Reinforcement Learning Optimization for Energy-Efficient Cellular Networks with Coordinated Multipoint Communications

<table><svg height="14.3125" id="M148" style="vertical-align:-1.90608pt" version="1.1" viewbox="0 0 13.175 14.3125" width="13.175" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,11.4)"><path d="M745 361q0 -134 -83.5 -233t-214.5 -130l16 -11q97 -67 250 -132l-8 -23q-76 3 -131 16q-81 19 -242 125l-20 13q-129 8 -209 91t-80 208q0 160 116 271t289 111q136 0 226.5 -83t90.5 -223zM645 356q0 127 -57.5 201.5t-169.5 74.5q-126 0 -210.5 -104.5t-84.5 -248.5
q0 -97 46 -166.5t129 -87.5l84 15l29 -19q104 21 169 121.5t65 213.5z" id="x1D444"></path></g>
</svg> value changes in different User Count.</table>

Mathematical Problems in Engineering

fig7

Figure 7

Figure 7: Reinforcement Learning Optimization for Energy-Efficient Cellular Networks with Coordinated Multipoint Communications 