Delay-Aware and Energy-Efficient Carrier Aggregation in 5G Using Double Deep Q-Networks

Khoramnejad, Fahime; Joda, Roghayeh; Sediq, Akram Bin; Abou-zeid, Hatem; Atawia, Ramy; Boudreau, Gary; Erol‐Kantarci, Melike

doi:10.1109/tcomm.2022.3204846

Cited by 18 publications

(13 citation statements)

References 52 publications

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“…• Energy Risk Cost (C e ): As the UAV's current battery power after the action (Υ R ja ) goes down, the situation becomes more serious. This is reflected in exponential growth (g) seen in (3).…”

Section: A Deep Risk Sensitive Learning With Risk Measurementmentioning

confidence: 97%

“…Mollahasani et al [2] proposed actor-critic-based learning to reduce energy consumption in an open radio access network architecture. Khoramnejad et al [3] addressed energy consumption and quality of service performance metrics in 5G networks by using a multi-agent double deep Q-network. Pamuklu et al [4] included solar panels in their architecture as an alternative energy source for reducing the dependency on grid energy.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

Catherine

Pamuklu

Syed

et al. 2022

2022 IEEE Future Networks World Forum (FNWF)

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A deep reinforcement learning technique is presented for task offloading decision-making algorithms for a multi-access edge computing (MEC) assisted unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT) environment. The task offloading technique uses financial concepts such as cost functions and conditional variable at risk (CVaR) in order to quantify the damage that may be caused by each risky action. The approach was able to quantify potential risks to train the reinforcement learning agent to avoid risky behaviors that will lead to irreversible consequences for the farm. Such consequences include an undetected fire, pest infestation, or a UAV being unusable. The proposed CVaR-based technique was compared to other deep reinforcement learning techniques and two fixed rulebased techniques. The simulation results show that the CVaRbased risk quantifying method eliminated the most dangerous risk, which was exceeding the deadline for a fire detection task. As a result, it reduced the total number of deadline violations with a negligible increase in energy consumption.

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Section: A Deep Risk Sensitive Learning With Risk Measurementmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

Catherine

Pamuklu

Syed

et al. 2022

2022 IEEE Future Networks World Forum (FNWF)

View full text Add to dashboard Cite

show abstract

“…On the other hand, the 3rd Generation Partnership Project (3GPP) introduces carrier aggregation (CA) as one of the most important features in Rel-10, and subsequently enhances in Rel-11 and Rel-12. CA technology enables the aggregation of multiple resource blocks (RBs) from different component carriers (CCs) into a virtual carrier with larger bandwidth to achieve higher throughput and lower latency [11,12]. Without a doubt, as a remedy with the understanding that it cannot be used to achieve the needed bandwidth for the currently allocated licensed frequency spectrum, CA is introduced as one of the most momentous features in 5G networks [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the integration of 5G-U and CA technology issues a challenge to existing networks, which cannot be addressed by the currently proposed schemes. In [12,21], Qlearning and double Q-learning methods are used for carrier selection and discontinuous transmission under efficient coexistence of 5G and Wi-Fi in unlicensed spectrum bands. However, existing studies represented by [12,21] did not consider multiple types of services in LTE-Advanced (LTE-A), which has already been one of the most fundamental and important network environments.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band

Li,

Zheng,

Guan

et al. 2023

Applied Sciences

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Carrier aggregation (CA) technology in the Fifth-Generation (5G) network can be considered as a major feature for high-rate data services, which can be combined with the concept of 5G in unlicensed band (5G-U) for data offloading to further enhance the efficiency of the scarce licensed spectral resource. However, the introduction of cellular services in an unlicensed spectrum can force unlicensed band users to silent mode. Meanwhile, the coexistence among different radio access technologies operating on the same frequency band is a complex issue, especially considering the requirement diversity of quality of service (QoS) in networking. In this paper, we propose an optimal resource allocation mechanism to maximize system performance satisfaction for multiple types of services with different QoS requirements in the 5G-U network with a CA feature to address the challenge. Additionally, the performance of services in Wi-Fi system is protected by reserving the minimum spectrum resource when offloading for the 5G-U network. Analytical results on the mean ergodic achievable rate of Wi-Fi services and the mean packet delay of 5G services are provided to maximize the system performance satisfaction. Numerical results demonstrate the effectiveness of the proposed algorithm, wherein optimal spectrum and power allocation can contribute to the maximum system satisfaction in a 5G-U network with a CA feature.

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“…Carrier aggregation is mainly divided into three types: in band continuous carrier aggregation, in band discontinuous carrier aggregation, and inter band discontinuous carrier aggregation [10][11] . By integrating and allocating multiple idle carriers to the same user, the bandwidth and transmission rate of the carrier system are improved [12][13] .…”

Section: Introductionmentioning

confidence: 99%

Greedy algorithm-based power line carrier aggregation technique

Shi,

Li,

Luo

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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In order to meet the communication requirements of the new power system where a large number of power electronic objects are concurrently connected to the network, this paper first proposes a multi-layer hierarchical networking architecture for low-voltage power line carrier communication adapted to the new power system. Then, a greedy algorithmbased low-voltage power line carrier packet aggregation method is proposed to improve the overall transmission rate of the system by aggregating the filtered carriers. The simulation results show the effectiveness of the proposed algorithm.

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Delay-Aware and Energy-Efficient Carrier Aggregation in 5G Using Double Deep Q-Networks

Cited by 18 publications

References 52 publications

Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band

Greedy algorithm-based power line carrier aggregation technique

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