Optimal Time Allocation Research Articles

Max-Min Fairness of Rate-Splitting Multiple Access With Finite Blocklength Communications

Rate-Splitting Multiple Access (RSMA) has emerged as a flexible and powerful framework for wireless networks. In this paper, we investigate the user fairness of downlink multi-antenna RSMA in short-packet communications with/without cooperative (user-relaying) transmission. We design optimal time allocation and linear precoders that maximize the Max-Min Fairness (MMF) rate with Finite Blocklength (FBL) constraints. The relation between the MMF rate and blocklength, as well as the impact of cooperative transmission are investigated. Numerical results demonstrate that RSMA can achieve the same MMF rate as Non-Orthogonal Multiple Access (NOMA) and Space Division Multiple Access (SDMA) with smaller blocklengths (and therefore lower latency), especially in cooperative transmission deployment. Hence, we conclude that RSMA is a promising multiple access for guaranteeing user fairness in low-latency communications.

Joint Localization and Communication Study for Intelligent Reflecting Surface Aided Wireless Communication System

The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.

Delay-Sensitive Energy-Efficient UAV Crowdsensing by Deep Reinforcement Learning

Mobile crowdsensing (MCS) by unmanned aerial vehicles (UAVs) servicing delay-sensitive applications becomes popular by navigating a group of UAVs to take advantage of their equipped high-precision sensors and durability for data collection in harsh environments. In this paper, we aim to simultaneously maximize collected data amount, geographical fairness, and minimize the energy consumption of all UAVs, as well as to guarantee the data freshness by setting a deadline in each timeslot. Specifically, we propose a centralized control, distributed execution framework by decentralized deep reinforcement learning (DRL) for delay-sensitive and energy-efficient UAV crowdsensing, called DRL-eFresh. It includes a synchronous computational architecture with GRU sequential modeling to generate multi-UAV navigation decisions. Also, we derive an optimal time allocation solution for data collection while considering all UAV efforts and avoiding much data dropout due to limited data upload time and wireless data rate. Simulation results show that DRL-eFresh significantly improves the energy efficiency, as compared to the best baseline DPPO, by 14% and 22% on average when varying different sensing ranges and number of PoIs, respectively.

SQ3R Techniques with Let's Read Application for Improvement of Reading Comprehension in Low Class Elementary Students

The purpose of this study was to determine the planning, process, student responses and the obstacles faced in the application of the SQ3R method with illustrated story media in learning reading comprehension for fourth grade elementary school students. This research is a qualitative descriptive study which was conducted in the fourth grade of SDN 03 Baosan Lor, Ponorogo with a total of 31 students. The methods of data collection were observation, interviews, questionnaires, and documentation. Data analysis uses the Milles and Huberman model, namely data reduction, data presentation and conclusion drawing. The results of this study indicate that lesson plans such as syllabus, lesson plans and media are well structured and planned. The learning process creates an effective atmosphere, runs smoothly and pleasantly, students respond to learning with a very positive response, and the obstacles faced are less than optimal time allocation, and there are 2-3 students who have difficulty in following the learning stage because they are still confused and do not understand. pay attention to the teacher during the learning process.

The Way to Spend a Workday Matters in School Principals' Somatic and Psychological Discomfort.

School principals usually have to sacrifice health and family obligations to obtain sufficient work time. This study investigates school principals' somatic and psychological discomfort related to their time allocation to diverse work contexts and life domains, so as to test the optimal allocation of time to each context and domain. This study is based on survey data of 347 school principals, from the preexisting 2021 Survey of School Teachers' Living Conditions in Shanghai. Generalized linear regression modeling was adopted to analyze the data according to the research purpose. This study finds that school principals' daily time spent on work at home, sleep, breakfast, exercise, and family obligations significantly predict their somatic or psychological discomfort. However, their time spent on work at school, daytime napping, lunch, and dinner are not of significance. This study reveals several unhealthy ways of working and lifestyle habits among school principals from a perspective of time allocation, such as extended periods working at home, sleep deficits, hurried breakfast, lack exercise, and failure to meet familial obligations.

Secrecy Rate Maximization for Multicarrier-Based Cognitive Radio Networks With an Energy Harvesting Jammer

Cognitive radio (CR) is becoming an increasingly significant component of Internet-of-Things technologies because it overcomes the challenge of spectrum resource scarcity for wireless communications. For the cooperative CR network (CRN) based on the multicarrier system, this article investigates the physical layer security (PLS) to guarantee information privacy for the secondary network while guaranteeing the quality of service (QoS) of the primary network. First, we propose one communication scheme for this type of CRN. The secondary transmitter (ST) and secondary jammer (SJ) first harvest RF energy from the primary signal transmitted by the primary transmitter (PT). Then, ST transmits confidential information with the spectrum vacated by the primary network to the secondary receiver (SR), and SJ simultaneously generates the jamming signal to interfere with the eavesdropper (ED). Second, for the scenarios with the instantaneous channel state information (CSI) and the statistical CSI of the ED, respectively, we aim to maximize the secrecy rate of the secondary network while guaranteeing the QoS requirement of the primary network by jointly optimizing the time allocation between the two phases and the transmit power allocation for ST and SJ over subcarriers. For each scenario, the secrecy rate maximization is formulated as the nonconvex optimization problem. Finally, we design efficient algorithms for both two nonconvex problems. The optimal time allocation is obtained by the 1-D search method at the outer layer, and the optimal transmit power allocation is obtained by the difference of concave (DC) programming at the inner layer, where the Lagrange duality method is used to solve the convex approximation problem. Simulation results verify that the proposed scheme essentially improves the secrecy rate of the secondary network compared to benchmark schemes.

Performance optimization of pillow sheet set sewing assembly line by SAM analysis and lean manufacturing techniques of method study & work measurement

This research was conducted to enhance the productivity of the targeted sewing line at the ABC textile company. The existing SAMs, the capacity of the sewing line for various operations, and several required machines were collected. Moreover, the obtained figures were observed and analyzed by using time study and motion study, and certain improvements were made at the sewing line. Results indicated that SAMs for operations A and B were minimized by -13.64% and -14.54% respectively; whereas, SAM for operation C was increased by 16.67%. Machine requirement for operation C was increased by 100%; moreover, the capacity for operations A, B, and C was increased by 12%, 12.69%, and 40% respectively. When it is to production activities, the little improvements play a significant role in boosting the productivity of the production system. Optimal allocation of human resources, machines, and time are the benefits of line balancing. This can be made possible with the application of the line-balancing framework. With every passing day, the nature and type of article vary at the sewing line thus it is highly needed for the company to get the model developed for an automated line balancing application. Moreover, this work can be extended by the development of a line-balancing framework considering the nature of production.

Minimax Persistent Monitoring of a network system

We investigate the problem of optimally observing a finite set of targets using a mobile agent over an infinite time horizon. The agent is tasked to move in a network-constrained structure to gather information so as to minimize the worst-case uncertainty about the internal states of the targets. To do this, the agent has to decide its sequence of target-visits and the corresponding dwell-times at each visited target. For a given visiting sequence, we prove that in an optimal dwelling time allocation the peak uncertainty is the same among all the targets. This allows us to formulate the optimization of dwelling times as a resource allocation problem and to solve it using a novel efficient algorithm. Next, we optimize the visiting sequence using a greedy exploration process, using heuristics inspired by others developed in the context of the traveling salesman problem. Numerical results are included to illustrate the contributions.

Throughput Maximization for the Full-Duplex Two-Way Relay System with Energy Harvesting

The full-duplex technique can improve the transmission capacity of the communication systems, and energy harvesting (EH) is a promising operation to prolong the lifespan of a wireless node by utilizing the radio-frequency signals. In this paper, the throughput performance of a full-duplex two-way energy EH capable relay system is investigated. In particular, a practical EH protocol, named the time-switching-based relaying (TSR) protocol, is used for EH and the decode-and-forward (DF) policy for information transmission. The outage probability is successfully obtained, and the corresponding system throughput for TSR protocol can be derived by it. The derived throughput is a function of different system parameters, including the time-switching (TS) ratio, power allocation ratio, and the length of the communication time slot. Meanwhile, the throughput is used to characterize a joint time and power allocation scheme for the system, and we aim to find the optimal time and power allocation to achieve the optimal throughput. Due to the existence of three variables and the integral form of throughput expression, an optimization for the throughput is difficult. However, a modified simulated annealing-based search (SABS) algorithm can be used to optimize the throughput. The modified SABS algorithm overcomes being highly impacted by the initial point, and derives the optimal solution fast. Simulation results show that the analytical throughput expression is related with the TS ratio, power allocation ratio, and the length of the communication time slot. The analytical curve of the throughput matches with the simulated one well, which shows that the obtained analytical system throughput for the TSR protocol is valid. Meanwhile, the proposed modified SABS algorithm could be used to derive accurate throughput when SNR is higher than 10 dB.

Exploiting Hybrid SWIPT in Ambient Backscatter Communication-Enabled Relay Networks: Optimize Power Allocation and Time Scheduling

Ambient backscatter communication (ABC) has become an innovative technique for energy-efficient and short-range communication. In this article, we propose a hybrid simultaneously wireless information and power transfer (SWIPT)-assisted relay transmission scheme to extend the communication range of the ABC mode. That is, SWIPT with time-switching (TS) and power-splitting (PS) approaches is adopted into the relay for harvesting energy and processing information. Moreover, after harvesting energy, all the relays simultaneously transmit data according to power-domain nonorthogonal multiple access (NOMA) technology. Therefore, in the ABC-enabled hybrid SWIPT relay networks with power-domain NOMA, there is a problem of how to maximize the network throughput by allocating available power and time resources. We first formulate an optimization problem to achieve the maximum network throughput by finding the optimal PS ratio of relays as well as the optimal time allocation among users and relays. In order to reduce the computational complexity of the problem, we decompose it into two subproblems: 1) PS ratio optimization and 2) time allocation optimization. For PS ratio optimization, we derive the closed form of the optimal PS ratio for relays by the Lagrange dual theory and subgradient method. For time allocation optimization, the Lagrange dual theory and subgradient method are also adopted to obtain the closed form of the optimal time allocation among users and relays. Then, we propose a joint optimization of the PS ratio and time allocation-based iterative algorithm to solve the problem. Finally, simulation results show that our proposed scheme has an obvious throughput superiority over other two benchmark schemes.

The maximal secondary throughput in cognitive radio networks with energy harvesting

In terms of spectrum reuse and lifetime prolongation, the energy-harvesting cognitive radio networks (EH-CRNs) have been a hot issue in the wireless networking research community. While satisfying the minimal throughput demand of primary users (PUs), it is aimed to maximize the throughput of secondary users (SUs) in the EH-CRN with multiple SUs. Specifically, the problem of secondary throughput maximization (STM) is first formulated as a non-linear optimization problem, then its convexity is proven, and finally an efficient algorithm is proposed that jointly uses the Fibonacci search method and equal interval search method to obtain the optimal time allocation among primary transmitter (PT)'s energy transfer and each SU's packet transmission, and the optimal transmit power of PT. Furthermore, for the scenarios where the circuit power is negligible, the convex problem is first proven, and a more efficient algorithm is presented for the problem of STM. Simulation results demonstrate that, with the increase of the minimal throughput demand of PUs, the reduction rate of the maximal secondary throughput increases.

NURBS Interpolator with Scheduling Scheme Combining Cubic and Quartic S-shaped Feedrate Profiles Under Drive and Chord Error Constraints

Traditional S-shaped feedrate profiles only account for the initial and end feedrates during scheduling; they fail to consider whether the feed rate should continuously satisfy the constraints of the chord error in the processing step. To overcome this problem, this paper proposes a feedrate scheduling scheme that processes S-shaped quartic feedrate profiles at the initial stage followed by S-shaped cubic feedrate profiles to ensure machining quality and efficiency. First, the scheme traverses the feedrate points that satisfy the chord error to determine a slope suitable for the jerk of S-shaped quartic feedrate profile, in order to ensure that the increase in the feedrates does not exceed its constraints. Second, an iterative method is used to optimally allocate processing time between the S-shaped cubic and quartic feedrate profiles to optimize processing efficiency. Finally, a method for determining the new maximum feedrate under feedrate interference is discussed. Simulations and experiments are performed to verify the effectiveness of the proposed method relative to other methods. The results reveal that, when using the proposed approach, the processing and running times of the computer are reduced by 39.48% and 118.34%, respectively, as compared with those of other methods. • Combining S-shaped quartic and cubic feedrate profile, and then calculating the optimal time allocation between them. • Ensure that the speed, acceleration and jerk meet the given constraints. • The method avoids the problem when the profile is complex the maximum feedrate can’t be updated and reduces the caculation. • The problem of low machining efficiency caused by uniform motion near the minimum feedrate point is avoided.

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Unmanned Aerial Vehicles (UAVs) play an essential role in information collection where routing and time scheduling are two critical factors and are considered sequently. Given the paths of UAVs, metaheuristic algorithms are explored to allocate searching time of UAVs, which are hard to guarantee the optimal solution for time allocation. In this paper, a novel searchinG timE allocatioN undEr coopeRative pAth pLaning (GENERAL) is proposed to solve the optimal solution of the time allocation given paths of UAVs. GENERAL adopts a semi-greedy construction and a repair procedure to initialize and amend the routing solutions during iterations. Motivated by the Newton’s method from convex optimization domains, we introduce a new Perturbed Parametric Nonlinear Complementarity Problem function (PPNCP-function), which reformulates the time allocation problem as a smoothing system of equations according to Karush-Kuhn-Tucker (KKT) theorem. Then a smoothing Newton method is introduced to obtain the optimal time allocation solution with superlinear convergence. Experimental results empirically indicate the GENERAL’s effectiveness compared to Ant Colony Optimization with Simulated Annealing (ACO-SA) and Genetic Algorithm (GA). Besides, some numerical results indicate that the smoothing Newton method with PPNCP function is promising. The emerging aspects of this paper include: 1) it integrates the path planning with time allocation problem for maximizing the information collection reward; 2) a new PPNCP-function and a smoothing Newton method have been proposed to solve the optimal time allocation under path planning of UAVs; 3) the theoretical convergence analysis of the smoothing Newton method has been provided in this paper.

A novel hybrid backscatter and conventional algorithm for multi‐hop Internet of Things networks

AbstractThis article investigates a multi‐hop cognitive radio network in terms of end‐to‐end bit delivery. The network exploits backscatter communication (BackCom) and harvest‐then‐transmit (HTT) mode in a hybrid manner. Such a network can be used in Internet of Things (IoT) applications in which IoT users coexist with a primary network and use the primary spectrum to transmit data in both BackCom and HTT modes. Besides, such users can harvest energy from the primary signals. A novel hybrid backscatter and conventional transmission (HBCT) algorithm is proposed in order to maximize end‐to‐end bit delivery by jointly optimizing time and power allocations. For this goal, we formulate a nonconvex optimization problem. Next, we transform the problem into a convex one and develop a new analytical formulation by which we calculate the optimal power and time allocation in closed‐form equations. The numerical results demonstrate the superiority of HBCT compared with current schemes.

Maximizing Secondary Users’ Sum-Throughput in an In-Band Full-Duplex Cognitive Wireless Powered Backscatter Communication Network

This article studies the secondary users’ sum-throughput in an in-band full-duplex (I-FD) cognitive wireless powered backscatter communication network. The secondary network consists of a hybrid access point (H-AP) and a finite number of geographically distributed secondary backscatter sensors (SBSs), each capable of using either the conventional or backscatter communication. The secondary network shares the primary network’s spectrum using an underlay spectrum sharing model. In this model, the H-AP and SBSs operate in I-FD mode to achieve improved spectral and time efficiency. Moreover, when an SBS has little available energy, it switches to the low-energy consumption backscatter method instead of the conventional transmission method. The goal is to maximize the sum-throughput of the SBSs. It is shown that such a problem is a convex optimization problem. Closed-form expressions for the optimal allocated time and energy to SBSs are derived and solved via a low complexity and efficient algorithm called joint optimal time and energy allocation (JOTEA). Numerical results illustrate that the JOTEA algorithm achieves a higher sum-throughput than the benchmark equal time allocation method. Furthermore, if the self-interference cancellation circuit considerably cancels self-interference in the H-AP, the I-FD mode achieves a higher sum-throughput performance than that of the half-duplex mode. Moreover, using backscatter communication results in a further increase in the sum-throughput.

Enabling the Internet of Bodies Through Capacitive Body Channel Access Schemes

The Internet of Bodies (IoB) is an imminent extension of the vast Internet of Things (IoT) domain, where wearable, ingestible, injectable, and implantable smart objects form a network in, on, and around the human body. The highly radiative nature of radio-frequency (RF) IoB devices unnecessarily extends the coverage range beyond the human body, which reduces energy efficiency, causes co-existence and interference issues, and exposes sensitive personal data to security threats. Alternatively, capacitive body channel communication (BCC) confine signal transmission to the human body to reduce signal leakage, experience less propagation loss, and reach pJ/b energy efficiency levels. Therefore, capacitive BCC is a key enabler to reach the ultimate design goals of ultra low power, high throughput, and small form-factor IoB devices. Albeit these attractive features, the communication and networking aspects of the capacitive BCC are not thoroughly explored yet. Therefore, this article proposes orthogonal and nonorthogonal capacitive body channel access schemes with or without cooperation among the IoB nodes. In order to address the Quality of Service (QoS) demand scenarios of different IoB applications, we present and formulate the max–min rate, max-sum rate, and QoS sufficient operational regimes, and then provide closed-form and numerical solution optimal power and phase time allocations. Extensive numerical results are analyzed to compare the performance of orthogonal and nonorthogonal schemes with and without cooperation for various design parameters under prescribed QoS regimes. The obtained results show that capacitive body channel access schemes can provide several Mb/s rates even at low transmission powers ranging between −60 and −90 dBm. Moreover, the cooperative schemes are shown to be effective to avoid performance degradation caused by increasing network size, low transmission power, and poor channel quality.

Optimal Transmission Strategy and Time Allocation for RIS-Enhanced Partially WPSNs

Wireless powered sensor networks (WPSNs) have evolved as a promising paradigm for energy-efficient communications. Recently, the proliferation of reconfigurable intelligent surface (RIS) has further been envisioned as a cost-effective solution for improving wireless power transfer (WPT) efficiency. In this paper, from the practical perspective of balancing the network sustainability and reliability, we consider a RIS-enhanced partially WPSN that composed of wireless-powered energy receivers (ERs) and battery-powered information receivers (IRs). Assuming the partially WPSN operates in time division multiple access (TDMA) mode, the joint optimization of covariance matrices, downlink/uplink (DL/UL) time allocation and RIS reflecting coefficients are investigated under the minimum DL rate constraint among all IRs for maximizing the achievable UL sum rate. Specifically, the single-IR single-ER (SISE) case is first studied based on the assumption of separate DL/UL RIS reflecting coefficients, in which an alternating optimization algorithm is proposed with semi-closed-form optimal solutions. In order to reduce the hardware overhead and signal processing complexity, we also investigate the case of identical DL/UL RIS reflecting coefficients, in which an iterative optimization algorithm is developed to tackle the coupled DL/UL transmissions. Then, we extend our work to the multiple-IRs multiple-ERs (MIME) case, where both the optimization problems corresponding to separate and identical DL/UL RIS reflecting schemes become more challenging to solve. To circumvent this intractability, we propose a successive convex relaxation (SCA) based alternating optimization algorithm and a low-complexity two-step algorithm. Finally, numerical results demonstrate the superior UL sum rate performance of our proposed algorithms over the adopted benchmarks.

Optimal Time Allocation for Energy Harvesting Cognitive Radio Networks with Multichannel Spectrum Sensing

From the perspective of time domain and frequency domain, we investigate the energy harvesting cognitive radio networks (EH-CRNs) with multichannel, where the secondary transmitter (ST) opportunistically accesses the licensed subchannels to transmit packets by consuming the harvested energy. To explore the spectrum holes and improve the lifetime of the EH-CRNs, the ST scavenges energy from the radio-frequency (RF) signal in the wide band during the energy harvesting (EH) phase and exploits the harvested energy for sequential sensing and packet transmission during the rest of the time slot. Under the energy constraint, the secondary throughput is improved by optimizing the time allocation among the EH phase, sensing phase, and transmission phase. We formulate the secondary throughput with respect to the durations of the three phases, prove the existence of the optimal time allocation, and discuss the secondary throughput in three cases of the EH-CRNs. Finally, numerical results validate the theoretical results about the secondary throughput and explore the impacts of key system parameters.

Optimal Time Trajectory Generation and Tracking Control for Over-Actuated Multirotors With Large-Angle Maneuvering Capability

This paper presents an optimal time trajectory generation method for over-actuated multirotors. Different from underactuated multi-rotors that can only track a 4-D trajectory, over-actuated multi-rotors have the ability to track a 6-D trajectory. The proposed method can generate a 3-degree of freedom(DoF) position trajectory and a 3-DoF attitude trajectory. The method is composed of two steps: first, on the basis of polynomials, position and attitude trajectories satisfying geometric constraints are generated by solving unconstrainted problem in the spatial domain. Second, attitude and position dynamics constraints are added in the temporal domain, in order to achieve large-angle maneuvers. Afterwards, optimal time allocation is achieved by solving a mapping function in the temporal domain. The mapping function describes the mapping relationship between spatial variables and temporal variables. A comparative experiment is presented to verify that time allocated to trajectory by the proposed algorithm is shorter compared to other algorithms, while a tracking controller is constructed for overactuated multirotors. The tracking controller is verified to be feasible for the experimental platform through flight experiment.

Relay Selection-Based Cooperative Backscatter Transmission With Energy Harvesting: Throughput Maximization

In this letter, an optimal time allocation scheme is proposed to improve the throughput of energy harvesting based backscatter communication. The base station (BS) serving as the power supply is transmitting continuously with constant power. All the relays can harvest radio frequency (RF) energy from the transmission of BS while the user performs backscatter transmission, and then the selected relay decodes and forwards the information from user with harvested RF energy. We propose an optimal time allocation scheme that considers the adaptive backscatter coefficient and relay selection to maximize throughput. Simulation results demonstrate that the proposed scheme outperforms the benchmark strategies in system throughput improvement.