The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

中文翻译：

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车载网络中基于机器学习的网络切片资源分配策略

车载网络中多样化的服务要求刺激了人们进行调查，以开发合适的技术来满足车辆的需求。在这种情况下，网络切片已被认为是满足各种严格服务要求的最有前途的体系结构技术之一。然而，由于复杂的通信环境而导致的每个片的服务业务的不可预测性导致分配的片资源的利用率较弱。因此，在本文中，我们使用基于长短期内存（LSTM）的资源分配来减少总的系统延迟。特别地，我们首先将无线电资源分配问题表述为凸优化问题，以最小化系统延迟。其次，为了进一步减少延迟，我们设计了基于卷积LSTM-（ConvLSTM-）的流量预测，以预测车辆网络中复杂切片服务的流量，该流量用于资源分配处理中。考虑了三种类型的流量，即SMS，电话和Web流量。最后，基于预测结果，即每个切片的流量和用户负载分布，我们利用原始-双内点方法探索资源的最佳切片权重。数值结果表明，预测的SMS，电话和Web流量的平均错误率分别为25.0％，12.4％和12.2％，总延迟显着降低，这验证了流量预测的准确性和有效性。建议的策略。在资源分配处理中使用。考虑了三种类型的流量，即SMS，电话和Web流量。最后，基于预测结果，即每个切片的流量和用户负载分布，我们利用原始-双内点方法探索资源的最佳切片权重。数值结果表明，预测的SMS，电话和Web流量的平均错误率分别为25.0％，12.4％和12.2％，总延迟显着降低，这验证了流量预测的准确性和有效性。建议的策略。在资源分配处理中使用。考虑了三种类型的流量，即SMS，电话和Web流量。最后，基于预测结果，即每个切片的流量和用户负载分布，我们利用原始-双内点方法探索资源的最佳切片权重。数值结果表明，预测的SMS，电话和Web流量的平均错误率分别为25.0％，12.4％和12.2％，总延迟显着降低，这验证了流量预测的准确性和有效性。建议的策略。我们利用原始对偶内点法来探索资源的最佳切片权重。数值结果表明，预测的SMS，电话和Web流量的平均错误率分别为25.0％，12.4％和12.2％，总延迟显着降低，这验证了流量预测的准确性和有效性。建议的策略。我们利用原始对偶内点法来探索资源的最佳切片权重。数值结果表明，预测的SMS，电话和Web流量的平均错误率分别为25.0％，12.4％和12.2％，总延迟显着降低，这验证了流量预测的准确性和有效性。建议的策略。