Resource Allocation for D2D Communications Underlaying Cellular Networks Over Nakagami-&lt;inline-formula&gt; 
                     &lt;tex-math notation="LaTeX"&gt;$m$ &lt;/tex-math&gt;
                  &lt;/inline-formula&gt; Fading Channel

Lu, Baoshan; Lin, Shijun; Shi, Jianghong; Wang, Yan

doi:10.1109/access.2019.2894721

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Extensive works have been studied on channel allocation for D2D enabled network. In sparse D2D communication networks, which means the quantity of D2D users are no more than quantity of CUEs, resource allocation problem of D2D users is usually modeled as a 0-1 integer combinational optimization problem [7][8][9][10][11][12][13]. One kind of them concentrates on the optimal allocation with an attempt to exhaustive searches, for example, Hungarian or graph theory-based algorithm [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Whereas, the above-mentioned algorithm requires the BS to have knowledge of the instantaneous CSI of all links, and the algorithm is only suitable for one-to-one matching mode. Lu et al [13] investigated the D2D communication channel allocation problem under the Nakagami-m fading channel environment, and proposed an allocation algorithm based on weighted bipartite graph matching. When the number of D2D users and the number of cellular users do not match, the number of both is equalized by adding virtual nodes.…”

Section: Introductionmentioning

confidence: 99%

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Fast Channel Allocation for Ultra-dense D2D-enabled Cellular Network with Interference Constraint in Underlaying Mode

2021

KSII TIIS

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We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Channel Allocation for Ultra-dense D2D-enabled Cellular Network with Interference Constraint in Underlaying Mode

2021

KSII TIIS

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“…Guaranteed bit rate (GBR) service is required in many applications, such as real-time gaming and audio/video communications, requiring a minimum data rate and strict latency. In previous works, power control and resource allocation methods were proposed to guarantee QoS requirements, such as SINR or data rate [12][13][14][15][16][17][18][19]. However, since most of these works allocate a single channel of a fixed bandwidth to one CU, there is still a problem in that some users have a SINR or a data rate below their QoS requirements.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for GBR Services in D2D-Enabled Communication

2020

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In device-to-device (D2D)-enabled communications, a channel can be matched to a cellular user (CU)–D2D user (DU) pair, where either two separate resources can be allocated in a dedicated mode, or a single resource is shared in a shared mode. It affects the channel capacity and the channel bandwidth needed to guarantee the target data rate. The transmit power also needs to be adaptively controlled according to channel bandwidth, as well as channel quality. In this paper, we propose a resource allocation scheme to minimize the total channel bandwidth while guaranteeing the target data rate of guaranteed bit rate services. The original problem is separated into two sub-problems; (1) the channel bandwidth calculation and channel sharing mode selection for arbitrary matching (2) the 3-D matching problem of the CU, DU and channel. To mitigate computational complexity, the 3-D matching problem is transformed into two 2-D matching problems, and a sub-optimal solution is derived using a sub-gradient algorithm. From numerical results, it was confirmed that the average channel bandwidth was almost the same even though the computational complexity of the sub-optimal algorithm was very low compared to the original algorithm. We also investigate the impact of various parameters, such as maximum D2D distance and the number of users.

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“…Adopting D2D technology, the number of supported devices can be improved in mMTC scenarios [18]. By allowing unauthorized devices to reuse the frequency bands of authorized cellular users, bandwidth utilization can be improved [19,20,21]. The literature [22] proposes a heuristic subcarrier allocation method to set the user’s signal-to-noise ratio (SNR) threshold to meet the QoS of the system.…”

Section: Introductionmentioning

confidence: 99%

“…In [25], the authors propose two solutions to manage the communication between D2D devices and the BS to lighten the overhead of MTC devices on the 5G network. However, resource allocation [19,20,21,22,23,24,25] mainly focuses on throughput maximization or interference minimization. In an IIoT enabled by D2D communications, massive devices demand access to the network via D2D mode.…”

Section: Introductionmentioning

confidence: 99%

Interference-Aware Subcarrier Allocation for Massive Machine-Type Communication in 5G-Enabled Internet of Things

Sun

Zhou

et al. 2019

Sensors

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Massive machine-type communication (mMTC) is investigated as one of three typical scenes of the 5th-generation (5G) network. In this paper, we propose a 5G-enabled internet of things (IoT) in which some enhanced mobile broadband devices transmit video stream to a centralized controller and some mMTC devices exchange short packet data with adjacent devices via D2D communication to promote inter-device cooperation. Since massive MTC devices have data transmission requirements in 5G-enabled IoT with limited spectrum resources, the subcarrier allocation problem is investigated to maximize the connectivity of mMTC devices subject to the quality of service (QoS) requirement of enhanced Mobile Broadband (eMBB) devices and mMTC devices. To solve the formulated mixed-integer non-linear programming (MINLP) problem, which is NP-hard, an interference-aware subcarrier allocation algorithm for mMTC communication (IASA) is developed to maximize the number of active mMTC devices. Finally, the performance of the proposed algorithm is evaluated by simulation. Numerical results demonstrate that the proposed algorithm outperforms the three traditional benchmark methods, which significantly improves the utilization of the uplink spectrum. This indicates that the proposed IASA algorithm provides a better solution for IoT application.

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Resource Allocation for D2D Communications Underlaying Cellular Networks Over Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math> </inline-formula> Fading Channel

Cited by 11 publications

References 24 publications

Fast Channel Allocation for Ultra-dense D2D-enabled Cellular Network with Interference Constraint in Underlaying Mode

Fast Channel Allocation for Ultra-dense D2D-enabled Cellular Network with Interference Constraint in Underlaying Mode

Resource Allocation for GBR Services in D2D-Enabled Communication

Interference-Aware Subcarrier Allocation for Massive Machine-Type Communication in 5G-Enabled Internet of Things

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