A Deep Learning Based Social-aware D2D Peer Discovery Mechanism
A Deep Learning Based Social-aware D2D Peer Discovery Mechanism
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DOI：
10.23919/icact.2019.8701911
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Yunhan Long;R. Yamamoto;Taku Yamazaki;Y. Tanaka
中科院分区：
文献类型：
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作者：
Yunhan Long;R. Yamamoto;Taku Yamazaki;Y. Tanaka
随着对信息快速交换的需求，设备到设备（D2D）通信成为下一代网络架构的重要组成部分之一。为了实现高效的D2D通信，对等体发现起着重要的作用，因为发现结果强烈地影响进一步的性能。D2D通信中对等点发现的研究大多基于发现邻近设备以识别附近的目的设备。特别地，一些研究集中于时隙分配以广播用于发现邻近设备的地址信息。此外，其他研究关注具有不同信标探测信号的用户分组。然而，这些对等体发现机制没有考虑源设备在真实的情况下可能遇到恶意设备的风险。针对此问题，提出了一种利用社交网络关系信息排除恶意设备的对等点发现机制。提出的机制有助于降低遇到恶意设备的概率，并通过排除恶意设备，提高对等点发现的效率。仿真结果表明，基站（BS）提取信任的候选人之间的设备，以量化的信任度的基础上潜在的社会信息的设备。
With the demand for rapid exchanges of information, device to device (D2D) communications become one of the essential components of next-generation network architecture. To realize efficient D2D communication, peer discovery plays an important role since the discovery result strongly affects further performance. Most of the researches on peer discovery in D2D communication are based on discovering proximity devices to recognize nearby destination devices. In particular, some researches focus on time slot distribution to broadcast address information for discovering proximity devices. Moreover, other researches pay attention to user grouping with different beacon probing signals. However, these peer discovery mechanisms do not consider the risks that source devices may encounter malicious devices in real situations. As a solution to this, this paper proposes a peer discovery mechanism which applies the social network relationship information to exclude malicious devices. The proposed mechanism contributes to decrease the probability of encountering malicious devices and enhances the efficiency of peer discovery by excluding malicious devices. Simulations clarify that the base station (BS) extracts trusted candidates among devices to quantify the trust degree of devices based on the potential social information.