# A Grant-Free Random Access Process for Low-End Distribution System Using Deep Neural Network
## 2 Related Work
In [29], it was derived that the optimal transmission probability can be written as:  
\[p_{opt}=\min\left(1,\frac{M}{n}\right), \tag{1}\]  
where \(M\) is the number of preambles (or channels), and \(n\) is the number of devices. The focus is now shifting to the grant-free transmission, i.e., when \(p_{opt}=1\). With \(p_{opt}<1\), there can be some devices that are barred from the transmission and hence experience delay. If \(p_{opt}=1\), all the devices transmit, reducing the delay. The four-step handshake process also induces a delay. With the grant-free procedure, each device can transmit at any time of its choice. It does not need to take permission from the eNodeB for transmission. However, with the grant-free transmission, there comes a challenge. If all the devices transmit simultaneously, collisions can take place. If the number of resources is larger than the number of devices, the grant-free process outperforms the conventional handshake-based random access process. However, if number of devices is larger than the number of resources, which is generally the case, collisions can take place and the performance will be worse than the conventional random access process. Therefore, in order to make the grant-free process work, a sophisticated technique or algorithm is required such that the collisions are minimized and success is maximized. In [36], the authors studied grant-free transmission, where the resource pool was increased virtually, such as in pattern division multiple access (PDMA) [37]. In [38], the authors used multiple antennas to increase the success probability of the preambles; however, the model used in our work is simpler as it only considers a single antenna. In [39], the authors used collision reduction to solve the problem of congestion in grant-free random access process in a massive MIMO scenario. Again, the receiver complexity was the issue, which is nonexistent in our scenario. The authors in [40] used sparsity and then used signal processing techniques at the receiver to decode which transmitter had transmitted on the channel; however, it increased the receiver complexity, while the algorithm in our work requires a simple receiver. In this way, there are enough resources for the devices to choose separate channels and hence collisions can be reduced. So far, we have discussed grant-based random access, grant-free random access, and the techniques that are used to implement the grant-free transmission. Most of the techniques used signal processing techniques or nonorthogonal transmission to resolve the problem of collision. We did not find any work where grant-free transmission was obtained without complex signal processing at the receiver and orthogonal transmission. In this paper, we take this challenge and propose an algorithm for grant-free transmission without receiver complexity and orthogonal transmission with the objective to minimize collisions. We use deep learning techniques to realize our algorithm. In the first step, our deep learning model is trained based on the scenarios of idle, success, and collisions. Then, the deep learning model is used to predict the run time and which device needs to transmit such that there is a maximum chance of success and a minimum chance of collision. Figure 2: Techniques to improve random access process.