# Three-Dimensional Resource Allocation in D2D-Based V2V Communication
## 3 Matching and Resource Block Allocation Scheme
### Optimal Power Allocation
Topology of the network and the differential Quality of Service (QoS) of the users can make different sets of optimization problems with varying constraints. Methods used to solve these vary from matching theory [18] to machine learning-based methods [19; 20], which have been investigated recently. Hybrid resource allocation architecture in [21], where cellular V2X is combined with IEEE 802.11p is an interesting area of future research. Other optimization techniques like the one proposed in [22], based on the Lyapunov optimization and heuristic techniques like cluster-based resource block sharing and power allocation [23] can be used to solve the resource allocation problem. Future work on Vehicle to Pedestrians (V2P) communication scenarios could be an interesting area of research for D2D-based resource allocation along with the impact of using multiple antennas. ## 6 Conclusions

This paper is based on the resource allocation in D2D-based V2V with a network scenario consisting of three types of users with different QoS requirements. An optimization problem has been proposed in which the objective is to increase the capacity of the V2I links while keeping the constraints in mind. The optimal power for the V2V and V2I links is calculated, keeping in mind the minimum required QoS for the cellular users. Matching using hypergraphs and RB allocation using hypergraph coloring has been presented. The impact of the SINR of the CUE on the capacity of the V2I link has been studied along with the impact of increasing the V2V users on the V2I capacity. In future, more network scenarios could be studied with users with varying requirements and using different optimization techniques to solve the problem. Out of coverage scenarios and relays can bring a whole new challenge in terms of resource allocation in D2D-based V2V communication. **Author Contributions:** Conceptualization, U.A.K. and S.S.L. ; Methodology, U.A.K. and S.S.L. ; Software, U.A.K. and S.S.L. ; Validation, U.A.K. and S.S.L. ; Formal Analysis, U.A.K. and S.S.L. ; Investigation, U.A.K. and S.S.L. ; Resources, U.A.K. and S.S.L. ; Data Curation, U.A.K. and S.S.L. ; Writing-Original Draft Preparation, U.A.K. and S.S.L. ; Writing-Review & Editing, U.A.K. and S.S.L. ; Visualization, U.A.K. and S.S.L. ; Supervision, U.A.K. and S.S.L. ; Project Administration, U.A.K. and S.S.L. ; Funding Acquisition, U.A.K. and S.S.L. **Funding:** This research was supported by the Technology Innovation Program (10062375, Development of Core Technologies Based on V2X and In-Vehicle Sensors for Path Prediction of the Surrounding Objects (Vehicle, Pedestrian, Motorcycle)) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea). **Conflicts of Interest:** The authors declare no conflict of interest. ## Appendix A

Considering the reliability requirement of the V2V users and minimum guaranteed SINR of CUE. The probability of outage becomes:

\[Pr\bigg{\{}\frac{P_{i}h_{i}}{P_{j}h_{j,t}+P_{k}h_{k,t}}\leq\gamma_{0}\bigg{\}} \leq p0\]

where,

\[P_{j}=\frac{\gamma_{th}\Big{(}P_{k}h_{k,t}+P_{l}h_{t,t}\Big{)}}{h_{j}}\]

and

\[h^{a,b}=a^{a,b}g^{a,b}\]Replacing the channel power gain with small- and large-scale fading parameters and assuming that the small-scale fading parameter is independent and identically distributed (i.i.d.) exponential random variables. The integral becomes:

Figure 4:0

Inserting the values of the gains and integrating the triple integral would result in:

Figure 4:1

Figure 4:2

Figure 4:3

Figure 4:4

Figure 4:5

Figure 4:6

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