  In response to the advent of the 5G era, enhancing throughput and increasing
transmission efficiency within limited spectrum resources is an important
research topic. In the LTE system, utilizing unlicensed spectrum to assist
traditional mobile networks, known as License Assisted Access, has emerged as a
viable solution to effectively improve transmission efficiency. However, as the
unlicensed spectrum also accommodates other users, such as Wi-Fi for mobile
communication, there is a need to address the issue of spectrum resource
allocation, aiming to achieve fair transmission among different mobile
communication users. This research project aims to explore and compare two
approaches: traditional communication algorithms and reinforcement learning
method Q-leaning, under the condition of achieving maximum system throughput,
in order to determine the differences between the two methods.
