Scheduling In Heterogeneous Networks Research Articles

DCQSH: Dynamic Conflict-Free Query Scheduling in Heterogeneous Networks during Emergency

There can be disasters such as tsunami, fire-related incidents, etc. in several ways. Mobile devices and the cloud occupy a significant position in connectivity and relief operations in these circumstances. This would be more important an efficiently performing query facility in mobile devices in a crisis situation. To achieve the mentioned facility, a Dynamic conflict-free query scheduling approach for heterogeneous networks during the emergency situation (DCQSH) is suggested in this paper. DCQSH is specifically built to schedule queries for the heterogeneous communication networks. DCQSH's key feature would be that it can optimize the query efficiently and often operates with complex tasks and adjusts the query rate without rebuilding the existing transfer schedule. DCQSH operates within heterogeneous networks, as it could accommodate the condition where the mobile devices become low energy-efficient on the networks. The experimental findings reveal that DCQSH outperforms in a heterogeneous scenario in terms of its relation to baseline algorithms. MATLAB framework was utilized to validate the simulation performance.

Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory

In the context of Industry 4.0, extensive deployment and application of advanced manufacturing equipment and various sensors is leading to a growing demand for data exchange between different devices. In smart factories, network transmission has multiprotocol features of wired/wireless communication, and different data flows have different real-time requirements. In this article, a heterogeneous network architecture based on software-defined network is proposed for realizing cross-network flexible forwarding of multisource manufacturing data and optimized utilization of network resources. Subsequently, the mechanism of cross-network fusion and scheduling (CNFS) is analyzed from the perspective of high dynamic characteristics and different delay requirements of data flows. Based on this analysis, a route-aware data flow dynamic reconstruction algorithm is proposed. The proposed algorithm improves the efficiency of manufacturing data cross-network fusion, especially for multivariety and small-batch intelligent manufacturing systems. Furthermore, for meeting the bandwidth requirements of different delay flows, a delay-sensitive network bandwidth scheduling algorithm is proposed. Finally, the effectiveness of the proposed CNFS mechanism is verified using a candy packaging intelligent production line prototype platform.

A multi-level grammar approach to grammar-guided genetic programming: the case of scheduling in heterogeneous networks

The scale at which the human race consumes data has increased exponentially in recent years. One key part in this increase has been the usage of smart phones and connected devices by the populous. Multi-level heterogeneous networks are the driving force behind this mobile revolution, but these are constrained with limited bandwidth and over-subscription. Scheduling users on these networks has become a growing issue. In recent years grammar-guided genetic programming (G3P) has shown its capability to evolve beyond human-competitive network schedulers. Despite the performance of the G3P schedulers, a large margin of improvement is demonstrated to still exist. In the pursuit of this goal we recently proposed a multi-level grammar approach to generating schedulers. The complexity of the grammar was increased at various stages during evolution, allowing for individuals to add more complex functions through variation operations. The goal is to evolve good quality solutions before allowing the population to specialise more as the grammar functionality increased in a layered learning way. In this paper the results of this initial study are replicated, and confirmed, and it is seen that this approach improves the quality of the evolved schedulers. However, despite the gain in performance, we notice that the proposed approach comes with an acute sensitivity to the generation at which the grammar complexity is increased. Therefore, we put forward a novel seeding strategy and show that the seeding strategy mitigates the shortcomings of the original approach. The use of the seeding strategy outperforms the original approach in all the studied cases, and thus yields a better overall performance than the state-of-the-art G3P generated schedulers.

Revisiting Scheduling in Heterogeneous Networks When the Backhaul Is Limited

We study the impact of the limited capacity of backhaul links on downlink user scheduling in a heterogeneous network comprising macro base stations and small cells. Assuming a tree topology of the backhaul network, we formulate a backhaul-aware global $\alpha$ -fair time-domain user scheduling problem and study it under three different scenarios of backhaul limitations. For the scenario where the backhaul links are not the bottleneck, we derive closed-form scheduling solutions to the scheduling problem under certain assumptions. For the scenario where the backhaul links between the macro base station and the small cells are the bottleneck, we show that the global $\alpha$ -fair user scheduling problem can be decomposed into a set of independent local $\alpha$ -fair user scheduling problems. However, unlike the previous case, a local scheduler in this case is not of a unique type but can be of one of three types, depending on the available backhaul capacity. We completely characterize these three types and also propose a simple heuristic for optimal $\alpha$ -fair scheduling. When the link between the macro base station and the core network is a potential bottleneck, we show how each base station can still perform a local scheduling as in the previous case as long as there is a master problem that allocates feasible virtual backhaul capacities to each BS. However, computing the optimal virtual capacities is complex and expensive in terms of the amount and frequency of information exchanges. For this scenario, we propose realization-agnostic heuristic schemes that are simple to implement and perform quite well.

Unified Analysis of Multiuser Scheduling for Downlink MIMO Systems with Imperfect CSI

In this paper, unified BER performance and outage probability analyses of downlink multiuser scheduling for multiple-input multiple-output (MUS-MIMO) transmission/combining (transceiving) systems with imperfect channel state information (CSI) caused by feedback over Rayleigh fading channels is presented. The unified analysis is applicable to MIMO transceiving systems whose received signal-to-noise ratio (SNR) is given as largest order statistic of chi-square distribution. The performance of MUS-MIMO employing selective transmission/selective combining (ST/SC), ST/maximum ratio combining (MRC) and space-time block codes (STBC) can be analyzed by this general framework. Accurate and approximate unified closed-form BER and outage probability expressions of the MUS-MIMO transceiving system with normalized SNR-based scheduling in heterogeneous networks are derived. Based on the approximate BER and outage probability expressions, diversity gains are analyzed at high SNR. The results indicate that the MUS-MIMO for using the three transceiving schemes can achieve full diversity order for perfect CSI, while the diversity gain is degraded differently for the three transceiving schemes under imperfect CSI. Computer simulation shows that the theoretical analysis is in good agreement with simulation results, and the approximate BER expressions are very close to the accurate ones but with lower computational complexity.

Collaborative Opportunistic Scheduling in Heterogeneous Networks: A Distributed Approach

We consider a collaborative opportunistic scheduling problem in a decentralized network with heterogeneous users. While most related researches focus on solutions for optimizing decentralized systems’ total performance, we proceed in another direction. Two problems are specifically investigated. (1) With heterogenous users having personal demands, is it possible to have it met by designing distributed opportunistic policies? (2) With a decentralized mechanism, how can we prevent selfish behaviors and enforce collaboration? In our research, we first introduce a multiuser network model along with a scheduling problem constrained by individual throughput requirement at each user’s side. An iterative algorithm is then proposed to characterize a solution for the scheduling problem, based on which collaborative opportunistic scheduling scheme is enabled. Properties of the algorithm, including convergence, will be discussed. Furthermore in order to keep the users staying with the collaboration state, an additional punishment strategy is designed. Therefore selfish deviation can be detected and disciplined so that collaboration is enforced. We demonstrate our main findings with both analysis and simulations.