# A Grant-Free Random Access Process for Low-End Distribution System Using Deep Neural Network
## 3 System Model  
As already mentioned above, for the system model, we consider a grant-free transmission. Let us denote by \(M\) the total number of preambles or channels, and \(D\) the total number of IoT devices trying to transmit to the network. Unlike in [36], in which the resource pool was increased to incorporate a large number of IoT devices, we take a slightly different approach by introducing stations \(N\). The IoT devices, instead of transmitting directly to the eNodeB, transmit to any of the \(N\) stations randomly. If there is no queue in the station, the device directly transmits to the eNodeB, otherwise, it waits until the queue ends. Each station has an infinite queue to incorporate a large number of IoT devices. The IoT device emulates a unit buffer queue or a node, and it has one packet. Any real time IoT device can be considered, especially the devices that send data on a consistent basis. The devices may have the following properties: a unit buffered queue, small size of the transmitted data, and a Poisson based distribution. The devices can be considered as operating from batteries because of the small size of the packet and sporadic transmission. The device leaves the system as soon as it achieves success and competes for the channel again after obtaining a packet. The pictorial representation of the system model is given in Figure 3.  
We see that in Figure 3, the rows represent different stations and the columns in each row represent the devices. The number of stations is limited while the number of devices that transmits via stations is infinite. \(D_{N1\_1}\) represents the first device in station \(N_{1}\) and \(D_{N3\_2}\) represents the second device in station 3, etc. The devices arrive in the system based on a Poisson arrival process, with \(\lambda\) being the arrival rate. Each device has only one packet, and as soon as it gets successful, it leaves the system and tries again when it has a packet. So, in our scenario, devices and packets are the same things and may be used interchangeably throughout the text.  
When the device arrives at the station, it checks whether there is a queue ahead of it or not. If there is no queue, which means there is only one device in that station, the device immediately transmits, otherwise, it enters the queue. If there are multiple devices in a queue, the device with maximum waiting time transmits, i.e., it follows the first-in-first-out (FIFO) rule.  
At the start of each time slot, the station which has the device transmits the packet to the eNodeB. If only one station transmits and others remain idle, the event is a success, whereas if more than one of the stations have a packet and they transmit, it results in a collision. If queues of all the stations are empty, the event is idle. This is depicted in Figure 4.  
Since the handshake process and ACB are not used in grant-free transmission, it needs a sophisticated algorithm to keep the number of collisions to a minimum and the number of successes to a maximum. The proposed algorithm is able to predict which station should transmit to have a minimum number of collisions and maximum number of successes. The algorithm is explained in the next section.  
Figure 3: System model: Queues of devices in stations.