## 3 Proposed System Model
### Communication Model  
We consider a radio network system to be an edge server consisting of a set \(N=\{1,2,\ldots n\}\), where \(N\) refers to the number of user devices. Next, we set \(J=\{1,2,\ldots j\}\), where \(J\) refers to Radio Remote Heads (RRHs), and \(M=\{1,2,\ldots m\}\) refers to the number of MEC servers  
Figure 1: Network system scenario.  
used in our network scenarios. In our network scenario, all the RRHs are interconnected through a fast gigabit switch to a common processing center comprising a set of Baseband Units (BBUs) denoted as \(K=\{1,2,\ldots k\}\). We assume that each BBU can serve one or more RRHs, and the RRHs can cooperate with each other for downlink transmissions and uplink transmissions to the user device. The channel gain between UE \(n\) and RRH \(j\) is denoted as \(g_{nj}\). The system bandwidth allocated from RRH \(j\) to UE \(n\) is denoted as \(w_{nj}\). The fixed transmission power of user device n for offloading task \(i\) to RRH \(j\) is denoted as \(p_{n}^{tr}\). The noise power is denoted as \(n_{0}\). Next, intercell interference is a type of interference that occurs in cellular networks when multiple cells (i.e., base stations) use the same frequency band to communicate with their respective user devices. The signals transmitted by neighboring cells can interfere with each other and cause degradation of the received signal quality, leading to errors in data transmission. In our work, we denote inter-cell interference as \(Los_{nj}\). The transmission rate of computational task \(i\) from user device \(n\) to base station \(j\) is expressed as follows:  
\[R_{nj}= W_{nj}log_{2}\Bigg{(}1+\frac{p_{n}^{tr}g_{nj}}{n_{0}+Los_{nj}}\Bigg{)} \tag{1}\] \[t^{loc}=\frac{C_{i}}{V_{i}^{loc}} \tag{2}\]  
where \(t^{loc}\) is the local completion time for user devices n for accomplishing task \(i\) in milliseconds (ms). \(V_{i}^{loc}\) is the computing capacity of user devices \(n\) in Megahertz (MHz). The computing capacity of an IoT device can vary greatly depending on the specific device and its purpose. IoT devices can range from simple sensors that collect data and transmit it to a central server for processing to more complex devices that have on-board processing capacity. In our work, we use simple sensors on IoT devices which have small computing capacity for implementations. \(C_{i}\) is the total number of computational capacities required to complete computational task \(i\) measured in task CPU cycles. In addition, we can obtain the local energy consumption for user devices to accomplish task \(i\) as follows:  
\[e^{loc}=\alpha C_{i}\Big{(}V_{i}^{loc}\Big{)}^{2} \tag{3}\]  
where \(\alpha\) denotes a coefficient associated with the chip architecture [40]. Next, \(e^{loc}\) is the local energy consumption for user devices \(n\) to accomplish task \(i\). In the context of computing and user devices, energy consumption is typically measured in units of watt seconds (Ws). One watt second is equal to the energy consumed by a device that consumes one watt of power for one second. To convert energy consumption from watt seconds to Joules (\(j\)), the formula is as follows: energy (\(j\)) = power (W) \(\times\) time (s). Therefore, energy consumption is a standard unit of energy measurement.  
Also, the computation cost for offloading task \(i\) to the edge server refers to the process of initializing latency and energy consumed for offloading task \(i\) from user devices to the edge server. The cost formula can be denoted as  
\[cost^{loc}= t^{loc}+e^{loc} \tag{4}\]