A Survey of Device-to-Device Communications: Research Issues and Challenges

Jameel, Furqan; Hamid, Zara; Jabeen, Farhana; Zeadally, Sherali; Javed, Muhammad Awais

doi:10.1109/comst.2018.2828120

Cited by 438 publications

(207 citation statements)

References 272 publications

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“…D2D paradigm in 5G WCNs is being extensively investigated in the literature. Some of the comprehensive surveys on D2D communication are available in other works . This section states some important resource allocation algorithms in respect of latency reduction, use of sector antennas, and improving EE with optimal power.…”

Section: Related Workmentioning

confidence: 99%

E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

Gandotra

Jha

Jain

2018

Trans Emerging Tel Tech

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The massive spread of the smart devices demanding high data rate and delay‐sensitized applications has escalated energy consumption levels in the fifth‐generation (5G) wireless communication networks (WCNs). It thus becomes imperative for the mobile network operators to meet the tremendous subscriber demands using energy‐efficient techniques. The limited battery capacities of the mobile terminals (MTs) further make energy efficiency (EE) critical. Device‐to‐device (D2D) communication ensues to be a key enabler technology in the realization of an energy‐efficient 5G WCN. It also serves as a paramount technology for green communication. This paper studies a resource allocation scheme, which iteratively accentuates adaptive reuse of resource blocks (RBs) from the active cellular users to the D2D pairs in a tri‐sectored cell. The cellular users involved in RB allocation to pairs are termed as active. Required count of RBs for each D2D pair is chosen by employing the hidden Markov model. The RB reuse strategy favors enhancement in throughput. The proposed energy‐efficient adaptive RB allocation with low complexity (E2ARC) scheme aims to guarantee ample RBs to individual D2D pairs to optimize the power consumption levels. With advanced throughput and optimal power, augmentation in EE is achieved with a high degree of fairness. E2ARC also comprehends low complexity. The efficacy of the proposed scheme is demonstrated by the simulation results.

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Section: Related Workmentioning

confidence: 99%

E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

Gandotra

Jha

Jain

2018

Trans Emerging Tel Tech

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“…Device‐to‐Device (D2D) communication is an essential part of IoT, in which devices cooperatively provide services without intermediation of any central entity . The involved devices may communicate directly using the wireless medium or via a base station.…”

Section: Introductionmentioning

confidence: 99%

“…1 Device-to-Device (D2D) communication is an essential part of IoT, in which devices cooperatively provide services without intermediation of any central entity. 3,4 The involved devices may communicate directly using the wireless medium or via a base station. In such a context, some constrained devices in terms of processing power, battery lifetime, and memory may prefer or be obliged to offload their tasks to other devices.…”

Section: Introductionmentioning

confidence: 99%

T‐D2D: A trust model for service offloading in device‐to‐device communication

Movahedi

Hosseini

2019

Trans Emerging Tel Tech

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With the advances in pervasive computing, Internet of things (IoT) has gained considerable attention from both research and industrial communities. While IoT devices are able to provide computational services to other devices via device‐to‐device (D2D) communications, they are not guaranteed to be honest and collaborative. In such a context, the trust model can help to detect malicious service providers. However, malicious nodes may perform trust‐distortion attacks to mislead the trust model. They may perform on‐off attacks to remain undetected or bad‐mouth about others to make it difficult to infer if a contradictory recommendation comes from the on‐off nature of the evaluated node or dishonesty of the recommender. To address these issues, we propose T‐D2D, a lightweight trust model capable to face simultaneous trust‐distortion attacks. T‐D2D evaluates a node's nature using both short‐term and long‐term evaluation intervals to detect different types of on‐off attacks. Moreover, it keeps track of marginal misbehaving over several successive intervals to recognize the nature of suspicious on‐off nodes with light misbehaving attitude. To face bad‐mouthing attackers, T‐D2D limits its dependence on recommendations to when the direct trust is not decisive. Moreover, it evaluates the honesty of a recommender based on the correctness of its recommendations over time. Simulation results prove that T‐D2D exhibits significantly better performance than other counterparts in terms of trust level, correctness of calculated trust, percentage of selected malicious providers, total wasted execution time, and energy consumption in presence of simultaneous trust‐distortion attacks.

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“…For this reason, recent research efforts target to improve the spatial efficiency. D2D communications constitute a prominent example, where cellular devices are allowed to communicate directly with each other without passing their messages through the Base station (BS) [4]- [7]. This paradigm entails higher throughput and lower latency in the communication for two reasons: first, a traditional cellular communication between two devices requires one time slot in the uplink and one time slot in the downlink, whereas a single time slot suffices in D2D communications.…”

Section: Introductionmentioning

confidence: 99%

“…k→∞ p[k] =p wherep is a stationary point of(15), and (ii) |p[k] −p| ≤ Ck −(1−θ)/(2θ−1) for some C > 0.Proof: see Appendix AAfter solving the power allocation subproblems in both the ERM or MRM cases, a channel allocation problem similar to(7) will arise, and a similar solution based on integer relaxation can be used. Algorithm 1 highlights the operation of the separate resource allocation method with all the previously discussed cases, with B (d) (k), B (u) (k), P (k) are the values of each variable at the k-th iteration, and P(d) D j , P (d) C j are the j-th columns of P (d) D , P (d) C matrices.…”

mentioning

confidence: 99%

Underlay Device-to-Device Communications on Multiple Channels

Elnourani

Hamid

Romero

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-stateinformation (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the-art alternatives.

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A Survey of Device-to-Device Communications: Research Issues and Challenges

Cited by 438 publications

References 272 publications

E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

T‐D2D: A trust model for service offloading in device‐to‐device communication

Underlay Device-to-Device Communications on Multiple Channels

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