A traffic flow-based and dynamic grouping-enabled resource allocation algorithm for LTE-D2D vehicular networks

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

Wavelength division multiplexing/time division multiplexing (WDM/TDM) passive optical networks (PON) is being viewed as a promising solution for delivering multiple services and applications. The hybrid WDM / TDM PON uses the wavelength and bandwidth allocation strategy to control the distribution of the wavelength channels in the uplink direction, so that it can ensure the high bandwidth requirements of multiple Optical Network Units (ONUs) while improving the wavelength resource utilization. Through the investigation of the presented dynamic bandwidth allocation algorithms, these algorithms can't satisfy the requirements of different levels of service very well while adapting to the structural characteristics of mixed WDM / TDM PON system. This paper introduces a novel wavelength and bandwidth allocation algorithm to efficiently utilize the bandwidth and support QoS (Quality of Service) guarantees in WDM/TDM PON. Two priority based polling subcycles are introduced in order to increase system efficiency and improve system performance. The fixed priority polling subcycle and dynamic priority polling subcycle follow different principles to implement wavelength and bandwidth allocation according to the priority of different levels of service. A simulation was conducted to study the performance of the priority based polling in dynamic resource allocation algorithm in WDM/TDM PON. The results show that the performance of delay-sensitive services is greatly improved without degrading QoS guarantees for other services. Compared with the traditional dynamic bandwidth allocation algorithms, this algorithm can meet bandwidth needs of different priority traffic class, achieve low loss rate performance, and ensure real-time of high priority traffic class in terms of overall traffic on the network.

The slicing technology can meet the diversified and personalized service needs of 5G networks, schedule virtual networks dynamically and improve the operation efficiency of the network. The dynamic resource allocation algorithm among slices is the key for the application of slicing technology, which has also become a research hotspot in recent years. However, the current dynamic resource allocation algorithm of 5G network slicing has many problems such as insufficient fairness, difficulty in ensuring user satisfaction and low resource utilization rate, etc. Under the circumstances of a wide variety of 5G services, it is difficult to meet the needs of user satisfaction under differentiated service conditions only from the perspective of the network. From the perspective of Quality of Experience (QoE), a QoE evaluation system for 5G network is constructed, and a QoE-based dynamic resource allocation algorithm of 5G network slicing on the basis of this system is built in this research. The results of the simulation experiments indicate that the algorithm has significant effect in improving QoE of 5G communities, and it also has good performance in improving the resource utilization rate in the communities.KeywordsQoE5GSliceResource allocationParticle swarm optimization

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

MIMODSA:Non-Pre-emptive Scheduling in Cognitive Radio Ad-Hoc Networks (CRAHNs)

In view of the fact that the limited wireless resources and the asymmetric traffic between uplink and downlink in the trunking communication system based on TD-SCDMA network, a design scheme of dynamic resource allocation (DCA) is given. DCA is a very effective technology to solve those problems and enhance the utilization of resource. Based on the characteristics of the trunking communication system, we combine with the DCA algorithms of TD-SCDMA network in this paper, and give an optimal DCA strategy for the truking communication system in TD-SCDMA network. Finally, the simulation result shows the DCA strategy markedly improve the system performance and gain higher resource utilization.

Cloud computing’s simulation and modeling capabilities are crucial for big data analysis in smart grid power; they are the key to finding practical insights, making the grid resilient, and improving energy management. Due to issues with data scalability and real-time analytics, advanced methods are required to extract useful information from the massive, ever-changing datasets produced by smart grids. This research proposed a Dynamic Resource Cloud-based Processing Analytics (DRC-PA), which integrates cloud-based processing and analytics with dynamic resource allocation algorithms. Computational resources must be able to adjust the changing grid circumstances, and DRC-PA ensures that big data analysis can scale as well. The DRC-PA method has several potential uses, including power grid optimization, anomaly detection, demand response, and predictive maintenance. Hence the proposed technique enables smart grids to proactively adjust to changing conditions, boosting resilience and sustainability in the energy ecosystem. A thorough simulation analysis is carried out using realistic circumstances within smart grids to confirm the usefulness of the DRC-PA approach. The methodology is described in the intangible, showing how DRC-PA is more efficient than traditional methods because it is more accurate, scalable, and responsive in real-time. In addition to resolving existing issues, the suggested method changes the face of contemporary energy systems by paving the way for innovations in grid optimization, decision assistance, and energy management.

Resource allocation is an important part of non-orthogonal multiple access technology. How to allocate resources reasonably to achieve high service quality has become an important research topic. The existing resource allocation algorithms have the disadvantages of low system performance improvement, inability to achieve dynamic resource allocation, and high complexity. In view of the limitations of the existing resource allocation methods, this paper proposes to minimize the power consumption of the base station by obtaining the minimum power required by the terminal to reach the target rate under certain fairness constraints, and at the same time obtain the channel state through the base station. The information calculates the actual rate reached by the terminal in real time. If the rate is lower than a certain value, the resources are reallocated to achieve dynamic resource allocation. Finally, the simulation shows that the performance of the proposed algorithm is improved when the terminal is fixed. When the terminal moves, the system performance is greatly improved and the base station energy consumption is reduced.

This paper proposes a dynamic resource allocation (DRA) algorithm that makes use of the Hopfield neural network (HNN) methodology, which provides a fast way of finding the optimum resource when formulated as a combinational problem. The proposed algorithm is applied to both the scheduling of downlink transmissions in a CDMA scenario with delay-oriented services, and real time services, although by a proper modification of the constraints imposed in the energy function, it could be easily extended to other services or access technologies. The algorithm is evaluated by means of simulations, revealing its ability to adapt to the specific service and traffic conditions. Finally, the convergence issues related to HNN methodology are considered in the light of the obtained results

Future wireless communication systems are expected to provide a broad range of multimedia services that have a significant traffic asymmetry between uplink and downlink. The code division multiple access‐time division duplex (CDMA‐TDD) system is a promising solution to cope with the problem of traffic asymmetry. However, the TDD system is subject to inter‐cell interference compared to frequency division duplex (FDD) system. Since both uplink and downlink share the same frequency in TDD, uplink and downlink may interfere each other especially when neighboring cells require different rates of traffic load. Thus, the resource allocation among cells is an important issue in TDD. In this paper, the resource allocation in the CDMA‐TDD is formulated as a mixed integer programming (MIP) problem. A dynamic resource allocation algorithm (DRAA) is provided that effectively solves the traffic asymmetry problem. The MIP problem is also solved by a well‐known branch and bound procedure. Both the crossed slot and non‐crossed slot allocation are examined and compared to the DRAA. Computational result shows that proposed DRAA gives a good performance as the traffic asymmetry increases between the uplink and downlink. Copyright © 2003 John Wiley & Sons, Ltd.

This paper considers a joint optimal design of admission control and resource allocation for multimedia services delivery in high‐speed railway (HSR) wireless networks. A stochastic network optimization problem is formulated which aims at maximizing the system utility while stabilizing all transmission queues under the average power constraint. By introducing virtual queues, the original problem is equivalently transformed into a queue stability problem, which can be naturally decomposed into three separate subproblems: utility maximization, admission control, and resource allocation. A threshold‐based admission control strategy is proposed for the admission control subproblem. And a distributed resource allocation scheme is developed for the mixed‐integer resource allocation subproblem with guaranteed global optimality. Then a dynamic admission control and resource allocation algorithm is proposed, which is suitable for distributed implementation. Finally, the performance of the proposed algorithm is evaluated by theoretical analysis and numerical simulations under realistic conditions of HSR wireless networks.

Virtual MIMO, also known as Multiuser-MIMO, has been widely investigated to improve data transmission rate of various wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference. To obtain good trade-off between the system throughput and transmission performance, we investigate joint user grouping and resource allocation under the consideration of system throughput and average MSE performance in uplink SC-FDMA systems. Based on linear MIMO detection, we first develop MSE-oriented user grouping criterion for evaluation of transmission performance, then propose a dynamic user grouping and optimal resource allocation algorithm with elastic average MSE constraint. We evaluate the proposed joint resource allocation algorithms in LTE uplink scenarios and the results show that it achieves alterable trade-off between system throughput and average MSE.

Traditional algorithms for dynamic OFDMA resource allocation have relatively deterministic system capacity and user fairness. Thus, in this letter, an efficient scheme is proposed to flexibly adjust quality-of-service for users, which is achieved by appropriately setting minimum data-rate of each user.

Dynamic resource allocation combined with multiple antennas (MIMO) can farther increase the system capacity and the quality of service (QoS) of conventional OFDM systems. Selection diversity is the simplest way to realize spatial diversity, but the performance improvement of this form of diversity is limited. Adaptive antenna arrays promise to achieve significant increases in system capacity and performance in wireless communications, but they are characterized by a relatively higher implementation complexity than that of selection diversity. In this paper, we propose a dynamic resource allocation scheme with selective beamforming for MIMO/OFDM systems. By applying eigenvalue decomposition on the subset of all available channel correlation matrices, the best beamforming, which corresponds to the largest eigenvalue in the subset, is adaptively selected. Then the proposed dynamic resource allocation algorithm adaptively assigns the bit and power distribution according to the channel gain corresponding to the selected best beamforming, under the constraint of fixed power and overall bit rate. By doing so, the maximal SNR on each sub-carrier obtained by selective beamforming is further maximized. Numerical results show that under frequency-selective multipath fading, the proposed system yields significant performance improvements over the conventional OFDM transmission.