Methods for Modelling of Overall Telecommunication Systems

<strong></strong>The Fine Thread between Stability and Randomness of the Non-stationary D/M/1 Queue’s GI/M/1 Pointwise of Stationary Fluid Flow Approximation Model with Application Ultra-Low Latency of Autonomous Driving Service

“…We believe that the non-stationary load will exhibit sinusoidal mean behaviour, which will describe the cyclic load pattern over a specified time period (for example, day) in accordance with the prior research on non-stationary analysis of communication networks [2][3][4][5][6], namely 𝜆(𝑡) = 𝐴 + 𝐵𝑠𝑖𝑛(𝑤𝑡 + 𝐷), for more details see [7][8][9].…”

<strong></strong>The Fine Thread between Stability and Randomness of the Non-stationary D/M/1 Queue’s GI/M/1 Pointwise of Stationary Fluid Flow Approximation Model with Application Ultra-Low Latency of Autonomous Driving Service

“…We believe that the non-stationary load will exhibit sinusoidal mean behaviour, which will describe the cyclic load pattern over a specified time period (for example, day) in accordance with the prior research on non-stationary analysis of communication networks [2][3][4][5][6], namely 𝜆(𝑡) = 𝐴 + 𝐵𝑠𝑖𝑛(𝑤𝑡 + 𝐷), for more details see [7][8][9].…”

Effect of the root parameter on the stability of the Non-stationary <em>D/M/1 </em> queue’s <em>GI/M/1 </em> model with PSFFA applications to the Internet of Things (IoT)

“…Both Figures 1 and 2(c.f., [2]) provide two real life applictions of Time Varying queueing systems. In Figure 1, the Mean number of calls per minute at a central office switch -measured in 15 minutes intervals averaged over 10 work days are observed .…”