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\begin{document}

\title{An Adaptive Channel Modulation  and Coding Scheme for 5G–TSN Integration\\
}

\author{\IEEEauthorblockN{Mahmoud Alqudah}
\IEEEauthorblockA{\textit{University of Siegen} \\
Siegen, Germany \\
mahmoud.alqudah@uni-siegen.de}
\and
\IEEEauthorblockN{Ala' Khalifeh}
\IEEEauthorblockA{\textit{German Jordanian University} \\
Amman, Jordan \\
Ala.Khalifeh@gju.edu.jo}
\and
\IEEEauthorblockN{Roman Obermaisser}
\IEEEauthorblockA{\textit{University of Siegen} \\
Siegen, Germany \\
roman.obermaisser@uni-siegen.de}
\and
\IEEEauthorblockN{Kai Daniel}
\IEEEauthorblockA{\textit{University of Applied Sciences Ruhr West} \\
Mülheim an der Ruhr, Germany \\
Kai.Daniel@uni-siegen.de}
\and
\IEEEauthorblockN{Usman Ahmed Saeed}
\IEEEauthorblockA{\textit{University of Siegen} \\
Siegen, Germany \\
Usman.Saeed@student.uni-siegen.de}
}

\maketitle

\begin{abstract}

The dynamic behaviour of the wireless channel poses significant challenges in addressing the Time-Sensitive Networking (TSN) requirements such as having low delay and packet loss which is increasingly adopted as a deterministic communication standard in industrial environments. Unlike wired links, wireless channels experience unpredictable variations that can disrupt time guarantees unless carefully controlled. Fifth Generation (5G) networks offer flexibility, high capacity, and reliability, making it a strong candidate for forming a heterogeneous TSN-capable network. Adapting to channel fluctuations is therefore an essential step toward achieving deterministic wireless communication. This paper introduces a link adaptation scheme based on the adaptive modulation and Coding Scheme (AMCS) that accounts for Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), and channel stability. The proposed scheme dynamically alternates between aggressive and conservative modes, maximizing the data rate under favorable channel conditions while ensuring robustness during high-BER periods. The solution is evaluated using the OMNeT++ simulation environment.

\end{abstract}

\begin{IEEEkeywords}
Adaptive Modulation Coding Scheme (AMCS), 5G-TSN, Link Adaptation, Wireless Channel
\end{IEEEkeywords}

\section{Introduction}
The integration of 5G networks with TSN is gaining increasing attention due to the promising potential of combining their respective strengths. While 5G provides security, flexibility, high speed, and reliability, TSN represents a suite of standards designed to ensure deterministic communication \cite{b1}. The ultimate objective of this convergence is to create a deterministic heterogeneous network that unifies 5G and TSN, offering a wireless alternative to traditional wired systems while preserving their performance guarantees \cite{b2}.

One of the key enablers of deterministic communication in Ethernet has been the stability of the transmission medium. In contrast, wireless systems such as 5G inherently suffer from channel variability, where fluctuations in signal conditions can lead to packet losses and transmission delays \cite{b9}. These variations pose significant challenges to achieving deterministic behavior over wireless links, making adaptive mechanisms essential for maintaining reliability and timing precision \cite{b3} \cite{b4}.

In 5G New Radio (NR), the Modulation and Coding Scheme (MCS) selection is driven by the interaction between the gNodeB (gNB) within the Radio Access Network (RAN) and the User Equipment (UE). The UE measures the Downlink (DL) SNR and derives a corresponding Channel Quality Indicator (CQI) using standardized SNR-to-CQI lookup tables \cite{b14}. This CQI value quantifies the instantaneous channel condition and is periodically reported to the gNB. Upon reception, the gNB maps the CQI to a specific MCS using predefined CQI-to-MCS tables, determining the modulation order and coding rate for the next transmission interval. This procedure, known as AMCS, enables the system to adjust transmission robustness and data rate according to real-time channel variations. As demonstrated in \cite{b12}, AMCS significantly enhances throughput and latency performance, underscoring the importance of accurate and timely MCS selection for maintaining reliable and efficient communication in 5G NR networks \cite{b3}, \cite{b11}.

Selecting a higher-order MCS increases the number of bits transmitted per symbol, thereby enhancing the data rate and overall throughput while reducing packet delay. However, this improvement comes at the cost of higher sensitivity to channel variations or unstable channel conditions can significantly increase packet loss. Achieving consistent transmission performance therefore requires a more adaptive approach to MCS selection that accounts for multiple factors influencing link quality \cite{b10}.

The main contribution of this paper is the development of a dynamic decision-making model for MCS selection that considers SNR, BER, channel stability, and the performance of recent transmissions. Unlike traditional methods that rely on static CQI-to-MCS lookup tables \cite{b14}, the proposed model dynamically adjusts to variations in channel quality, whether improving or degrading. The objective is to minimize packet loss while maintaining low and stable latency. The proposed approach is implemented and evaluated using the OMNeT++ simulation environment, with Simu5G serving as the baseline for performance comparison \cite{b15}.

The remainder of this paper is organized as follows: Section II presents the related work and state of the art, Section III describes the proposed methodology, Section IV discusses the evaluation results and analysis, and finally Section V concludes the paper and proposes future work.




\section{State of the Art}

Several studies have explored enhancements to AMCS due to their significant impact on overall network performance. The authors in \cite{b5} established a theoretical relationship between Ultra-Reliable Low Latency Communication (URLLC) performance metrics, channel quality, and transmission configuration, linking them to key parameters such as delay, bandwidth, and reliability. Based on these thresholds, the MCS was dynamically selected. Although the approach achieved promising results, it introduced considerable computational overhead and lacked real-time feedback or learning mechanisms. In contrast, the authors in \cite{b6} proposed a conservative adaptation strategy that aggregates CQI reports over time to estimate the worst-case channel behavior, selecting a conservative MCS to guarantee URLLC reliability. While effective in meeting stringent latency and reliability targets, this method resulted in excessive resource consumption compared to other AMCS strategies.

In another study, the authors in \cite{b7} implemented a Reinforcement Learning (RL) framework using Q-learning, enabling the gNB to learn an optimal MCS selection policy that maximizes spectral efficiency while maintaining a target Block Error Rate (BLER). The RL-based model outperformed traditional lookup table methods and Outer Loop Link Adaptation (OLLA) in terms of throughput and adaptability; however, its iterative feedback process introduced additional computational complexity. Similarly, the authors in \cite{b8} proposed a non-CQI-based adaptive approach that reduces signaling overhead by avoiding reliance on CQI reports. Instead, the method employs an ACK/NACK counter ratio combined with a Gaussian-filtered SNR metric to adjust the MCS dynamically. Laboratory simulations demonstrated improved adaptability to small-scale channel variations, though further validation in large-scale network scenarios remains necessary.


\section{Proposed Adaptive Modulation and Coding Scheme}
In conventional 5G NR systems, the selection of the MCS is primarily based on the CQI periodically reported by the UE. Each CQI value corresponds to a fixed entry in a predefined lookup table mapping channel quality to a modulation and coding level. While this approach simplifies link adaptation, it overlooks critical aspects such as BER, instantaneous SNR variations, and the temporal stability of the wireless channel. Consequently, CQI-based selection often fails to meet the stringent reliability and latency requirements demanded by URLLC and TSN applications, especially under fast-fading or interference-prone conditions. To overcome these limitations, this work proposes an AMCS selection algorithm that dynamically adjusts modulation and coding levels based on SNR, BER, and channel stability. The objective is to minimize packet loss, a key component of real-time determinism, while maintaining low and consistent latency.

During the initialization phase, QPSK is selected as the initial MCS to ensure reliable transmission and minimize packet loss, particularly when channel stability estimation is not yet precise enough to support higher-order modulation. The gNB gathers SNR values reported by the UE, which serve as raw indicators of channel quality. These values are used to compute the effective SNR. The calculation incorporates the Interference Overlapping Ratio (IOR) to more accurately capture the true link quality, it is calculated based on the measured overlapping bandwidth and the power ratio between the interferer and the desired signal. The collected SNR measurements are accumulated over several transmission intervals and used to determine the SNR variance, which quantifies channel stability and its degree of fluctuation. To avoid frequent MCS switching, the system applies a fixed number of iterations before adjusting modulation levels.

Channel fluctuation is characterized by variations in SNR over time, and these fluctuations directly influence the stability of the wireless link. At the same time, BER indicates whether the channel is operating under clean or degraded conditions. To determine an appropriate stability threshold for the proposed model, one that captures both channel clarity and reliability, an experiment was conducted to analyze the relationship between SNR variance and BLER. The evaluation focused on 256QAM as a high order MCS, as lower-order MCS levels are not typically used for aggressive transmission under optimal channel conditions, the experiment involved a representative set of randomly generated SNR cases which ensured the threshold was tested under realistic and varied channel conditions. As shown in Fig. \ref{fig:snr-variance}, BLER nearly doubles when the SNR variance reaches 5 dB², after which the curve exhibits an exponential increase compared to the near-linear behavior observed below this point according to curve fitting procedure. Based on these results, a variance of 5 dB² is adopted as the SNR stability threshold in the proposed algorithm.

The instability of the wireless channel is illustrated in Fig. \ref{fig:channel}, where the orange curve represents a low-variance channel and the blue curve represents a high-variance channel, highlighting the temporal fluctuations of SNR measurements observed across two distinct channel conditions. The figure clearly reveals significant fluctuations, characterized by periodic drops and recoveries in signal strength. This behavior reflects sudden channel degradations that increase the risk of packet loss. In such scenarios, reducing the data rate by selecting a lower-order MCS becomes a more reliable approach to maintain transmission stability and minimize losses. Conversely, a stable channel would exhibit minimal SNR fluctuations and consistent signal levels over time, indicating favorable conditions for higher-order MCS selection.


\begin{figure}
    \centering
    \includegraphics[width=1\linewidth]{SNR Variance Test.png}
    \caption{Experiment for SNR Variance Threshold Estimation}
    \label{fig:snr-variance}
\end{figure}

\begin{figure}
    \centering
    \includegraphics[width=1\linewidth]{channel.png}
    \caption{Channel Fluctuation}
    \label{fig:channel}
\end{figure}

\begin{algorithm}[ht]
\caption{Adaptive MCS Selection Algorithm (mcs\_amc)}
\label{alg:mcs_amc}
\SetAlgoLined
\KwIn{Recent BER, SNR History (7 Iterations), lastModulation}
\KwOut{Selected MCS}

\textbf{Initialization:}\\
Set initial MCS $\leftarrow$ QPSK \\
Set initial BER threshold $B_{th} \leftarrow \alpha$ \\

\BlankLine
\textbf{Step 1: SNR Processing}\\
Obtain current SNR reported by UE \\
% Map CQI $\rightarrow$ SNR using lookup table \\
Compute effective SNR: 
$SNR_{eff} = 10\log_{10}\left(\frac{10^{SNR/10}}{1 - 10^{IOR/10}}\right)$ \\
Append $SNR_{eff}$ to SNR history (keep last 8 samples) \\

\BlankLine
\textbf{Step 2: Channel Stability Assessment}\\
Calculate variance $\sigma^2_{SNR}$ from SNR history \\
\If{$\sigma^2_{SNR} > 5$}{
    Mark channel as \textit{unstable} according to experiment \\ 
    Reduce $SNR_{eff}$ by safety margin (1 dB)
}
\Else{
    Mark channel as \textit{stable}
}

\BlankLine
\textbf{Step 3: Candidate MCS Evaluation}\\
For each modulation $M \in \{QPSK, 16QAM, 64QAM, 256QAM\}$:\\
\Indp
Compute predicted BER$(M)$ using empirical SNR–BER curves \\
Combine predicted and recent BER: 
$BER_{avg} = 0.3\times BER_{recent} + 0.7\times BER_{pred}$ \\
\Indm
Select highest $M$ satisfying $BER_{avg} \leq B_{th}$

\BlankLine
\textbf{Step 4: Decision Logic}\\
\If{channel unstable OR $BER_{recent} > \beta$}{
    downgrade MCS to lower order (e.g., 16QAM or QPSK)
}
\ElseIf{SNR and BER indicate improvement $\geq 20\%$}{
    upgrade to next higher MCS
}
\Else{
    maintain current MCS
}

\BlankLine
\textbf{Step 5: Output}\\
Return selected MCS \\
Update $lastModulation \leftarrow selectedMCS$ \\
\end{algorithm}

In the proposed framework, two types of BER are considered: the reported BER, which is directly measured by the UEs and sent to the gNB, and the estimated BER, which is calculated from predefined SNR–BER relationships corresponding to different MCS candidates, as shown in Fig. \ref{fig:snr} \cite{b13}. The SNR–BER curve illustrates the inverse relationship between signal quality and error probability, higher SNR values generally lead to lower BER, indicating more reliable communication. During each transmission cycle, the framework computes the estimated BER for all MCS options and compares them with a dynamic BER threshold. This adaptive threshold ensures that MCS selection aligns with the required reliability and latency constraints, a crucial aspect for maintaining deterministic performance in time-sensitive and real-time communication scenarios.

The MCS selection process starts with evaluating channel stability based on recent SNR readings and their variance. If the variance exceeds the defined stability threshold, the framework progressively reduces the effective SNR across iterations as a safety measure, based on experiment, 1dB reduction in the effective SNR was sufficient to significantly reduce packet error rates during variance spikes, without causing excessive or oscillatory reductions in the selected MCS. When instability persists, the system switches to a conservative mode, selecting a lower-order MCS to maintain link reliability and minimize packet loss. Under stable channel conditions, the algorithm considers both the reported and estimated BER values to calculate a weighted performance metric, the weight metric calculates the average of both predicted BER and reported BER where the predicted BER is given higher significance than the reported one. The average ratio was found to be the optimal balance during extensive calibration. This approach allows the system to explore potential MCS upgrades while keeping selections within a predefined reliability threshold. If a higher-order MCS demonstrates sufficient improvement in BER and remains within acceptable limits, the system dynamically upgrades to that MCS, enhancing throughput efficiency without compromising transmission stability.

\begin{figure}
    \centering
    \includegraphics[width=1\linewidth]{SNR1.png}
    \caption{Probability of Bit Error Rate vs SNR}
    \label{fig:snr}
\end{figure}

In the proposed framework, the BER threshold is adaptive rather than fixed, but remains bounded between minimum BER threshold $\alpha = 2 \times 10^{-2}$ and maximum BER threshold $\beta = 5 \times 10^{-2}$. These limits align with 3GPP specifications, where acceptable BLER levels are closely tied to the number of allowable Hybrid Automatic Repeat Request (HARQ) retransmissions. Consequently, the chosen bounds provide a practical balance between performance and retransmission overhead. Under favorable channel conditions, the BER threshold is relaxed, enabling more aggressive MCS selection to increase data rate and improve throughput. Conversely, when the channel degrades or the BER exceeds the allowable range, the system immediately switches to a conservative mode to minimize packet loss. This dual-mode strategy maintains an effective balance between reliability and spectral efficiency.

Overall, the proposed framework introduces a context-aware and feedback-driven decision mechanism, in contrast to the traditional static CQI-based approach. By combining SNR history tracking, BER prediction, and variance-based stability assessment, the algorithm effectively emulates real-world 5G modem behaviour under varying network conditions. The enhanced responsiveness enables near real-time adaptability and improved deterministic performance even in dynamic wireless environments. The implementation, developed and evaluated in the OMNeT++ simulation environment, demonstrated significant improvements in reliability and delay stability compared to conventional CQI-based schemes.

\section{Evaluation}
The proposed implementation referred to as mcs\_amc in this paper was evaluated using OMNeT++, a modular and extensible simulation framework widely utilized for advanced network research. To accurately model real-world 5G network behaviour and wireless channel dynamics, additional frameworks, INET and Simu5G were integrated into the simulation environment. Simu5G is a simulation library built on top of the OMNeT++ framework, providing detailed modelling capabilities for 5G networks, primarily focusing on the data plane while supporting all relevant protocol layers \cite{b15}. It serves as an effective tool for analyzing and evaluating 5G network performance across various metrics. Simu5G was used as the baseline reference, providing a detailed representation of 5G architecture, radio access mechanisms, and link-layer functionalities. The performance of the proposed algorithm was analyzed using key evaluation metrics, including the number of MCS selections, average packet loss rate, and average packet delay, allowing for a comprehensive assessment of reliability and latency under dynamic channel variations. The simulation setup consisted of a 5G network with 10 UEs randomly distributed within the coverage area, where Constant Bit Rate (CBR) DL transmissions were carried out to all UEs over 3GPP channel model, HARQ is enabled with 3 retransmissions limit. Multiple simulation runs were executed to ensure the consistency and accuracy of the reported results.

A comparative analysis between the proposed mcs\_amc algorithm and the standard Simu5G implementation is presented in figure \ref{fig:num_alternations}. The figure illustrates the distribution of MCS selections over the course of a representative simulation. Results show that mcs\_amc maintains overall consistency with the baseline, exhibiting only minor variations at medium and high SNR levels. However, the proposed implementation demonstrates a smoother distribution and slightly higher MCS selection frequency at elevated SNR values, indicating a more adaptive and context-aware modulation adjustment based on recent channel conditions. While both approaches perform similarly under low and stable signal conditions, mcs\_amc exhibits enhanced responsiveness as signal quality improves, enabling more efficient transitions to higher modulation levels without compromising stability.
\begin{figure*}
    \centering
    \includegraphics[width=1\linewidth]{num_implementation.png}
    \caption{Count of MCS Selections between mcs\_amc and Simu5G}
    \label{fig:num_alternations}
\end{figure*}

Figure \ref{fig:packetloss} presents a comparison of the packet loss rate between the default Simu5G implementation and the proposed mcs\_amc algorithm across varying SNR levels. The results indicate that mcs\_amc consistently achieves lower packet loss rates, particularly in high-SNR regions where network performance is expected to improve. While both implementations exhibit comparable behaviour at lower SNR values, the difference becomes more pronounced as signal quality increases. The default system shows sharper fluctuations in packet loss, whereas mcs\_amc maintains a more stable and controlled loss profile. These findings demonstrate that the proposed algorithm adapts more effectively to changing channel conditions by selecting modulation levels that preserve target BER constraints, thereby sustaining reliable transmission with reduced packet loss.
\begin{figure}
    \centering
    \includegraphics[width=1\linewidth]{packetloss1.png}
    \caption{Average of Packet Loss Rate between Simu5G and mcs\_amc}
    \label{fig:packetloss}
\end{figure}
Adapting the MCS in response to changing channel conditions naturally causes variations in the data rate, which may increase or decrease depending on channel quality. The primary goal of this work is to minimize packet loss while maintaining stable and low packet delay. As depicted in Fig. \ref{fig:delay}, the proposed implementation exhibits significantly improved delay stability and reduced latency compared to the default Simu5G configuration. The figure also reveals that while MCS transitions can introduce temporary delay fluctuations, the proposed algorithm effectively regulates these variations through adaptive control, ensuring smoother transitions between modulation levels. The results confirm that the proposed framework responds more efficiently to dynamic channel conditions, maintaining consistent and reliable transmission performance relative to the baseline model.
\begin{figure}
    \centering
    \includegraphics[width=1\linewidth]{delay1.png}
    \caption{Average of Packet Delay (s)}
    \label{fig:delay}
\end{figure}

\section{Conclusion and Future Work}
This paper presented the simulation and evaluation of a proposed adaptive modulation and coding scheme that dynamically adjusts to channel conditions in 5G networks. The approach utilizes SNR reports from UEs to estimate the BER for each potential MCS and incorporates the variance of historical SNR values to assess channel stability. A variable BER threshold was introduced based on channel stability, complemented by a decision mechanism that evaluates the potential improvement gained from upgrading the MCS. Simulation results demonstrated that the proposed model outperforms the standard Simu5G implementation and other state-of-the-art methods across key performance metrics, achieving enhanced reliability, reduced packet loss, and improved delay stability under dynamic channel conditions.
This work represents a step toward achieving deterministic packet delay and timing behavior. The next phase will focus on incorporating explicit delay constraints to ensure that transmission latency does not exceed predefined bounds as an essential requirement for real-time communication and compliance with TSN standards. Advancing in this direction will further support the realization of reliable and deterministic 5G–TSN integration.

\section*{Acknowledgment}

This research work is part of the BANNER project (EFRE-20801481) which is financially supported by the European Regional Development Fund (EFRE) and the State of NRW in Germany. 



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