Effective Capacity in MIMO Channels With Arbitrary Inputs

Cross-layer analysis has been gaining an increasing attention as a powerful tool to study and assess different qualityof-service (QoS) mechanisms in wireless networks. Regarding the physical and data-link layers, in this paper we provide a cross-layer study for visible light communication (VLC) systems operating under statistical QoS constraints, which are inflicted as limits on the delay violation and buffer overflow probabilities. We assume that the VLC access point (AP) is unaware of the channel conditions, thus the AP sends the data at a fixed rate. Under this assumption, and considering an ON-OFF data source, we employ the maximum average data arrival rate at the AP buffer and the non-asymptotic bounds on buffering delay as the main performance measures. Through numerical results, we illustrate the impacts of different physical and data-link parameters on the system performance.

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“…and the maximum average arrival rate, denoted as δ ref (θ), has the same expression as in (10) with D = E d h {e −θR }.…”

Section: Numerical Resultsmentioning

confidence: 99%

VLC Systems with Fixed-Rate Transmissions under Statistical Queueing Constraints

Hammouda

Peissig

2018

2018 11th International Symposium on Communication Systems, Networks &Amp; Digital Signal Processing (CSNDSP)

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“…Hammouda et al. obtained the optimal input covariance matrix to maximize the EC under the short‐term average power constraint [22].…”

Section: Related Workmentioning

confidence: 99%

Effective capacity optimization for UDN

Xie

Ai²,

2022

IET Communications

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For the ultra‐dense distributed network (UDN), the intensive deployment leads to the collision problem of the unlicensed spectrum access due to the random contention access mechanism of the unlicensed spectrum, which is difficult to improve the user quality of service (QoS). Here, the wireless access process of the licensed assisted access (LAA) network is modelled as a four‐state semi‐Markov model. Then, the effective capacity (EC) of the UDN based on the random access is derived, and the network capacity limit under the QoS requirements in the unlicensed spectrum is given. Based on the obtained EC, two power control algorithms are proposed to maximize the EC and effective energy efficiency (EEE) respectively under the user QoS requirements. Finally, numerical results verify the accuracy of the proposed theory, and the capacity‐delay domain of the UDN is given. The simulation results show that the proposed algorithms improve the EC and the EEE compared with existing classical schemes.

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“…Step (a) comes from using the maximum service rate R since the service process depends on the fading coefficients that vary independently every block. We are interested in finding the maximum average arrival rate of Markovian sources that can support a certain fading channel while satisfying the QoS requirement in (17). Regarding this, the QoS requirements are satisfied when the effective bandwidth of the arrival process becomes equal to the effective capacity of the service process, i.e., a(θ, c) � E C (θ, R) [39].…”

Section: Effective Capacitymentioning

confidence: 99%

“…us, effective capacity is equivalent to effective bandwidth that helps in analyzing the resources needed for supporting different time-varying arrival processes. Recently, the effective capacity theory has been used as a relevant cross-layer designing tool that allows to link PHY to the statistical QoS performance of upper layers in several different scenarios [15][16][17], while ensuring high security fidelity.…”

Section: Introductionmentioning

confidence: 99%

“…A detailed analysis of the effective capacity for a MIMO wireless system with lowpower, wideband, and high signal-to-noise ratio (SNR) regimes was carried out in [16], where statistical queuing constraints were imposed as a limitation on buffer violation probabilities in the large-queue-length regime. Meanwhile, the authors in [17] investigated the throughput and energy efficiency of a MIMO system subject to a statistical QoS constraint. However, as the traffic originated from IoT devices is massive due to the large number of devices in a IoT network, such networks require the support of per-link bit rate, delay and reliability, and security, which is not addressed in the above works.…”

Section: Introductionmentioning

confidence: 99%

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

Irfan

Alves

López

2021

Security and Communication Networks

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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.

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Effective Capacity in MIMO Channels With Arbitrary Inputs

Cited by 6 publications

References 75 publications

VLC Systems with Fixed-Rate Transmissions under Statistical Queueing Constraints

VLC Systems with Fixed-Rate Transmissions under Statistical Queueing Constraints

Effective capacity optimization for UDN

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

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