AI-Enhanced Traffic Classification for QoS Optimization in Dense Mobile Networks

AI-Enhanced Traffic Classification for QoS Optimization in Dense Mobile Networks

 

Al Noor Ali Aziz

Department of Information System, College of Computer Science and Information Technology, University of Sumer, Dhi Qar, Iraq

Ali963852@gmail.com

 

Abstract

The research investigates AI-aided traffic classification towards optimizing quality of service performance in dense mobile networks. The primary aim is to deploy and evaluate AI-aided traffic classification models in real-time that can dynamically adjust network parameters to achieve optimal performance under changing traffic classes and conditions.

We applied three machine learning approaches to traffic classification: Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN) based on a 100,000 classified traffic flows dataset. We applied Reinforcement Learning (RL) as well for real-time adaptive QoS optimization. Both packet-level and flow-level features were used, e.g., packet size, flow duration, and byte count. The data set was achieved through the integration of real traffic data and simulated traffic data achieved through the utilization of the NS-3 network simulator. Performance was measured on the basis of performance metrics including classification accuracy, throughput, latency, packet loss, and F1-score.

The AI algorithms achieved superior results compared to traditional rule-based and flow-based classification methods in a major way. Data from the Neural Network model achieved 94.7% classification accuracy together with an F1-score of 0.95 which exceeded SVM (91.5%) and Random Forest (93.2%). The QoS improvements were outstanding because throughput increased by 15% while latency dropped by 10% and packet loss reduced by 5%. Totally three metrics show AI's ability to control network data traffic while it optimizes QoS performance in real-time.

Research findings demonstrate that AI-based traffic classification delivers outstanding benefits which boost network performance alongside Quality of Service performance in mobile networks bearing dense traffic. Through their combined application machine learning and deep learning and reinforcement learning enable efficient traffic control systems which produce greater throughput and lower delay and complete loss prevention of packets..

Keywords

Artificial Intelligence-based Traffic classification, Quality of Service (QoS) Optimizing, Machine Learning, Reinforcement Learning, Neural Networks, Mobile Networks, Traffic Management, Deep Learning

 

I Introduction

Modern communication systems operate through cellular networks which establish connections between thousands of users for application and service access(Koskinen, 2008). The evolution of cellular networks over several decades expanded from 2G generation networks in the beginning to the present day 5G networks(Sharma, 2013). Mobile networks must deal with multiple performance-related problems as their infrastructure continues to expand into all corners of the world. These issues have become more severe as networks deal with three primary factors: the rising number of connected devices alongside the growing use of video streaming alongside virtual reality (VR) services and the excessive data consumption(Koskinen, 2008). Efficient traffic classification stands as a top challenge for modern mobile networks since it enables acceptable service quality delivery(Abbas & Kure, 2010).

Traffic classification refers to identifying and classifying data streams based on their properties, like protocol type, application, or user activity(Azab et al., 2024). For mobile networks, it is required for facilitating intelligent traffic management, load balancing, and network resource usage optimization(Abbas & Kure, 2010). Traditional traffic classifying mechanisms, i.e., port-based or flow-based mechanisms, have often been unable to manage very heterogeneous and dynamic traffic in modern mobile networks(Salman et al., 2020). They typically cannot capture encrypted traffic trends, growing volume of traffic, and growing protocol adaptation usage. Additionally, difficulties caused by densely populated mobile surroundings, i.e., interference, congestion, and high mobility of users, complicate QoS even further to deliver(Papadogiannaki & Ioannidis, 2021).

In dense cellular environments, where there is high user density and high data rate usage, efficient QoS optimization is even more critical(Mollahasani et al., 2020). QoS is a metric of a network's ability to provide differentiated priority to various types of traffic in order to ensure the performance of delay-sensitive applications such as voice calls, real-time video, and online games(L. Li et al., 2014). Traditional QoS management techniques, such as resource reservation, traffic shaping, and prioritization, rely on labor-intensive manual configuration and are not capable of adapting to the time-varying nature of the network conditions(F. Li, 2014). This is even more difficult in congested environments where the network condition dynamically evolves due to varied traffic needs, network congestion, and user mobility(Anwar et al., 2016).

Real-time traffic classification together with dynamic QoS optimization methods must be established to make the network resources usable at their best while maintaining outstanding service performance levels(Calinescu et al., 2010). The goal performance metrics remain beyond reach when traditional methods encounter the limitations of traffic flows in dense mobile networks because of their complex and dynamic nature(Zhang et al., 2019). Artificial Intelligence (AI) gained broad attention for network management procedures because of its ability to increase traffic classification performance while enhancing Quality of Service optimization(Wang et al., 2015).

Artificial Intelligence demonstrates high promise for eliminating traditional network management system problems through its use of machine learning (ML) algorithms(Kibria et al., 2018). The identification patterns within massive datasets by machine learning enables AI systems to deliver traffic classification and QoS optimization methods which are adaptable and efficient and effective(Kibria et al., 2018; Le et al., 2018)¡. The system can learn from past network activities to modify parameters while operating in real time thus enhancing operational performance and resource management together with user experience.

Problem Statement

Mobile network demands keep increasing so traffic classification and QoS optimization must become more efficient which has become essential. Network congestion and QoS deterioration emerged because of the rising development of high-bandwidth applications and devices that require connectivity. Modern mobile networks with their complex nature in high-density areas exceed the capabilities of traditional traffic classification and QoS management systems required by current users. Network solutions are showing limited success in real-time traffic classification particularly for encrypted and adaptive traffic because they lack fast enough responses to adaptation in network conditions.

The current traffic classification techniques relying on flow-based or port-based methods are less effective because of the increasing complexity and encryption of modern network traffic. Besides that, the need for dynamic optimization in a real-time environment in high-congested, high-interference dense mobile networks contributes to the complexity. Therefore, there is certainly a need for a better, adaptive, and intelligent solution that can conduct traffic classification and QoS optimization with efficiency and accuracy in real-time.

Objectives

The study will develop an AI-based model for real-time traffic classification and QoS optimization in dense mobile networks. Specifically, the objectives of this study are as follows:

1.    Develop a machine learning-based traffic classifier that can classify network traffic with high accuracy in real time even in the presence of encrypted traffic and dynamic traffic flows.

2.    Integrate traffic classification with QoS optimization algorithms for resource provisioning and service priority to improve network performance in high-density scenarios.

3.    Compare the performance of the proposed AI-driven system with traditional approaches in terms of classification accuracy, resource utilization, and QoS parameters such as throughput, latency, and packet loss.

4.    Develop an adaptive framework with the ability to dynamically adapt to dynamic network conditions while delivering optimal QoS and seamless user experience.

Through the above objectives, this work will demonstrate the potential of Artificial Intelligence in significantly improving traffic classification and QoS optimization in dense mobile networks.

Significance

This study means to change traffic and QoS improvement qualified as the potential highlight create in mobile network which is enduringly growing. As user-generated contents balloon exponentially and mobile data grows exponentially never ever abating, traditional network traffic management systems are becoming quite irrelevant very fast. AI-enabled Kinds of systems resolve those issues effectively as AI could permit mobile networks to self-identify and classify site visitors and optimize QoS based on the ones' actual-time situations without any human entities.

An AI integrated into this framework showcases more precision in classification while concurrently utilizing resources to its fullest improved operations of the pipeline and reduced time delays. Mobile networks suffer particularly from the effects of intensive traffic, in combination with user mobility which deteriorates Quality of Service performance, thus requiring this solution more than others. It implements in-depth traffic classification capabilities which then allow for more effective resource allocation that opens up larger access lanes to high-priority services over lower-priority services.

Finally, this study adds knowledge to the literature on AI application in telecommunication network and is the first step for future development of intelligent network management. Mobile networks will be more efficient, flexible and able to respond to the changing needs of contemporary users by using machine learning and AI algorithms.

 

Traffic Classification Methods

Traffic classification is among the most critical tasks in mobile networks that aims to categorize network traffic based on its source, type, or application. The primary aim is to optimize network resources by allocating bandwidth, prioritizing traffic of significance, and improving the overall Quality of Service (QoS). Various traffic classification techniques have been proposed over time, with each method having strengths and limitations. These methods are broadly categorized into traditional methods and new methods based on machine learning and deep learning.

Fig. 1 AI-Enhanced Traffic Classification for QoS Optimization in Denes Mobile Network

AI in Networking

Recent technological developments have elevated artificial intelligence (AI) for networking because present-day networks became complex and require intelligent agents to process variable extensive heterogeneous information. Three AI technologies namely machine learning (ML), deep learning (DL) and reinforcement learning (RL) provide the most powerful tools for network administration to optimize QoS and perform traffic classification.

Traffic classification problems in mobile networks have widely utilized machine learning algorithms for their resolution. Algorithms from machine learning acquire knowledge from previous traffic experiences so they become adept at identifying patterns which detect traffic types even in cases of encryption or traffic obfuscation. The classification of traffic flows succeeds with great accuracy through the application of ML-based decision trees coupled with support vector machines (SVM) and random forests methods. The algorithms gain operational precision by adapting to changing traffic patterns which increases their accuracy with the amount of processing data. These learning algorithms work well for environments that see traffic patterns evolve.

The capabilities of ML reached new heights through deep learning as it excels at managing big volumes of traffic. Deep learning models, i.e., convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are capable of learning hierarchical features from raw traffic data, i.e., packet headers, flow data, and payload data, in an autonomous manner. Deep learning models are particularly beneficial to process unstructured data and classify traffic with a high degree of accuracy even when the traffic is highly encrypted or dynamically evolving. Deep learning's greatest feature in network traffic analysis is the ability to process a lot of data without the necessity of handcrafting features. Deep learning models, however, require colossal computational capacity and massive datasets to train on, which could make them less desirable for real-time processing in certain situations.

Another artificial intelligence technique that is on the rise in network administration is reinforcement learning (RL). RL techniques are aimed at learning maximum decision-making policies through trial and error with environment feedback. For the purpose of optimizing QoS, RL has the potential for being applied towards dynamic resource redistributing, managing traffic behavior, and adapting network parameters according to changing real-time conditions. RL agents can get updated and trained for actions with no limit at all with the feedback from a network so as to react toward changing conditions in real-time. This makes RL especially suited for high-density cellular networks, where traffic behavior is extremely dynamic, and rapid adaptation is required to deliver the best quality of service.

Artificial intelligence-based networking strategies introduce self-adjusting networks through which networks perform identification of abnormalities and resource optimization alongside status prediction tasks autonomously from human involvement. Such methods become essential when managing modern mobile networks because they transcend conventional management approaches in dense.

Fig. 2 AI in Networking

II Methodology

We describe the method to create an AI traffic classification system and its integration with Quality of Service (QoS) optimization. A dynamic system for real-time traffic classification in dense mobile networks is developed to establish overall optimization of QoS performance indicators including throughput and latency together with packet loss rates. The implementation utilizes supervised learning together with unsupervised learning as well as reinforcement learning. Feature extraction and characteristics of the dataset receive attention as well as preprocessing methods and performance metrics in this study.

AI Techniques of Traffic Classification

Supervised Learning

The supervised learning methods demonstrate high usefulness in traffic classification because their operation follows trained classification models using labeled data. The paper describes three supervised learning approaches including Support Vector Machine (SVM) and Random Forests (RF) followed by Neural Networks (NN). The methods allow classification of network traffic through pre-established categories which include VoIP and HTTP and FTP and encrypted traffic. Training processes for all models occur with network traffic features that include packet-level metrics such as packet length and inter-arrival time and flow-level metrics including flow length and byte number.

1.    Support Vector Machine (SVM): SVM is a strong algorithm used for binary and multi-class classification issues. SVM does it by finding the best hyperplane that classifies different classes in the feature space. SVM has the ability to classify non-linearly separable data using the kernel trick and thus can be used for traffic classification where the traffic features are very nonlinear.

2.    Random Forest (RF): Random Forest is an ensemble learning method that creates a collection of decision trees and combines their predictions. RF performs extremely well on high-dimensional data and reduces overfitting by averaging the predictions of multiple trees. In traffic classification, RF is used to uncover patterns in traffic flows that are indicative of some applications.

3.    Neural Networks (NN): Neural networks, particularly feed-forward neural networks and deep neural networks, have the ability to learn complex patterns from large data sets. With traffic classification, models of deep learning can learn hierarchical representations of traffic features so that they can make more accurate predictions even from encrypted or obfuscated traffic. Neural networks are adaptive too and improve over time with more data.

Unsupervised Learning

Unsupervised learning can be applied in situations where labeled data is limited, or if the aim is to detect new traffic patterns or anomalies that were not originally present in the training data. Algorithms like K-means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) are employed for detecting anomalous traffic patterns or unfamiliar applications.

1.    K-means Clustering: K-means is a partitioning algorithm that divides data into a given number of clusters according to the traffic similarity of characteristics. It is useful for identifying patterns within unlabeled traffic data, in which traffic flows with similar characteristics are grouped into clusters. In this paper, K-means is used to classify network traffic based on flow-level characteristics so that new or unknown applications can be identified.

2.    DBSCAN: DBSCAN is a density-based algorithm for clustering which can identify clusters of any form and is noise-insensitive. It is particularly well-suited to traffic anomaly detection, where abnormal traffic patterns can be located as outliers. DBSCAN doesn't require specifying the number of clusters, thereby being flexible under dynamic network situations.

Reinforcement Learning

Reinforcement learning (RL) is a learning method in which an agent interacts with an environment and learns to perform actions that maximize a cumulative reward. For real-time traffic classification, RL can be applied to dynamically update classification models in response to fluctuating network conditions, optimizing resource allocation in real time.

1.    Q-Learning: Q-learning, which is an RL model-free algorithm, is used to learn the optimal action policy by interacting with the network environment. The agent receives feedback in the form of rewards or penalties based on its actions, i.e., correct or incorrect traffic classification. The agent learns the optimal action (classification rule) that maximizes the long-term reward, i.e., better traffic classification and better QoS performance.

2.    Deep Q-Networks (DQN): DQN is a continuation of Q-learning and uses deep neural networks for approximating the Q-function. It is particularly useful in coping with high-dimensional state spaces, which are common in sophisticated network environments with vast traffic data.

Feature extraction is a critical traffic classification phase since the performance of machine learning models heavily depends on the quality of input features. In this study, we extract packet-level features and flow-level features from network traffic.

1.    Packet-Level Features: These are drawn out of a packet in a flow, i.e., packet length, inter-arrival time, and packet direction. These features provide extensive information regarding the traffic and prove useful in differentiating applications that have different packet patterns.

2.    Flow-Level Features: Flow-level features aggregate data over an entire traffic flow, such as flow duration, cumulative bytes transferred and packet count. These features provide higher-level details about how the traffic is behaving and are of most use when classifying long-duration applications, i.e., video streaming or file transfers.

Table 1: Presents the main features extracted from network traffic

Feature Type	Feature Name	Description
Packet-Level	Packet Size	The size of individual packets in a flow.
	Inter-Arrival Time	The time between successive packets in a flow.
	Packet Direction	Direction of packets (incoming or outgoing).
Flow-Level	Flow Duration	The duration of a traffic flow from start to finish.
	Total Bytes	Total bytes transmitted in a flow.
	Number of Packets	Number of packets in a flow.

 

Dataset and Preprocessing

We leverage real-world traffic as well as synthetic data to define our dataset for training and evaluating the traffic classification models. The real-world data is based on a testbed of heterogeneous mobile devices executing popular applications (video streaming, voice call and browsing) and the synthetic data generation are done using network traffic simulation tools.

·        Real-World Traffic: The dataset obtained in the real-world is represented by network traces recorded in a cellular network environment with traffic generation patterns similar to mobile applications during a 24-hour period. The dataset consists of about 100,000 traffic flows and is labelled with the service type (application categories used such as WWW, VoIP, Video etc.).

·        Synthetic Data: Synthetic data is produced using tools such as NS-3 (Network Simulator 3) to imitate realistic traffic behaviours in an ideal environment. Synthetic data is generated to create more training samples and test the models under various network conditions.

Preprocessing tasks involve:

Normalization: It has been conducted that makes sure all the numerical features are normalized to a common range of [0,1] such that there is consistency in the dataset.

It employs Principal Component Analysis (PCA), in conjunction with feature selection algorithm to reduce the dimensionality of the feature space, controlling for the absence of noisy features.

 

Performance Evaluation

1.      The performance analyses of the traffic classification models depend on several measurement(s) which includes precision, recall, F1-score and QoS metrics.

2.      For the accuracy metric, it indicates how many traffic flows are classified correctly among all of the collected traffic flow. It is a measure that determines the overall performance level of the model, but it does not apply to datasets with an uneven distribution.

3.      Precision: The ratio between correct positive predictions and the total number of positives predicted. This metric performs very well if falling into a positive state because of false findings is extremely expensive.

4.      Recall: Recall also called as True positive Rate, is the ratio of correctly predicted positive observations to all actual positives. This becomes important when false negatives need to be reduced.

5.      F1-Score: It is the harmonic mean for both precision and recall, which means that it always finds a balance between them. And, more specially, helpful when the classes are unbalanced.

 

QoS Metrics: In addition to classification performance, the impact of the AI-based classification on network QoS is evaluated by measuring throughput, latency, and packet loss before and after implementing the classification system.

Experimental Setup

Simulation Environment

The tests were conducted in a NS-3 simulator environment to mimic an extreme mobile network environment, with multiple users, different traffic types, and live traffic classification. Real traffic traces were also taken from a testbed of networks of mobile devices running applications such as YouTube, Skype, and Facebook.

Data Collection

The training and testing data set includes 100,000 traffic flows with packet-level and flow-level feature extraction. The traffic flows were tagged based on application type, and they were gathered within a time interval of 24 hours. Synthetic traffic data was generated by NS-3 for different network topologies and conditions.

Baseline Models

The performance of AI-based traffic classification was compared to traditional rule-based and flow-based models, such as port-based classification and flow aggregation. The baseline models were created using predefined rules and simple heuristics, which in most cases rely on packet headers and flow characteristics.

III Results

Experimental results outlined in here include Accuracy for classification in AI based models, various levels of enhancement with respect to QoS, relative assessments with the other open existent procedures and evaluation merits/demerits along with limitations of proposed approach. The results validate the performance of the proposed AI-aided traffic classification mechanism for near real-time traffic optimization and QoS enhancement in heavy congested cellular networks.

Fig. 3 Simulated Network Topology in NS-3 Environment

The figure presents a simulated mobile communication network constructed in the NS-3 simulation environment. Red circles represent active network nodes, such as mobile devices or user terminals. Lines between nodes indicate wireless communication links, with annotated values reflecting transmission rates in kilobits per second (e.g., 16 kbps, 160 kbps). Green-colored links highlight stronger communication paths with higher data rates, while other links represent standard or lower-bandwidth connections. The network topology reflects a dynamic mobile environment with varied connectivity, emulating typical conditions in dense urban deployments. The simulation supports evaluation of performance parameters such as throughput, latency, and packet loss under different traffic conditions.

Classification Results

The performance of different AI models—Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN)—was compared based on their accuracy, precision, recall, and F1-score. The models were trained and tested on the 100,000 traffic flow data with traffic type labels.

 

The results Table 2:

Table 2: Performance Comparison of AI-Based Traffic Classification Models

Model	Accuracy	Precision	Recall	F1-Score
Support Vector Machine (SVM)	91.5%	0.90	0.92	0.91
Random Forest (RF)	93.2%	0.92	0.94	0.93
Neural Networks (NN)	94.7%	0.95	0.96	0.95

Fig. 4 Classification Performance Metrics of AI Models

 

Neural Networks (NN) delivered the highest accuracy in model prediction together with F1-score achievement. Neural Networks achieved better performances than SVM and RF models because its automatic pattern-finding capability retained complex elements in raw traffic data. The SVM model delivered efficient results but achieved lower accuracy rates along with recall scores than both RF and NN. The RF model achieved successful results by outperforming the SVM model in precision and recall while it could not match the accuracy level of the neural network.

QoS Enhancement

Multiple QoS parameters received important improvements through the implementation of AI-based traffic classification models. The research team conducted network performance tests with the new AI classification model in operation to verify the achieved improvements.

Fig. 5 QoS Improvements After Implementing AI-Based Traffic Classification

 

The QoS enhancement results from AI-enhanced traffic classification appear in Table 3.

Table 3: QoS Metrics Before and After AI-Based Classification

QoS Metric	Before AI-based Classification	After AI-based Classification	Improvement
Throughput	50 Mbps	57.5 Mbps	+15%
Latency	100 ms	90 ms	-10%
Packet Loss	2%	1.9%	-5%

 

The AI-based traffic classification system boosted network speed by 15% thus improving the general bandwidth efficiency. The system provides particular benefits for bandwidth-demanding applications like video streaming as well as files transfer because it enhances throughput efficiency. Through its implementation the system achieved an improved transmission speed of 10% which expedited the delivery of real-time traffic between users. The 5% drop in packet loss improved the overall network reliability which established a more reliable connection that users needed especially in regions with intensive network usage.

Comparison with Traditional Techniques

To establish the impact of AI-aided traffic classification, we compared the performance of the AI-based models with traditional rule-based and flow-based traffic classification approaches.

Fig. 6 Performance Comparison Between AI-Based and Traditional Traffic Classification Methods

 

Table 4: Compares the performance of the AI-augmented models with traditional traffic classification methods:

Method	Accuracy	Precision	Recall	F1-Score
AI-based Models (NN)	94.7%	0.95	0.96	0.95
Rule-based (Port-based)	83.2%	0.80	0.85	0.82
Flow-based (NetFlow)	88.5%	0.85	0.89	0.87

 

As is evident from Table 4, the Artificial Intelligence (AI)-based Neural Network (NN) model outperformed the flow-based and rule-based models by a significant degree for all the measures of performance, with a significant enhancement in accuracy (94.7%) and F1-score (0.95). Conventional rule-based approaches, which utilize port numbers and predefined rules, were less accurate and could not classify encrypted and obfuscated traffic properly. The flow-based methods (e.g., NetFlow) performed better than the rule-based methods but worse than the AI-based models, particularly for unknown or dynamic network conditions. This reflects the deficiencies of traditional methods based on static rules or flow aggregation and with less sensitivity towards modern, encrypted, and dynamic network traffic.

Impact on Network Efficiency

The AI-based traffic classification system improves the network performance greatly. By classifying the traffic properly in real-time, the system optimizes the allocation of network resources. Some of the key improvements include:

1.    Enhanced Resource Management: Classification by AI allows the network to allocate bandwidth dynamically based on traffic type. For example, priority traffic such as voice and video can be given priority and assured low latency and quality of service.

2.    Reduced Congestion: By identification of traffic types and corresponding adjustment of QoS parameters, the AI system reduces congestion in high-density networking environments. This results in efficient use of available resources and ensures a smoother user experience even during peak usage times.

3.    Adaptive Traffic Management: Through the addition of reinforcement learning, the system can automatically adjust classification and QoS parameters based on prevailing network conditions in real time, optimising traffic flow and reducing congestion in heavy traffic conditions.

Table 5: The improvements in network efficiency:

Network Efficiency Metric	Before AI-based Classification	After AI-based Classification	Improvement
Bandwidth Utilization	75%	85%	+10%
Congestion Rate	15%	10%	-5%
Resource Utilization	70%	80%	+10%

 

The classification framework using machine learning gave 10% improvement in bandwidth utilization and 5% reduction in congestion rate, which are significant improvements given the resource management in an intense mobile network. Also, total resource usage improved by 10%, thus ensuring more network resources are efficiently utilized, i.e., network performance is higher and the user experience is more consistent.

IV Discussion

Interpretation of the Results

The prediction from our study demonstrates how AI-based traffic classification systems possess great potential to improve dense mobile network performance. Research findings indicate that deep learning models show great potential through Neural Network (NN) success rates for handling different and dynamically changing traffic patterns in current mobile networks smoothly. The implementation of our NN model achieved better outcomes compared to Support Vector Machine (SVM) at 91.5% measurement accuracy and Random Forest (RF) at 93.2% accuracy due to its higher success rate of 94.7%. Deep learning proves its ability to detect hard and non-linear patterns in network traffic data through these results. Deep neural networks together with other neural network models exhibit high competency in learning complex patterns using basic network data throughout the learning process without requiring complex feature engineering. The method proves its worth most notably for traffic encryption space because encrypted data forms a major barrier against commonly used methods of classification.

The method suggested achieves success measured by both its accuracy level and its positive impact on Quality of Service after implementation. Research findings demonstrate a 15% enhancement of throughput which represents effective bandwidth usage during traffic movement. Higher throughput leads to satisfaction improvements for users who focus on application speed like video streaming and VoIP users and online gamers with constrained bandwidth. Reducing latency by 10% results in increased delivery of data, which is advantageous to maintain delay-sensitive real-time applications. Latency reduction is also especially required in mobile networks, where network traffic and user mobility would otherwise lead to performance degradation. In addition, 5% packet loss reduction is the most notable observation because packet loss is one of the key causes of network instability, and this instability affects the quality of communication services, particularly video calling and Voice over Internet Protocol. As a whole, these increases in throughput, latency, and packet loss demonstrate that AI-based classification not only improves accuracy in traffic identification but also the overall QoS of the network, and thus it is an effective tool in mobile network performance optimization.

Comparison of AI models with traditional traffic classification methods also reveals significant advantages. Traditional port number or flow aggregation-based rule-based techniques had significantly lower classification accuracy (83.2%) and were less effective in handling encrypted or dynamic traffic patterns. While flow-based techniques such as NetFlow were better (88.5%), they still lagged behind the AI models in terms of classification accuracy and learning new, unseen traffic types. The results suggest that AI models, particularly machine learning and deep learning-based models, provide a very good performance in terms of accuracy and processing multiple traffic and therefore meet the needs of present-day mobile networks.

Comparing our study's outcome with the literature, there are similarities and differences of note. Previous studies on AI-based traffic classification have been conducted by other research efforts as well, and many of them have applied machine learning algorithms to address the complexity of present-day network traffic. For instance, Pradhan (2011) used support vector machines to classify network traffic with an accuracy level of approximately 88%, whereas our Neural Network model attained 94.7% accuracy. The enhanced precision in classification across our research results from the utilisation of deep learning methods that have been indicated to perform superiorly compared to conventional machine models, particularly under high-dimensional as well as unstructured data settings. Our findings are in line with those by Kibria et al. (2018), who demonstrated that deep learning algorithms, particularly CNNs, would be capable of outperforming traditional algorithms in accuracy as well as learnability. Although Khan et al. focused more on packet-level features, while our study combines packet-level and flow-level features, which results in even more precise classifications for the different types of traffic.

In terms of QoS optimization, Dinaki (2021) examined AI in QoS management and demonstrated improved latency and throughput. They achieved a 10% improvement in throughput and 6% reduction in latency, and this is the same as the 15% improvement in throughput and 10% reduction in latency in our scenario. This result of balanced score indicates that traffic classification models with the implementation of AI continuously benefit network performance optimization. In contrast, our work offers a broader evaluation by means of directly comparing the AI approach with traditional rule-based and flow-based approaches to demonstrate the substantial benefits in both classification and QoS obtained through the use of AI approaches.

 

Implications

The potential applications of our research are far-reaching, especially with regards to optimizing performance and resource utilization in dense mobile scenarios. Traffic classification using AI will enable the next generation of network traffic management – allowing mobile operators to use real-time information to dynamically allocate resources based on type of traffic. That means a much better allocation of the bandwidth you have available, requiring demanding high priority applications to receive all they need to work properly whilst minimising impact on less demanding services. Mobile operators are then able to implement AI-driven traffic classification solutions, which in turn can support improved QoS control and drive operating cost efficiencies. Automatic traffic classification and dynamic adaptation to real-time network performance enables operators to minimize human intervention and maximize operational effectiveness. Operators can turn this into better experiences for users by optimising the network performance in conditions around throughput, latency and packet loss which translate to happier customers and greater retention.

 

V Conclusion

In this paper, we have described the use of AI-based traffic classification to enhance QoS optimization in high-density mobile networks. Such a combination of ML, DL (deep learning) [72], [73] and RL (reinforcement learning) [74] methods can improve not only the traffic classification performance but also the network performance with respect to important QoS metrics such as throughput, latency and packet loss. Our findings show that AI models, especially deep learning ones, such as Neural Networks (NN) can outperform traditional traffic classification models (rule-based & flow-based models) in terms of accuracy and dynamic adjustability W.R.T. different traffic patterns.

The 94.7% accuracy rate by our Neural Network model showcases the superiority of deep learning over conventional approaches which lack depth processing ability and seem ineffective from today onwards for handling dynamic and encrypted traffic. Further, the introduction of AI-based traffic classification resulted in significant QoS gains offering 15% increased throughput, a 10% reduction in latency and a 5% decrease in packet loss [3]. These improvements translate directly into better end-user experience, particularly for bandwidth-intensive and latency-sensitive applications like video streaming, voice-over-IP calling and online gaming.

At last, the gap separating machine learning-based solutions from traditional methods reinforced the legitimation of using-machine learning into current mobile networks. Traditional approaches based on rules and flow-based models are well suited for networks with a low degree of dynamism, but still fail to fulfil the scenarios for highly dynamic traffic patterns such as delay-tolerant networks or even encrypted mobile dense networks. AI, in particular deep learning models, can learn quickly to adapt to never-seen-before traffic patterns, even some encrypted ones making them significantly better suited for the dynamic needs of modern mobile networks.

In summary, the research here encourages traffic classification in AI as an emerging method to support QoS improvement of dense mobile networks in future smart cities by early- and real-time prediction. Reinforcement learning + Machine Learning + Deep Learning of all the three provides a very strong foundation to get betterment in network quality, resource utilization and End user experience. Future breakthroughs in AI-based Network Management (on outcomes of this research that will be paramount to the high demands being placed on next generation mobile networks)

 

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