Effective Capacity Model Research Articles

A simple but effective capacity model for check and design of beam-column joints in RC seismic buildings

The design of beam-column joints is a crucial step of the design of seismic-resistant RC framed structures. Premature joint failure may prevent yielding of beams and the achievement of the target collapse mechanism. Hence, an effective capacity model is essential. In the past, Italian building code in force before NTC (2008) required that a minimum amount of ties, equal to that used in the column, be provided in the joint, but it did not stipulate any verification of the joint. Eurocode 8 (2005) and subsequently Italian NTC (2008 and 2018), recognize that shear in beam-column joint is higher than in columns and recommend the explicit verification of the joint. The suggested capacity model is based on stress analysis and is very demanding in terms of joint ties. The new draft of Eurocode 8 (2020) provides a different capacity model, based on research carried on by Fardis (2021), which better reflects the state of art on this topic under both theoretical and experimental point of view. This paper examines the capacity model for shear resistance of joints adopted in Eurocode 8 draft, underlining its advantages but also some difficulties in practical application. Based on a parametric analysis of a wide set of RC joints, it is showed that it is possible to assume that the joint rebars are yielded when the joint attains its ultimate limit state. This assumption does not determine any significant loss of precision in evaluating the shear resistance of the joint and leads to a capacity model consistent with other models adopted in Eurocode 8, e.g. those for shear resistance of RC beams. Furthermore, it allows a closed form solution of the problem that leads to a very simple procedure both for the verification of joint resistance and for the optimization of the design of horizontal and vertical rebars of the joint.

Secure Rate Control and Statistical QoS Provisioning for Cloud-Based IoT Networks

The Internet of Things (IoT) facilitates physical things to detect, interact, and execute activities on-demand, enabling a variety of applications such as smart homes and smart cities. However, it also creates many potential risks related to data security and privacy vulnerabilities on the physical layer of cloud-based Internet of Things (IoT) networks. These can include different types of physical attacks such as interference, eavesdropping, and jamming. As a result, quality-of-service (QoS) provisioning gets difficult for cloud-based IoT. This paper investigates the statistical QoS provisioning of a four-node cloud-based IoT network under security, reliability, and latency constraints by relying on the effective capacity model to offer enhanced QoS for IoT networks. Alice and Bob are legitimate nodes trying to communicate with secrecy in the considered scenario, while an eavesdropper Eve overhears their communication. Meanwhile, a friendly jammer, which emits artificial noise, is used to degrade the wiretap channel. By taking advantage of their multiple antennas, Alice implements transmit antenna selection, while Bob and Eve perform maximum-ratio combining. We further assume that Bob decodes the artificial noise perfectly and thus removes its contribution by implementing perfect successive interference cancellation. A closed-form expression for an alternative formulation of the outage probability, conditioned upon the successful transmission of a message, is obtained by considering adaptive rate allocation in an ON-OFF transmission. The data arriving at Alice’s buffer are modeled by considering four different Markov sources to describe different IoT traffic patterns. Then, the problem of secure throughput maximization is addressed through particle swarm optimization by considering the security, latency, and reliability constraints. Our results evidence the considerable improvements on the delay violation probability by increasing the number of antennas at Bob under strict buffer constraints.

Dynamic Multiconnectivity Based Joint Scheduling of eMBB and uRLLC in 5G Networks

Multiple use cases are the dominant feature for the fifth-generation mobile communication system (5G). Enhanced mobile broadband (eMBB) and ultrareliability low latency communication (uRLLC) are considered as two main scenarios. Different from the eMBB, which requires high data rate, the uRLLC requires extremely high reliability and very low latency. With multiple demands, the coexistence of eMBB and uRLLC will be an urgent challenge for the 5G networking strategy. In this article, a dynamic multiconnectivity (MC)-based joint scheduling framework with traffic steering for eMBB and uRLLC is proposed. The eMBB and uRLLC are specifically sliced with each other, which avoids the queue of uRLLC. A modified effective capacity (EC) model is proposed to measure the performance of our framework, which improves the throughput of eMBB under the guarantee of uRLLC latency. The proposed EC model turns the analytical target to the queue of users, different from the queue of base stations in traditional EC model. By a two-step optimization, the typical mixed integer nonlinear programming conducted by the EC model is decomposed into the integral programming and the nonlinear programming. The system-level simulation results show the proposed framework enhances the system throughput effectively while guaranteeing the uRLLC latency.

Cross-layer framework for capacity analysis in multiuser ultra-dense networks with cell DTx

Cell discontinuous transmission (Cell DTx) is a key technology to mitigate inter-cell interference (ICI) in ultra-dense networks (UDNs). The aim of this work is to understand the impact of Cell DTx on physical-layer sum rates of SBSs and link-layer quality-of-service (QoS) performance in multiuser UDNs. In this work, we develop a cross-layer framework for capacity analysis in multiuser UDNs with Cell DTx. In particular, we first extend the traditional one-dimensional effective capacity model to a new multidimensional effective capacity model to derive the sum rate and the effective capacity. Moreover, we propose a new iterative bisection search algorithm that is capable of approximating QoS performance. The convergence of this new algorithm to a unique QoS exponent vector is later proved. Finally, we apply this framework to the round-robin and the max-C/I scheduling policies. Simulation results show that our framework is accurate in approximating 1) queue length distribution, 2) delay distribution and 3) sum rates under the above two scheduling policies, and further show that with the Cell DTx, systems have approximately 30% higher sum rate and 35% smaller average delay than those in full-buffer scenarios.

Resource Allocation for Low-Latency Vehicular Communications: An Effective Capacity Perspective

Vehicular communications face a tremendous challenge in guaranteeing low latency for safety-critical information exchange due to fast varying channels caused by high mobility. Focusing on the tail behavior of random latency experienced by packets, latency violation probability (LVP) deserves particular attention. Based on only large-scale channel information, this paper performs spectrum and power allocation to maximize the sum ergodic capacity of vehicle-to-infrastructure (V2I) links while guaranteeing the LVP for vehicle-to-vehicle (V2V) links. Using the effective capacity theory, we explicitly express the latency constraint with introduced latency exponents. Then, the resource allocation problem is decomposed into a pure power allocation subproblem and a pure spectrum allocation subproblem, both of which can be solved with global optimum in polynomial time. Simulation results show that the effective capacity model can accurately characterize the LVP. In addition, the effectiveness of the proposed algorithm is demonstrated from the perspectives of the capacity of the V2I links and the latency of the V2V links.

Effective Capacity in Wireless Networks: A Comprehensive Survey

Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical-layer channel models, however, do not explicitly consider quality-of-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks, such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications, such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

A Close Approximation of the Nonempty Buffer Probability Over Nakagami- <inline-formula> <tex-math notation="LaTeX">$m$</tex-math></inline-formula> Fading Channels

Based on the effective capacity model, the tail probability of backlog size in a wireless communication system can be characterized by two effective capacity functions, namely, the QoS exponent and the nonempty buffer probability. The QoS exponent is well understood in the literature; however, there is little known about the other function, i.e., the nonempty buffer probability, because this function has not been exactly derived. In this letter, we derive a new approximation formula for this function over Nakagami- $\boldsymbol{m} $ fading channels. Simulation results indicate that our formula can closely approximate this function and is more accurate than existing approximation formulae.

A novel Effective Capacity-based framework for providing statistical QoS guarantees in IEEE 802.11 WLANs

This article proposes a performance model of the IEEE 802.11 MAC layer that employs the notion of Effective Capacity. In particular, the paper establishes that an IEEE 802.11 mobile station can be regarded as a Semi-Markovian bursty server of the On/Off type, with known distributions for the On and Off periods, and subsequently applies known results for Semi-Markovian models to derive the Effective Capacity function of this On/Off server. The general Effective Bandwidth/Capacity theory can then be used for computing buffer overflow probabilities and for employing simple traffic control policies to enforce related QoS guarantees. The policies guarantee a soft bound on the buffer overflow probability and are suitable for real-time traffic control over WLANs. The Effective Capacity model of IEEE 802.11 stations is originally developed by assuming that the other competing stations are saturated. This is a conservative assumption that becomes very accurate in a highly loaded network. Subsequently, the model is adapted to encompass lightly loaded networks as well. In the adapted model, each mobile station directly measures a few model parameters, instead of calculating them on the basis of the saturation assumption, and uses these measurements in the computation of its Effective Capacity function. The theoretical results are checked against simulations, validating the appropriateness of the model.

Delay-Constrained Optimal Link Scheduling in Wireless Sensor Networks

We consider the optimal link scheduling problem in wireless sensor networks. The optimal link scheduler under our consideration is intended to assign time slots to different users to minimize channel usage subject to constraints on data rate, delay bound, and delay bound violation probability; we study the problem under fading channels and a signal-to-interference-plus-noise-ratio (SINR)-based interference model. To the best of our knowledge, this problem has not been studied previously. We use the effective capacity model to formulate the optimal link scheduling as a mixed-integer optimization problem. We first discuss a simple case, namely, the scheduling with a fixed power allocation, and then extend to the case with variable transmit power. Moreover, because the mixed-integer optimization problem is NP-hard, we propose a computationally feasible column-generation-based iterative algorithm to search for a suboptimal solution to the problem. Finally, we design a medium access control (MAC) protocol to implement our optimal link scheduling strategy in practical wireless networks. Simulation results demonstrate that our proposed scheme achieves a larger throughput, a larger admission region, and a higher power efficiency than a benchmark time-division multiple-access (TDMA) system.