Minimum Power Scheduling under Rician Fading in Full-Duplex Relay-Assisted D2D Communication

Penda, Demia Della; Νομικός, Νικόλαος; Charalambous, Themistoklis; Johansson, Mikael

doi:10.1109/glocomw.2017.8269122

Cited by 9 publications

(4 citation statements)

References 38 publications

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“…In this setting, a hybrid FD/SOR/HD policy was developed to maximize the throughput per energy unit of the transmission. Another study by Della Penda et al presented joint relay mode selection and power allocation, assuming Rician fading channels [28]. The proposed algorithm, activated a set of wireless links for power consumption minimizing, and provided success probability guarantees.…”

Section: A Backgroundmentioning

confidence: 99%

Bandit-Based Learning-Aided Full-Duplex/Half-Duplex Mode Selection in 6G Cooperative Relay Networks

Nomikos,

Charalambous,

Trakadas

et al. 2024

IEEE Open J. Commun. Soc.

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The high level of autonomy and intelligence that is envisioned in sixth generation (6G) networks necessitates the development of learning-aided solutions, especially in cases in which conventional Channel State Information (CSI)-based network processes introduce high signaling overheads. Moreover, in wireless topologies characterized by fast varying channels, timely and accurate CSI acquisition might not be possible and the transmitters (CSIT) only have statistical CSI available. This work focuses on the appropriate selection of relaying mode in a cooperative network, comprising a single information source, one buffer-aided (BA) relay with full-duplex (FD) capabilities, and a single destination. Here, prior to each transmission, the relay should select to operate either in FD mode with power control, or, resort to half-duplex (HD) relaying when excessive self-interference (SI) arises. Targeting the selection of the best relaying mode, we propose an FD/HD mode selection mechanism, namely multi-armed bandit-aided mode selection (MABAMS), relying on reinforcement learning and the processing of acknowledgements/negative-acknowledgements (ACK/NACK) packets for acquiring useful information on channel statistics. As a result, MABAMS does not require continuous CSI acquisition and exchange and nullifies the negative effect of outdated CSI. The proposed algorithm's average throughput performance is evaluated, highlighting a performance-complexity trade-off over alternative solutions, based on pilot-based channel estimation that result in spectral and energy costs while obtaining instantaneous CSI.INDEX TERMS 6G, full-duplex, buffer-aided relays, multi-armed bandits (MAB), relay mode selection, reinforcement learning.

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Section: A Backgroundmentioning

confidence: 99%

Bandit-Based Learning-Aided Full-Duplex/Half-Duplex Mode Selection in 6G Cooperative Relay Networks

Nomikos,

Charalambous,

Trakadas

et al. 2024

IEEE Open J. Commun. Soc.

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“…Thus, D2D communication has been proposed, as a remedy for excessive cellular traffic, enabling users to directly exchange or relay data for cell edge users. Decentralized caching at the users devices and intelligent D2D resource allocation can provide several gains to wireless networks, minimizing energy consumption, delay and backhaul usage [131]- [134].…”

Section: B Device-to-device Networkmentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.INDEX TERMS 6G, edge caching, heterogeneous networks, machine learning, mobile edge networks, reinforcement learning.

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“…As NOMA relies on user pairs, it is easy to observe that the cooperation between them, in cases where the weak user fails to decode its own message, can lead to robust communication and guaranteed fairness. In various studies, the advantages of device-to-device (D2D) communication have been investigated [12,13]. Thus, the adoption of the D2D communication paradigm opens up tremendous opportunities to further strengthen the performance of integrated satellite-terrestrial NOMA networks, as long as the channel conditions and rate requirements of each pair are jointly considered with the user pairing process of NOMA.…”

Section: Introductionmentioning

confidence: 99%

“…In the next step, the algorithm calculates the rate R D2D (i,j) weak that the weak satellite user is able to achieve via the corresponding D2D link if this pair is used, through (10), and the rate R DECODED (i,j) weak that the weak user is able to achieve in case that the strong user forwards the received signal from the satellite to the weak user using (13). Next, considering the rate R D2D (i,j) weak that the D2D link is able to offer to the weak user, and the rate R DECODED (i,j) weak that the weak user is able to achieve when the strong user forwards the weak user signal, the algorithm calculates the achievable rate of the weak user by calculating the minimum of these two rates through (14), and practically uses this information to identify whether or not the D2D link is preferable compared to the directly received coded satellite signal at the weak user.…”

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confidence: 99%

Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication

Karavolos

Νομικός

Vouyioukas

2020

Telecom

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The currently deployed terrestrial wireless networks experience difficulties while coping with the massive connectivity demands of coexisting users and devices. The addition of satellite segments has been proposed as a viable way of providing improved coverage and capacity, leading to the formation of integrated satellite-terrestrial networks. In such topologies, non-orthogonal multiple access (NOMA) can further enhance the efficient use of wireless resources by simultaneously serving multiple users. In this paper, an integrated satellite-terrestrial NOMA network is studied where cooperation between ground users is allowed, following the device-to-device (D2D) paradigm. More specifically, the proposed satellite NOMA cooperative (SANOCO) D2D scheme optimally selects pairs of users, by considering the channel conditions of the satellite and the terrestrial D2D links. In SANOCO-D2D users are served through NOMA in the satellite link, and then, if the weak user fails to decode its signal, terrestrial D2D communication is activated to maintain the total sum rate of the system. Comparisons with conventional orthogonal multiple access (OMA) and an alternative NOMA optimal user pairing scheme show that significant sum rate and spectral efficiency gains can be harvested through SANOCO-D2D under varying channel conditions and terrestrial D2D bandwidth.

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Minimum Power Scheduling under Rician Fading in Full-Duplex Relay-Assisted D2D Communication

Cited by 9 publications

References 38 publications

Bandit-Based Learning-Aided Full-Duplex/Half-Duplex Mode Selection in 6G Cooperative Relay Networks

Bandit-Based Learning-Aided Full-Duplex/Half-Duplex Mode Selection in 6G Cooperative Relay Networks

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication

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