Conventional Resource Allocation Schemes Research Articles

An adaptive MLP-based joint optimization of resource allocation and relay selection in device-to-device communication using hybrid meta-heuristic algorithm

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications.

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

Self‐improved moth flame for optimal container resource allocation in cloud

SUMMARYResource allocation in the cloud is becoming more complicated and challenging due to the rising necessities of cloud services. Effective management of virtual resources in the cloud is of large significance since it has a great impact on both the operational cost and scalability of the cloud environment. Nowadays, containers are becoming more popular in this regard due to their characteristics like reduced overhead and portability. Conventional resource allocation schemes are usually modeled for the migration and allocation of virtual machines (VM), as a result; the question may arise on, “how these strategies can be adapted for the management of a containerized cloud”. This work evolves the solution to this issue by introducing a new fitness oriented moth flame algorithm (F‐MFA) for optimizing the allocation of containers. Further in this work, the optimal allocation relies on certain constraints like balanced cluster use, system failure, total network distance (TND), security and threshold distance, and credibility factor as well. In the end, the supremacy of the presented model is computed to the conventional models in terms of cost and convergence analysis.

SDFA: A Service-Driven Fragmentation-Aware Resource Allocation in Elastic Optical Networks

To support the fifth-generation bandwidth-hungry applications, such as the Internet of Things, virtual reality, augmented reality, and cloud computing, elastic optical networks have become the most promising infrastructure that allocates bandwidths for services flexibility. Fragmentation caused by dynamic resource allocation deteriorates the availability of resources in networks, increasing the blocking of requests. The fragmentation occurs not only in the used path but also in the neighboring links that are not included in the used path; they are connected to the used path. This paper proposes a service-driven fragmentation-aware (SDFA) resource allocation scheme to enhance resource utilization by avoiding fragmentation with the joint consideration of the used path and neighboring links. A service-driven fragmentation metric (SDFM) is, for the first time, presented to estimate the fragmentation in the used path and neighboring links. The SDFA scheme prefers to assign services at the spectrum slots, which leads to the minimum value of SDFM. Simulation results indicate that SDFA outperforms four conventional fragmentation-aware resource allocation schemes in terms of blocking probability and resource utilization due to a lower fragmentation in the network.

ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics.

Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, ABV-CoViD (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a [Formula: see text]- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the [Formula: see text] model, and [Formula: see text] model for ANN modelling. We considered the [Formula: see text](12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of [Formula: see text]-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme's efficacy in ABV measurement over conventional and manual resource allocation schemes.

Resource allocation for downlink non-orthogonal multiple access in joint transmission coordinated multi-point networks

Joint transmission coordinated multi-point (JT-CoMP) and non-orthogonal multiple access (NOMA) are key enabling technologies of 5G ubiquitous broadband infrastructures. These technologies are jointly expected to exploit multi-cell and non-orthogonal resource transmissions; thus, conventional resource allocation schemes that only consider either one of them fail to efficiently exploit resources of 5G networks. In this paper, we bridge this gap by proposing a practical and comprehensive joint sub-carrier assignment and power allocation scheme for network sum-rate maximization in JT-CoMP-enabled NOMA networks. We formulate the problem as a mixed integer non-linear programming (MINLP) problem, which is NP-hard. The problem is decoupled into two sub-problems, where the sub-carrier assignment is modeled as a two-sided many-to-many matching game and the power allocation is formulated as a difference of convex (DC) programming problem. The matching algorithm is proved to converge to a two-sided exchange stable matching. Furthermore, the solution computed by the proposed scheme is verified against a baseline solution computed by a commercial optimization package, and has been shown to achieve 91.38% of the baseline solution for JT-CoMP-NOMA networks. Simulation results illustrate that the proposed scheme enhances cell-edge users’ achievable rates by 0.1−27.7% in JT-CoMP-NOMA over conventional NOMA.

Cellular Licensed Band Sharing Technology Among Mobile Operators: A Reinforcement Learning Perspective

Next-generation wireless networks will need to support of very high data rates and low–latency communications, which will require a new wireless radio technology paradigm. The growing number of mobile users is causing spectrum scarcity; and hence, an efficient spectrum utilization method is required. Conventional scheduling-based resource allocation scheme in wireless networks under limited resources is a challenging due to the complex network situations, dynamic network environment, and diverse needs for future networks. To overcome resource scarcity in mobile networks, spectrum sharing among multiple operators may be an efficient solution. Traditional methods of dynamic spectrum sharing are model-dependent, and they are not robust to the changing wireless environments. To enable low-latency communications for complex future wireless networks, efficient machine learning algorithms can be used across the wireless network infrastructure. Integrating machine learning for resource allocation can leverage intelligent and efficient mechanisms for dynamic wireless networks. To efficiently and intelligently utilize the scarce resources of dynamic networks, this paper proposes an efficient machine learning-based spectrum sharing method among multiple mobile network operators (MNOs). A mobile network operator uses the idle slots of the another operator and transmits the information efficiently. Using the neural network model, each MNO learns the spectrum utilization of other MNOs and selects the idle slots of other MNOs. Simulation results have been computed and compared with the conventional scheme where resources are not shared. These simulation results show that the proposed neural network model can efficiently learn the network quickly, and spectrum sharing can lead to improved network performance in terms of the delay, user-perceived throughput, resource usage, packet drop, and sum throughput of the network.

UAV-assisted wireless powered Internet of Things: Joint trajectory optimization and resource allocation

Due to affordable price, high mobility and flexible maneuverability, Unmanned Aerial Vehicle (UAV)-assisted communication can play an important role in the deployment of the Internet of Things (IoT) in emergency. Since UAV network performance is highly dependent on UAV deployment location, trajectory design becomes the research hotspot in UAV-assisted communication. To this end, a UAV-assisted multicarrier wireless powered communication model is proposed for IoT scenarios in this paper. Specifically, as an aerial base station, the UAV transmits Orthogonal Frequency Division Multiplexing (OFDM) signals to IoT nodes, while IoT nodes decode information and harvest energy from the signals. Afterwards, IoT nodes transmit information to the UAV by using the harvested energy. A joint UAV trajectory optimization and resource allocation scheme based on OFDM is proposed. The aim is to maximize the minimum achievable rate in the uplink among all IoT nodes by jointly optimizing UAV trajectory, subcarrier, power and subslot allocation, subject to the achievable sum rate of all IoT nodes in the downlink. Due to the non-convexity and complexity of the formulated optimization problem, an alternative iteration algorithm is proposed to deal with the problem. Simulation results that the proposed algorithm can optimize the UAV trajectory and adapt to the node movement. Compared with conventional resource allocation schemes, the proposed scheme not only significantly enhances the minimum achievable rate, but also works well for two flight modes.

Sparse Code Multiple Access-Based Edge Computing for IoT Systems

In this paper, a sparse code multiple access (SCMA)-based edge computing scheme is proposed for Internet-of-Things (IoT) systems. The aim of implementing SCMA, which is a nonorthogonal multiple access resource allocation technique, is to improve network connectivity and maximize data rate provision. The proposed edge-IoT system is investigated under different SCMA configurations to explore the various performance aspects such as connectivity, throughput, task completion time, and complexity. First, the problem is formulated as a data rate maximization problem for SCMA-based heterogeneous networks under power constraints. Then, the problem is subdivided into a power allocation problem, which is solved using the water filling approach, and a codebook allocation problem that is solved using a heuristic algorithm. The results show that the SCMA scheme can significantly improve the IoT performance compared to the conventional orthogonal frequency-division multiple access resource allocation scheme in terms of connectivity, throughput, and task completion time provided that SCMA configurations are suitable with IoT processing capabilities to avoid undesired detection latency.

Energy efficient dynamic resource allocation for wireless systems using receiver puncturing technique

Given the massive increase in network infrastructure required to provide the high capacities expected in current wireless systems due to the exponential growth in mobile data consumption, substantial improvement in energy efficiency of the current 4G systems is necessary. High energy efficiency results in low operational cost and sustainable network deployment. In this paper, we propose a new receiver puncturing technique and a novel energy efficient dynamic resource allocation scheme. Improvements in BER performance and 20%~25% transmit-energy expenditure reduction, as compared to conventional proportional fair resource allocation schemes, are shown by both link level and system level simulations of a multi-cell downlink orthogonal frequency-division multiple access (OFDMA) system. Finally, the complexity issues inherent to the proposed technique are also evaluated.

Energy-Efficient Resource Allocation for Massive MIMO Amplify-and-Forward Relay Systems

Energy-efficient resource allocation is investigated for multi-pair massive MIMO amplify-and-forward relay systems, where a dedicated relay assists pairwise information exchange among many pieces of single-antenna user equipment (UE). The system energy efficiency (EE) is theoretically analyzed by employing large system analysis and random matrix theory. This analytical result provides excellent approximation for the system with a moderate number of antennas, and it also enables several efficient algorithms, working with a different knowledge of channel state information (CSI), to maximize the system EE by scheduling the optimal numbers of relay antennas and UE pairs as well as the corresponding relay transmission power. In contrast to the conventional resource allocation schemes, the proposed algorithms avoid complicated matrix calculations and the instantaneous CSI of small-scale fading; therefore, they are computationally efficient with low CSI overhead. The proposed optimization framework sheds light on the optimized system configurations, and it also offers an efficient way to achieve EE-oriented resource allocation for the multi-pair massive MIMO relay systems.

Temporal-aware rate allocation in mission-oriented WSNs with sum-rate demand guarantee

Conventional resource allocation schemes for single-purpose WSNs are unable to capture the dynamicity of network and/or application specifications. This compels us to design mechanisms that support multi-purpose mission-oriented WSNs, where several missions with different characteristics coexist in an integrated WSN. The paper aims to propose an efficient temporal-aware resource allocation mechanism for mission-oriented WSNs. Furthermore, our design supports minimum sum-rate demand guarantee for active sessions of each mission. We use contention graph notion to analytically model the interference in our underlying WSN. Moreover, in-network processing operators play important role in our design scheme. Using a time-variant mission-oriented WSN model, we formulate our resource allocation problem as a dynamic network utility maximization problem, which aims to maximize a hybrid mix of an aggregated objective function of missions’ utilities and energy consumption, considering capacity, interference and sum-rate demand constraints. Finally, using dual decomposition method, we solve the problem and propose a distributed temporal-aware algorithm called TARC-SD that optimally allocates the shared bandwidth to the sources. The conducted experiments indicate that the TARC-SD algorithm achieves higher network utilization and wider feasible scenarios compared to conventional time-invariant schemes.

BER-Driven Resource Allocation for Scalable Bitstreams Over OFDMA Networks

Conventional resource-allocation schemes in orthogonal frequency-division multiple-access (OFDMA) systems consider only one bit-error-rate (BER) requirement. When these schemes are applied to transmit bitstreams with multiple BER requirements, low resource utilization will occur since the target BER has to be the strictest one. We develop a BER-driven resource allocation for generic bitstreams (BRA-G) algorithm by exploiting multiple predetermined target BERs. Then, this scheme is extended to transmission of realistic scalable bitstreams (BRA-SB) where more important (MI) layers possess lower target BERs as compared with less important (LI) layers. The objective here is to maximize the expected quality of all JPEG 2000 (J2K) scalable bitstreams subject to a total power constraint. Instead of using static target BERs for different layers, BRA-SB water-fills over all layers to determine the number of transmit layers and the target BER of each layer simultaneously based on the importance of each layer in terms of reconstructed quality and average channel conditions. Bit-level results show that BRA-SB provides significant image quality improvement over a scheme that ignores the layer's importance in terms of target BER and a BRA-SB-like scheme with static target BERs. A suboptimal algorithm is also proposed to reduce the computational complexity and its performance is close to the optimal one.

Optimized BS assignment and resource allocation in cooperative OFDM networks

Cooperative transmission (CT) and orthogonal frequency division multiple (OFDM) are promising technologies for extending coverage and increasing throughput in broadband wireless access (BWA) networks. Therefore, we propose a novel BWA network architecture, that can set up inter-cell collaboration using physical layer cooperative transmissions among distributed wired access networks with a powerful coordination capability at the central office. However, conventional base station (BS) assignment and resource allocation schemes cannot be used directly because a user can be serviced by more than one BS with cooperative transmission technology. This study proposes a novel framework of BS assignment and resource allocation in a cooperative OFDM network. We provide three approaches of resource allocation for minimizing bandwidth usage, minimizing transmission power consumption, and balancing resource costs respectively. An optimized resource allocation scheme can be implemented by flexibly choosing one of these approaches based on network load. The simulation results show the efficiency of the proposed mathematical formulations and linearization approach of our scheme. The performance benefit of CT technology on the bandwidth saving is demonstrated by comparing the new BS assignment and resource allocation scheme with conventional non-cooperative transmission.

Joint packet scheduling and resource allocation with quality fairness for wireless VoD system

This paper investigates the problem of multiuser packet scheduling and resource allocation for video transmission over downlink OFDMA networks. A cross-layer approach is proposed to maximize the received video quality under the video quality fairness constraint. Unlike the previous methods in which the objective index is estimated the video quality in the unit of bit, the proposed algorithm develops the objective index in unit of packet, which is more fit for video transmission. In order to solve the optimization problem, a suboptimal algorithm of joint packet scheduling and resource allocation is proposed. The algorithm is compatible with the emerging wireless standards, such as IEEE 802.16. The simulation results show that the proposed method outperforms the conventional resource allocation schemes in terms of received video qualities and quality fairness.

FFR 기반의 Femtocell 네트워크를 위한 적응 주파수 자원 할당 방법

According to distribute of resource of macro cell and reduce distance between transmitter and receiver, Femto cell system is promising to provide costeffective strategy for high data traffic and high spectral efficient services in future wireless cellular system environment. However, the co-channel operation with existing Macro networks occurs some severe interference between Macro and Femto cells. Hence, the interference cancellation or management schemes are imperative between Macro and Femto cells in order to avoid the decrease of total cell throughput. First, we briefly investigate the conventional resource allocation and interference cancellation scheme between Macro and Femto cells. So we found that cell throughput and frequency reuse ware decreased Then, we propose an adaptive resource allocation scheme based on the distribution of Femtocell traffic in order to increase the cell throughput and also maximize the spectral efficiency over the FFR (Fractional Frequency Reuse) based conventional resource allocation schemes. Simulation Results show that the proposed scheme attains a bit similar SINR (Signal to Interference Noise Ratio) distribution but achieves much higher total cell throughput performance distribution over the conventional resource allocation schemes for FFR and future IEEE 802.16m based Femtocell network environment.

An Intelligent Priority Resource Allocation Scheme for LTE-A Downlink Systems

In this paper, we propose an intelligent priority resource allocation (iPRA) scheme for long term evolution-advanced (LTE-A) downlink systems with multimedia traffic. The iPRA scheme intelligently computes priorities for users by using fuzzy inference system so as to enhance system throughput and attain high Jain fairness index while guarantee QoS requirements. Simulation results show that the iPRA scheme can achieve higher system throughput and better QoS requirement guarantee than conventional radio resource allocation schemes in high traffic load, while attains a rather large Jain fairness index.

Improved Resource Allocation Scheme in LTE Femtocell Systems based on Fractional Frequency Reuse

Femtocells provide high quality indoor communications with low transmit power. However, when femtocells are applied in cellular systems, a co-channel interference problem between macrocells and femtocells occurs because femtocells use the same spectrum as do the macrocells. To solve the co-channel interference problem, a previous study suggested a resource allocation scheme in LTE cellular systems using FFR. However, this conventional resource allocation scheme still has interference problems between macrocells and femtocells near the boundary of the sub-areas. In this paper, we define an optimization problem for resource allocation to femtocells and propose a femtocell resource allocation scheme to solve the optimization problem and the interference problems of the conventional scheme. The evaluation of the proposed scheme is conducted by System Level Simulation while varying the simulation environments. The simulation results show that the proposed scheme is superior to the conventional scheme and that it improves the overall performance of cellular systems.

Energy efficient radio resource management strategies for green radio

Mobile communication industries are increasingly contributing to the worldwide energy consumption and CO2 emission. This study addresses a number of key radio resource management (RRM) strategies across PHY and MAC layers for reducing base station energy consumption, as measured by a ‘Joules per bit’ metric. These strategies including power efficient link adaptation, exploitation of multi-user diversity and trading bandwidth for energy efficiency. By collectively taking advantage of those RRM strategies, a multi-user adaptive power and resource allocation algorithm is proposed to ease the power requirements of a base station, while maintaining the same levels of service to the user. The scheduling algorithm is applied to an LTE downlink simulator and its performance is evaluated for various traffic load conditions. The results show that the proposed algorithm achieves a significant energy saving (up to 86%) over a conventional non-energy aware resource allocation scheme. Furthermore, the energy efficiency performance of various multiple antenna techniques is evaluated along with the impact of control signalling overhead. These multiple antenna schemes are then incorporated into the proposed scheduling algorithm and the additional achievable energy savings are quantified.

A Multimedia Quality-Driven Network Resource Management Architecture for Wireless Sensor Networks With Stream Authentication

Media integrity, transmission quality, and energy efficiency are critical for secure wireless image streaming in a wireless multimedia sensor network (WMSN). However, conventional data authentication and resource allocation schemes cannot be applied directly to WMSN due to the constraints on limited energy and computing resources. In this paper, we propose a quality-driven scheme to optimize stream authentication and unequal error protection (UEP) jointly. This scheme can provide digital image authentication, image transmission quality optimization, and high energy efficiency for WMSN. The contribution of this research is two-fold as summarized below. First, a new resource allocation-aware greedy stream authentication approach is proposed to simplify the authentication process. Second, an authentication-aware wireless network resource allocation scheme is developed to reduce image distortion and energy consumption in transmission. The scheme is studied by unequally protected image packets with the consideration of coding and authentication dependency. Simulation results demonstrate that the proposed scheme achieves a performance gain of 3 ~ 5ndB in terms of authenticated image distortion.