چكيده به لاتين
The Intelligent reflecting surface, which is also called the reconfigurable Intelligent surface, is a new technology to improve the performance of beyond-5th generation mobile phone telecommunication systems, which consists of a large number of small and low-cost elements that are able to reflect the waves that hit them. The elements present in the IRS are passive in the sense that they reflect the wave without amplification and only with the ability to change the phase. Failure to amplify the wave power causes a problem called double path fading, which will cause a severe fading of the transmitted signal.
Recently, a new type of Intelligent reflective surfaces has been introduced, which, in addition to changing the phase, also have the ability to amplify the signal power. This feature will overcome the double path fading and the resulting problems. The active surface consumes more power compared to the passive surfaces, but unlike common amplifiers, it does not use the RF chain, and as a result, it can be a good substitute for the passive surface.
The purpose of this thesis is to compare active surfaces with common passive surfaces in conditions close to reality based on deep reinforcement learning as a branch of machine learning. First, a problem with the aim of maximizing the sum rate of a multi-user and MISO system in the presence of an active reflective surface will be investigated. Then, with the help of the DDPG algorithm as a reinforcement learning agent, this problem will be solved continuously, and further changes will be applied to this model to solve the problem discretely.
The obtained results show a severe performance drop of the discrete active surface compared to the continuous active surface. As the number of elements of the discrete surface increases, its performance will be lower than that of the passive surface. It is also proven that the proposed method will have much less computational complexity than the existing classical methods in continuous and discrete mode.
