# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 1 Introduction
### Previous Works on Resource Allocation and Detection  
In [15], an approach based on the measurement of the reference signals' received power (RSRP) was described. During the sensing window, the transmitting UE measures the RSRP of all subchannels, and if a subchannel does not exceed a threshold, this subchannel becomes a candidate. Moreover, a transmitting UE needs to detect the PSCCH transmitted by other UEs and receive the Sidelink Control Information (SCI) to know which subchannels are occupied. For this purpose, it is not needed all the ELDs of SCI, so they are transmitted in two stages. The first stage of SCI is transmitted over PSCCH, while the second one is transmitted over Physical Sidelink Shared Channel (PSSCH). The first stage SCI decoding is based on a blind detection. When the first stage SCI decoding fails, the second stage SCI decoding also fails. After the sensing window, we have the selection window, where the candidate subchannels are selected and occupied. In [16], an open-source, fullstack, end-to-end, standard-compliant network simulator was presented, where the physical layer receives the first stage SCI transmitted over PSCCH to measure the RSRP and to get the information about the possible PSSCH. The RSRP is computed using the three resource elements (REs) per resource block (RB), carrying the first stage SCI, because the simulator does not explicitly have PSCCH DMRS. In [17], the authors proposed an enhancement to the sensing-based semi-persistent scheduling to reflect the stochastic nature of the aperiodic traffic on the resource allocation, to reduce the packet collisions. The authors of [18] proposed a method for pedestrian UEs that operates with partial sensing, which is used to reduce the power consumption, in order to enhancing the throughput. For this, an additional UE, i.e., an RSU, is used to collect the sensing results from each UE. Then, the RSU aligns all of the partial sensing windows to provide a more efficient sensing result. In [19], the collision probability via the random selection of resources was analyzed, and the deep reinforcement learning algorithm was investigated, which decreases the collision probability by using a platoon leader to learn from the communication environment. In [20], a group scheduling mechanism is proposed, where a platoon leader takes charge of the whole platoon. It is the platoon leader that grants the resources by the network and distributes the scheduling information among the remaining platoon. The authors of [21], to address the latency problem, proposed the hyper-fraction channel allocation method, where the road is divided into several zones and for each zone, a channel is allocated. Then, when a UE is located within a certain zone, this one uses the channel allocated to the zone. In [22], a cluster-based resource selection scheme is proposed to reduce the resource collision. In this scheme, the resources are divided into different resource sets, then each cluster head selects its resource set based on measurement. The authors of [23] proposed a clustering-based resource management scheme for latency and sum rate optimization. First, an optimization of the total average sum rate of each link is performed, and then cluster-based optimum algorithms are proposed to obtain the optimum resource management. In [24], a method to minimize the interference between UEs was proposed. For that, an optimization problem is expressed as a mixed binary integer nonlinear programming, subject to the quality of service, the total available power, the interference threshold, and the minimal transmission rate. A resource allocation mechanism adaptive to the environment was proposed in [25]. This approach is based on reinforcement learning, and intends to prioritize the channel access to UEs with urgent needs. The reinforcement learning framework is modeled as a contextual multi-armed bandit to provide efficiency and accuracy.