Energy efficient resource allocation in two-tier OFDMA networks with QoS guarantees

Masoudi, Meysam; Zaefarani, Hamidreza; Mohammadi, Abbas; Çavdar, Çiçek

doi:10.1007/s11276-016-1442-5

Cited by 11 publications

(7 citation statements)

References 37 publications

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“…To resolve the challenges caused by the binary variables, one approach is to relax the troublesome constraints, sub-channel allocation for instance, to shape the problem into a convex form and then making a hard decision in the end as we did in [49]. An alternative approach is to add auxiliary constraints to enforce the solution to be in our desired form as we will describe later.…”

Section: Solution Methodologymentioning

confidence: 99%

Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption

Masoudi¹,

Çavdar²

2017

Preprint

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A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. Utilizing the abundant processing capabilities of the clouds, mobile edge computing enables mobile devices with limited batteries to run resource hungry applications and to save power. However, it is not always true that edge computing consumes less power compared to device computing. It may take more power for the mobile device to transmit a file to the cloud than running the task itself. This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile edge computing networks. We consider the maximum acceptable delay as QoS metric to be satisfied in our network. We formulate the problem as a mixed integer nonlinear problem which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making. Simulation results illustrate that by utilizing the proposed algorithms, considerable power savings can be achieved, e.g., about 60% for large bit stream size compared to local computing baseline.

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Section: Solution Methodologymentioning

confidence: 99%

Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption

Masoudi¹,

Çavdar²

2017

Preprint

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“…To reduce complexity and increase design flexibility, recent work focuses on a two-level framework [10]–[11]. This framework divides the radio resource technique into frequency domain (FD) and time domain (TD) schedulers working independently of each other.…”

Section: Related Workmentioning

confidence: 99%

Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink

et al. 2019

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The increasing demand for network applications, such as teleconferencing, multimedia messaging and mobile TV, which have diverse requirements, has resulted in the introduction of Long Term Evolution (LTE) by the Third Generation Partnership Project (3GPP). LTE networks implement resource allocation algorithms to distribute radio resource to satisfy the bandwidth and delay requirements of users. However, the scheduling algorithm problem of distributing radio resources to users is not well defined in the LTE standard and thus considerably affects transmission order. Furthermore, the existing radio resource algorithm suffers from performance degradation under prioritised conditions because of the minimum data rate used to determine the transmission order. In this work, a novel downlink resource allocation algorithm that uses quality of service (QoS) requirements and channel conditions to address performance degradation is proposed. The new algorithm is formulated as an optimisation problem where network resources are allocated according to users’ priority, whereas the scheduling algorithm decides on the basis of users’ channel status to satisfy the demands of QoS. Simulation is used to evaluate the performance of the proposed algorithm, and results demonstrate that it performs better than do all other algorithms according to the measured metrics.

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“…The literature study reveals that most previous studies have been focusing on the investigation of performance limits for single technology scenarios, and hence, the impact of coexistent technologies with partial time/frequency overlapping has been neglected. While the literature study of interference management for cellular and WiFi networks is mature [15], there is a crucial need to investigate the interference impact for large-scale, heterogeneous, and short-packet communication, which is the focus of this work. The main contributions of this paper are as follows.…”

Section: A Literature Studymentioning

confidence: 99%

Grant-Free Radio Access IoT Networks: Scalability Analysis in Coexistence Scenarios

Masoudi

Azari

Yavuz

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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IoT networks with grant-free radio access, like SigFox and LoRa, offer low-cost durable communications over unlicensed band. These networks are becoming more and more popular due to the ever-increasing need for ultra durable, in terms of battery lifetime, IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this paper, we study the impact of coexisting competing radio access technologies on the system performance. Considering K technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, and experienced delay as a function of distance to the serving access point. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how introduction of a new coexisting technology may degrade the system performance in terms of success probability and battery lifetime. We further investigate solutions in which this destructive effect could be compensated, e.g., by densifying the network to a certain extent and utilizing joint reception.

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Energy efficient resource allocation in two-tier OFDMA networks with QoS guarantees

Cited by 11 publications

References 37 publications

Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption

Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption

Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink

Grant-Free Radio Access IoT Networks: Scalability Analysis in Coexistence Scenarios

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