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10 March 2024

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

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Department of Engineering, King’s College London, Strand, London WC2R 2LS, UK
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Computers2024, 13(3), 74;https://doi.org/10.3390/computers13030074
This article belongs to the Special Issue Emerging Trends and Challenges of Software-Defined Networking (SDN) Technologies
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Abstract

Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources.

1. Introduction

Various elements are enabled in the development progress beyond 5G, including artificial intelligence (AI) and ubiquitous connectivity. Telecommunication intelligence (specifically in 6G networks) refers to the ability of a self-contained ecosystem, the self-awareness of state conditions, and optimal appropriate response reactions [1]. Since 6G technology delivers extended distributed intelligence and control mechanisms, artificial intelligence (AI) and machine learning (ML) can benefit the control and optimization processes. For example, the radio function in the access layer is efficiently controlled and coordinated using ML. ML enhances the gNodeB (gNB) functions in bearer coordination, interference management, and radio resource allocation [2,3,4].
The progression toward 6G promotes innovation in telecommunication services and functionalities, in which a blueprint comprising potential use cases is set [5]. This blueprint includes in-depth and new use cases, producing numerous traffic taxonomies in an infrastructure. The blueprint also improves the handling mechanism for traffic classifications in an infrastructure. Generally, a 5G network using the Quality of Flow Index (QFI) method ensures a granular traffic classification. This outcome enhances the Quality of Service (QoS) approach in the previous generation. Nevertheless, the upcoming 6G network is more holistic due to the end-to-end network flexibility. This flexibility enables the network to adapt to traffic behaviors along the entire connection regardless of the terrestrial type [5].
The increasing number of telecommunication devices is directly proportional to the growing spatial demand for these services. This observation necessitates the provision of viable services to every populated area. Given that network adoption can increase in the forthcoming years, higher transport network variations concerning their selection and deployment can be observed. One example of addressing the less populated areas (rural land areas) is through network adoption by providing offshore services. The 5G Infrastructure Public–Private Partnership (5G PPP) introduced a vision to have an architecture design as a single access network comprising terrestrial networks (TNs) and non-terrestrial networks (NTNs) to advance ubiquitous connectivity [1]. An example of NTN technology is satellite technology. Satellite ubiquity aptitude demonstrates its presence for quick and accessible options for service provisioning.
The advancement of technology towards 6G is encouraged by diverse use cases and supported by advanced transport networks. Nonetheless, there are missing areas in integrating the heterogeneity of use cases and transport networks that could lead to more efficient infrastructure. This study proposed an intelligent steering mechanism for regrouping the data that shared the same attributes and matched the transport network properties. The data or traffic in this study were reorganized and regrouped into a total number of available transport networks arranged in a particular service area. Each data group was redirected towards optimum-matched transport technologies compared to the conventional traffic-splitting method. For example, in an urban area where the current terrestrial transport network is reaching capacity, expansion works are not time- and operationally feasible; therefore, integrating a non-terrestrial network is the most viable alternative, and steering the traffic to alternative paths is a feasible approach. Therefore, the most viable alternative is integrating an NTN technology and redirecting the traffic to alternative paths. Hence, utilizing existing satellite and cable TV is an option in which these two technologies are dynamically provisioned and readily available [6,7,8].
This paper defines an optimization problem that aims to maximize overall resource utilization by steering pertinent traffic types to appropriate transport networks that resolve unnecessary issues such as traffic delays and congestion. The process assigned relevant traffic clusters to appropriate transport networks, using machine learning (ML) and mixed integer linear programming (MILP) techniques. Subsequently, the resource assignment method was manipulated to determine the optimal assignment between the cluster and the transport network. The objectives of this study are as follows:
i.
Introduce a traffic steering scheme that optimizes the coexistence of heterogeneous transport networks.
ii.
Demonstrate the comparative measures for traffic steering using ML and MILP against traffic classifications.
iii.
Demystify the factors contributing to the cluster-transport assignment.
The remainder of this study is organized as follows: Section 2 refers to several research studies related to this study. Section 3 details the foreseeable issues discovered by these studies. Section 4 discusses the framework for the traffic-to-transport network assignment proposal. Section 5 highlights the findings and analysis of the observations from the simulation scenario incorporated in the framework. Finally, Section 6 describes the conclusion and future works.

3. Problem Statement

The role of the non-terrestrial transport network in 6G was anticipated to acquire a more significant function over a secondary or backup link (see Figure 2). However, there are challenges in determining the optimum uses of the respective transport networks based on various traffic classifications. A report [1] synthesized the coexistence of terrestrial networks and the upcoming network topology as a single 6G access network. However, the current approach leads to an apparent gap of inefficient resource management. The regular approach is to incorporate a novel path and route optimization using ML. This study developed a traffic steering mechanism to analyze the traffic and direct relevant traffic to the appropriate transport network types using ML and MILP methodology. This study also assessed the accuracy of decisions based on the ML clustering output and the transport network selection. Consequently, traffic was assigned optimally to designated transport networks in an ideal environment.
Figure 2. The non-terrestrial transport networks for the 6G infrastructure.

4. Proposed ML Techniques, MILP Framework, and Methodology

4.1. Network Model

Figure 3 highlights that this study operates within the defined network model, with K numbers of transport network technology (connectivity alternatives between the access layer node and the core node). Even though each transport technology possessed various links to form mesh connectivity in optical and DOCSIS cables, the technology was treated as a single connectivity line. This observation was similar to the inter-satellite links from satellite technology. The transport network to the provided list mentioned in the previous section was scoped using a K value of 3. Meanwhile, the different annotations of arrow colors and directions indicating the downlink (DL) and uplink (UL) traffic represented the traffic classifications. Specifically, the code color represented the traffic types exchanged inside the infrastructure. Figure 3a,b illustrate the difference in traffic classification treatment between the conventional and the proposed intelligent traffic engineering for 6G architectures.
Figure 3. The coexistence of heterogeneous transport networks for the 6G infrastructure using (a) conventional and (b) the proposed architectures.
The traffic classification in the conventional architecture was treated with different priorities and scheduling. Nonetheless, the proposed architecture in this study complemented the approach by adding preliminary traffic segregation works before accessing the transport networks. The post-clustering and assignment process indicates that a group of packets that shares the same attributes is steered to optimal-matched transport network attributes and subsequently undergoes a conventional traffic prioritization process. This proposed architecture mission aimed to maximize end-to-end efficiency by enabling relevant packets to be steered into optimal paths following their respective transport network technologies. Equation (1) describes the calculation of the total volume of packets, U , from the i-th index of packets that sums up the total traffic volume in an instance, T t .
T t = ∑ i = 1 N U i ( t ) = ∑ m = 1 5   ∑ n = 1 N u m n t
where Ui(t) is the packet in the instance, and umn(t) represents a packet based on their traffic classification types denoted by m, representing traffic classification types ranging from 1 to 5 and the n-th index of packets. T(t) equals the total number of packets in an instance in the respective m category, where n ranges from 1 to N. T(t) underwent unsupervised learning, a clustering technique to group the total UE and traffic per instance.

4.2. Clustering Model

The clustering process transformed the packets from T(t) into the aggregated volume of packets within clusters, denoted as Dj(t), where j is the cluster label set to equal the number of transport network technologies equal to three, represented as j ϵ {C#1, C#2, C#3}. Thus, Tj(t) = DC#1(t) + DC#2(t) + DC#3(t). Meanwhile, the series of instances, t, exhibited different Dj(t) values over time based on the dataset and clustering output. Subsequently, a variation of traffic types in a cluster, umn(t,j), determines the value Dj(t). Each Dj(t) was analyzed to produce the total downlink (DL) or uplink (UL) denoted by Vdl/ul, minimum error rate, εmin, and packet delay budget, βmax, values in forming Dj(t):(Vdl/ul, εmin, βmax). Each instance in the format in the clusters, Dj(t), incorporated the total sums of packets handled by the transport network. Given that the number of transport network technologies is K, λk is the respective denotation of K transport network technology. Moreover, a similar format, λk:(VT-dl/ul, εT-min, βT-max), represents the transport network technology, where VT-dl/ul is the capacity of the transport network, εT-min is the error rate, and βT-max is the packet trip time.

4.3. Clustering Process

This study examined the functionality of unsupervised ML under clustering algorithms. The clustering algorithms were conducted with unlabeled data, in which the attributes of each data point were analyzed, and the similarity in a cluster was exhibited. An emulator that emulated the generated inbound and outbound data transacting from UE to gNB and the core (including vice versa) was used to derive the data for this study. These data included the Reference Signal Received Power (RSRP), DL, UL, and QFI values. A clustering algorithm was employed to learn from each data point and generate a vector value, Dj(t), representing a point’s attributes or properties. Subsequently, the process combined all points with similar attributes into clusters.
A different perspective on the resource assignment was also obtained based on different input types, and this study performed traffic clustering based on the use cases of the traffic. Three use cases were considered in this study: eMBB, URLLC, and mMTC. These use cases were represented with Fi(t), where i = [eMBB, URLLC, mMTC]. The datasets were classified into three categories, resulting in a format that resembled the previous cluster format, Fi(t):(Vdl/ul, εmin, βmax). This process asserted uniform traffic types within the group, contradicting the clustering process performed using the ML clustering technique. The clustering process then had to undergo another unsupervised learning technique for detailed execution.
The dimensionality reduction algorithm produced two-dimensional data from the multivariate data. Before clustering, this process reshaped the data into a vector value using the t-distributed stochastic neighboring embedding (T-SNE) method. The data were filtered based on the referenced signal receive power (RSRP) values to exclude the farthest UE from the serving radio unit (RU). Only the UE inside the serving area was considered in the subsequent stages (step 3 in Figure 4). Finally, the filtered list of downlink and uplink values with QoS Flow ID (QFI) underwent the T-SNE transformation process to create an unlabeled dataset before the data execution for the clustering process (step 4 in Figure 4). The multivariate data from the prior processes underwent initial steps of scaling and transformation to produce a standardized scale to enable the balance dominance of every dataset field before applying T-SNE to reduce the multivariate dataset into a single unlabeled value.
Figure 4. The end-to-end flow of the clustering and resource assignment.
A clustering process was performed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to identify patterns from the T-SNE transformation output. The clustering process identified the attributes of each point and grouped them into three clusters: Tj(t) = DC#1(t) + D C#2(t) + DC#3(t). Each point (represented as UE) was assigned a cluster label (C#1, C#2, and C#3) after the clustering procedure was completed, indicating the group of each point (UE) (steps 5, 6, and 7 in Figure 4).
Subsequently, each label on a UE was stored and recorded for future data training and learning processes. The post-clustering process then shifted to the resource assignment stages. Steps 8 and 9 were the preprocessing stages before the resource assignment step. During this stage, the data were collected and converted into a matrix format. The transport network data comprised capacity information, round trip time, and packet loss rate, representing a transport network attribute value in a particular instance, λk:(Vdl/ul, εmin, βmax).

4.4. Matching Process

The MILP was employed to determine the optimal resource assignment based on the cluster transport network assignment. Each value from the UE cluster and the transport network was checked similarly during the resource assignment process. Step 9 in Figure 4 implies the iteration process of the similarity check between each cluster and the transport network properties or attributes. xij is the decision variable where the value of ‘i’ represents the clusters assigned to the available ‘j’ transport network as resources. Hence, xij  ∈ 1 to determine the output of the cluster transport assignment. xij ∈ {0,1}, i = the transport networks, j = the clusters
This study evaluated the assignment output by matching clusters with transport networks with qualities compatible with the cluster. Similarly, the same processes were applied to examine the corresponding matching assignment on the clusters formed by various UE types (see Figure 5). Consequently, three significant clusters were created from the eMBB, URLLC, and mMTC UE types, and the same matching assignment output using MILP was evaluated. The process also considered clustering each UE type in an instance. Furthermore, the total DL or UL size, maximum packet delay budget values, and minimum packet loss rate of the cluster were recorded and represented as cluster attributes. Lastly, similarity checks were performed between the attributes, clusters, and transport networks.
Figure 5. The end-to-end flow of the resource assignment process based on various UE types.

5. Discussion and Findings

The parameters in table below defines the simulation to generate the UE dataset (see Table 1). Table 2 shows the compositions of clustering output for the first instance. The percentage in each cluster indicates the types of UE traffic classification in the clusters.
Table 1. The list of parameters for UE and transport network simulations.
Table 2. The outcome of the clustering process.

5.1. Proportion Results of the UEs in the Cluster

The unsupervised ML technique clustered the data into three groups. Thus, this study opted for a density-based clustering technique called Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Figure 6 and Figure 7 reveal that the HDBSCAN clustering technique produces the DL and UL traffic proportions generated by the UEs. The data displayed the traffic size in different clusters, highlighting the significance of traffic size compared to the total number of UEs. In addition, the number of UEs had no impact on the UE distribution in the cluster. This result observed a random distribution pattern of the number of UEs in the clusters (blue line). Nonetheless, traffic size produced increasing patterns along the increase of the traffic size (red bar). Each cluster also comprised several traffic classifications that were determined by similar attributes. The previous clustering process involved reducing the dimensions of a point or UE by representing its vector values and properties. Figure 8 and Figure 9 depict the clustering process output using dimensionality reduction, which transforms the DL and UL datasets.
Figure 6. The proportions of UE in the clusters for DL.
Figure 7. The proportions of UE in the clusters for UL.
Figure 8. The post-TSNE DL dataset of UEs for the clustering process.
Figure 9. The post-TSNE UL dataset of UEs for the clustering process.

5.2. DL and UL Cluster-Transport Assignment Process Results

This study clarified the matching process output between the clusters and transport network attributes in a vector value produced by the unsupervised dimensional reduction T-SNE method. A post-MILP process assigned a vector value of clusters to the transport types. The vector value was then reformed to the initial state, demonstrating the distinctive parameters of traffic size, time, and error rate.

5.2.1. The Performance of the Cluster-Transport Assignment in Scenario 1 (Dedicated Transport Network to a Single Node in a Service Area)

Table 3 indicates the total DL and UL results, indicating the performances of the clustering process based on machine learning techniques and assignments. The parameters for the assignment performance included the traffic size (DL and UL), time, and error rate. Moreover, the compatible assignment of the cluster was based on the computed parameters (see Table 1 and Table 2). Each cluster’s performance is measured by the total number of clusters, complying with the performance matrix in Table 3. The highest scoring percentage indicates the highest number of compliances for every instance and cluster formed. In each cluster, total traffic (DL or UL) is summed and matched with the available link capacity. Thus, both DL and UL traffic are within the set resources. Nonetheless, the PDB and PER show low scores because of the maximum value of PDB captured from UEs to represent the cluster, though many other UEs have lower values than the RTT of the transport link. The inverse proposition involves PER because the minimum PER value generated represents the cluster.
Table 3. The performance of the clustering process: Scenario 1.
Each cluster was assigned to the appropriate transport type following the UL and DL parameters and transport type. Consequently, the values in each cluster against the designated transport network populating the high traffic usage were assigned to the optical network. This outcome was attributed to the high capacity of transport links (see Table 4). Table 4 indicates that at every instance, the maximum traffic size from every cluster formed is assigned to optical network because of the high-capacity link, whereas DOCSIS cable shows the adoption of handling 52% of the minimum traffic size captured from the clusters in each instance.
Table 4. The composition of DL and UL traffic size to the transport network: Scenario 1.

5.2.2. The Performance of the Cluster-Transport Assignment in Scenario 2 (A Shared Transport Network in an Area with Multiple Service Nodes)

The following result showed a shared resources scenario that elucidated the selection mechanism behavior if the primary transport network (optical network) was shared and possessed low link capacity. Additionally, the cluster assignment to the transport network shifted by assigning high-traffic clusters with the highest capacity to the satellite transport network. This finding was similar to the output in Table 3, except that there are instances where the total traffic in DL and UL in the clusters computed higher than the available resources link, thus contributing to the two per cent drop in the score (see Table 5). The cluster assignment to the transport network revealed minor changes in the UL parameter. Notably, the UL capacity for DOCSIS cable was set to 6 Gbps per link instead of 10 Gbps of DL streams (see Table 1).
Table 5. The performance of the clustering process: Scenario 2.
The assignment process in shared transport networks followed a similar pattern (inclination towards high-capacity link). Thus, the net available capacity in the optical network was low, shifting the high traffic size to satellite networks and minimum DL values in every instance proportionately shared among three transport links (see Table 6). The satellite network prevalently handles UL stream traffic, with 85% of the maximum traffic size in all 60 instances assigned to the satellite network (approximately for maximum DL in all 46 instances assigned to the satellite network). A total of 52% of the minimum traffic size for UL flows to DOCSIS cable.
Table 6. The composition of DL and UL traffic size to the transport network: Scenario 2.
The simulation generated sixty instances, which comprised three clusters assigned to three transport network types (optical network, satellite network, and DOCSIS coaxial cable). Overall, in 60 instances, the composition of the clusters and assignment of the cluster to the transport network varied and showed no specific pattern or relations between the cluster and transport network. The assignment of a cluster to the transport network was performed unanimously across all recorded instances and was created based on the dynamic attributes of UEs.

5.3. DL and UL Use Case Results: Transport Assignment Process

5.3.1. The Performance of the Cluster-Transport Assignment in Scenario 1 (Dedicated Transport Network to a Single Node in a Service Area)

This study improved the traffic load proportioning process into multiple transport network types to determine the most efficient assignment. The process was achieved by clustering the traffic based on the UE type or use cases. A similar approach was also applied to this process (see Figure 5). Table 7 displays the proportions of use cases for the UL stream in the dedicated resources of the transport network. Each traffic was grouped based on its classifications and was then assigned using the MILP method. From Table 7, DL and UL traffic sizes are generated within the available link’s resources. Similar to the observation from Table 3, PDB and PER values produce values higher and lower than the link’s RTT and PER, respectively, thus producing very low scoring to the performance matrix.
Table 7. The performance of the clustering process (use case): Scenario 1.
The high DL traffic is fully assigned to the optical network originating from the eMBB use case traffic type. In contrast, the maximum list of UL traffic from every instance is distributed to two transport network types (optical network and DOCSIS cable). The highly generated upload traffic based on the nature of the mMTC use case results in assignment to the optical network (48.33%), with satellite network handling a minimal percentage of mMTC traffic types (3.33%). On the other hand, the list of minimum UL traffic is assigned to the optical network (see Table 8).
Table 8. The composition of DL and UL traffic size (use case) to the transport network: Scenario 1.

5.3.2. The Performance of the Cluster-Transport Assignment in Scenario 2 (A Shared Transport Network in an Area with Multiple Service Nodes)

Table 9 depicts the use case transport assignment for UL streams containing shared transport resources, in which the net available optical network is used to accommodate traffic from other nodes. Nonetheless, each cluster formed from the use cases shows traffic size within the available resources except in 13% of the total instances where clusters produced higher UL traffic size than the link’s capacity. The time parameters show full compliance within the full round-trip time of links.
Table 9. The performance of the clustering process (use case): Scenario 2.
Notably, Table 10 explains the assignment of high DL traffic shifting towards the next highest link’s capacity because a low and exhaustive capacity of the optical network was observed in several instances. The eMBB and mMTC generated high and low UL traffic and the assignment to the satellite network and DOCSIS cable transport options, respectively. Elaborating on UL traffic from Table 10, a shift from assigning the mMTC and URLLC traffic across all available transport options can be observed. This finding was attributed to the satellite network capacity consumed by most of the UL traffic from both maximum and minimum lists of UL values.
Table 10. The composition of DL and UL traffic size (use case) to the transport network: Scenario 2.
The assignment result exhibited an unpredictable correlation between time and error rate values in determining the UE traffic and transport network selection process. Comparable results were also noted for the output from the selection process implemented in the cluster-transport assignment in Section 5.1. This study provided additional findings, in which the outcome pattern only produced disputable assignments due to low transport network capacity. From the findings, DOCSIS cable was selected as one of the transport options because of its ability to handle low traffic.

6. Conclusions and Future Works

This study successfully constructed an intelligent mechanism to comprehend traffic load while assigning it to suitable transport networks. Unsupervised ML techniques were used to retrieve the optimal traffic cluster configurations. The result from clustering works shows the formation of cluster groups based on the density plots of the data. The cluster was then translated to numerical values representing the size of the traffic, delay, and packet loss. The cluster’s numerical result was then relayed to the decision-making process to determine the optimum match to transport network numerical values. An intelligent mechanism was effectively demonstrated in choosing the appropriate transport type based on the traffic and transport attributes. This observation was shown from the movement of cluster assignment on transport network type demonstrated in scenarios 1 and 2. Therefore, an intelligent mechanism using ML techniques and solver-based solutions was successfully introduced and demonstrated to achieve a high percentage (98%) of dynamic traffic steering capability based on resource links.
The decision-making process was assessed in this study by performing transport selection based on the standard use cases (scenario 2) defined in 5G (eMBB, mMTC, and URLLC). Thus, the UE clustering process (ML and use cases) observed a marginal performance difference with the same dataset. Moreover, using ML approaches in clustering yielded more definitive assignment outcomes for the selected transport network type. Nonetheless, this study identified the necessity for additional functionalities in traffic-transport assignment throughout the decision-making stage. Even though the assignment performed the selection based on the similarity attributes between the two sides, the UE traffic size and transport network capacity strongly influenced the assignment. Time and error rate attributes were not fully understood and demystified.
Future studies should determine newer approaches to reconfigure the entire process and replicate the cluster configuration. This process requires repeating the decision-making stages to guarantee an indisputable assignment of traffic-transport type and produce a highly efficient assignment. Consequently, a finer approach shall be explored in depth, considering parameters such as time and error rate to produce more influential decisions on the assignment process.

Author Contributions

Conceptualization, H.A.H.N. and T.M.; methodology, H.A.H.N.; validation, H.A.H.N. and T.M.; formal analysis, H.A.H.N.; investigation, H.A.H.N.; resources, H.A.H.N.; data curation, H.A.H.N.; writing—original draft preparation, H.A.H.N.; writing—review and editing, H.A.H.N.; visualization, T.M.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest, and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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