Resource Allocation for Low-Latency Vehicular Communications with Packet Retransmission

Abstract

Vehicular communications have stringent latency requirements on safety-critical information transmission. However, lack of instantaneous channel state information due to high mobility poses a great challenge to meet these requirements and the situation gets more complicated when packet retransmission is considered. Based on only the obtainable large-scale fading channel information, this paper performs spectrum and power allocation to maximize the ergodic capacity of vehicular-toinfrastructure (V2I) links while guaranteeing the latency requirements of vehicular-to-vehicular (V2V) links. First, for each possible spectrum reusing pair of a V2I link and a V2V link, we obtain the closed-form expression of the packets' average sojourn time (the queueing time plus the service time) for the V2V link. Then, an optimal power allocation is derived for each possible spectrum reusing pair. Afterwards, we optimize the spectrum reusing pattern by addressing a polynomial time solvable bipartite matching problem. Numerical results show that the proposed queueing analysis is accurate in terms of the average packet sojourn time. Moreover, the developed resource allocation always guarantees the V2V links' requirements on latency.

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

“…In this paper, the total bandwidth at the BSs is maximized via a formulated optimization problem under the QoS constraints. Similarly, in [46], spectrum and transmit power are effectively allocated to ensure the safety-critical information transmission for Internet of vehicle (IoV). The ergodic capacity of V2I links is maximized under latency constraints.…”

Stackelberg Game-Based Power Allocation for V2X Communications

“…Calculate optimal price and power 3: Use Equations (15) and (19), calculate the optimal p * and P * ;…”

“…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 [17,18], the congestion control problem in C-V2X was investigated. The authors of [19][20][21] investigated the radio resource management (RRM) for V2X communications, including both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The author of [22] proposed the joint clustering method and power control scheme in V2X communications to reduce the communication complexity, as well as the total power consumption of base station (BS).…”

Reconfigurable Intelligent Surface Enhanced Device-to-Device Communications

“…Recently, machine learning (ML) methods, especially deep learning (DL), have become promising tools to address explosive mass data, mathematically intractable nonlinear nonconvex problems and high-computation issues. DL based approaches can significantly reduce the complexity, and have been adopted in wireless communication systems, e.g., physical layer communications [13] and resource allocation [14]. Motivated by the potential applications of DL in solving sophisticated optimization problems, the authors in [15] have adopted the DL method for designing the RIS reflection matrices with restricted channel state information (CSI).…”