## 3 System Model
The reason behind this is to separate the cell-edge and cell-centered user and to give more priority to the cell-edge users who are in the outer region by allocating more RBs to them. The distance between user and the attached eNB is considered along with SINR for accurate classification of the user. The maximum length from the eNB to the edge of inner region is \(\Omega\). Any user who is located outside this area will be treated as a cell-edge user and the rest of the user will be considered as a cell-centered user. The proposed EMLWDF algorithm is explained in Equation (3) below:  
\[m_{i,j}{}^{EMLWDF}=\frac{-log\Big{(}\frac{\delta_{i}}{W_{i}}\Big{)}}{\tau_{i }}.D_{HOL,i}.\psi_{i,j}, \tag{4}\]  
where  
\[W_{i}=\left\{ \begin{array}{r@{\quad}l}\frac{R_{i}}{\tau_{i,j}}\quad&if\quad R _{i}>\Omega\\ 1\quad&if\quad R_{i}\leq\Omega\end{array} \right., \tag{5}\]  
\[R_{i}=\sqrt{\sum_{k=1}^{u}\left|x_{k}-y_{k}\right|^{2}}, \tag{6}\]  
\[\gamma_{i,j}=\frac{G_{ij}P_{i,j}}{\sigma^{2}+I}. \tag{7}\]  
Here, \(m_{i,j}{}^{EMLWDF}\) denotes the metric value of the EMLWDF algorithm, \(\gamma_{ij}\) is the SINR of user \(i\) RB \(j\) and \(R_{i}\) is the distance between eNB and user \(i\). The higher the metric \(m_{i,j}{}^{EMLWDF}\) value for a particular user in Equation (4), the higher the chance for that user to get the required RBs. Based on Equations (4) and (5), it can be seen that the variable \(W_{i}\) will vary according to the location of the user \(i\). If the distance of the user \(i\) from eNB is more than the radius of the inner region \(\Omega\), the proposed EMLWDF algorithm will consider the distance \(R_{i}\) and SINR \(\gamma_{i,j}\) of user \(i\) RB \(j\) to calculate the metric \(m_{i,j}{}^{EMLWDF}\). Otherwise, the user will be considered as a cell-centered user and the EMLWDF algorithm will not consider the distance and SINR of that particular user. After exceeding the inner region value, the higher the distance from eNB and the less SINR of the user, the higher the metric value and thus higher the possibility to get RBs. The distance between the eNB position \(x\) and user position \(y\) with \(n\) dimensions is measured in Equation (6) by the Pythagorean theorem considering Euclidian space [24]. Equation (7) calculates the received SINR \(\gamma_{i,j}\) of user \(i\) on RB \(j\), where \(G_{ij}\) and \(P_{i,j}\) are the channel gain and transmit power of serving eNB, respectively [25]. \(\sigma^{2}\) is the white noise power spectral density and \(I\) is the inter-cell interference. The users are affected by various noise and interference in the real-life wireless environment. Large scale errors occur when the users are receiving the data packets. Therefore, this paper implements the two-state Markov Model to measure the probability of packet loss \(\delta_{i}\)[26]. Success state and failure state are the two states in this model. The success state has low error probability and that helps to transmit packet successfully. On the other hand, the failure state has higher error probability. Thus, packets cannot be transmitted, which makes this state unusable. If the packet transmission fails, the packet is retransmitted until it exceeds the delay threshold value. After exceeding the threshold value, the packet will be discarded and the next packet transmission will begin. The packet-dropping operation has a significant impact on the probability of packet loss. This paper considers that the success state is the usable system scenario as packets can be transmitted successfully in this state. The delay threshold and error probability are considered in the simplified equation of success state, which is described in the following Equation (8):  
\[\delta_{i}=\varepsilon_{i}(1-S_{i})^{\tau_{i}}. \tag{8}\]  
Here, \(\varepsilon_{i}\) is the error probability and \(S_{i}\) is the probability from failure state to success state. The \(\varepsilon_{i}\) is assumed as a small value and \(\varepsilon_{i}\) should be smaller than \(S_{i}\) in this state to make the system usable.