Joint Allocation Scheme Research Articles

Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network

Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network

Joint Aperture and Power Allocation Strategy for a Radar Network Localization System Based on Low Probability of Interception Optimization

In the process of using the Distributed Radar Network Localization System (DRNLS) further to improve the survivability of a carrier platform, the random characteristics of the system's Aperture Resource Allocation (ARA) and Radar Cross Section (RCS) are often not fully considered. However, the random characteristics of the system's ARA and RCS will affect the power resource allocation of the DRNLS to a certain extent, and the allocation result is an essential factor determining the performance of the DRNLS's Low Probability of Intercept (LPI). Therefore, a DRNLS still has some limitations in practical application. In order to solve this problem, a joint allocation scheme of aperture and power for the DRNLS based on LPI optimization (JA scheme) is proposed. In the JA scheme, the fuzzy random Chance Constrained Programmin model for radar antenna aperture resource management (RAARM-FRCCP model) can minimize the number of elements under the given pattern parameters. The random Chance Constrained Programmin model for minimizing Schleher Intercept Factor (MSIF-RCCP model) built on this basis can be used to achieve DRNLS optimal control of LPI performance on the premise of ensuring system tracking performance requirements. The results show that when RCS has some randomness, its corresponding uniform power distribution result is not necessarily the optimal scheme. Under the condition of meeting the same tracking performance, the required number of elements and power will be reduced to a certain extent compared with the number of elements in the whole array and the power corresponding to the uniform distribution. The lower the confidence level is, the more times the threshold is allowed to pass, and the lower the power is, so that the DRNLS can have better LPI performance.

Joint allocation of emergency medical resources with time-lag correlation during cross-regional epidemic outbreaks

During cross-regional epidemic outbreaks, patients with suspected infection have a high demand for testing resources, while patients with confirmed infection have a high demand for treatment resources. The allocation of testing resources affects the demand for treatment resources in multiple regions and at different periods, which features a time-lag correlation. In this study, we developed a joint allocation model for allocating testing and treatment resources with consideration of their time-lag correlation to minimize the loss of patients and maximum the fairness of allocation in all regions. We devised a sequential solution generation strategy (SSGS) and combined it with the NSGA2 and MOPSO algorithms to solve the proposed joint allocation problem. We demonstrated numerically that NSGA2 coupled with SSGS yields a better solution. Furthermore, we compared the solutions of our joint allocation model with those of an independent allocation model. Our numerical results show that the joint allocation scheme, which considers the time-lag correlation between the two types of resources, has better performance—with a lower loss of patients and higher fairness of allocation. In particular, when the impact of the allocation of testing resources on the demand for treatment resources was increased, the joint allocation scheme performed better in terms of both the objectives.

Optimized Joint Allocation of Radio, Optical, and MEC Resources for the 5G and Beyond Fronthaul

In 5G and beyond telecommunication infrastructures a crucial challenge in achieving the strict Key Performance Indicators (KPIs) regarding capacity, latency, and guaranteed quality of service, is the efficient handling of the fronthaul bottleneck. This part of the next generation networks is expected to comprise the New Radio (NR) access and the Next Generation Passive Optical Network (NGPON) domains. Latest developments load the fronthaul with computing tasks as well (e.g., for AI-based processes) in the context of Mobile Edge Computing (MEC). Towards efficient management of all resource types, this paper proposes a joint allocation scheme with three optimization phases for radio, optical, and MEC resources. This scheme, which has been developed in the context of the blueSPACE 5G Infrastructure Public Private Partnership (5G PPP) project, exploits cutting-edge technologies, such as radio beamforming, spatial-spectral granularity in optical networks, and Network Function Virtualization (NFV), to provide dynamic, adaptive, and energy efficient allocation of resources. The devised model is mathematically described and the overall solution is evaluated in a realistic simulation scenario, demonstrating its effectiveness.

Allocation algorithm of CPS communication resources based on cooperative game

In order to improve the communication rate of the cyber–physical fusion system, and at the same time guarantee the quality of system demand of each user, a CPS (Cyber–Physical Systems) communication resource allocation algorithm based on cooperative game was first proposed. The resource allocation process of the orthogonal frequency division multiple access network downlink in CPS is modeled as a cooperative game among multiple users, and the Pareto optimality among users is achieved by solving the Nash bargaining solution. Secondly, this paper studies the cooperative task assignment of communication resources for actuators in CPS execution network, and proposes a feasible task assignment method of communication resources based on K–M algorithm. Simulation experiments show that the task allocation result obtained by this method is better than that of auction method, and the algorithm running time can also meet the limit of actual demand. Finally, in consideration of the control center’s need for fine control of the actuator, this paper studies the wireless resource allocation problem between a control center and the actuator, and maximizes the control effect of the actuator as the daily standard, and proposes a dynamic Joint allocation scheme of subchannels and power. Simulation results show that the proposed scheme can maintain a high level of control effect even when the transmission power of the control center is small, which effectively saves the energy consumption of the control center.

Computation Offloading Strategy Based on Joint Allocation in Mobile Device Cloud

The contradiction between mobile terminal limited energy and users’ high requirements is more and more intensified, to provide satisfactory computation performance for real time applications, offloading workloads to Mobile Device Cloud (MDC) will be adopted. In this paper, we consider the problem of communication competition in the network, and put forward the offloading strategy based on the Joint Allocation Scheme (JAS) for lower latency. In the physical layer, the channel bandwidth allocation mode of OFDMA is used to make transmission rate realistic, combined with the task assignment, a delay model belongs to the NP problem can be established. We use the step-by-step solution, firstly consider the allocation of continuous task, and then use the simulated annealing algorithm (SAA) to get the optimal solution.

Joint ABS and user grouping allocation for HetNet with picocell deployment in downlink

In order to resolve the co-channel inter-cell interference problem in heterogeneous networks (HetNet), the feature of almost blank subframes (ABS) in the time domain of the enhanced inter-cell interference coordination (eICIC) is utilized. In this paper, an ABS configuration design is developed on downlink in HetNet and the associated resource allocation problem for maximizing the system performance with fairness among user equipments (UEs) is considered. Compared to conventional problems, the resource assignment problems include the configuration of ABS pattern and the resource allocation for macro UEs and pico UEs, which aims to maximize the downlink throughput and balance the traffic offloading in intra-frequency HetNet deployments. First, this paper introduces an ABS pattern design by using the channel condition, which is developed in terms of the time domain resource. Subframes are categorized as protected or normal subframes for reducing interference impact to pico UEs. Based on the configuration of the ABS pattern, we develop a grouping strategy to determine which pico UEs use either protected or normal subframes. Besides, the assignment of resource blocks with respect to the resource in the frequency domain is developed along with the fairness among UEs. The proposed joint allocation scheme takes the system throughput and the fairness into account, and has better performance than the existing schemes. Simulation results also reveal that the performance of the proposed joint allocation scheme approximates the optimal solution with the full search scheme.

Energy-efficient downlink resource management in self-organized OFDMA-based two-tier femtocell networks

Abstract Femtocell is a novel technology that is used for escalating indoor coverage as well as the capacity of traditional cellular networks. However, interference is the limiting factor for performance improvement due to co-channel deployment between macrocells and femtocells. The traditional network planning is not feasible because of the random deployment of femtocells. Therefore, self-organization approaches are the key to having successful deployment of femtocells. This study presents the joint resource block (RB) and power allocation task for the two-tier femtocell network in a self-organizing manner, with the concern to minimizing the impact of interference and maximizing the energy efficiency. In this study, we analyze the performance of the system in terms of the energy efficiency, which is composed of both the transmission and circuit power. Most of the previous studies investigate the performance regarding the throughput requirement of the two-tier femtocell network while the energy efficiency aspect is largely ignored. Here, the joint allocation task is modeled as a non-cooperative game which is demonstrated to exhibit pure and unique Nash equilibrium. In order to reduce the complexity of the proposed non-cooperative game, the joint RB and power allocation task is divided into two subproblems: an RB allocation and a particle swarm optimization-based power allocation. The analysis of the proposed game is carried out in terms of not only energy efficiency but also throughput. With practical 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) parameters, the simulation results illustrate the superior performance of the proposed game as compared to the traditional methods. Also, the comparison is carried out with the joint allocation scheme which only considers the throughput as the objective function. The results illustrate that significant performance improvement is achieved in terms of energy efficiency with slight loss in the throughput. The analysis in regard to energy efficiency and throughput of the two-tier femtocell network is carried out in terms of the performance metrics, which include convergence, impact of varying RBs, impact of femtocell density, and the fairness index.

Fairness-insured Aggressive Sub-channel Allocation and Efficient Power Allocation Algorithms to Optimize the Capacity of an IEEE 802.16e OFDMA/TDD Cellular System

This paper aims to find a suitable solution to joint allocation of sub-channel and transmit power for multiple users in an IEEE 802.16e OFDMA/TDD cellular system. We propose the FASA (Fairness insured Aggressive Sub-channel Allocation) algorithm, which is a dynamic channel allocation algorithm that considers all of the users' channel state information conditionally in order to maximize throughput while taking into account fairness. A dynamic power allocation algorithm, i.e., an improved CHC algorithm, is also proposed in combination with the FASA algorithm. It collects the extra downlink transmit power and re-allocates it to other potential users. Simulation results show that the joint allocation scheme with the improved CHC power allocation algorithm provides an additional increase of sector throughput while simultaneously enhancing fairness. Four frames of time delay for CQI feedback and scheduling are considered. Furthermore, by addressing the difference between uplink and downlink scheduling in an IEEE 802.16e OFDMA TDD system, we can employ the uplink channel information directly via channel sounding, resulting in more accurate uplink dynamic resource allocation.