Robust channel quality indicator reporting for multi-carrier and multi-user systems

Ahmad, Ayaz; Hassan, Naveed Ul; Shah, Nadir

doi:10.1016/j.comnet.2014.09.007

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…After ensuring the availability of donor nodes, the response time is a key metric that decides the selection of the node for a possible task offloading process. 59 In this context, the "data transmission" of the tasks to be offloaded plays a primary role. Given the importance of data-transmission-dependent total response time, we now elaborate on the design that we followed for the purpose in the following text.…”

Section: System Modelmentioning

confidence: 99%

“…The pseudocodes for both algorithms are located in the Section 4.1 as we have explained the entire PAM distributed scheduling and allocation process there. After ensuring the availability of donor nodes, the response time is a key metric that decides the selection of the node for a possible task offloading process 59 . In this context, the “data transmission” of the tasks to be offloaded plays a primary role.…”

Section: System Modelmentioning

confidence: 99%

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PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

Khattak,

Yuan,

Ahmad

et al. 2024

Trans Emerging Tel Tech

Self Cite

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Recent advancements in distributed computing systems have shown promising prospects in enabling the effective usage of many next‐generation applications. These applications include a wide range of fields, such as healthcare, interactive gaming, video streaming, and other related technologies. Among such solutions are the evolving vehicular fog computing (VFC) frameworks that make use of IEEE and 3GPP protocols and use advanced optimization algorithms. However, these approaches often rely on outdated protocols or computationally intensive mathematical techniques for solving or representing their optimization models. Additionally, some of these frameworks have not thoroughly considered the type of application during their evaluation and validation phases. In response to these challenges, we have developed the “predictive analytics and modules” (PAM) framework, which operates on a time and event‐driven basis. It utilizes up‐to‐date 3GPP protocols to address the inherent unpredictability of VFC‐enabled distributed computing systems required in smart healthcare systems. Through a combination of a greedy heuristic approach and a distributed offloading architecture, PAM efficiently optimizes decisions related to task offloading and computation allocation. This is achieved through specialized algorithms that provide support to computationally weaker devices, all within a time frame of under 100 ms. To assess the performance of PAM in comparison to three benchmark methodologies, the evaluation pathways that we employed are average response time, probability density function, pareto‐analysis, algorithmic run time, and algorithmic complexity.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

Khattak,

Yuan,

Ahmad

et al. 2024

Trans Emerging Tel Tech

Self Cite

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show abstract

“…For practical implementations, the eNodeB classifies the active UEs into GR and BR UEs according to the received wideband CQI feedback. In fact, the wideband CQI is a discrete integer [26] that reflects the average channel quality of a given UE across the entire cell bandwidth. Once the CQI feedback is received by the eNodeB, it decides whether the concerned UE is a GR UE, i.e.…”

Section: Icic Approachesmentioning

confidence: 99%

Cooperative resource management and power allocation for multiuser OFDMA networks

et al. 2017

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Mobile network operators are facing the challenge to increase network capacity and satisfy the growth in data traffic demands. In this context, Long Term Evolution (LTE) networks, LTE-Advanced networks, and future mobile networks of the Fifth Generation seek to maximize spectrum profitability by choosing the frequency reuse-1 model. Due to this frequency usage model, advanced radio resource management and power allocation schemes are required to avoid the negative impact of interference on system performance. Some of these schemes modify resource allocation between network cells, while others adjust both resource and power allocation. In this article, we introduce a cooperative distributed interference management algorithm, where resource and power allocation decisions are jointly made by each cell in collaboration with its neighboring cells. Objectives sought are: increasing user satisfaction, improving system throughput, and increasing energy efficiency. The proposed technique is compared to the frequency reuse-1 model and to other state-of-the-art techniques under uniform and non-uniform user distributions and for different network loads. We address scenarios where throughput demands are homogeneous and non-homogeneous between network cells. System-level simulation results demonstrate that our technique succeeds in achieving the desired objectives under various user distributions and throughput demands.

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TSM: Temporal segmentation and modules-based computation offloading using predictive analytics and NR-V2X

Khattak,

Yuan,

Ahmad

et al. 2023

Internet of Things

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Robust channel quality indicator reporting for multi-carrier and multi-user systems

Cited by 4 publications

References 32 publications

PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

Cooperative resource management and power allocation for multiuser OFDMA networks

TSM: Temporal segmentation and modules-based computation offloading using predictive analytics and NR-V2X

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