial vehicle (UAV) enabled network where an energy-constrained rotary-wing UAV is employed as a mobile base station to serve multiple ground users (GUs), while the data transmission between the UAV and each GU faces security threats from both multiple external eavesdroppers (Eves) and other GUs that potentially act as internal Eves. To ensure the user fairness, we maximize the minimum average secrecy rate among all GUs via jointly optimizing the UAV's trajectory and transmit power as well as the user scheduling, subject to the constraint on the UAV energy budget. To this end, an iterative suboptimal algorithm is proposed by leveraging the block coordinate descent and successive convex approximation techniques. Simulation results are provided to demonstrate the efficacy of the proposed algorithm.

Periodic water waves through suspended canopy

Small-amplitude water waves passing through a suspended canopy is studied. The area of suspended canopy is modeled by an array of vertical rigid cylinders with periodic spacing. Assuming that the diameter of cylinders and their spacing are much smaller than the typical incident wavelength, the homogenization theory (method of multiple-scale perturbation) is applied to create coupled micro-scale (cylinder spacing) and macro-scale (wavelength) problems. The micro-scale problem describes turbulent flows within a unit cell of the cylinder array, being driven by the macro-scale pressure gradients. Employing the concept of averaged energy balance over a wave period, the micro-scale flows determine the eddy viscosity, which damps the waves in the macro-scale flows. Eigenfunction expansions method is used to solve the macro-scale problem, in which a complex frequency dispersion relation is solved numerically by a multiple successive approximation technique. The potential decomposition method, which can avoid solving a complex frequency dispersion relation, is also employed to validate the accuracy of the eigenfunction expansions method. Both methods yield accurate solutions. However, the eigenfunction expansions method is relatively straightforward and can unify the solutions for suspended canopy and emergent vegetation. A new set of flume experiments of waves through suspended canopy is conducted and experimental data are used to check present solutions. Very good agreement has been observed. Finally, the effectiveness of suspended canopy, submerged and emergent vegetation on wave attenuation is discussed.

Efficiency‐enhanced and high‐precision of input common‐mode feedback control using OSA–CLS technique

Efficiency-enhanced and high-precision input common-mode feedback control (ICMFB) is proposed for a fully differential readout circuit of the MEMS capacitive sensor. This ICMFB improves the efficiency and performance of input common-mode voltage control using a smaller feedback capacitor. The noise performance of the readout circuit is improved as a decrease of a feedback capacitor. The correlated level shift (CLS) technique is used to improve the output swing of the amplifier therefore reduce the value of a feedback capacitor. Also, the oversampling successive approximation (OSA) technique is used to improve the accuracy of input common-mode voltage control using a low-gain op-amp. Simulation results show that the input common-mode voltage error decreases to 0.01% when the value of the feedback capacitor is the same as the MEMS sensor. Also, the noise power spectral density of the readout circuit increases by 10 dB.

Power-Efficiency Evolution of Capacitive Sensor Interfaces

Recent years have witnessed an improvement in the energy efficiency of capacitive sensor interfaces by more than three orders of magnitude. This article reviews the architectural and circuit innovations that have contributed to this progress. The fundamental limit on the energy consumption of capacitive sensor interfaces is discussed, as well as the widely used figure-of-merit (FoM). Interfaces based on period modulation feature simple circuitry, but their power efficiency at higher resolution deteriorates. Those employing ΔΣ modulation achieve high resolution with improved efficiency but require operational transconductance amplifiers that do not easily scale with process and supply voltage. Interfaces using successive approximation techniques feature mostly digital circuitry achieving good power efficiency at medium resolution. To achieve higher resolution, they can also be employed as the front-end in a hybrid architecture, where a back-end based on ΔΣ modulation or a voltage-controlled oscillator (VCO) performs a fine measurement on the front-end's residue, resulting in high resolution and excellent energy efficiency simultaneously.

Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization

This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex approximation technique. For the non-ideal IRS scenario, constant modulus IRS elements are further divided into continuous phase shifts and discrete phase shifts. To tackle the passive beamforming problem with continuous phase shifts, a novel algorithm is developed by utilizing the sequential rank-one constraint relaxation approach, which is guaranteed to find a locally optimal rank-one solution. Then, a quantization-based scheme is proposed for discrete phase shifts. Finally, numerical results illustrate that: i) the system sum rate can be significantly improved by deploying the IRS with the proposed algorithms; ii) 3-bit phase shifters are capable of achieving almost the same performance as the ideal IRS; iii) the proposed IRS-aided NOMA systems achieve higher system sum rate than the IRS-aided orthogonal multiple access system.

Max-Min QoS Power Control in Generalized Cell-Free Massive MIMO-NOMA With Optimal Backhaul Combining

This paper studies the uplink (UL) transmission of a generalized cell-free massive multiple-input multiple-output (massive MIMO) system in which multiple base stations (or access points), each equipped with a multiple-antenna array and connected to a central processing unit (CPU) over a backhaul network, simultaneously serve multiple users in a cell-free service area. The paper focuses on the non-orthogonal multiple access (NOMA) approach for sharing pilot sequences among users. Unlike the conventional cell-free massive MIMO-NOMA systems in which the UL signals from different access points are equally combined over the backhaul network, this paper first develops an optimal backhaul combining (OBC) method to maximize the UL signal-to-interference-plus-noise ratio (SINR). It is shown that, by using OBC, the correlated interference can be effectively mitigated if the number of users assigned to each pilot sequence is less than or equal to the number of base stations (BSs). As a result, the cell-free massive MIMO-NOMA system with OBC can enjoy unlimited performance when the number of antennas at each BS tends to infinity. A closed-form SINR expression is derived under Rayleigh fading and used to formulate a max-min quality-of-service (QoS) power control problem to further enhance the system performance. To deal with the NP-hardness of the concerned optimization problem, a successive inner approximation technique is applied to convert the original problem into a series of convex optimizations, which can be solved iteratively. In addition, a user grouping algorithm is also developed and shown to be better than random user grouping and a grouping method recently proposed in the literature. Numerical results are presented to corroborate the analysis and demonstrate the superiority of the proposed optimal backhaul combining over both equal-gain backhaul combining and zero-forcing backhaul combining.

Throughput maximization for multi-UAV enabled millimeter wave WPCN: Joint time and power allocation

In this paper, we investigate the effective deployment of millimeter wave (mmWave) in unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In particular, a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed. In the considered model, multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations (mBSs) with the harvested energy via mmWave. In this case, we propose an algorithm for jointly optimizing transmit power and energy transfer time. To solve the non-convex optimization problem with tightly coupled variables, we decouple the problem into more tractable subproblems. By leveraging successive convex approximation (SCA) and block coordinate descent techniques, the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm. Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.

Robust Design for Turning and Climbing Angle-Constrained UAV Communication Under Malicious Jamming

This letter studies an unmanned aerial vehicle (UAV)-enabled uplink communication system with turning and climbing angle constraints, and the communication process suffers from the attack from one ground jammer with imperfect location information. Under such setup, we aim to maximize the uplink throughput by jointly considering the UAV's three dimensions (3D) trajectory design and the ground node (GN)'s transmit power control. However, the optimization problem is non-convex and is hard to be tackled. To overcome this difficulty, we develop an alternating algorithm by leveraging the block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and S-procedure to acquire the suboptimal solution. Simulation results show that our proposed algorithm significantly improves the uplink throughput compared with the benchmark algorithms.

Energy-Efficient Trajectory Design for UAV-Enabled Communication Under Malicious Jamming

In this letter, we investigate a UAV-enabled communication system, where a UAV is deployed to communicate with the ground node (GN) in the presence of multiple jammers. We aim to maximize the energy efficiency (EE) of the UAV by optimizing its trajectory, subject to the UAV's mobility constraints. However, the formulated problem is difficult to solve due to the non-convex and fractional form of the objective function. Thus, we propose an iterative algorithm based on successive convex approximation (SCA) technique and Dinkelbach's algorithm to solve it. Numerical results show that the proposed algorithm can strike a better balance between the throughput and energy consumption by the optimized trajectory and thus improve the EE significantly as compared to the benchmark algorithms.

Securing wireless relaying communication for dual unmanned aerial vehicles with unknown eavesdropper

Unmanned aerial vehicles (UAVs) face a lot of security challenges due to the openness of their wireless communication. In this paper, we investigate how to improve the security of wireless communication when the UAV is utilized as a wireless relay. We consider a new scenario where the dual UAVs are employed cooperatively to transmit confidential information and an eavesdropper whose actual position is unknown attempts to eavesdrop on the information illegally. We propose an optimization problem for the average secrecy rate under multiple constraints. The optimization problem we proposed is non-convex and complicated. We design an iterative algorithm that jointly optimizes the trajectories and transmit power to make the average secrecy rate maximized via the block coordinate descent and successive convex approximation techniques. The feasibility of this method is verified by numerical simulation.

Joint energy efficient power and subchannel allocation for uplink MC‐NOMA networks

SummaryDue to the exponential rise in the number of subscribers in existing wireless networks, energy‐efficient distribution and utilization of network resources have become the preliminary objective of researchers nowadays. Nonorthogonal multiple access (NOMA) has attained widespread significance in this regard and has become a strong candidate for enhancing energy efficiency (EE) performance of existing networks. Multicarrier NOMA (MC‐NOMA) technique is an extension of NOMA that breaks the available resource block into various subchannels and allocates them efficiently to NOMA users (NUs). MC‐NOMA networks have the ability to further enhance system performance by efficiently exploiting channel diversity for addressing the problems of spectral inefficiency and power limitations. Therefore, in this paper, we have considered MC‐NOMA technology for an uplink scenario to investigate its performance for enhancing system EE by performing energy‐efficient power and subchannel allocation. To this end, we have formulated a joint user clustering, subchannel allocation, and power allocation problem for EE maximization (JSPEE) of uplink MC‐NOMA scenario. Due to the nonconvex, combinatorial nature of the problem, we propose a two‐step solution: that is, for each subchannel, first, subchannel allocation and user clustering are attained through low‐complexity suboptimal algorithm, which is followed by energy‐efficient power allocation of NUs. For subchannel allocation and user clustering, we exploit the channel diversity in the form of difference in channel gain values of various users. For power allocation, the nonconvex nature of the formulated problem is tackled by employing Dinkelbach and successive convex approximation (SCA) techniques to attain an energy‐efficient solution. Our simulation results portray that the JSPEE algorithm clearly outperforms the current NOMA as well as OMA works and enhances the system EE by efficient user clustering and exploiting channel diversity.

Energy-Efficient Data Uploading for Cellular-Connected UAV Systems

Integrating unmanned aerial vehicles (UAVs) into cellular networks offers a promising solution to support their efficient operations and achieve high-quality communication with the ground. In this paper, we consider a cellular-connected UAV communication system, in which one energy-constrained UAV flies from a given initial location to a final location while uploading data to the ground base stations (GBSs) along its flight. We study the joint design of UAV operation time, communication scheduling, as well as UAV trajectory and transmit power to maximize the data uploading throughput, subject to the communication quality of service (QoS) requirement and UAV energy budget constraints. We first consider an offline design approach by utilizing only the channel distribution information (CDI) that is available prior to the UAV's flight, which is formulated as a non-convex optimization problem and challenging to solve. By using path disretization and successive convex approximation (SCA) techniques, an efficient alternating optimization algorithm is proposed, which can converge to a solution that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Then, we further study the online design approach by utilizing the instantaneous channel state information (CSI) that is available to the UAV in real time along its flight. As the online design problem has similar structure as that of the offline design, an adaptive online optimization algorithm is proposed. To further reduce the computational complexity, a low-complexity online algorithm based on receding horizon optimization (RHO) is developed by utilizing a combined offline and online design approach. Simulations are conducted to corroborate our study and the results demonstrate the performance gain of proposed designs as compared to various baseline schemes. Furthermore, our results unveil the tradeoff between system throughput and UAV endurance in the considered system.

A mathematical model of COVID-19 using fractional derivative: outbreak in India with dynamics of transmission and control

Since the first case of 2019 novel coronavirus disease (COVID-19) detected on 30 January, 2020, in India, the number of cases rapidly increased to 3819 cases including 106 deaths as of 5 April, 2020. Taking this into account, in the present work, we have analysed a Bats–Hosts–Reservoir–People transmission fractional-order COVID-19 model for simulating the potential transmission with the thought of individual response and control measures by the government. The real data available about number of infected cases from 14 March, 2000 to 26 March, 2020 is analysed and, accordingly, various parameters of the model are estimated or fitted. The Picard successive approximation technique and Banach’s fixed point theory have been used for verification of the existence and stability criteria of the model. Further, we conduct stability analysis for both disease-free and endemic equilibrium states. On the basis of sensitivity analysis and dynamics of the threshold parameter, we estimate the effectiveness of preventive measures, predicting future outbreaks and potential control strategies of the disease using the proposed model. Numerical computations are carried out utilising the iterative Laplace transform method and comparative study of different fractional differential operators is done. The impacts of various biological parameters on transmission dynamics of COVID-19 is investigated. Finally, we illustrate the obtained results graphically.

Nested Sparse Successive Galerkin Approximation for Nonlinear Optimal Control Problems

Solving the Hamilton-Jacobi-Bellman (HJB) equation for nonlinear optimal control problems usually suffers from the so-called curse of dimensionality. In this letter, a nested sparse successive Galerkin method is presented for HJB equations, and the computational cost only grows polynomially with the dimension. Based on successive approximation techniques, the nonlinear HJB partial differential equation (PDE) is transformed into a sequence of linear PDEs. Then the nested sparse grid methods are employed to solve the resultant linear PDEs. The designed method is sparse in two aspects. Firstly, the solution of the linear PDE is constructed based on sparse combinations of nested basis functions. Secondly, the multi-dimensional integrals in the Galerkin method are efficiently calculated using nested sparse grid quadrature rules. Once the successive approximation process is finished, the optimal controller can be analytically given based on the sparse basis functions and coefficients. Numerical results also demonstrate the accuracy and efficiency of the designed nested sparse successive Galerkin method.

Distributed Big-Data Optimization via Blockwise Gradient Tracking

We study distributed big-data nonconvex optimization in multiagent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex function, i.e., the agents' sum-utility, plus a convex (possibly) nonsmooth regularizer. Our interest is on big-data problems in which there is a large number of variables to optimize. If treated by means of standard distributed optimization algorithms, these large-scale problems may be intractable due to the prohibitive local computation and communication burden at each node. We propose a novel distributed solution method where, at each iteration, agents update in an uncoordinated fashion only one block of the entire decision vector. To deal with the nonconvexity of the cost function, the novel scheme hinges on successive convex approximation techniques combined with a novel blockwise perturbed push-sum consensus protocol, which is instrumental to perform local block-averaging operations and tracking of gradient averages. Asymptotic convergence to stationary solutions of the nonconvex problem is established. Finally, numerical results show the effectiveness of the proposed algorithm and highlight how the block dimension impacts on the communication overhead and practical convergence speed.

Joint Power Control and Passive Beamforming in IRS-Assisted Spectrum Sharing

In cognitive radio (CR) communication systems, it is challenging to achieve high rate for the secondary user (SU) in the presence of strong cross-link interference with the primary user (PU). In this letter, we exploit the recently proposed intelligent reflecting surface (IRS) to tackle this problem. Specifically, we investigate an IRS-assisted CR system where an IRS is deployed to assist in the spectrum sharing between a PU link and an SU link. We aim to maximize the achievable SU rate subject to a given signal-to-interference-plus-noise ratio (SINR) target for the PU link, by jointly optimizing the SU transmit power and IRS reflect beamforming. Although the formulated problem is difficult to solve due to its non-convexity and coupled variables, we propose an efficient algorithm based on alternating optimization and successive convex approximation techniques to solve it sub-optimally, as well as some heuristic designs of lower complexity. Simulation results show that IRS is able to significantly improve the SU rate, even for the scenarios deemed highly challenging in conventional CR systems without the IRS.

Delay Minimization for Massive MIMO Assisted Mobile Edge Computing

Mobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices, by enabling computational task offloading. In this article, we employ massive multiple-input multiple-output methods to facilitate offloading in MEC. Our objective is to minimize the maximum delay for offloading and computing among the users, which requires a joint allocation of wireless and computational resources. Both perfect and imperfect channel state information (CSI) are considered. Under perfect CSI, we derive a semi-closed-form solution for the formulated problem. Under imperfect CSI, since the formulated problem is non-convex, we transform it into a convex one using a successive convex approximation technique and propose an iterative algorithm to solve it. Presented numerical results show the benefits of having a large number of antennas at the base station, and the necessity of performing joint radio and computational resource allocation.

Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks

Limited bandwidth resources and higher energy efficiency requirements motivate incorporating multicast and broadcast transmission into the next-generation cellular network architectures, particularly for multimedia streaming applications. Layered division multiplexing (LDM), a form of non-orthogonal multiple access (NOMA), can potentially improve unicast throughput and broadcast coverage with respect to traditional orthogonal frequency division multiplexing (FDM) or time division multiplexing (TDM), by simultaneously using the same frequency and time resources for multiple unicast or broadcast transmissions. In this paper, the performance of LDM-based unicast and broadcast transmission in a cellular network is studied by assuming a single frequency network (SFN) operation for the broadcast layer, while allowing arbitrarily clustered cooperation among the base stations (BSs) for the transmission of unicast data streams. Beamforming and power allocation between unicast and broadcast layers, the so-called injection level in the LDM literature, are optimized with the aim of minimizing the sum-power under constraints on the user-specific unicast rates and on the common broadcast rate. The effects of imperfect channel coding and imperfect channel state information (CSI) are also studied to gain insights into robust implementation in practical systems. The non-convex optimization problem is tackled by means of successive convex approximation (SCA) techniques. Performance upper bounds are also presented by means of the S-procedure followed by semidefinite relaxation (SDR). Finally, a dual decomposition-based solution is proposed to facilitate an efficient distributed implementation of LDM in each of the SCA subproblems, where the unicast beamforming vectors can be obtained locally by the cooperating BSs. Numerical results are presented, which show the tightness of the proposed bounds and hence the near-optimality of the proposed solutions.