SINR Model With Best Server Association for High Availability Studies of Wireless Networks

Ohmann, David; Awada, Ahmad; Viering, Ingo; Simsek, Meryem; Fettweis, Gerhard

doi:10.1109/lwc.2015.2495303

Cited by 23 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To minimize the influence of noise in the channels with high interference levels (e.g. due to the coexistence with IEEE 802.11), channel blacklisting is utilized by considering the wireless channel quality such as signal-to-interferencenoise ratio (SINR) [30], received signal strength indication (RSSI) [31] and packet reception ratio (PRR) [32]. The medium access control layer enables the TDMA technique that performs channel-hopping at each slot boundary to arbitrate and coordinate network communications.…”

Section: Network Model a Brief Overview Of Wirelesshartmentioning

confidence: 99%

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

et al. 2021

View full text Add to dashboard Cite

The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, the opportunistic spectrum access capability of cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast network, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the successful transmission probability of the packets among field devices by opportunistically allocating the primary channels to the devices for data transmissions. In this paper, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process by considering the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe to maximize the received packets at the GW. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.INDEX TERMS wirelessHART, cognitive radio, markov decision process, industrial scientific medical

show abstract

Section: Network Model a Brief Overview Of Wirelesshartmentioning

confidence: 99%

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Even with these models, deriving the packet loss probability in URLLC is very challenging. To overcome this difficulty, a numerical method for evaluating packet loss probability was proposed by D. Öhmann et al [182]. More recently, C. She et al analyzed the impact of shadowing correlation on the network availability, where a packet is transmitted via a cellular link and a Device-to-Device (D2D) link [183].…”

Section: Communication Theory Tools 1) Characterizing Correlations Of...mentioning

confidence: 99%

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

She¹,

Sun²,

Gu³

et al. 2020

Preprint

View full text Add to dashboard Cite

As one of the key communication scenarios in the 5th and also the 6th generation (6G) cellular networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. The state-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLC. A holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how to integrate theoretical knowledge (models, analysis tools, and optimization frameworks) of wireless communications into different kinds of deep learning algorithms for URLLC. We first introduce the background of URLLC and review promising network architectures and deep learning frameworks in 6G. To better illustrate how to improve learning algorithms with theoretical knowledge, we revisit model-based analysis tools and cross-layer optimization frameworks for URLLC. Following that, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLC and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

show abstract

“…The error was examined by them. They also addressed how the signaling lag relates to the size of the area For network availability analysis, the SINR model was proposed in [15] in which the likelihood of long blocklength short-lived error decoded in the SINR model was not considered. In addition, it was suggested a semi-analytical SINR method.…”

Section: Literature Reviewmentioning

confidence: 99%

Delay Analysis of Wireless Cellular Networks for Better Qos

Palle,

Shivashankar

2019

IJEAT

View full text Add to dashboard Cite

The message for call requests is created by mobile devices during a call which is then sent to a base station (BS). A BS processes the response of a call request and chooses to accept or deny the call. Signals such as location notifications, paging and switching due to user mobility take a significant share of the total traffic load within mobile cellular networks. Therefore, between signaling packets, the maximum allowable delays may differ. This time will be delayed because if the time is longer than the allowable pause. The quality of service is therefore reduced, which for service providers is not acceptable. In this paper, we propose an empirical model to determine an overall delay in the processing of wireless cell network signaling packets, which involves the delay in the radio channel and the wired component delay in processing. We are demonstrating the effectiveness of priority processing in reducing handoff delays. We also assess the delay between cells according to their positions in the area and their influence on processing delays by the number of nodes. In addition, we evaluate the difference in delay between cells depending on their position within the network area and how many stations influence time delayed processing.

show abstract

SINR Model With Best Server Association for High Availability Studies of Wireless Networks

Cited by 23 publications

References 10 publications

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

Delay Analysis of Wireless Cellular Networks for Better Qos

Contact Info

Product

Resources

About