Opportunistic Resource Allocation Research Articles

Opportunistic admission and resource allocation for slicing enhanced IoT networks

Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things (IoT) as it provides an enhanced user experience and elastic resource configuration. In the context of slicing enhanced IoT networks, both the Service Provider (SP) and Infrastructure Provider (InP) face challenges of ensuring efficient slice construction and high profit in dynamic environments. These challenges arise from randomly generated and departed slice requests from end-users, uncertain resource availability, and multi-dimensional resource allocation. Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments. To this end, we propose an Opportunistic Admission and Resource allocation (OAR) policy to deal with the issues of random slicing requests, uncertain resource availability, and heterogeneous multi-resources. The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources. To cope with the random slice requests and uncertain resource availability, we formulated this issue as a Markov Decision Process (MDP) to obtain the optimal admission policy, with the aim of maximizing the system reward. Furthermore, the buyer-seller game theory approach was adopted to realize the optimal resource allocation, while motivating each SP and InP to maximize their rewards. Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily, and outperforms the baseline schemes in terms of the system reward.

Energy-efficient opportunistic multi-carrier NOMA-based resource allocation for beyond 5G (B5G) networks

The interplay between the non-orthogonal multiple access (NOMA) and the opportunistic cognitive radio (CR)-based orthogonal frequency multiple access (OFDMA) has been recently realized as a promising paradigm to support the unprecedented massive connectivity demands of future beyond fifth-generation (B5G) wireless communication systems. In such systems, which are called multi-carrier NOMA CR-based systems, each licensed band reserved for primary users can be opportunistically utilized based on power-domain NOMA to serve a group of secondary users simultaneously. An important challenge in this domain is how to provide energy-efficient resource allocation techniques that attempt to strike a balance between the total throughput (i.e., the achieved sum-rate) and the power required to achieve that rate while satisfying network QoS demands and being aware of the unique characteristics of the CR operating environment. In this paper, we propose an energy-efficient resource allocation technique for multi-carrier NOMA CR-based systems, which aims at maximizing the overall energy efficiency (EE) of the system under a set of CR and NOMA constraints. The EE maximization problem is shown to be a fractional non-convex optimization, which is, in general, hard to optimize. To deal with the fractional and the non-convexity nature of the formulated EE maximization problem, we exploit the Dinkelbach’s algorithm to transfer the EE problem to a parameterized optimization problem. Then we use an iterative optimization approach to obtain the solution for the EE maximization problem. Simulation results reveal that this EE maximization-based resource allocation technique outperforms the existing resource allocation techniques in terms of the overall EE of the system while striking a good balance between the sum-rate and the transmit power consumption.

Attaining the recesses of the cognitive space.

Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.

New YARN Non-Exclusive Resource Management Scheme through Opportunistic Idle Resource Assignment

Efficiently managing resources and improving throughput in a large-scale cluster has become a crucial problem with the explosion of data processing applications in recent years. Hadoop YARN and Mesos, as two universal resource management platforms, have been widely adopted in the commodity cluster for co-deploying multiple data processing frameworks, such as Hadoop MapReduce and Apache Spark. However, in the existing resource management, a certain amount of resources are exclusively allocated to a running task and can only be re-assigned after that task is completed. This exclusive mode unfortunately leads to a potential problem that may under-utilize the cluster resources and degrade system performance. To address this issue, we propose a novel opportunistic and efficient resource allocation scheme, named OpERA, which breaks the barriers among the encapsulated resource containers by leveraging the knowledge of actual runtime resource utilizations to re-assign opportunistic available resources to the pending tasks. OpERA avoids incurring severe performance interference to active tasks by further using two approaches to efficiently balances the starvations of reserved tasks and normal queued tasks. We implement and evaluate OpERA in Hadoop YARN v2.5. Our experimental results show that OpERA significantly reduces the average job execution time and increases the resource (CPU and memory) utilizations.

Characterization of sparse WLAN data traffic in opportunistic indoor environments as a prior for coexistence scenarios of modern wireless technologies

CR enabled radio environment provides the seamless operating framework to cope with the requirements of next generation wireless systems like higher data rates, massive machine time communication and interference free coexistence scenarios of wireless technologies. Traffic characterization studies assist to optimize coexistence framework by providing necessary information about the usage patterns of wireless services in observed radio bands. Prior knowledge about the observed sparse wireless local area network (WLAN) data traffic is a key for opportunistic radio resource allocation and utilization in the given coexistence scenarios of WLAN either with cellular systems (including 5G-NR/4G Long Term Evolution (LTE)) or with low power wireless systems (including Bluetooth and ZigBee). Parametric methodology is adapted to model the sparse WLAN data traffic observed in 2.4 GHz Industrial, Scientific and Medical (ISM) band as spectral and temporal activity. The multivariate Gaussian mixture model (MGMM) is proposed in both scenarios where either dependency is considered between the observed WLAN data traffic or assuming that the observed traffic is independent and identically distributed (i.i.d). It is to be validated that in order to have an efficient characterization of sparse WLAN data traffic, the dependency (correlation) between neighbored frequency subbands must be considered. By considering the dependency either between neighbored frequency subbands or neighbored time domain signals actually help to characterize the sparse WLAN data traffic in more realistic way which could not be possible by assuming them i.i.d. Such statistics to characterize sparse data traffic really help as user activity for efficient allocation and utilization of radio resources in CR enabled coexistence scenarios of wireless technologies where WLAN acitivity is considered nominal either as secondary user or classify it grey spectrum as primary user.

Moving Towards Universal Health Coverage in Haiti

ABSTRACT Haiti announced in 2018 its aim to achieve universal health coverage. In this paper, we discuss what this objective means for the country and what next steps should be taken. To contextualize the notion, we framed Haiti en route to the 2030 goal and analyzed qualitatively the status quo in terms of geographic, financial, and service access. For each dimension, we focused on the context, the government’s policies and political agendas, their implementation progress, and key influential factors. Our analysis found little progress and numerous challenges. Geographic access was limited due principally to the insufficient number of facilities, difficulties in reaching health facilities, and local customs. Financial coverage was low because of the government’s insufficient budgets, inefficient budget allocation, and ineffective management. Service access also had room for significant improvement for a lack of basic infrastructure and resources, gaps between the essential service package guidelines, health professionals’ skills, and the needs, as well as deficiencies in people-centered care. These factors affected not only health service coverage but also its quality. We found that the root causes of these issues were composed of unstable financing mechanisms, opportunistic resource allocation, and ineffective management control systems. We suggest that to overcome these issues and achieve universal health coverage with decent service quality, Haiti’s health system needs to be reformed by implementing strategic financing, decentralized management systems, and community engagement in primary health care.

Utility-Based Opportunistic Scheduling Under Multi-Connectivity With Limited Backhaul Capacity

Multi-connectivity is a 5G key enabler to fulfill multi-service quality-of-service (QoS) requirements. This letter examines an opportunistic resource allocation problem under multi-connectivity and limited backhaul capacity in evolved LTE using two utility functions: 1) proportional fairness (PF) and 2) utility proportional fairness (UPF). We then propose an efficient algorithm to deal with the formulated NP-hard problem. Such algorithm guarantees to produce a solution with an explicit bound on the optimal solution. Finally, simulation results justify that UPF utility function with opportunistic scheduling in multi-connectivity can better satisfy QoS requirements.

Link state opportunistic routing for multihop wireless networks

Enhancing the quality of service is the crucial issue of future wireless networks. In this paper, we propose a new multihop wireless routing protocol inspired by opportunistic resource allocation strategies that take into account the variability of the radio conditions due to path loss, shadowing and multipath fading. Thanks to this knowledge, our proposition dynamically adapts the selected path accross time. The adaptation is function of each link state and the amount of channel information available. This allows to improve system performance in terms of delay and throughput. This solution can be used in all multihop wireless contexts but can have a special interest in wireless coverage zone extension context. Simulation results will show that the proposed routing protocol greatly outperforms the other existing protocols such as ad-hoc on-demand distance vector, optimized link state routing and extremely opportunistic routing protocols reducing mean packet delays by more than 50% in several scenarii.

Opportunistic Capacity-Based Resource Allocation for Chunk-Based Multi-Carrier Cognitive Radio Sensor Networks.

The spectrum allocation for cognitive radio sensor networks (CRSNs) has received considerable research attention under the assumption that the spectrum environment is static. However, in practice, the spectrum environment varies over time due to primary user/secondary user (PU/SU) activity and mobility, resulting in time-varied spectrum resources. This paper studies resource allocation for chunk-based multi-carrier CRSNs with time-varied spectrum resources. We present a novel opportunistic capacity model through a continuous time semi-Markov chain (CTSMC) to describe the time-varied spectrum resources of chunks and, based on this, a joint power and chunk allocation model by considering the opportunistically available capacity of chunks is proposed. To reduce the computational complexity, we split this model into two sub-problems and solve them via the Lagrangian dual method. Simulation results illustrate that the proposed opportunistic capacity-based resource allocation algorithm can achieve better performance compared with traditional algorithms when the spectrum environment is time-varied.

Performance of opportunistic fixed-beam spatial multiplexing in OFDMA uplink

Adaptive spatial multiplexing (SM) Multiple Input Multiple Output (MIMO) techniques enhance the spectral efficiency of wideband wireless communications in favorable radio channel conditions. In this study, we show the benefits of combining a traditional fixed-beam scheme and multiuser opportunistic radio resource allocation in space-time-frequency domains. This combining is feasible in wideband Orthogonal Frequency Domain Multiple Access (OFDMA) systems such as the Universal Mobile Telecommunications System (UMTS) Long Term Evolution (LTE). This study brings novel knowledge which indicates that an orthogonal fixed beam approach benefits from opportunistic radio resource allocation clearly more than a conventional antenna domain approach in frequency-selective radio channels. It was found out that the fixed beam radio link performance is enhanced in wideband adaptive radio links due to the fact that orthogonal beams reduce correlation between the MIMO channels in the allocated sub-bands. Moreover, beamforming gains bring higher Eigenvalues of the MIMO channel matrix in the opportunistically selected sub-bands. It is shown that the fixed beam deployment changes the distribution of the MIMO channel correlation values and Eigenvalues in a manner which enhances opportunistic multiuser gains in wideband time-frequency-selective MIMO radio channels. Simulation results with 2 × 2 spatial multiplexing in an OFDMA uplink indicate that the proposed beam domain scheme gives up to 80% data throughput gain over the corresponding antenna domain scheme in a pedestrian microcell environment.

A Distributed Optimization Framework for Multi-Channel Multi-User Small Cell Networks

Small cell enchantment is emerging as the key technique for wireless network evolution. One challenging problem for small cell enhancement is how to achieve high data rate with as-low-as-possible control and computation overheads. As a solution, we propose a low-complexity distributed optimization framework in this paper. Our solution includes two parts. One is a novel implicit information exchange mechanism that enables channel-aware opportunistic scheduling and resource allocation among links. The other is the sub-gradient based algorithm with a polynomial-time complexity. What is more, for large scale systems, we design an improved distributed algorithm based on insights obtained from the problem structure. This algorithm achieves a close-to-optimal performance with a much lower complexity. Our numerical evaluations validate the analytical results and show the advantage of our algorithms.

Opportunistic sharing scheme for spectrum allocation in wireless virtualization

The past few decades have witnessed an increasing growth in mobile and wireless network, leading to a corresponding fast growth in mobile demands. However, the proliferating mobile demands compel wireless network to face several challenges, such as the conflict between spectrum crisis and low resource utilization ratio, and the poor quality of service and quality of experience. Wireless virtualization, enabling multiple concurrent virtual networks running on shared wireless substrate resource, has been proposed as a promising way to overcome the plights of the current mobile and wireless network. How to efficiently allocate the resource, especially the spectrum resource, of physical network to multiple virtual networks is one fundamental and important challenge in wireless virtualization. This paper rethinks the characteristics of virtual networks' requirements, and then divides the requirement into a baseline part and a fluctuant part. Based on it, this paper introduces an opportunistic spectrum sharing approach, through which we formulate the spectrum resource allocation problem as an NP-Hard problem. Then, we propose our opportunistic spectrum resource allocation scheme for the wireless virtualization. Simulations validate the performance advantages of our approach and show that opportunistic spectrum sharing significantly improves the revenue, resource utilization and acceptance ratio of physical wireless network while decreasing the payments of virtual networks.

An opportunistic resource allocation approach for mixed QoS and non-QoS connections in OFDMA wireless networks

An opportunistic resource allocation approach is proposed to guarantee both fair resource allocation and high system throughput under combinations of QoS and non-QoS connections in OFDMA networks. This approach features dynamic connection classification and packet prioritization based on real-time network conditions and QoS constraints. A classifier is first employed to prioritize QoS connections by observing the channel state of each subscriber station and the utilization of network resources. It performs a finite-horizon Markov decision process with dynamic rules affected by system load. The transmission order of packets is then determined by an opportunistic multiservice scheduler according to the QoS requirements of connections and the output of the classifier. Having the scheduling result, an allocator assigns slots to the scheduled packets, and its output is linked back to the connection classifier through a resource usage observer for all subscriber stations. The sub-channel allocation problem is also solved by cooperation between the slot allocator and the packet scheduler. Results of numerical analysis and NS2 simulation confirm the advantages claimed above. The same conclusion can also be drawn from the comparison with several existing approaches in terms of system throughput, service successful ratio, average spectral efficiency, and system revenue.

Capacity Analysis of Downlink MIMO-OFDMA Resource Allocation with Limited Feedback

Opportunistic resource allocation for multiple users utilizing multiple-input multiple-output (MIMO) channel in orthogonal frequency division multiple access (OFDMA) is used to attain the spectral efficiency required in future wireless communications networks. In this paper, we express analytical outage capacity results and average capacity results with optimal rate adaptation for MIMO-OFDMA systems with limited feedback. A resource block (RB)-wise SNR quantization feedback scheme and a best-M feedback method are studied for RB allocation. Feedback based transmit antenna selection (AS) is considered in the analysis. An imperfect feedback channel has a significant impact on the performance of practical feedback based communication. The proposed analysis also considers imperfect feedback in the RB allocation and AS.

A Study of Trade-Off Between Opportunistic Resource Allocation and Interference Alignment in Femtocell Scenarios

One of the main problems in wireless heterogeneous networks is interference between macro- and femto-cells. Using Orthogonal Frequency-Division Multiple Access (OFDMA) to create multiple frequency orthogonal sub-channels, this interference can be completely avoided if each sub-channel is exclusively used by either macro- or a femto-cell. However, such an orthogonal allocation may be inefficient. We consider two alternative strategies for interference management, opportunistic resource allocation (ORA) and interference alignment (IA). Both of them utilize the fading fluctuations across frequency channels in different ways. ORA allows the users to interfere, but selecting the channels where the interference is faded, while the desired signal has a good channel. IA uses precoding to create interference-free transmissions; however, such a precoding changes the diversity picture of the communication resources. In this letter we investigate the interactions and the trade-offs between these two strategies.

Joint Sensing Adaptation and Resource Allocation for Cognitive Radio with Imperfect Sensing

In cognitive radio networks, imperfect spectrum sensing causes harmful interferences or low utilization of unused bands, depending on an access policy of the secondary system. Since these losses have a trade-off relationship, it is hard to decide proper sensing and access policies for the licensed bands. To deal with this problem, we propose a joint algorithm for sensing adaptation and opportunistic resource allocation. The joint optimization problem minimizes the total expected cost of the losses and utilities which are likely generated for the secondary system. To protect the primary system and ensure the secondary system's utility, we employ an average interference threshold constraint and a QoS constraint for the secondary system. Two types of average secondary utilities are considered for the QoS constraint; one is averaged over only transmitted frames and the other is averaged over total frames. Based on energy detection, we derive feasible ranges of sensing threshold to satisfy the constraints. Simulation results show that the proposed scheme dynamically adapts sensing threshold depending on the network environment and minimizes the total cost of the secondary system. In addition, we investigate the performance of the proposed scheme with the two types of the secondary utility.

Antenna Selection in MIMO Cognitive Radio

Wireless users use radio frequency (RF) channels for data and message communication. The recent research reviles that the most appropriate to tackle the issues related to spectrum utilization is a function of time and space calls for dynamic access strategies that adapt to the electromagnetic environment. Cognitive radio is one such solution with the ability to sense the RF channel evaluation and adaptively react intelligently in order to optimize the usage of the available spectrum. In this paper we focus on opportunistic resource allocation between the access points (AP) and the wireless stations (STA) for the required spectrum management policies of the wireless systems. A concurrent communication of the cognitive users, competing over the physical resources for the end users. Based on the requirements of this we propose and analyze a channel capacity [6] enhancement technique to design a cognitive multiple input multiple output (MIMO) transceiver system and propose low complexity antenna selection [15] algorithms. Using this technique only a subset of the available antennas to transmit or receive signal greatly reduce the cost and complexity of the physical layer resources of cognitive MIMO system.

Opportunistic delay‐margin‐based resource allocation for next‐generation wireless networks

AbstractThis paper studies and develops efficient traffic management techniques for downlink transmission at the base station (BS) of multi‐service IP‐based networks by combining quality‐of‐service (QoS) provision and opportunistic wireless resource allocation. A delay‐margin‐based scheduling (DMS) for downlink traffic flows based on the delays that each packet has experienced up to the BS is proposed. The instantaneous delay margin, represented by the difference between the required and instantaneous delays, quantifies how urgent the packet is, and thus it can determine the queuing priority that should be given to the packet. The proposed DMS is further integrated with the opportunistic scheduling (OPS) to develop various queueing architectures to increase the wireless channel bandwidth efficiency. Different proposed integration approaches are investigated and compared in terms of delay outage probability and wireless channel bandwidth efficiency by simulation. Copyright © 2010 John Wiley & Sons, Ltd.

Control of the trade-off between resource efficiency and user fairness in wireless networks using utility-based adaptive resource allocation

This work addresses the fundamental problem of the trade-off between resource efficiency and user fairness in wireless networks that use opportunistic radio resource allocation. The concept of managing the trade-off by controlling the system fairness index is applied. In order to do that, two adaptive utility-based resource allocation frameworks consisting of subcarrier assignment and power allocation algorithms are proposed. These frameworks are named utility-based alpha-rule and beta-rule, and are suitable for non-real-time and real-time services, respectively. Not only can both frameworks be designed to work as wellknown classic policies found in the literature, but also as adaptive policies, which are able to meet a desired system fairness target. System level simulations show that the proposed frameworks are powerful tools to the network operator, since they can decide in which trade-off point of the efficiency-fairness plane they want to operate the system.

Biological Swarm Intelligence Based Opportunistic Resource Allocation for Wireless Ad Hoc Networks

Particle swarm optimization (PSO) is one of the most important biological swarm intelligence paradigms. However, the standard PSO algorithm can easily get trapped in the local optima when solving complex multimodal problems. In this paper, an improved adaptive particle swarm optimization (IAPSO) is presented. Based on IAPSO, a joint opportunistic power and rate allocation (JOPRA) algorithm is proposed to maximize the sum of source utilities while minimize power allocation for all links in wireless ad hoc networks. It is shown that the proposed JOPRA algorithm can converge fast to the optimum and reach larger total data rate and utility while less total power is consumed by comparison with the original APSO. This thanks to the dynamic change of the maximum movement velocity of the particles, the use of the modified replacement procedure in constraint handling, and the consideration of the state of the optimization run and the population diversity in stopping criteria. Numerical simulations further verify that our algorithm with the IAPSO outperforms that with the original APSO.