On the Performance of Underlay Device-to-Device Communications

Nguyen, Tan N.; Nguyen, Van‐Dinh; Nguyen, Hoai Giang; Tu, Lam-Thanh; Chien, Trinh Van; Hoa, Nguyen Tien

doi:10.3390/s22041456

Cited by 8 publications

(5 citation statements)

References 72 publications

(99 reference statements)

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“…The former calculates the probability that the instantaneous e2e SINR of both destinations is below the predefined threshold, the latter, on the other hand, computes the potential throughput of the whole network. Mathematical speaking, these metrics can be formulated as follows [ 48 ]:

…”

Section: Performance Analysis and Trendsmentioning

confidence: 99%

Performance Analysis of Multihop Full-Duplex NOMA Systems with Imperfect Interference Cancellation and Near-Field Path-Loss

Phan

Nguyen

et al. 2023

Sensors

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Outage probability (OP) and potential throughput (PT) of multihop full-duplex (FD) nonorthogonal multiple access (NOMA) systems are addressed in the present paper. More precisely, two metrics are derived in the closed-form expressions under the impact of both imperfect successive interference cancellation (SIC) and imperfect self-interference cancellation. Moreover, to model short transmission distance from the transmit and receive antennae at relays, the near-field path-loss is taken into consideration. Additionally, the impact of the total transmit power on the performance of these metrics is rigorously derived. Furthermore, the mathematical framework of the baseline systems is provided too. Computer-based simulations via the Monte Carlo method are given to verify the accuracy of the proposed framework, confirm our findings, and highlight the benefits of the proposed systems compared with the baseline one.

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…”

Section: Performance Analysis and Trendsmentioning

confidence: 99%

Performance Analysis of Multihop Full-Duplex NOMA Systems with Imperfect Interference Cancellation and Near-Field Path-Loss

Phan

Nguyen

et al. 2023

Sensors

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“…The performance of the CRNs studied widely in [4][5][6][7][8][9][10][11][12] while the performance of FCs was investigated in [13][14][15][16][17][18][19]. Particularly, authors in [4] addressed the channel estimation problem in the overlay CRNs based on the state transition probability method.…”

Section: Introductionmentioning

confidence: 99%

“…A novel spectrum sensing algorithm for CR-based Internet-of-thing (IoT) was given in [5] to facilitate the efficiency and time sensitivity compared with other approaches. The outage performance of both primary and secondary networks in cognitive device-to-device (D2D) network was derived in the closed-form expressions in [6]. Besides, the secrecy performance of CRNs in Nakagami-𝑚 distribution was addressed in [7].…”

Section: Introductionmentioning

confidence: 99%

Performance on Cognitive Broadcasting Networks Employing Fountain Codes and Maximal Ratio Transmission

Nguyen¹,

Tu²,

Nguyen³

et al. 2023

RADIOENGINEERING

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The comprehensive performance of cognitive broadcasting networks employing Fountain codes (FC) and maximal ratio transmission (MRT) is investigated in the present paper. More precisely, the secondary transmitter (ST) employs Fountain code to effectively broadcast a common message such as a safety warning, security news, etc., to all secondary receivers (SRs) via underlay protocol of cognitive radio networks (CRNs). Different from works in the literature that are interested in studying the outage probability (OP), and the ergodic capacity of the CRNs. The present paper, on the other hand, focuses on the characteristics of the number of needed time slots to successfully deliver such a message. Particularly, we derive in closed-form expressions the cumulative distribution function (CDF), the probability mass function (PMF), and the average number of the required time slot to broadcast the message to all SRs. Additionally, we also provide the throughput of secondary networks (SNs). We point out the impact of some key parameters, i.e., the number of SRs and the number of transmit antennae at the secondary transmitter, on the performance of these considered metrics. Numerical results via the Monte-Carlo method are given to verify the accuracy of the derived framework as well as to highlight the influences of some essential parameters. Furthermore, we also compare the performance of the proposed networks with state-of-the-art and simulation results unveiling that the considered system consistently outperforms works in the literature.

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“…Thus, the resource allocation (RA) for D2D devices in the existence of cellular devices is an inherent issue of D2D communications in theoretical and practical aspects. A large number of research studies have been conducted regarding RA for D2D communications, especially underlay cellular networks [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. When different D2D links occupy distinct cellular channels, efficient one-to-one mapping techniques may be applied to RA [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks

Lee

2022

Sensors

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In this paper, a resource allocation (RA) scheme based on deep reinforcement learning (DRL) is designed for device-to-device (D2D) communications underlay cellular networks. The goal of RA is to determine the transmission power and spectrum channel of D2D links to maximize the sum of the average effective throughput of all cellular and D2D links in a cell accumulated over multiple time steps, where a cellular channel can be allocated to multiple D2D links. Allowing a cellular channel to be shared by multiple D2D links and considering performance over multiple time steps require a high level of system overhead and computational complexity so that optimal RA is practically infeasible in this scenario, especially when a large number of D2D links are involved. To mitigate the complexity, we propose a sub-optimal RA scheme based on a multi-agent DRL, which operates with shared information in participating devices, such as locations and allocated resources. Each agent corresponds to each D2D link and multiple agents perform learning in a staggered and cyclic manner. The proposed DRL-based RA scheme allocates resources to D2D devices promptly according to dynamically varying network set-ups, including device locations. The proposed sub-optimal RA scheme outperforms other schemes, where the performance gain becomes significant when the densities of devices in a cell are high.

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On the Performance of Underlay Device-to-Device Communications

Cited by 8 publications

References 72 publications

Performance Analysis of Multihop Full-Duplex NOMA Systems with Imperfect Interference Cancellation and Near-Field Path-Loss

Performance Analysis of Multihop Full-Duplex NOMA Systems with Imperfect Interference Cancellation and Near-Field Path-Loss

Performance on Cognitive Broadcasting Networks Employing Fountain Codes and Maximal Ratio Transmission

Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks

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