Interference Minimization Resource Allocation for V2X Communication Underlaying 5G Cellular Networks

In this paper, the resource allocation for vehicle-to-everything (V2X) underlaying 5G cellular mobile communication networks is considered. The optimization problem is modeled as a mixed binary integer nonlinear programming (MBINP), which minimizes the interference to 5G cellular users (CUs) subject to the quality of service (QoS), the total available power, the interference threshold, and the minimal transmission rate. To achieve that, the original MBINP is decomposed into three steps: transmission power initialization, subchannel assignment, and power allocation. Firstly, the minimum transmission power required by the V2X users (VUs) is set as the initial power value. Secondly, the Hungarian algorithm is used to obtain the appropriate subchannel. Finally, an optimization mechanism is proposed to the power allocation. Simulation results show that the proposed algorithm can not only ensure the minimal transmission rate of VUs but also further improve the CUs’ channel capacity under the premise of guaranteeing the QoS of the CUs.

[1]  Li Zhao,et al.  Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G , 2017, IEEE Communications Standards Magazine.

[2]  Eneko Osaba,et al.  Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles , 2017, Sensors.

[3]  Xuemin Shen,et al.  Performance Analysis of Vehicular Device-to-Device Underlay Communication , 2017, IEEE Transactions on Vehicular Technology.

[4]  Rose Qingyang Hu,et al.  D2D communication underlay in uplink cellular networks with distance based power control , 2016, 2016 IEEE International Conference on Communications (ICC).

[5]  Yuanwei Liu,et al.  Full-Duplex Cooperative NOMA Relaying Systems With I/Q Imbalance and Imperfect SIC , 2020, IEEE Wireless Communications Letters.

[6]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[7]  Ekram Hossain,et al.  Resource allocation for spectrum underlay in cognitive radio networks , 2008, IEEE Transactions on Wireless Communications.

[8]  Jingjing Li,et al.  Performance Analysis of Impaired SWIPT NOMA Relaying Networks Over Imperfect Weibull Channels , 2020, IEEE Systems Journal.

[9]  Ming Xiao,et al.  Energy-Efficient Cognitive Transmission With Imperfect Spectrum Sensing , 2016, IEEE Journal on Selected Areas in Communications.

[10]  Xuelong Li,et al.  Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues , 2016, IEEE Communications Surveys & Tutorials.

[11]  Derrick Wing Kwan Ng,et al.  Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Hassan Halabian,et al.  Distributed Resource Allocation Optimization in 5G Virtualized Networks , 2019, IEEE Journal on Selected Areas in Communications.

[13]  Alagan Anpalagan,et al.  Joint Communication and Computing Resource Allocation in 5G Cloud Radio Access Networks , 2019, IEEE Transactions on Vehicular Technology.

[14]  Qing Wang,et al.  A Survey on Device-to-Device Communication in Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.

[15]  Gengfa Fang,et al.  Resource Allocation for Underlay D2D Communication With Proportional Fairness , 2018, IEEE Transactions on Vehicular Technology.

[16]  Yi Wang,et al.  New Radio (NR) and its Evolution toward 5G-Advanced , 2019, IEEE Wirel. Commun..

[17]  Guanding Yu,et al.  Service Oriented Resource Management in Spatial Reuse-Based C-V2X Networks , 2020, IEEE Wireless Communications Letters.

[18]  Zhiguo Ding,et al.  Residual Transceiver Hardware Impairments on Cooperative NOMA Networks , 2020, IEEE Transactions on Wireless Communications.

[19]  Li Wang,et al.  Resource Allocation for D2D Communications Underlay in Rayleigh Fading Channels , 2017, IEEE Transactions on Vehicular Technology.