# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 2 Related Works  
### The Legacy Contention-Based Random Access Procedure  
The contention-based RA procedure is a reference method used in the existing RA scheme and it has difficulty supporting massive connectivity. Therefore, a number of studies on contention-based RA considering the massive RA of 5G have been conducted [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. A study was conducted on the access class barring method proposed by the 3 GPP standard organization to solve the high collision problem of mMTC. The authors of [11] designed a random access channel (RACH) success probability analysis model for the three coverage enhancement groups based on the 3 GPP NB-IoT standard. The author suggested that with the existing access class barring (ACB) and back-off (BO) algorithms, the RA success rate can be approximately doubled through RA management for the three coverage enhancement groups compared to the massive RA situation in a single group. The authors of [12] analyzed the performance of power ramping, back-off, and access class barring (ACB) algorithms to solve the congestion situation of mMTC. Studies on the ACBs utilized in the 3 GPP NB-IoT standard were conducted in [13]. The authors improved the collision rate by proposing an ACB configuration method that selects the optimal barring parameters in real time. In [14], joint optimization of the preamble selection and access barring methods was performed. The author derived a solution to the average RA throughput maximization problem using the block coordinate descent method, which is a non-convex problem. Numerous studies have been conducted on increasing the service satisfaction rate by allocating prioritized preambles according to the requirements of each device. The authors of [15] proposed a distributed queuing-based content resolution scheme for MAC layer load estimation. Through the design of the distributed queue-based framework, the author solved the congestion of massive access and guaranteed the priority of device access. In [16], various device access requirements were considered. Using the proposed access priority provisioning technology, devices can satisfy the delay requirement by transmitting multiple preambles according to the priority order. The authors of [17] studied a method for distinguishing devices in the access control process using a tagged preamble. The authors of [17] studied a method for distinguishing devices in the access control process using a tagged preamble. Through joint optimization of the tagged preamble and access-class barring parameters, the author improved the RA success rate. In [18], the MAC and PHY layer enhancement of RA was studied to consider the QoS requirements of the various service types of mMTC. To improve the RA performance in mMTC, a machine-learning-based approach was studied. The authors of [19] proposed a reinforcement learning (RL)-based ACB method that can adaptively select the barring rate. The proposed scheme improved the access success probability through RL-based dynamic barring factor selection. The authors of [20] studied the ACB method by considering the energy consumption and delay constraints of the device. In this study, a deep reinforcement learning approach was used to obtain the real-time mean barring time and barring rate. To resolve preamble collisions, [21] proposed a preamble selection method based on multi-agent reinforcement learning. In addition to the aforementioned studies, various other approaches to mMTC have been studied. In [22], a method for utilizing unused PUSCH resources was proposed to support devices that failed RA in mMTC. The proposed PUSCH resource reallocation algorithm increased the RA success rate at the cost of energy consumption. To support a large number of connections in mMTC, the authors of [23] used a compressive sensing method. The authors reduced the RA collision rate by utilizing the sporadicity characteristics and a non-orthogonal pilot.