# A Grant-Free Random Access Process for Low-End Distribution System Using Deep Neural Network
## 8 Conclusions and Future Works  
The conventional LTE-A random access procedure uses an access-barring scheme to control congestion that arises due to a large number of IoT devices. This induces extra delay during the transmission, and the overall efficiency of the system is affected. This paper dealt with the grant-free transmission of IoT devices and coping with the collision problem which is associated with it. The paper proposed an efficient algorithm that took channel conditions into account and managed the transmission such that the successes were maximum and the collisions were minimum. The algorithm was used to train a DNN model using an optimal threshold and then the DNN model was used to predict which device needed to transmit to have maximum chances of success. We see that by using the proposed algorithm in grant-free transmission, the delay can be reduced and the overall system efficiency can be increased. As machine/deep learning can be used to maximize the success rate, it can also be used in other issues in random access networks. One such example is choosing eNodeB in dense deployments such that the probability of success is maximum. Since the eNodeBs can be large in number in dense deployments and the available traffic of devices may not be uniform, it is possible that some eNodeBs face sever congestion while others may not have any traffic. If the devices do not select the eNodeB intelligently, they may end up facing long queuing delays and consequently have less throughput. We observe that the devised algorithm selected the eNodeB such that the probability of success was maximum and hence it improved the overall efficiency of the system. So this algorithm can help alleviate congestion issues in dense deployment scenarios.  
There can be different directions in which the work related to this topic can be done in the future. One research direction can be to prioritize the access of devices based on their importance using machine/deep learning. Currently, ACB is widely used as an access-barring technique and the devices compete for the transmission. However, there are certain devices which have time-critical data such as banks, hospitals, etc. A large delay can hinder their performance. Therefore, an algorithm can be devised to set the priorities of the devices and transmissions can be made based on the proposed algorithm such that the throughput is maximum, and the access delay of time-critical devices is minimum. Machine/deep learning can be used to predict the traffic of certain prioritized classes and the traffic can be routed to more lightly loaded eNodeBs in case of congestion. Different machine/deep learning techniques can also be used to select the best among them.  
Another research direction can be to increase the throughput in grant-free random access, and again, machine/deep learning can help to solve the problem. Currently the transmission in random access is orthogonal, i.e., only one device can transmit on one channel in a given time and frequency resource. If more than one of the devices transmit on the same channel, it results in a collision, i.e., the receiver is unable to decode the transmission of each of the transmitting devices. Nonorthogonal transmission can be a solution to the problem where instead of taking it as a loss, it can be thought of as an advantage, and deliberately, more than one of the devices transmit on a single channel. At the receiving end, the receiver decodes the transmission of each of the transmitting devices using machine/deep learning. Based on the available parameters, machine/deep learning can help the receivers in decoding the received signals.  
Investigation, A.A.; Project administration, D.Q.; Software, A.A.; Supervision, D.Q.; Writing--original draft, A.A.; Writing--review & editing, D.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.