Dynamic Allocation Scheme Research Articles

AN IMPROVED CHANNEL SELECTION ALGORITHM (ICSA) FOR SPECTRUM HAND-OFF IN COGNITIVE RADIO AD HOC NETWORK

The exponential growth in wireless communication technology has led to spectrum scarcity. Because of this, the world has moved from Fixed Spectrum Allocation (FSA) Strategy to Dynamic Spectrum Allocation (DSA) Strategy. Cognitive Radio (CR) is a rapidly growing technique that makes use of DSA, where the licensed users, otherwise known as Primary Users (PUs), share their channel with the unlicensed users, known as Secondary Users (SUs). The SUs can use the PUs channel when they are not being used for transmission, but they have to vacate to another vacant channel when the PUs arrive in their channel. Switching to another vacant channel on the arrival of the PUs to their channels is known as spectrum hand-off. Finding another suitable vacant channel for the SUs to continue their interrupted transmission is challenging. Several researchers have used different techniques to address the challenge of target channel selection for spectrum hand-off by considering channel occupancy alone. Still, they suffer challenges like the high number of spectrum hand-offs, high delay, and low throughput. Therefore, this research focuses on developing an Improved Channel Selection Algorithm (ICSA) that considers channel occupancy serially with signal quality requirements for selecting a particular backup channel for spectrum hand-off. The simulation was carried out using Network Simulator (NS), and the results were plotted using Matlab. The results showed that the ICSA had better performance when compared with the Novel Proactive Hand-off Scheme (NPHS) regarding the number of hand-offs, average delay, and average throughput.

Self-Disciplined Nonsmooth Coordination Control for Battery Energy Storage System in Autonomous DC Microgrids Toward Large-Signal Stability

For a Battery Energy Storage System (BESS)-based autonomous DC microgrid, owing to the coupling complexity between multiple control objectives under a hierarchical control framework, coordination control for large-signal stabilization is well-acknowledged as a non-trivial problem. This paper aims to present a self-disciplined nonsmooth coordination control strategy to address the multi-objective task within one framework. Firstly, a nonsmooth composite regulation law is designed to mitigate the increasing constant power loads (CPLs) by employing a feedforward compensation. Secondly, a dynamic droop strategy aiming for dynamic power allocation for state-of-charging (SOC) balancing of BESS is built based on a nonsmooth dynamic average consensus protocol with low communication cost. Then by utilizing a dynamic power allocation strategy and a large-signal stabilizer for each energy storage unit (ESU), both SOC dynamic balancing and system level large-signal stability can be ensured. Moreover, a fast performance recovery ability can be promised by bringing into two convergence rate coordination factors. Simulation and comparative experimental studies have demonstrated the efficacy of the proposed control strategy.

Ground-to-Air Communications Beyond 5G: A Coordinated Multipoint Transmission Based on Poisson-Delaunay Triangulation

This paper designs a novel ground-to-air communication scheme to serve unmanned aerial vehicles (UAVs) through legacy terrestrial base stations (BSs). In particular, a tractable coordinated multi-point (CoMP) transmission based on the geometry of Poisson-Delaunay triangulation is developed, which provides reliable and seamless connectivity for UAVs. An effective dynamic frequency allocation scheme is designed to eliminate inter-cell interference by using the theory of circle packing. For exact performance evaluation, the handoff probability of a typical UAV is characterized, and then the coverage probability with handoffs is attained. Simulation and numerical results corroborate that the proposed scheme outperforms the conventional CoMP scheme with three nearest cooperating BSs in terms of handoff and coverage probabilities. Moreover, as each UAV has a fixed and unique CoMP BS set, it avoids the real-time dynamic BS searching process, thus reducing the feedback overhead.

Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems

Evolutionary algorithms and swarm intelligence ones are commonly used to solve many complex optimization problems in different fields. Yet, some of them have limited performance when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and some of them may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-Learning-Based Optimizer (STLBO) is designed to dynamically adjust parameters for balancing exploration and exploitation abilities. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress a search space into a lower-dimensional one for more efficiently guiding a population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate one to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate STORA by comparing it with several state-of-the-art algorithms through eight benchmark functions. We further test its actual performance by applying it to solve a real-world computation offloading problem.

Fault Tolerant Control of Drone Interceptors Using Command Filtered Backstepping and Fault Weighting Dynamic Control Allocation

This paper proposes a fault tolerant control strategy for drone interceptors with fixed wings and reaction jets subject to actuator faults. The drone interceptors have both continuous and discrete actuators, which pose a challenge to the control system design. The proposed fault tolerant control system consists of two parts, a nonlinear virtual control law and a dynamic control allocator. To deal with system uncertainty and quantization error, a virtual control law with a parameter update law is designed by command filtered backstepping. Then, a fault weighting dynamic control allocation algorithm is developed to distribute the virtual control signal to the actuators on the drone interceptor. When an actuator fault occurs, the proposed fault weighting dynamic control allocation scheme can redistribute the control signals to the remaining actuators. The effectiveness of the proposed algorithm is confirmed by numerical simulation.

Accelerated resource allocation based on experience retention for B5G networks

The Beyond-Fifth-Generation (B5G) Wireless Communication Systems will require efficient resource allocation (RA) policies to fulfill future applications' increasing data rate and bandwidth demands. Reusing spectrum allows efficient use of the available spectrum, but at the same time, dynamic power allocation strategies are necessary for controlling interference among spectrum-sharing wireless devices. Recently, Deep Reinforcement Learning (DRL) approaches have been applied to find feasible resource allocation solutions demonstrating higher adaptability and robustness than iterative optimization algorithms. However, DRL-based models must constantly explore the network conditions to adapt to channel fluctuations during learning. Adjusting this model to the differences between the source and the new target model requires additional training time, and depending on its length, it may cause a system performance drop and hinder the DRL-based model's learning. One strategy to accelerate learning a new network environment is exploiting the generated knowledge from the already trained models. In this work, we focus on the Experience Replay (ER) retention mechanism to manage, during the learning, the knowledge that DRL models have about the wireless network environment. Our proposal transfers the parameters of a Deep Q-Network (DQN) model from the source to target environment to reduce the training time. We implement a Dual-Buffer (DB) retention mechanism to keep relevant experiences longer and increase the training samples' diversity to ease learning in new environments. This approach is evaluated in a B5G network to control the interference through optimal power transmission allocation. The experiment results show that our proposal stabilizes and accelerates the model learning compared to the standard FIFO experience replay (ER) retention mechanism reducing the training time and increasing the model's generalizability.

Dynamic task allocation based on auction in robotic mobile fulfilment system

Task allocation is one of the important factors affecting the efficiency of robotic mobile fulfilment system (RMFS). In this paper, a dynamically changing task allocation model is constructed, with the overall maximum profit as the optimization objective, and allows robots that are performing tasks to participate in the task allocation. Using the auction algorithm, three dynamic allocation strategies are developed: queued allocation strategy (QAS), immediate allocation strategy (IAS), and reservable allocation strategy (RAS). This paper conducts simulation experiments to compare and analyze the proposed three dynamic allocation strategies, static allocation strategy (SAS) as well as a heuristic algorithm (HAS). Simulation results show that RAS, with robots' tasks changing dynamically, is better at increasing the number of picking orders and reducing the distance travelled by robots than other proposed strategies, which improves the picking efficiency of RMFS.

Preventing online disinformation propagation: Cost-effective dynamic budget allocation of refutation, media censorship, and social bot detection.

Disinformation refers to false rumors deliberately fabricated for certain political or economic conspiracies. So far, how to prevent online disinformation propagation is still a severe challenge. Refutation, media censorship, and social bot detection are three popular approaches to stopping disinformation, which aim to clarify facts, intercept the spread of existing disinformation, and quarantine the source of disinformation, respectively. In this paper, we study the collaboration of the above three countermeasures in defending disinformation. Specifically, considering an online social network, we study the most cost-effective dynamic budget allocation (DBA) strategy for the three methods to minimize the proportion of disinformation-supportive accounts on the network with the lowest expenditure. For convenience, we refer to the search for the optimal DBA strategy as the DBA problem. Our contributions are as follows. First, we propose a disinformation propagation model to characterize the effects of different DBA strategies on curbing disinformation. On this basis, we establish a trade-off model for DBA strategies and reduce the DBA problem to an optimal control model. Second, we derive an optimality system for the optimal control model and develop a heuristic numerical algorithm called the DBA algorithm to solve the optimality system. With the DBA algorithm, we can find possible optimal DBA strategies. Third, through numerical experiments, we estimate key model parameters, examine the obtained DBA strategy, and verify the effectiveness of the DBA algorithm. Results show that the DBA algorithm is effective.

DYNAMIC POWER ALLOCATED SWIPT NOMA OVER RAYLEIGH FADING CHANNEL

Cooperative NOMA with simultaneous wireless information and power transfer is one possible method for enhancing the energy efficiency (EE) and spectral efficiency (SE) of fifth generation (5G) networks. Non-orthogonal multiple access (NOMA) offers superior spectrum efficiency than the traditional OMA method providing huge connectivity for machine-to-machine communication and enormous machine-type communication. In this paper, we first present a comparative study of dynamic power allocation method over fixed power allocation method to find the better schemes among them. Later, we use a dynamic power allocation scheme with cooperative communication (Cooperative NOMA) to find the performance of the far user with other transmission methods. Finally, we use the dynamic power allocation method for the far end user with SWIPT NOMA for the transmission and compare it with fixed power allocated SWIPT NOMA.

Enhancing Performance of Downlink NOMA-Based C-RAN Topology Through Optimal User Pairing and Dynamic Power Allocation Scheme

Enhancing Performance of Downlink NOMA-Based C-RAN Topology Through Optimal User Pairing and Dynamic Power Allocation Scheme

Study on Balanced Allocation of Water Resources in the Yellow River Basin Based on Water Benefit Sharing

In recent years, the shortage of water resources and the deterioration of water ecological environment have led to the increasing contradiction between supply and demand of water resources in river basins. How to realize the balanced allocation and scientific regulation of water resources in river basins is a serious challenge for China to face water resources problems. In this paper, the dynamic process and allocation scheme of water resources and water benefit allocation under different cooperative scenarios are simulated by constructing the water resources equilibrium allocation model of water benefit sharing. The influencing factors of water benefit sharing mechanism and the process of allocation compensation are studied, and the benefit relationship in regional coordinated utilization of water resources is revealed. The upstream, midstream and downstream water users of the Yellow River Basin are formed into alliances, respectively. Based on the principle of master–slave game theory, a basin system optimization problem with multi-level hierarchical structure is established. The initial allocation of water resources is carried out with the maximum overall benefit of water resources allocation, and the incremental benefits of the system optimization compared with the current individual and overall benefits are analyzed. The fuzzy cooperative alliance is used to allocate incremental benefits, improve the stability of cooperation among alliances, realize the cooperation and interaction of water resources in distribution, and finally achieve a dynamic equilibrium state. This paper focuses on the allocation mechanism of water resources competition and cooperation under water benefit sharing, which can provide a scientific basis for improving water resources security in water shortage basins and adapt to new problems and challenges brought by changing environments.

Protection Schemes in HPON Networks Based on the PWFBA Algorithm.

In this paper, possibilities for network traffic protection in future hybrid passive optical networks are presented, and reasons for realizing and utilizing advanced network traffic protection schemes for various network traffic classes in these networks are analyzed. Next, principles of the Prediction-based Fair Wavelength and Bandwidth Allocation (PFWBA) algorithm are introduced in detail, focusing on the Prediction-based Fair Excessive Bandwidth Reallocation (PFEBR) algorithm with the Early Dynamic Bandwidth Allocation (E-DBA) mechanism and subsequent Dynamic Wavelength Allocation (DWA) scheme. For analyzing various wavelength allocation possibilities in Hybrid Passive Optical Networks (HPON) networks, a simulation program with the enhancement of the PFWBA algorithm is realized. Finally, a comparison of different methods of the wavelength allocation in conjunction with specific network traffic classes is executed for future HPON networks with considered protection schemes. Subsequently, three methods are presented from the viewpoint of HPON network traffic protection possibilities, including a new approach for the wavelength allocation based on network traffic protection assumptions.

Improved Dynamic Power Allocation Scheme for Massive Connectivity in NOMA System

In a wireless network, the radio resources are allocated to the users based on the Orthogonal Multiple Access (OMA) technique. The performance of the network can be improved by introducing non-Orthogonal multiple access (NOMA) whenever the number of users increases tremendously. There is much scope in improving the outage probability, bit error rate (BER), high spectral efficiency and latency. To improve this, Non-Orthogonal Multiple Access (NOMA) technique has been discussed in this paper with fair power allocation technique. The effect of fixed power and dynamic power allocation has been discussed and compared, the Bit error rate (BER) and achievable rate have been analyzed and compared. The NOMA uses Successive interference cancellation (SIC) at the receiver for decoding the individual user’s data which is a complex process. The effect of imperfect SIC has also been evaluated and analyzed. In dynamic Power allocation technique higher power is devoted to the far users due to which the near user goes into outage. A new technique has been proposed to reduce the outage power while taking the target rate and SNR of each user into consideration. The results show an improvement in outage probability in comparison with the state of art technique.

A Deep-Q Learning Scheme for Secure Spectrum Allocation and Resource Management in 6G Environment

In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the backdrop of sixth-generation (6G) communication networks that address the challenges of fixed spectrum allocations (FSA). The scheme exploits the advantages of deep-Q-network (DQN) and minimizes the search state explosion through a reward-penalty framework. A dynamic allocation of unallocated resource blocks (RBs) to mobile units (MUs) is carried out and once the allocation of RBs is complete, we integrate blockchain (BC) to record the transactional ledgers. The resource usage of MUs is recorded through smart contracts (SCs). We model the proposed scheme as a convex optimization problem, and subproblems are decomposed into a Pareto-optimal solution via Techebyecheff decomposition. In the simulation, we compare our scheme against FSA, and fifth-generation (5G) based DSA schemes like reinforcement learning (RL), deep neural networks (DNN)-based, and duelling DQN based schemes. The comparative analysis of 6G-DQN is modeled in terms of reward formulation, scalability of 6G-DQN-assisted DSA, and profit scenarios of BC-based allocation through intelligent channel control. The scheme proposes significant findings, with the best fit learning rate of 0.0001, and takes 500 episodes to converge to 60 total resource blocks. The servicing latency of the scheme is 272.4 ms, compared to 2010 ms in the duelling DQN approach. In spectrum allocation, an improvement of 26.32% is observed against non-DQN approaches, and 13.57% in the fairness parameter for spectrum allocation due to BC inclusion. The findings present the scheme efficacy for DSA over the aforementioned conventional approaches.

Dynamic Picking and Storage Optimization of Robotic Picking Systems

With the development of e-commerce and the improvement of logistics requirements, more and more ‘parts-to-picker’ picking systems begin to replace the inefficient ‘picker-to-parts’ picking systems in various scenarios. As the mainstream ‘parts-to-picker’ system, the robotic mobile fulfillment system has been attracting much attention in recent years. In addition to the customer's changing requirements, the rapid response of the picking system to the order is particularly important. In the above context, to seek a breakthrough in the picking system's picking efficiency without increasing the cost of additional equipment, the storage allocation of the pods becomes very important. This article focuses on the dynamic storage allocation of robotic mobile fulfillment system, which has positive theoretical and practical significance. By analyzing the pod storage process of the robotic mobile fulfillment system, a dynamic pod storage allocation model suitable for the robotic mobile fulfillment system is established with the goal of minimizing the pod handling distance. Two dynamic pod storage allocation strategies are proposed for the system. By simulating the picking systems of different scales, the effectiveness of the dynamic storage allocation strategy is verified, which has a certain reference to the operation of the robotic mobile fulfillment system in practice.

An Optimal Allocation Method of Distributed PV and Energy Storage Considering Moderate Curtailment Measure

Increasing distributed generations (DGs) are integrated into the distribution network. The risk of not satisfying operation constraints caused by the uncertainty of renewable energy output is increasing. The energy storage (ES) could stabilize the fluctuation of renewable energy generation output. Therefore, it can promote the consumption of renewable energy. A distributed photovoltaic (PV) and ES optimal allocation method based on the security region is proposed. Firstly, a bi-level optimal allocation model of PV and ES is established. The outer layer is a nonlinear optimization model, taking the maximum power supply benefit as the objective function. The inner layer is a day-ahead economic dispatching model. Then, a quick model solving method based on the steady-state security region is proposed. An initial allocation scheme of PV and ES is determined with the redundancy capacity. In addition, the linear hyperplane coefficient of the security region is used to convert the nonlinear day-ahead economic dispatching model into a linear one. Finally, the proposed method is used to analyze the improved IEEE 33-node system. It is found that a moderate curtailment measure of distributed PV peak output and the allocation of energy storage have a significant effect on the power supply benefit of the distribution system. The optimal quota capacity of DG exceeds the sum of the maximum load and the branch capacity. In addition, the optimal allocation scheme is closely related to the cost and technical parameters of distributed PV and ES. Dynamic allocation schemes should be formulated for distribution network.

AoI-Aware Joint Spectrum and Power Allocation for Internet of Vehicles: A Trust Region Policy Optimization-Based Approach

In Internet of Vehicles (IoV), information freshness is a significant indicator to indemnify road traffic safety, which is measured by Age of Information (AoI). In this article, we consider the coexistence scenario of vehicular user pairs and cellular users, where the base station (BS) acts as an agent to allocate channels and transmit power for vehicular user pairs. With the goal of minimizing the sum of the average AoI of all links and the average power consumption of all vehicular user pairs, we formulate this optimization problem as a discrete-time Markov decision process (MDP) problem and adopt the trust region policy optimization (TRPO) algorithm, which has the advantage of fast convergence and high stability. Then, an AoI-aware joint spectrum and power dynamic allocation scheme based on the TRPO algorithm is proposed. Simulation results show that the TRPO-based scheme significantly outperforms both the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> network (DQN)-based scheme and the random scheme in terms of average cumulative reward, convergence speed, and stability.

Flexible and Dynamic Scheduling of Mixed-Criticality Systems.

A mixed-criticality system refers to an integrated embedded system in which tasks with different criticality levels run on a shared computing platform. In the design and development of mixed-criticality systems, how to schedule tasks to ensure that high-criticality tasks are executed in time and low-criticality tasks are served as much as possible is a major problem to be studied. Existing studies tend to consider pessimistic processing strategies to ensure the schedulability of functional tasks with high-criticality requirements. However, excessive pessimistic processing can lead to waste of system resources, thereby reducing the performance of functional tasks with low-criticality requirements. In this paper, we propose an adaptive-service-level adjustment strategy for low-criticality tasks, which solves the problem of waste of resources caused by invalid compensation in the low-criticality task compensation method of flexible mixed-criticality systems. In view of the problem that the existing methods mostly use static budget allocation and static independent mode switching without considering the actual operation of the task, this paper also proposes a flexible and dynamic mixed-criticality system scheduling scheme and designs a system execution framework, scheduling algorithm, and dynamic allocation strategy of maximum execution budget, in order to reduce unnecessary redundant resource expenditures and system switching costs and to improve the performance of low-criticality tasks. Experiments show that the proposed methods are effective compared to the state-of-the-art.

MASTER: Reclamation of Hybrid Scratchpad Memory to Maximize Energy Saving in Multi-Core Edge Systems

Most modern multi-core edge devices work in outdoor situations with limited power supplies like energy harvester and batteries. Therefore, energy consumption is a fundamental issue in which the memory subsystem has a significant role. Scratchpad memories (SPM) can provide a broad potential for energy saving. Still, due to the insufficient SPM capacity in such edge devices, a rigorous SPM data allocation scheme is necessary to reduce the energy consumption of the memory subsystem. Emerging non-volatile memories (NVMs) are very useful to reduce the energy consumption of the memory subsystem. Compared with SRAM, NVMs have lower leakage power and higher density, but the read and write latencies of the NVMs are higher than the SRAM. Therefore, embedded and edge devices can take advantage of hybrid SPM composed of both NVM and SRAM to achieve further energy saving. This paper proposes MASTER, a task mapping, task scheduling, and dynamic SPM allocation scheme that efficiently utilizes the hybrid SPM space. To this end, we model the hybrid SPM allocation on a multi-core system with integer linear programming formulation to minimize the energy consumption of the memory subsystem. Experimental results show that MASTER improves the energy saving of the memory subsystem by up to 34 percent compared to EADA, which is a heuristic dynamic data allocation algorithm for multi-core systems with hybrid SPM.

Maximum Flow Routing Strategy With Dynamic Link Allocation for Space Information Networks Under Transceiver Constraints

In this paper, we investigate the maximum flow routing strategy with dynamic link allocation under a constraint on the number of transceivers for space information networks (SINs). Specifically, the time-expanded graph (TEG) is exploited to characterize the dynamic topology of SINs. Furthermore, although there exist multiple feasible links for SINs, only a limited number of them can be actually established due to the constraint on the number of transceivers. Traditionally the established link is fixed within one time interval in the TEG. In order to fully exploit the resource of multiple feasible links, we divide each time interval in the TEG into multiple fine-grained time periods and design the maximum flow routing strategy by jointly optimizing both the fine-grained time period duration and the link allocation as well as the amount of data transmitted on each transmission link and the amount of data stored in each caching link. This problem can be formulated as a mixed-integer quadratic program (MIQP), which is difficult to solve. To overcome this difficulty, we transform the MIQP into an equivalent mixed-integer linear program (MILP), which can be effectively solved by existing methods. Simulation results show that the proposed dynamic link allocation strategy can significantly outperform the fixed link allocation strategy within each time interval.