Robust Resource Allocation With Imperfect Channel Estimation in NOMA-Based Heterogeneous Vehicular Networks

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Abstract
In heterogeneous vehicular networks, nonorthogonal multiple access (NOMA) with effective resource allocation improves spectrum efficiency by allowing multiple users to share the same channel. However, channel estimation errors caused by high mobility in vehicular networks affect system robustness and link reliability. As a result, resource allocation in high-mobility scenarios is a challenging issue. In this paper, robust resource allocation is studied to improve both the throughput performance and reliability of NOMA-based heterogeneous vehicular networks. A cascaded Hungarian channel assignment algorithm is proposed to simplify the formulated resource allocation problem with reliability requirements into a robust power allocation problem with chance constraints. With the approximation of non-central Chi-square distribution, the chance constraints are transformed into deterministic constraints. Furthermore, the optimal power allocation for the transformed problem is obtained in consideration of the requirements in NOMA. Simulation results illustrate the effectiveness of the proposed robust resource allocation scheme and its improvement over existing schemes.
Funding Information
  • National Science Foundation (1560437)

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