## 4 The Proposed Adaptive Channel Coding Technique
### The Outer Loop Link Adaptation Approach
Depending on the type of applications for which the NB-IoT system is deployed as well as the cost constraints, ultra low power and faster microcontrollers such as the Silicon Laboratories EFM32 microcontrollers can be used to implement the EEACC algorithm along with full LTE protocol stacks for complex applications such as the control of unmanned aerial vehicles (UAVs) [55]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Instruction Type** & **Percentage** \\ \hline Load & 52\% \\ Store & 23\% \\ R-type & 14\% \\ Branch & 9\% \\ Jump & 2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MATLAB instructions classification application results. ## 5 Performance Evaluation

### Evaluation Setup

In order to assess and validate the performance of the proposed EEACC approach, various simulations were carried out under the NB-IoT network conditions summarized within the set-up parameters given in Table 2.

Furthermore, it is important to note that that a 4:1 Multiple Input Multiple Output (MIMO) with Alamouti decoding technique as well as an eNB antenna design is considered in these simulations. As the study was carried out in the context of a Narrowband Internet of Things (NB-IoT) network deployed in an LTE cellular network, the Quadrature Phase Shift Keying (QPSK) LTE modulation scheme is considered. The eNodeBs in an LTE network is usually built to support QPSK, 16 QAM, and 64 QAM modulation techniques for the Down Link direction. But considering that the end nodes (NB-IoT motes) are often computationally limited devices that are micro-controller based in many cases (sensor nodes, smart water meters, etc. ), the choice of the modulation scheme is often oriented towards QPSK [56]. ### The EEACC MATLAB Simulation Approach

The proposed EEACC MATLAB simulation methodology consists of the following steps:

* First, a random NB-IoT network of nodes is generated on a planar using the uniform distribution functions of MATLAB. * Then, the Base Station (BS) is positioned using the geometry middle point as root to generate a tree topology by the [PERSON]'s algorithm. * A meshnet topology is formed as NB-IoT nodes are progressively deployed. Upon deployed, every node broadcasts a small interest message to identify its neighbours and path to the BS as \begin{table}
\begin{tabular}{c c} \hline System bandwidth & 200 kHz \\ \hline Carrier frequency & 900 MHz \\ \hline Subcarrier spacing & 15 kHz \\ \hline \multirow{2}{*}{Chanel estimation for NPDCCH} & Sequential \\  & Channel Estimation in the \\  & Presence of Random Phase Noise [52] \\ \hline Interference Rejection Combiner & MRC \\ \hline Number of Tx antennas & 1 \\ \hline Number of receive antennas & 2 \\ \hline Frequency offset & 200 Hz \\ \hline \(L_{min}\) & 4 \\ \hline \(L_{max}\) & 12 \\ \hline \(N_{min}\) & 2 \\ \hline \(N_{max}\) & 10 \\ \hline Time offset period & 2.5 μs \\ \hline Network deployment Model & Mesh Network (Meshnet) \\ \hline Channel Model & Time-invariant slow fading \\  & with random phase noise following \\  & an Additive White Gaussian Noise (AWGN) like distribution \\ \hline slow or almost no nodes mobility considered & Static nodes \\  & No fading channel consideration. \\ \hline LTE Modulation Scheme & Quadrature Phase Shift Keying (QPSK) \\ \hline \end{tabular}
\end{table} 
Table 2: Key Simulation parameters. guided by the Friis transmission equation. It is also important to mention that the Meshnet is the most preferred deployment approach for the NB-IoT networks [57; 58]. This is due to the self-forming and self-healing nature of Mesh Networks which presents the following advantages when used for NB-IoT systems:

1. Better efficiency: With its ability to quickly create robust ad-hoc networks, mesh technology is proven to be a high-performance data transfer solution which happens to be the core of most NB-IoT applications (example of Wireless Sensor smart metering NB-IoT networks and many more). 2.