ID	原文	译文
3033	采用 AUC、MAP、Error Rate 作为评价指标。Ns-3 仿真实验表明，与 Node2vec、DDNE、E-LSTM-D等方法相比，所提方法具有更高的预测准确率。	The simulation experiments based on NS-3 show that compared with Node2vec, DDNE andE-LSTM-D, the proposed method has a better accuracy.
3034	为了解决车联网中车辆用户使用基于位置的服务（LBS）时真实位置的泄露问题，提出一种基于公交车缓存的位置隐私保护方案。	To solve the problem of real location leakage when vehicles use location-based service (LBS) on the Internet of vehi-cles, a location privacy protection scheme based on bus cache was proposed.
3035	公交车先根据其线路信息向 LBS 提供商获取兴趣点（POI）池，	Firstly, a point of interest (POI) pool was obtained from the LBS provider based on its route information.
3036	然后在行驶时根据其当前位置从 POI 池中挑选部分 POI 数据形成 POI 列表并广播给周围私家车。	Then the data in the POI pool was selected form a POI list while driving.Finally, the POI list was broadcast to surrounding private vehicles.
3037	私家车在接收到广播信息后，验证公交车身份，然后将 POI 列表存储到车辆的本地缓存中。	After the private vehicle received the broadcast data, it veri-fied the identity of the bus and then stored the POI list in the vehicle's local cache.
3038	当私家车需要查询 POI 信息时，首先在本地缓存中进行检索，若缓存未命中再以 k-匿名的方式向 LBS 提供商发送查询请求。	When a private vehicle needed to queryPOI information, it would first retrieve it in the local cache, and if the cache was missed, it would send a query request to theLBS provider using the k-anonymity method.
3039	仿真实验结果表明，所提方案通过减少私家车与 LBSP 的通信次数，能够降低私家车真实位置泄露的可能性，从而有效提高私家车的位置隐私保护水平。	The simulation experiment results show that the proposed scheme can reducethe possibility of leakage of the real location of the private vehicle by reducing the number of communications between theprivate vehicle and the LBSP, thereby effectively improving the privacy protection level of the private vehicle.
3040	为了解决 5G 移动通信超密集场景下功耗较大、频谱紧张、能效不高等问题，针对两层异构蜂窝非正交多址接入网络，提出了一种基于能效最大的资源分配算法。	In order to solve problems of high power consumption, spectrum shortage and low energy efficiency in the ul-tra-intensive 5G mobile communication scenario, a resource allocation algorithm based on the maximum energy effi-ciency for the two-layer heterogeneous cellular non-orthogonal multiple access network was proposed.
3041	在超密集场景下行通信链路中，通过分步求解频率资源分配和功率分配方案将 NP-hard 优化问题转化为确定性的约束寻优问题，提出了基于谱聚类用户分组算法和改进的 k-means 基站聚类分簇算法，得到不同用户组的频率资源分配方案。	The original NP-hard optimization problem on the downlink communication link of ultra-dense scene was divided into two subprob-lem, such as frequency resource allocation and power allocation, which became a deterministic constraint optimization problem. The frequency resource allocation scheme of different user groups was obtained by using base station clustering based on the improved k-means algorithm and users grouping based on spectral clustering algorithm.
3042	基于 Dinkelbach 方法将能效优化的分式问题转化为可求解的连续凸优化问题，并通过拉格朗日乘子迭代算法实现功率分配。	The fraction of en-ergy efficiency optimization was transformed into a solvable continuous convex optimization problem and power distri-bution was realized by Dinkelbach method, and the Lagrange multiplier iterative algorithm, respectively.