User Scheduling Method Research Articles

Minimizing Costs in Fog Nodes Through Effective Resource and Task Scheduling

Background: This system comprises the fog control node, access node, and Internet of Things (IoT) application. This study aims to reduce scheduling-related cloud costs. A user scheduling method and an economical task are carried out for this reason. The algorithm for job scheduling is constructed to determine the best strategy to handle several jobs in a fog access node. Reallocation technology ultimately reduces delays in both time and service. For analytical objectives, extensive simulations were run and performance metrics were compared with those of other currently in use methods. It was discovered that the suggested technique lowers task latency and offers extreme cost-effectiveness by improving the concurrent capability in the fog node, users, and task scheduling with improved performance statistics that made it possible. Materials and Methods: The research paper explores fog computing, a three-layered architecture consisting of IoT devices, fog computing, and cloud computing. The fog computing layer sits between cloud networks and Internet of Things (IoT) devices, providing fast access to these devices and handling computation locally at the network edge. Results: The research paper focuses on reducing cloud costs in scheduling. Although the proposed system showed many similarities in execution times compared to the diverse early completion-time method, it consistently lags behind the heterogeneous method by 5 to 10%. Conclusion: This research aims to reduce the cloud scheduling costs for Internet of Things (IoT) devices. A user-based scheduling algorithm and economical task allocation are implemented. A constraint-based and user-defined strategy is recommended.

Neighbor-based joint spatial division and multiplexing in massive MIMO: user scheduling and dynamic beam allocation

AbstractTwo-stage precoding schemes have been developed to reduce the channel estimation overhead in FDD systems. By integrating user scheduling into these schemes, it becomes possible to meet the quality-of-service requirements of high-density wireless communication systems, despite the limitations on spatial resources and transmit power budget. In this paper, we propose a user scheduling and dynamic beam allocation method for neighbor-based joint spatial division multiplexing (N-JSDM) transmission. The user scheduling problem is formulated as a 0–1 quadratic programming problem to maximize effective spectral efficiency (ESE) using directional channel properties. To address the complexity issue, convex relaxation and linearization methods are employed to transform the problem into a 0–1 mixed integer linear programming, and a dimensionality reduction method is introduced. The proposed user scheduling-aided N-JSDM scheme reduces downlink training length and feedback of channel state information. Additionally, a dynamic configuration form is used for pre-beamforming matrix design based on user distribution, outperforming conventional approaches. Simulation results demonstrate higher ESE achieved by the proposed scheme compared to previous methods.

Spectrum Allocation and User Scheduling Based on Combinatorial Multi-Armed Bandit for 5G Massive MIMO.

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.

Low‐complexity user scheduling for LEO satellite communications

AbstractWith the increasing number of user terminals (UTs), the interference among UTs might significantly decrease the throughput of the low earth orbit satellite communication system. In this paper, the user scheduling method is investigated to suppress user interference. Specifically, leveraging the strong spatial directivity of satellite channels, a low‐complexity angle‐based orthogonal user selection (AOUS) algorithm is proposed, which selects UTs with nearly orthogonal channels via angle information of UTs. A rate‐based proportionally fair (PF)‐AOUS algorithm is further proposed to ensure fairness among UTs, which combines the AOUS with the PF criterion. To reduce complexity, an improved angle‐based PF‐AOUS algorithm that schedules UTs according to their pitch angles rather than their rates is proposed. In addition, efficient precoding schemes for orthogonal UTs are designed by combining the steering vector and power allocation matrix, and it is shown that precoding can be converted into power allocation problems that further balance fairness and throughput. The numerical results indicate that the AOUS achieves a near‐optimal sum rate performance, and the angle‐based PF‐AOUS has the similar performance to the rate‐based PF‐AOUS, which achieves a high fairness index with the proposed precoding scheme.

Analysis and outage performance evaluation of a fair scheduling for independent non‐identically distributed users in a cognitive radio using OSTBC with equally correlated transmit antennas

In this study, a fair user-scheduling method is considered for an underlay cognitive radio system, in which the secondary users (SUs) are sharing the licenced frequency spectrum of a single primary user (PU), where the PU and SUs utilise Alamouti orthogonal space-time block coding (OSTBC). The impacts of some important practical issues are investigated in the system. First, it is assumed that the signal-to-noise-ratios of the SUs are independent non-identically distributed. Second, the transmit antennas for OSTBC corresponding to the SUs are assumed to be equally correlated. Third, the interference from the PU to SUs as well as the interferences from SUs to the PU are taken into consideration. To investigate the outage performance of the SUs, a closed-form expression for the cumulative distribution function of the SUs signal-to-interference-noise-ratio is obtained and then used to derive an expression for evaluating the secondary system outage performance. The numerically evaluated results, validated by computer simulations, provide insights about system outage performance under various practical situations on the impacts of the spatial antennas correlation and the PU interference on to SUs.

Energy Efficiency Optimization for NOMA UAV Network With Imperfect CSI

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the channel errors of low-altitude communication links formed by mobile deployment of UAVs cannot be ignored. And the energy efficiency of the UAVs communication with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we focus on system performance optimization in non-orthogonal multiple access (NOMA) UAV network considering imperfect CSI between the UAV and users. A suboptimal resource allocation scheme including user scheduling and power allocation is designed for maximizing energy efficiency. Because of the nonconvexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a non-probability problem and then decoupled into two convex subproblems. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm by successive convex approximation method. The simulation results prove that the proposed algorithm is effective compared with existing resource allocation schemes.

Efficient Deployment with Geometric Analysis for mmWave UAV Communications

Unmanned aerial vehicle (UAV) communications with mmWave band is a promising candidate for future communication systems due to its high reliability, excellent flexibility and large bandwidth availability. Nevertheless, the high frequency makes the mmWave UAV communications vulnerable to blockage, which hinders the application of mmWave UAV communications. In this letter, we propose an efficient user scheduling method for mmWave multi-UAVs communications with blockage. First, we explore the angle space channel transmission of mmWave multi-UAVs communications. Then, we propose a geometric analysis method to detect the blockage in the multi-UAVs communication system. Next, we formulate the user scheduling of multi-UAVs communication systems as an optimization problem and propose a greedy user scheduling algorithm to decrease the probability of the blockage and enhance the spectral efficiency of the multi-UAVs communications. Numerical simulation results are provided to verify the effectiveness of the proposed method.

Blind Signal Classification Analysis and Impact on User Pairing and Power Allocation in Nonorthogonal Multiple Access

This paper studies blind signal classification (SC) in the nonorthogonal multiple access (NOMA) system, which especially determines whether or not the received NOMA signal requires successive interference cancellation (SIC) without a priori signal information. In this paper, two types of blind SC errors are analyzed: 1) the signal that has to cancel the superposed interference component is classified as one that does not require SIC and 2) the signal that has no need of performing SIC is classified as one that requires SIC. Here, the interesting observation is that the classification error of the first type decreases with the SNR, but that of the second type increases with the SNR. In this regard, this paper proposes the joint user pairing and power allocation policy for the NOMA system based on the above observation that the blind SC performance of the NOMA user who does not perform SIC decreases with the SNR. The joint optimization problem maximizes the sum-rate gain of NOMA over orthogonal multiple access (OMA) with constraints on the maximum classification error probability and the minimum data rate. Since the problem is nonconvex, we propose the numerical algorithm that iteratively finds the appropriate user scheduling and power allocation. Simulation results show that the proposed scheme outperforms existing user scheduling methods.

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

Deep Cognitive Perspective: Resource Allocation for NOMA-Based Heterogeneous IoT With Imperfect SIC

The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile networks and the smart cities. However, considering the large numbers of connectivity demands, it is vital to improve the spectrum efficiency (SE) of the IoT with an affordable power consumption. To improve the SE, the nonorthogonal multiple access (NOMA) technology is newly proposed through accommodating multiple users in the same spectrums. As a result, in this paper, an energy efficient resource allocation (RA) problem is introduced for the NOMA-based heterogeneous IoT. At first, we assume the successive interference cancelation (SIC) is imperfect for practical implementations. Then, based on the analyzing method for cognitive radio networks, we present a stepwise RA scheme for the mobile users and the IoT users with the mutual interference management. Third, we propose a deep recurrent neural network-based algorithm to solve the problem optimally and rapidly. Moreover, a priorities and rate demands-based user scheduling method is supplemented, to coordinate the access of the heterogeneous users with the limited radio resource. At last, the simulation results verify that the deep learning-based scheme is able to provide optimal RA results for the NOMA heterogeneous IoT with fast convergence and low computational complexity. Compared with the conventional orthogonal frequency division multiple access system, the NOMA system with imperfect SIC yields better performance on the SE and the scale of connectivity, at the cost of high power consumption and low energy efficiency.

Closed-Form Secrecy Outage Analysis of Cellular Downlink Systems in the Presence of Co-Channel Interference

This paper analyzes the secrecy outage probability of a cellular downlink system which consists of multiple users, one base station (BS), and one eavesdropper in the presence of co-channel interferers, where the BS transmits its signals to users which are distributed according to a Poisson point process with a fixed density, while the eavesdropper attempts to tap the transmission from the BS to the users. To enhance the transmission security against eavesdropping, we propose two multiuser scheduling schemes, namely, the partial channel state information (CSI) aided user scheduling (PCSI-US) scheme where only the CSIs of users are available without knowing the co-channel interferers’ CSIs and the full CSI aided user scheduling (FCSI-US) scheme which requires the CSIs of both users and interferers. For comparison purposes, the conventional round-robin scheduling scheme is also considered as a baseline. We derive closed-form expressions of the secrecy outage probability for the proposed PCSI-US and FCSI-US as well as conventional round-robin scheduling schemes in co-channel interference (CCI) environments. Numerical results show that the proposed FCSI-US and PCSI-US schemes outperform the conventional round-robin scheduling in terms of their secrecy outage probabilities, where FCSI-US achieves the best secrecy outage performance in CCI environments. It is also demonstrated that increasing the mean number of users has a marginal effect on the secrecy performance of conventional round-robin scheduling, which can significantly decrease the secrecy outage probabilities of PCSI-US and FCSI-US schemes. This further validates the secrecy effectiveness of the proposed user scheduling methods in terms of combating the CCI.

Beam Design and User Scheduling for Nonorthogonal Multiple Access With Multiple Antennas Based on Pareto Optimality

In this paper, an efficient transmit beam design and user scheduling method is proposed for multi-user (MU) multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) downlink, based on Pareto-optimality. The proposed beam design and user scheduling method groups simultaneously-served users into multiple clusters with practical two users in each cluster, and then applies spatical zeroforcing (ZF) across clusters to control inter-cluster interference (ICI) and Pareto-optimal beam design with successive interference cancellation (SIC) to two users in each cluster to remove interference to strong users and leverage signal-to-interference-plus-noise ratios (SINRs) of interference-experiencing weak users. The proposed method has flexibility to control the rates of strong and weak users and numerical results show that the proposed method yields good performance.

Energy-Efficient Dynamic Point Selection and Scheduling Method for Intra-cell CoMP in LTE-A

In this paper, we propose a novel dynamic point selection (DPS) and user scheduling method for improving the energy efficiency in distributed antenna systems without cell edge spectral efficiency degradation. When DPS is used, each user is served by a single transmission point that can be dynamically switched. The proposed method decreases the power consumption by switching off inactive radio frequency (RF) chains and additionally reduces the interference by a static inter-cell agreement on which transmission points are simultaneously active. The performance of the method is evaluated by computer simulations in a system that accurately models the LTE-Advanced (LTE-A) intra-cell coordinated multi-point scenario 4. Based on the performance simulations, the proposed method achieves a significant energy efficiency gain over closed-loop spatial multiplexing applied on localized or distributed transmitting antennas. In general, the proposed method performs well when the load-independent RF power consumption is high in the active mode and low in the sleep mode. When the proportion of the load-independent RF power consumption to the total load-independent power consumption exceeds a certain limit, which is 22 % in the case of ten users in the 3-sector layout, the proposed method brings always energy efficiency gain even when RF chain micro sleeping cannot be implemented. The usability of the method is not dependent on the traffic load. The same approach can be applied to any distributed antenna system.

제한된 궤환 채널 기반 상향링크 MIMO/FDD 시스템에서의 효율적인 간섭 정렬 기법

Assuming perfect channel state information (CSI), the conventional interference alignment (IA) algorithm in the uplink cellular system suppresses inter-cell interference (ICI) by aligning ICI to a randomly selected reference vector. However, IA in practice relies on limited feedback between base stations and users, resulting in residual ICI. In this paper, we propose the optimization of the reference vector that minimizes the upper-bound of residual ICI power. Secondly, the iterative IA that designs the direction of transmit and receive filter is proposed to minimize the residual ICI as well as maximize the desired signals. Moreover, we propose the user scheduling method combined with proposed IA schemes which provides the multiuser diversity gain in multi-cell environments. Finally, the performance gain of the proposed IA algorithms compared with the existing IA are analyzed and demonstrated by simulation results.

다중 안테나 빔형성 메트릭스 변환 기법 및 사용자 선택 기법

Random beamforming (RBF) uses the signal to interference plus noise ratio (SINR) feedback to select users in multiple-input multiple-output (MIMO) systems. A large number of users are required to obtain the gain of multi-user diversity for a downlink transmission. However, if the number is not large enough, it may be difficult to obtain multi-user diversity, leading to a rapid degradation in performance. To resolve this problem, we propose the beamforming matrix transformation and the user scheduling method. The beamforming matrix transformation scheme uses the SINRs of each users and have a better performance than conventional schemes over a small number of users. In addition, we propose the user scheduling scheme corresponding to the beamforming matrix transformation. In simulation results, we demonstrate that the sum-rate can be improved according to the number of users.

A Distributed Downlink Scheduling Method for Multi-user Communication with Zero-Forcing Beamforming

A distributed scheduling approach to multi-user beamforming at a base-station (BS) is proposed. The scheme assumes the BS is equipped with multiple antennas and uses zero-forcing beamforming to schedule multiple users on the same time-frequency uplink/downlink channels. The optimal user set that the BS can schedule is the one that maximizes the sum-rate for the communication channel. The computational complexity of finding the optimal user set becomes intractable as the number of users exceeds 20-30. Sub-optimal schemes for maximizing the sum-rate generally require large control overhead. We propose an effective sum-rate performance metric that accounts for the control overhead required by the scheduling method. We also propose a user scheduling method that has both low complexity and low overhead. The complexity and overhead are reduced by adding one user at a time and distributing the computation of the scheduling problem across the users. Each user calculates the sum-rate that would result if they were added to the existing user set using their own channel and knowledge of the channels of existing users that is broadcast by the BS. The user with greatest sum-rate increase is scheduled. We use simulations to show that this approach can achieve significantly higher effective sum rates than previously proposed sub-optimal schemes. We also derive an approximate expression for the expected sum-rate with our distributed scheduling method and demonstrate its accuracy with simulations.