# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 2 Related Work  
Keeping in mind the differentiated quality of service (QoS) of the different communication scenarios, resource allocation among users becomes a major milestone. Resource allocation in LTE is the distribution of resources like power and frequency among the users, such that the method is optimum and solves the network congestion problem. A survey on radio resource allocation for V2X communication [7] thoroughly investigated the communication modes, radio resource management techniques, and discussions on different methodologies. Resource allocation for D2D-based vehicular communication has been deeply researched in the recent past with techniques like traditional optimization techniques [8], matching [9], graph theory [10], and machine learning [11] being used to investigate the performance of the network under varying scenarios. A resource allocation based on an immune algorithm for D2D-based vehicular networks has been in investigated in [12], where the authors have incorporated an adaptive cloning and population update strategy to provide a highly efficient method for solving resource sharing among vehicles. Power control using a distributed deep deterministic policy gradient method [13], introduced two models to solve a multi-agent energy-efficient power allocation problem. The author proposed a model that employs neural networks to overcome problems with existing approaches. Resource allocation with low-latency vehicular communication has been studied by several authors. Low latency with packet retransmission is investigated in [14], where the authors have presented the queuing analysis and then derived an expression for average packet sojourn time. A twin-timescale scheme for low-latency vehicular networks [15], aimed for reduction of maximum transmission latency by using a two-stage process, by first minimizing the worst-case transmission latency and then the base station allocating a total power at short-term timescale. The idea for using a twin-timescale is to provide a more realistic approach for avoiding frequent exchange of near instantaneous channel state information. hybrid strategy, whereby, cellular-based V2V communications is introduced to IEEE 802.11p-based vehicular networks [16] to improve network latency, is also being studied. Matching and graph theory are among the most researched areas in D2D-based vehicular communication. Both techniques have primarily been used for frequency assignment problems. Frequency resource blocks (RB) are shared among users in the cellular network. In order to avoid communication impairment due to interference, the RB are assigned in an optimum manner. Graph theory-based resource assignment has been studied in both two and three dimensions. Interference is usually taken as a metric to define the edges between the vertices in a graph. Interference hypergraph-based 3D matching resource allocation for V2X [17] using a weighted 3-partite interference hypergraph based on greedy and iterative matching, has been shown to improve the network throughput. Dynamic proximity-aware resource allocation in V2V communications [18] employs zone formation based on traffic patterns for vehicles. Then, a matching game is proposed to allocate resources to V2V pairs within each zone. The zone formation reduces interference and signaling overhead and hence satisfies the quality of service in terms of SINR. Distance-based power control schemes have been employed using stochastic geometry [19] in a D2D setting by using distance-dependent path loss parameters. Location information has been used to monitor the QoS requirements in [20] and dynamically modifies resource assignment to interfering links. Most of the research has focused on creating a network scenario with V2V and V2I users and then the problem is formulated using an objective function and constraints. The constraints usually include power, latency, SINR, and outage probability. The formulated problem is then solved using the techniques mentioned above.