# A Grant-Free Random Access Process for Low-End Distribution System Using Deep Neural Network
## 2 Related Work  
As mentioned in the previous section, IoT devices can be large in numbers, which creates a serious challenge for managing the service with limited resources. After the registration of the device to the network, the provision of resources to the device is the responsibility of the eNodeB. The eNodeB schedules the transmission of the device such that the transmission is guaranteed albeit with some delay [24]. However, before the association with the network, getting connected to the network is a random process. So the access of a device is of two types, random and scheduled. Generally, more resources are allocated for the scheduled access than the random access. When the number of devices is large, the resources for random access may prove insufficient, resulting in congestion. The delay in random access becomes important since it affects the overall efficiency of the network. Moreover, if a device cannot complete the random access process successfully, it cannot have service from the network. Hence with IoT devices, the random access process becomes extremely important, and it is imperative that the devices experience less delay in this process [25; 26]. In LTE/LTE-A networks, random access is a four-step handshake process between the device and eNodeB [27; 28]. In the first step, the device chooses a preamble that was broadcast by eNodeB and transmits it to eNodeB. In the second step, the eNodeB responds with the random access response (RAR) message. In the third step, the device sends a connection request message to the eNodeB, and in the fourth and last step, the eNodeB sends a contention resolution to the eNodeB. The details of the random access process in LTE/LTE-A networks can be found in [29; 30]. For a device to have a successful random access process, this four-step handshake process needs to be completed successfully. The problem occurs when more than one of the devices select the same preamble, which leads to the collision and the failure of the random access process of the devices involved. Therefore, controlling congestion in the random access process means minimizing the number of collisions and increasing the successful transmission. There has been a lot of work in the literature on how to control the congestion in the random access process. The techniques are discussed in [31], and in pictorial form, they are given in Figure 2.  
Of all the techniques mentioned in Figure 2, access class barring (ACB) [32; 33] and extended access barring (EAB) [34] have been accepted by the third-generation partnership project (3 GPP) as potential solutions to overcome congestion [35]. In EAB, each device is assigned a class number ranging from zero to nine. Only one class is allowed to transmit while the other classes are barred from the transmission. The eNodeB broadcasts a bitmap consisting of 10 bits with all bits zero except the one which is allowed to transmit and it has a value of one. Each device is also assigned a paging frame (PF) and paging occasion (PO). Each device wakes up at its PO within its PF and checks whether its class is allowed to transmit or not. If allowed, it transmits, otherwise it waits for the next PF and PO. The chances of collision in EAB are less but the delay is larger due to a large idle time. The devices have to wait for longer periods of time for successful transmission. In ACB, the eNodeB broadcasts an ACB factor (a transmission probability). Each device generates a random number and if this random number is less than the ACB factor, the device transmits, otherwise it is barred from the transmission and it waits for the next chance. In ACB, the idle time is less, but the collisions are more, and the delay in ACB is less than that of EAB. In ACB, the ACB factor is of prime importance and needs to be optimal to have a maximum success probability.