Dynamic Point Selection Research Articles

Mixed-Precision Neural Architecture Search and Dynamic Split Point Selection for Split Computing

Mixed-Precision Neural Architecture Search and Dynamic Split Point Selection for Split Computing

Load-Aware Greedy Dynamic CoMP Clustering Mechanism for DPS CoMP in 5G Networks

Cooperative communications offered by the Coordinated Multipoint (CoMP) technique have been introduced and become an important technique in the 5G mobile networks to support very high system capacity and improve the end-user service quality, especially for the cell-edge users via the dynamic coordination of transceivers at different geographical sites. One major issue driving the performance of CoMP operation is the decision on the coopering clusters or the cluster formation strategies. In this work, an approach of CoMP clustering has been studied and proposed, the so-called load-aware greedy dynamic CoMP clustering mechanism. Our proposed CoMP clustering technique is deployed on top of the actual traffic-based load-aware Dynamic Point Selection CoMP (DPS CoMP) mechanism, which is our previously proposed mechanism. The approach is aimed at maximizing spectral efficiency with appropriate cell coordination based on well-balancing cell loads within the coordinating area of the 5G networks. System performance is numerically studied and observed for the 5G Non-Orthogonal Multiple Access (NOMA) systems embedded with our proposed mechanisms for the realistic scenarios, i.e., homogeneous network and Heterogeneous Network (HetNet) cases. Numerical results illustrate the benefit brought by our approaches in comparison with others CoMP clustering techniques as well as the potential for further improvement.

An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments.

Recent developments in robotics have heightened the need for visual SLAM. Dynamic objects are a major problem in visual SLAM which reduces the accuracy of localization due to the wrong epipolar geometry. This study set out to find a new method to address the low accuracy of visual SLAM in outdoor dynamic environments. We propose an adaptive feature point selection system for outdoor dynamic environments. Initially, we utilize YOLOv5s with the attention mechanism to obtain a priori dynamic objects in the scene. Then, feature points are selected using an adaptive feature point selector based on the number of a priori dynamic objects and the percentage of a priori dynamic objects occupied in the frame. Finally, dynamic regions are determined using a geometric method based on Lucas-Kanade optical flow and the RANSAC algorithm. We evaluate the accuracy of our system using the KITTI dataset, comparing it to various dynamic feature point selection strategies and DynaSLAM. Experiments show that our proposed system demonstrates a reduction in both absolute trajectory error and relative trajectory error, with a maximum reduction of 39% and 30%, respectively, compared to other systems.

User Pairing and Power Allocation for NOMA-CoMP Based on Rate Prediction

In this paper, we consider a non-orthogonal multiple access (NOMA) system with coordinated multi-point (CoMP), which is used in 5G cellular networks to guarantee the rate requirements from the different edge users. Based on the China Family Panel Studies (CFPS) dataset, we use several learning algorithms to predict users’ rate requirements according to their profiles. We propose a many-to-many two-side subchannel–user matching strategy, which can classify users into cell-center users, high-rate requirement edge users, and low-rate requirement edge users based on their status and learning prediction results, and pair users with different subchannels to form joint transmission CoMP (JT-CoMP) subchannels and dynamic point selection CoMP (DPS-CoMP) subchannels. Furthermore, a discrete power allocation algorithm based on group search is proposed. Simulation results show that our proposed algorithm outperforms the traditional NOMA-CoMP algorithm and maximum throughput (MT) NOMA-CoMP algorithm. It maximizes the rate of high-rate requirement edge users while guaranteeing user fairness.

Activity-aware prediction of Critical Paths Aging in FDSOI technologies

Modern CMOS technologies such as FDSOI are affected by severe aging effects that do not only depend on physical issues related to nanoscale technologies, but also on the circuit environment and its run-time activity. Therefore it is extremely difficult to reliably establish a-priori guard bands for Critical Path estimations, usually leading to both large delay penalties (and therefore loss of performances) or too short operating lifetime. In this paper, we propose an approach that uses Machine Learning techniques to obtain reliable predictions of the aging of the Near Critical Paths. Starting from a limited set of measurements and simulation data, our framework is able to accurately estimate Critical Path delay degradation in time depending on physical parameters, environment conditions and circuit activity. Further to that, the corresponding regression models are applied to obtain dynamic aging-aware Operating Performance Point selection strategies.

Energy Efficient Throughput Aware Traffic Load Balancing in Green Cellular Networks

With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of the internet access, and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint, and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing achievable throughput and energy efficiency (EE) by reducing utility grid consumption from a network level perspective. This paper investigates the radio efficiency, EE, and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of network setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination technique in a cluster. Provision of cell sleep approach is contemplated for further energy saving by turning off lightly loaded base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enables an adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterward, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.

Mobile Collector-Based Cost Balancing Scheme for Uniform Data Gathering Delay and Energy Consumption in Wireless Sensor Actuator Networking Systems

The sink hole problem is a critical issue for data collection in wireless sensor networks. Recent research has indicated that the mobility of actuators in wireless sensor actuator networks (WSANs) alleviates the sink hole problem. However, mobile actuator-based data collection may result in unsatisfactory data gathering delay, which is not acceptable for delay-sensitive real time applications. Thus, planning the optimal path for mobile collector is a critical challenge in WSANs to balance between the prolongation of network lifetime and the reduction of the data gathering delay. To address this issue, we propose a mobile collector-based cost balancing algorithm to achieve uniform data gathering delay and energy consumption based on the adaptive region partitioning and dynamic polling point selection. The proposed algorithm uses multiple mobile collectors and partitions the sensing area into several regions that are monitored by the mobile collectors according to the distribution of cost in the area. It also dynamically determines moving trajectory of each mobile collector by selecting the polling points to visit considering the distance to travel and the nodes’ energy consumption. The superiority of the proposed algorithm is validated by theoretical analysis and simulation results.

Enhanced scheduling schemes with energy conservation for dynamic point selection in cloud radio access networks

In 5G mobile communications, Cloud-RAN (C-RAN) (Camps-Mur et al. in IEEE Commun Mag 57:99–105, 2019) is proposed to provide broadband services. It separates computation entities, i.e., baseband units (BBUs), from base stations and puts all BBUs in a centralized cloud. Existing researches have investigated how to enhance user equipments (UEs) to receive data from multiple collaborative cells by leveraging the dynamic point selection(DPS) technology. Collaborative transmission may cause higher energy consumption for UEs. Although 3GPP has defined the discontinuous reception (DRX) mechanism by regulating UEs to turn off their radio interfaces in a periodical manner, how to well coordinate DRX with DPS under the C-RAN architecture is still left as an open issue. This paper is the first one addressing this resource optimization problem in C-RAN with DPS, which asks how to optimize DRX parameters of UEs by considering their quality-of-service (QoS). We prove this problem to be NP-complete, and then propose two effective and efficient DPS solutions, called serving-ratio(SR) scheme and cost-aware (CA) scheme. SR serves UEs based on a special ‘serving ratio’ to ensure UEs receiving continuous subframes, especially for those in cell intersections. On the other hand, CA exploits the strategies of minimal scheduling costs to balance energy and throughput efficiency in a perspective way. Extensive simulation results validate that our schemes can successfully achieve higher system throughput, provide better resource utilization, and serve more UEs while guaranteeing their QoS and saving considerable energy as compared to existing schemes.

Dynamic Access Point and Service Selection in Backscatter-Assisted RF-Powered Cognitive Networks

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

A Dynamic MAP Discovery and Selection Scheme for Predictive Hierarchical MIPv6 in Vehicular Networks

Recent developments and the ubiquitous availability of wireless technologies and inter-vehicle communication systems are anticipated to lead to the evolution of heterogeneous wireless networks. Based on standards such as the IEEE 802.11, 5G and WiMax, coupled with mobile IP for the content delivery of road safety and infotainment services to drivers over the new generation of vehicular networks. However, the performance of traditional mobility management protocols over vehicular networks is affected by network topology changes and vehicles' high mobility. Therefore, efficient mobility management solution that mitigates the challenges of vehicles' mobility is needed. In this paper, we present an adaptive mobility management protocol for predictive hierarchical mobile IPv6 in vehicular networks. We develop a dynamic Mobility Anchor Point (MAP) discovery and selection scheme that adjusts the network topology structure to fit the vehicles' mobility environment better. Moreover, the predictive handover scheme is based on a multi-observation hidden Markov model, which utilizes the vehicles' movement projections to estimate the subsequent point of access. The performance of our proposed protocol has been tested using NS-2 and several real and synthetic mobility traces. The results indicate that our protocol exhibits better performance results when compared to well-known handover protocols in reducing the handover latency and packet loss.

A predictive headway-based bus-holding strategy with dynamic control point selection: A cooperative game theory approach

Bus holding is a widely used control method to regularize bus headways and reduce bus bunching. The method works in such a way by delaying buses at control points if their departure times or headways deviate from the planned ones. However, it may result in reduced bus commercial speeds and increased passenger onboard travel time. To avoid this problem, researchers have suggested that control points be spaced cautiously along the route such that only a few are needed. This study proposes a predictive headway-based bus holding strategy with dynamic control point importance ranking and selection based on the cooperative game theory. The framework considers not only individual control points’ impact but also the collective group control effects. Specifically, the proposed framework consists of two components: a performance model and a cooperative game model. The performance model predicts headway performances of all running buses when different control point combinations are in effect. Dynamic bus running times and passenger demands are reflected in the model. Then, these headway performances are passed to the cooperative game model with control points being players and improvements in headway performances compared with that under no holding control being the utility function. The game is solved by Myerson value, a concept that extends Shapley value used for the normal cooperative game and considers the cooperation structure and potential worth of coalitions. We use Myerson value to rank the importance of control points on regularizing headways, as it measures the average marginal utility contribution of a control point to all possible coalitions that exclude that point. We prove that Myerson value lies in the Ω-core of the game and thus satisfies allocation efficiency, individual and coalition rationality. The proposed framework is applied to target headway control and two-way-looking self-equalizing headway control. Simulation results show that the framework can significantly reduce passenger waiting time and bus headway variation.

Energy-aware clustering of CoMP-DPS transmission points

The main idea behind CoMP operation is to efficiently coordinate multiple transmission points to provide better transmission quality and QoS, while improving spectral efficiency. This concept can be extended to the heterogeneous networking environment of 5G systems and is expected to be one of the key technologies of the future. In this work, we focus on the CoMP transmission with Dynamic Point Selection (DPS) due to its low complexity and the gains it can offer to the reduction of the size of the clusters and to the network’s power consumption. Therefore, in this paper, we present an Energy-Aware Clustering (EnAC) scheme to be utilized with CoMP-DPS in LTE-A. The goal of the proposed scheme is to reduce, as much as possible, the power consumption at each BS of a cluster without compromising the CoMP operation. In other words, we incorporate energy-related parameters into the CoMP clustering procedure aiming to raise the energy awareness of the overall system. These parameters are thoroughly studied and fine-tuned through simulation results which prove the energy efficiency of the proposed scheme.

Dynamic point selection CoMP enabled hybrid powered green cellular networks

Integration of renewable energy (RE) harvester has emerged as a long-term solution for the next generation cellular networks for reducing their operational expenditures and CO2 footprints. However, the dynamic nature of RE generation could lead to energy outage and service quality deterioration. Thus, utilization of commercial grid supply in conjunction with RE generators is a more realistic option for sustainable network operations. Therefore, this article proposes an energy-efficient hybrid framework for achieving envisaged green cellular networks. Under the proposed framework, each base station (BS) is equipped with on-site solar energy harvester as the primary energy source along with the traditional grid electricity supplying additional energy demand. Afterward, dynamic point selection type coordinated multi-point transmission technique is integrated for selecting the best serving BS for a user. A comprehensive investigation is carried out in the context of downlink LTE-A cellular networks for evaluating energy efficiency performance of the proposed framework.

Construction of Dynamic Multifidelity Locally Optimized Surrogate Models

The conceptual design phase for the development of new and revolutionary aircraft should include both low- and high-fidelity multidisciplinary engineering analyses to enable reasonable computational cost and accuracy tradeoffs. Multifidelity locally optimized surrogate models can be employed to fuse the multifidelity information to give an accurate representation of the underlying design space. These surrogates can be further enhanced with derivative information and augmented with dynamic training point selection to reduce overall computational cost. In this paper, a framework for multifidelity locally optimized surrogate models is developed, and the superior accuracy for the same computational cost or cheaper cost for the same accuracy compared to standard single-fidelity global surrogate models is demonstrated via analytic test functions. The multifidelity locally optimized surrogate models are also applied to the creation of an aerodynamic database involving the steady turbulent flow around a NACA 00xx airfoil, where the influence of Mach number, angle of attack, and thickness-to-chord ratio variations on the lift and drag coefficient are studied.

Optimal Dynamic Point Selection for Power Minimization in Multiuser Downlink CoMP

This paper examines a coordinated multi-point transmission/reception system where multiple base-stations (BSs) employ coordinated beamforming to serve multiple mobile-stations (MSs). Under the dynamic point selection mode, each MS can be assigned to only one BS at any time. This paper then presents a solution framework to optimize the BS associations and coordinated beamformers for all MSs. With target signal-to-interference-plus-noise ratios at the MSs, the design objective is to minimize either the weighted sum transmit power or the per-BS transmit power margin. Since the original optimization problems contain binary variables indicating the BS associations, finding their optimal solutions is a challenging task. To circumvent this difficulty, we first relax the original problems into new optimization problems by expanding their constraint sets. Based on the nonconvex quadratic constrained quadratic programming framework, we show that these relaxed problems can be solved optimally. Interestingly, with the first design objective, the obtained solution from the relaxed problem is also optimal to the original problem. With the second design objective, a suboptimal solution to the original problem is then proposed, based on the obtained solution from the relaxed problem. Simulation results show that the resulting jointly optimal BS association and beamforming design significantly outperforms fixed BS association schemes.

Call Blocking Probability and Effective Throughput for Call Admission Control of CoMP Joint Transmission

Coordinated multipoint transmission (CoMP) with the joint transmission (JT) scheme is employed in cellular networks to improve data rate and cell-edge throughput and/or to increase system throughput. In CoMP-JT, since the user equipment (UE) is capable of receiving the desired signal from an adjacent cell, as well as from its serving cell, it can achieve an improved signal-to-interference ratio (SIR). However, this scheme entails exhaustion of resources in serving base stations. In this paper, we propose a call admission control scheme (CAC) to improve the performance of CoMP-JT-based cellular systems. In this CAC scheme, the CoMP-JT technique is applied to UEs located near the edge of a cell. To establish an analytical model for CoMP-JT-based cellular systems, we introduce a model for traffic analysis using a 2-D Markov chain and approximate the computation of the power sum of multiple lognormal random components in a multicell environment. Performances of CoMP-JT-based cellular systems are evaluated with respect to two quality-of-service (QoS) constraints pertaining to call-blocking and outage probabilities, respectively, from the network-layer and the physical-layer perspectives. We first measure the resource utilization and call blocking probability for the downlink resources for various offered loads in the cell. Based on that, we obtain the outage probability and effective throughput of the system. The analytical results are compared with computer simulations. Finally, we consider the dynamic point selection (DPS) scheme that is a modified version of the JT scheme, in which some resources of adjacent cells are muted.

Discovering Analytic Associate Rule Filtering on Multi-Dimensional Data Streams

Multidimensional data points discover structural and chronological association inside the data streams. The PaDSkyline framework in a distributed environment utilized intra group optimization and multi filtering technique for skyline query processing. Skyline query processes within each group of distributed data sets, but dynamic filtering point selection was not performed with cost effective system. Similarity- Profiled temporal Association MINing mEthod (SPAMINE) used reference time sequences and threshold value to filter the information from real world data. Different similarity models for filtering temporal patterns were not very effective for performing the phase shift in time. To attain minimal phase shift based cost effective filtering on multidimensional data stream, Analytic Associate Rule Filtering (AARF) mechanism is proposed in this study. The main objective of AARF is to identify the relationship between attributes on multidimensional data and to filter out the independent attributes from the data streams. Initially, analytic association rule uses the weight computing factor to identify relationship and make inferences while testing multidimensional samples. Secondly, with the help of the analyzed relationship, the AARF mechanism uses the attribute independent criterion to discard negligible weight from association rule. Finally, to filter the analytic association rule with specified phase shift time, the ‘if-then’ strategy is used in AARF mechanism. AARF mechanism has an ability to make an analytic filtering with minimal phase shift time on multidimensional test dataset. The minimal phase shift time reduces the execution time factor and attains cost effective filtering system. Experiment is conducted using Japanese vowel multidimensional data set extracted from UCI repository for measuring the factors such as the average precision level, execution time, filtering query traffic efficiency and true positive rate.

Message-Passing-Based Dynamic Point Selection for Coordinated Multipoint Transmission

This letter develops a dynamic point selection strategy for coordinated multipoint transmission using a message-passing approach. The dynamic determination of the best transmit point for individual users with the objective of the sum-rate maximization can be cast as a bipartite $b$ -matching problem, the computational cost of which, however, becomes quickly intractable with the increasing number of users. Therefore, this letter develops a message-passing algorithm that solves this computationally demanding challenge. Simulation results show that the proposed algorithm outperforms existing greedy-style approaches and provides a very efficient solution for the maximal sum-rate configuration.

Vertical sectorization in self-organizing LTE-advanced networks

Cellular radio networks continuously evolve to respond the exponential growth in data traffic volume in mobile communications. Active antenna technology contributes to this evolution by introducing vertical sectorization, which splits the horizontal sector into two subsectors with respect to the elevation plane and doubles the number of cells that can be deployed. However, in order to guarantee a reliable and near-optimal operation of vertical sectorization in a system that applies universal frequency reuse, such as LTE-advanced, co-channel interference mitigation is essentially needed. In this article we propose a decentralized self-optimization method that can be used to mitigate the undesirable inter-cell interference by self-tuning the electrical antenna downtilt toward the optimal antenna elevation angle. The performance evaluations for the proposed self-optimization method are carried out for both coordinated and uncoordinated subsector transmission scenarios within the LTE-advanced framework using a dynamic LTE-advanced compliant system level SON simulator. Based on the extensive performance evaluations carried out for a realistic urban scenario, it is found that self-optimization improves the vertical sectorization performance 25 % in terms of virtual load. Furthermore, the performance gain reaches up to 30 % when vertical vectorization is provided with dynamic point selection and muting feature. Therefore, the article concludes that in LTE-advanced networks vertical sectorization can largely benefit from the antenna self-optimization and outperform the traditional horizontal sectorization approach with low algorithmic complexity .

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.