On small-scale wireless networks, traditional architectures deliver fast execution and nearly ideal performance, but when user density is high, the performance suffers significantly. The highly congested frequency band is one of the major problems for wireless communication technology. Numerous wireless users to share the same services in terms of time and/or frequency because of the constrained radio band. Interference develops from this configuration. The rising need for high data-rate transmission is driving current development in wireless communication technology. This has led to a major improvement in wireless networks’ performance in recent years. However, there is still a pressing demand for more effective wireless communications given the widespread usage of smart phones, laptops, and multimedia devices. For numerous networks, including cellular networks, wireless lan, and wireless ad-hoc networks, communication inside the presence significant interference has indeed been taken into consideration. One of the finest techniques for reducing wireless communication interference is deep learning. Each one operates by taking lessons from a dataset. One of the cutting-edge technologies that has been applied to address current wireless communication problems is deep learning. Deep learning employs two distinct methods: data-driven approach and model-driven approach. Neural networks were used in place of the traditional building blocks in the data-driven approach. The second method is the model-driven approach, which substitutes a neural network for parts of the traditional algorithms’ strategies.