Stackelberg Game-Based Power Allocation for V2X Communications

Abstract

Ultra-reliable low-latency communication (URLLC) is one of the three usage scenarios anticipated for 5G, which plays an important role in advanced applications of vehicle-to-everything (V2X) communications. In this paper, the Stackelberg game-based power allocation problem was investigated in V2X communications underlaying cellular networks. Assuming that the macro-cellular base station (MBS) sets the interference prices to protect itself from the V2X users (VUEs), the Stackelberg game was adopted to analyze the interaction between MBS and VUEs, where the former acts as a leader and the latter act as followers. For MBS, we aimed at maximizing its utility from interference revenue while considering the cost of interference. Meanwhile, the VUEs aimed at maximizing their utilities per unit power consumption. We analyzed the Stackelberg model and obtained the optimal prices for MBS and optimal transmit powers for VUEs. Simulation results demonstrated the superiority of the proposed Stackelberg game-based power allocation scheme in comparison with the traditional power allocation strategy. Meanwhile, the proposed scheme achieved a better trade-off between economic profit and power consumption.Recently, there have been volumes of existing literature on V2X communications [5][6][7][8][9][10]. In [5], a cooperative automated driving (CAD) system based on collective perception and cooperative maneuver coordination was proposed, which could be applied to several use cases. The authors of [6] investigated the impact of the new radio (NR) flexible numerology on the cellular-vehicle-to-anything (C-V2X) autonomous access mode. The authors of [7] examined the possibility of using the V2X systems at sea and discussed the challenges. In [8,9], the performance comparison of IEEE 802.11p and C-V2X was presented. Moreover, the temporal and spatial dynamics of the V2X network were investigated in [10].In spite of the above attractive features and application scenarios, there are also a great number of problems in the coexistence of V2X communication and other networks, such as the resource allocation (RA), interference management on account of co-channel interference (CCI) caused by spectrum reuse. In order to solve these problems and achieve technical breakthroughs, several related works have been done in terms of RA, power control, congestion control, link scheduling, interference coordination, and so on [11][12][13][14][15][16][17][18][19][20][21][22][23]. The authors of [11] focused on the existing RA algorithms for V2X communications, and these algorithms were classified and compared with each other according to selected criteria. In [12,13], an RA problem among safety VUEs, non-safety VUEs, and conventional cellular UEs (CUEs) was studied. The authors of [14] proposed a novel hybrid scheme based on C-V2X technology to improve latency and reliability performance. A cooperative solution for V2X communications was proposed in [15], which could guarantee reliability and latency requirements for 5G enhanced V2X services. In [16], th...

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“…The researchers' primary objective was to utilize a Lagrangian approach to address the problem through power allocation and resource block assignment. The authors [35] devised a methodology for addressing a Stackelberg game scenario by implementing power control techniques. The Stackelberg game was utilized in this study to examine the interaction between the BS and the user equipment (UE).…”

Resource allocation for Vehicle‐to‐Everything communications: A survey

“…The authors in ref. [98] propose a novel power allocation scheme to optimise energy efficiency for underlay cellularbased V2X communications network. Several constraints have been considered and simplified to a power allocation problem using a Lagrangian dual method.…”

SCO-AN:Improve Secrecy Capacity for Secure Transmission Using Power Allocation

“…Where (A Γ t ) i is the i-th row of A Γ t . (21) and (22) are equivalent to (29) the Jacobian matrix of (H z ) is…”

“…Algorithm 1 SQP step0:set β=0.5 ,σ=0.2,ε=1 × 10 −6 ,the initial vector d0 = (1, 1, 1) T ,µ 0 = 0,λ 0 = (0, 0, 0) T ,z 0 = (ε 0 , d 0 , µ 0 , λ 0 ),z 0 = (ε 0 , 0, 0, 0),i = 0 step1:if H(z i ) ≤ 0,stop iteration, else,ψ i = ψ(z i ),ψ i is shown in (34),H(z i ) is shown in (29). step2: Solve the following equations: H(z i ) + H (z i )∆z i = ψz 0 ,then get the solution of the equations:∆z i = (∆ε i , ∆d i , ∆µ i , ∆λ i ) step3:Let m be the smallest non-negative integer m that satisfies the following inequality:…”