Dynamic Spectrum Optimization for Internet-of-Vehicles with Deep-Learning-Based Mobility Prediction

Abstract

Internet-of-Vehicles (IoV) plays an important part of Intelligent Transportation Systems, and is widely regarded as one of the most strategic applications in smart cities development. Next generation wireless network is especially crucial for meeting the connectivity and bandwidth demands of IoVs. Smart spectrum resource management has received much attention of the research community as it is believed to be a promising approach for solving the spectrum resource challenge of IoV and Intelligent Transportation Systems. In this article, we propose a smart spectrum optimization technique based on a deep learning method for user mobility prediction. For this purpose, based on the Exploration and Preferential Return (EPR) model which can be used to investigate the movement trend and aggregation behavior of the target, we adopt the D-Exploration and Preferential Return (D-EPR) model as a deep learning technique to train a Long-Short Term Memory (LSTM) recurrent neural network (RNN) in order to predict the future locations of IoV nodes. With predicted user’s mobility, a graph theoretic algorithm is then applied to achieve spectrum reuse and optimization. Besides, our proposed deep-learningbased user mobility prediction is able to identify the user position. This paper then compares the performances of mobility prediction by traditional method and our proposal. The outcomes of spectrum efficiency and network capacity are also provided to show the effectiveness of the proposed solution.

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

“…Li et al [ 24 ] propose a method based on deep learning for the prediction of user mobility using a preferential exploration and return model as a deep-learning technique to predict the future locations of the nodes. According to the proposal made by Iftikhar et al [ 25 ], a decision-making algorithm using game theory is presented to model spectral mobility, which serves as a switching game that considers whether to change or remain in the channel.…”

Decision-Making Algorithm with Geographic Mobility for Cognitive Radios

“…This proposal is based on the weighted sum shown in (1), which is part of a conventional technique for solving multi-objective optimization problems [29]. To find a better channel in a CR environment, with the efficiency and simplicity of using the linear combination of weights, some attributes such as signal-to-interference plus noise ratio (SINR), bandwidth (BW), probability of channel availability (AP), channel estimated channel time of availability (ETA) are evaluated using the random waypoint mobility model (RWPM).…”

“…In the use of wireless networks, two important tasks are considered: location and handover management. Location management ensures that the location of network nodes is tracked, while handover management is responsible for maintaining connections while a node moves from one network to another [1]. This GM management needs to be considered for cognitive radio networks (CRN), because the available radio spectrum can change considerably with location and handover.…”