Dynamic Link Classification based on Neuronal

21st International Conference on Computer Communication Networks (ICCCN)

Dynamic Link Classification based on Neuronal Networks for QoS Enabled Access to Limited Resources ˇ Sebastian Subik, Dennis Kaulbars, Patrick-Benjamin B¨ok and Christian Wietfeld Communication Networks Institute TU Dortmund University Dortmund, Germany {sebastian.subik, dennis.kaulbars, patrick-benjamin.boek, christian.wietfeld}@tu-dortmund.de

Abstract—In this paper, the authors present a study of a network fingerprinting classification using monotone multilayer perceptron neuronal networks. It is part of an overall performance engineering approach. The classification is used to increase the performance of an active queue management on the quality of service for a next generation public safety communication system based on an IP overlay network. This network combines heterogeneous communication networks and technologies to increase the overall systems performance. Public safety users have higher requirements regarding coverage, data rates and quality of service than standard commercial ones. Main challenge for this study is the optimization of the overall system for voice group communication, which is still the most important communication within public safety scenarios. This paper shows that with the given parametrization, an ensemble of multi-layer perceptrons gives a satisfactory classification probability, if a setup of three technologies (EDGE, UMTS and LTE) is assumed to be in usage as communication technologies. This setup is ligthweight enough to have a chance to be implemented in a future system.

I. I NTRODUCTION Efficient public safety and national security services and operations rely heavily on secure information sharing through wired and especially mobile radio networks. Standardized Professional Mobile Radio (PMR)1 (Terrestrial Trunked Radio (TETRA)), have been in service for some time and provide voice and limited data services with a sufficient level of security [1]. Commercial mobile radio systems can provide high performance broadband services and worldwide coverage with nearly 100% uptime guarantee. In addition, public safety personnel ask for the same quality of service and experience for their public safety operations. To fulfill this requirement, intermediate systems based on gateways between heterogeneous networks (dedicated or commercial) can be used to close the performance gap between Public Safety Communication Systems (PSC) and commercial available systems. The challenge is to deal with the different link conditions influencing the quality of service (QoS). PSC gateway services need to provide solutions for an optimal usage of the interconnected systems without relying on special customization. 1 also

known as Land Mobile Radio systems (LMR)

Throughout this paper, the authors assume a scenario in which dedicated terrestrial radio networks are either damaged or at least have no core-network connection2 . This use case is mainly based on classical public safety and disaster relief operations [2]. In such a situation rescue forces should be enabled to switch to any available network without loosing the ability to be part of the group communication. If no network is available, rescue forces will start to build up an emergency network. In most cases this network is no homogeneous system but rather based on coupling different communication systems and technologies. To interconnect the different networks, an IP-based overlay network for rescue forces is used. This overlay network could be used as well for the interconnection of various heterogeneous transport networks as for the utilization of public mobile networks. To enhance quality of communication experience, the overlay network is interconnected via a dedicated PSC gateway with active quality management. Another option is to span a wireless mesh network based on unmanned autonomous systems (UAS) at the operation area. This can be used to provide a better coverage, but it still needs to be connected to a core network. This paper is structured as follows: After this introduction with an application scenario, an overview on the system architecture is presented. Starting from this overview, key performance indicators for an active QoS management in critical communication scenarios are derived to motivate the later proposed system’s optimization. Therefore the authors utilize the well know concept of multi-layer perceptrons for the identification and classification of the incoming link technologies. This classification is crucial for different optimization strategies to ensure public safety users’ quality of service requirements. In the end the performance of the proposed solution is evaluated and an overview of the best parameters is given. II. S YSTEM OVERVIEW The overall system is outlined in [1]. All networks are interconnected via an IP Overlay Network for Public Safety 2 In

this case, PSC systems work in a fall back stand alone mode

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Link to published version: tbd

PSC IP Active Gateway

N1 g1

Voice Queue Qv

gk N2

g1

gk Nn

g1

Output Voice

δ1=E[δ1(t)]

δ2=E[δ2(t)]

Dynamic Link Classification (DLC)

Active Queue Management (AQM)

gk Output Control PMR1

g1

δn=E[δn(t)]

Control Queue Qc

gk

Fig. 1. Public Safety Communication System based on an IP Overlay Network

Communication based on the ptx-protocol3 . The challenge is to deal with the different link conditions influencing the QoS in the system. Especially, if commercial networks are used based on temporary contracts, the possible range of user specific configuration is limited. Therefore, the PSC gateways need to provide solutions for an optimal usage of the interconnected systems without relying on special customization or protocol overhead. A. Minimizing Playback Delay for Voice Communication Different networks and their underlying technologies have significant influences on the behavior of transmitted data. Previous work ([3], [4]) has shown that the main influences can be modeled as a combination of a transmission delay and its variance (jitter). Based on the assumed emergency scenario, the focus of this study is the impact of heterogeneous transport networks on critical voice group communication. Our system model is depicted in Fig.1. It is assumed to have group members of a communication group spread over various heterogeneous mobile networks and a dedicated PMR network. Each network can be characterized with a minimal delay and a variable jitter. In such scenarios, especially voice communication is errorprone, because a lot of small packets are send with high frequency and a demand of a consistent transmission. Thus, for the quality of voice streams, the jitter is much more important than the overall delay. Instead of that, for all kinds of service usage (e.g. group call setup), the delay is important. A long delay lead to a negative quality of experience. At least the jitter can be compensated by buffering the audio stream before starting the playback (Voice Queue QV ). The challenge is to estimate the length of the initial playback delay. Within time variant environments, the estimation has to be done prior to the playback. Standard solutions use either long buffer (e.g. IP multimedia broadcast) or try to change the buffer size during a call between two talk bursts (enhanced 3 PTX: push-to-X; an IP based group communication and convergence layer protocol

VoIP software [5]). Another approach is the optimization of the underlying transport networks [6], but this approach is not suitable in the assumed scenario in this work. Thus, the buffer size need to be optimized, because a longer buffer results in longer delays and response times and so a more negative quality of experience within a critical communication call [7]. B. Fairness as KPI for Group Communication In critical communication scenarios it is crucial to guarantee a dedicated quality of service to all users. In network overload situations, at least the chance for all users to access a limiting resources should be identical and characterized only with predefined priority classes. As a limiting resource inside a group communication system a voice channel to a group can be identified. As a key performance indicator (KPI) for a heterogeneous group communication system, the fairness between all users can be utilized. This aspect is also depicted in Fig.1: The different mobile networks have a different impact on timing of the transmitted information. If two users from different networks send a packet to access a single resource (e.g. call setup) at a specific time, the packets will arrive at the Control Queue QC at different times depending of their origin network and the actual delay and jitter. So a user within a network with shorter delay has a greater probability to win the competition and reserve the limiting resource (resulting in a greater QoS). C. Usage of active queue management to gain fairness Summarizing the last aspect, users with a system dependent smaller average delay have a greater probability to reserve limiting resources in a heterogeneous network, which means an unfair deviation of chances for a user to win a competition about getting resources, i.e. for a call setup. In a fair system, all users have an equal chance for a successful resource allocation independent of their currently used network technology. To increase the level of fairness, the concept of AQM can be used. AQM delays Call setup requests of users which have a system-dependent low average delay. The length of this controlled delay is just as much as the difference between

Perceptron

TABLE I I NPUT T YPE A NALYSIS

Weight Matrix

No. Inputs 1 3 5

Inputs

samples

Input Type m m, sd, min m, sd, min, jit, med

No. Inputs 2 4

Input Type m, sd m, sd,min,jit

Outputs

learning is enabled by back propagation, which can be seen as the least mean squares algorithm within the linear perceptron. For the depicted scenario, a MLP would have a number of inputs, a number of hidden perceptrons and the output perceptrons (each for every network type).

x1, x2, x3, …, x

B. Ensamble of Multiple Multi-layer Perceptrons

Input Layer

Hidden Layer

Output Layer

Fig. 2. Scematic Multi-layer Perceptron with Input Sequence

the average delays between the various network technologies. This compensates the disadvantage of users with a high system-dependent delay, because now all call setup requests are handled equally. This results in a increasing amount of fairness. In order to calculate the controlled delay and so to enable the proposed optimization, it is very important that an incoming packet’s network can be classified reliably. Therefore, an Dynamic Link Classification (DLC) need to be installed to gain the necessary information for the AQM. In the following chapter, the dynamic link classification with multi-layer perceptrons is introduced. III. DYNAMIC L INK C LASSIFICATION WITH M ULTI - LAYER P ERCEPTRON To classify an unknown system, neuronal networks have shown to be a practicable way for machine learning [8] [9]. Especially systems with a back propagation of errors are flexible enough to be adoptable on different learning and classification scenarios. In this work, the conecpt of multilayer perceptrons (MLPs) is used to classify the incoming traffic of each user and to map the result on QoS rules for the active queue management. A. Multi-Layer Perceptron The proposed MLP model is depicted in Fig.2. The MLP consists of the input, the hidden and the output layer based on a set of perceptrons. Each layer is connected to the next with a weighted connection matrix. Changing the connection weights after an information is processed is the basic idea of the perceptron’s learning algorithm. The effectiveness of the learning depends on the occurrence of errors in the output detected by comparing with known results. The supervised

To gain even better results, an ensemble of multiple MLP could be utilized to classify the input into three groups (namely the mobile radio technologies EDGE, UMTS and LTE). The three MLPs are used with two output perceptrons each. This enables one MLP to classify two network profiles. When a classification is performed, the same inputs are used for all MLPs. It is obvious, that one MLP can not do a correct classification. Thus, a simple voting algorithm is used for decision making. Each MLP has one vote and has to vote for one of its outputs. After the voting phase, the sum for each network profile is compared and the one with the most votes is used as the ensemble’s result. For the rest of this paper, MLP is a synonym for an ensemble of three MLPs. The main challenge for the proposed system is the dimensioning of the MLP and the performance evaluation of the result. The next section gives an overview of MLP system’s design process and its attainable mean error rates. IV. PARAMETRIZATION OF M ULTI - LAYER P ERCEPTRON FOR M OBILE N ETWORK C LASSIFICATION As the foundation of the proposed parametrization within this section, measurements from real networks are analyzed for realistic results. No protocol overhead is necessary because all measurements are based on mandatory messages within the PTX group communication protocol. The samples from the different technologies and scenarios4 are each divided into two sets, one for training purposes and one for testing the classification accuracy. A. Input Layer Parametrization The number of samples needed for the calculation of the inputs for the MLP is crucial to gain sufficient classification accuracy. If more samples are chosen for the calculation of the inputs, the extraction of the characteristics of the different network profiles can be optimized. On the other hand, the collection of multiple samples leads to a high classification delay within the process. It is always a trade-off between classification accuracy and speed. Also, the type of inputs for providing the MLP with a high amount of information about the characteristics of the different network profiles is 4 no

mobility, urban and highway

0.30

0.30

0.10

0.20

0.20 0.10

0.00

0.00 Comb. 1

Comb. 3

Comb. 5

Comb. 1

(a) Based on 5 Samples

Comb. 3

Comb. 5

(b) Based on 20 Samples

0.6 0.4 0.2 0.0

important. In our study, we found out that statistical parameters calculated from the samples are sufficient to describe the networks’ characteristics. The parameters are: Mean (m), Standard Deviation (sd), Minimum (min), Median (med), Jitter (jit). The considered adoption of input parameters is very important in order to get a high classification accuracy. It depends on the number of samples used for input calculation and the decision which parameters should be used to provide the MLP with information. This dependency is depicted in Fig.3(a) and Fig.3(b). We analyzed different combinations of statistical parameters used as inputs while varying the number of inputs (rf. Table I). Fig.3(a) shows that for a small number of samples used for calculation, combination (3) provides the best description of the network characteristics whereas Fig.3(b) shows that for a high number of samples (that means that the classification process is delay-tolerant) combination (5) is the best one.

0.8

Fig. 3. Mean Error with Increasing Number of Inputs

1 Input So for a greater number of samples used for input calculation, it is better to use more inputs and for a smaller number of samples it is better to choose also a smaller number of inputs. This aspect is intensified in Fig.4, where we varied the number and the combination of inputs for a high number of samples used for input calculation (25 samples). The best results could be achieved with the combination of {m, sd} (set 2) and {m, sd, min, jit, med} (set 5). It can be seen, that the combination of mean, standard deviation and minimum is an important input parameter combination which holds a lot of information for the MLP even for a small number of samples.

3 Inputs

5 Inputs

Fig. 4. Mean Error Rate for Different Input Numbers and Combinations

B. Dimensioning of Training Sets Another important aspect is the dimension of the different data sets. One set is used by the MLP for learning (training set) and another set is used to validate the classification performance (validation set). It is very important that training set and validation set are uncorrelated from each other, so

0.0 0.1 0.2 0.3 0.4 0.5 0.6

0.00 0.01 0.02 0.03 0.04 0.05

Mean Error

that no identical data exist in both sets. It is also important that no set contains doubled entries. Fig.5 shows that the classification error is decreased with increasing dimension of the trainingset. It should be mentioned that an increasing length of the traningset requires more measurements. This leads to the trade-off between costs for getting measurement data and the accuracy of the classificator. In our study, we found out that even a relatively low number of learning data (55) is enough to provide good classification performance if ensemble learning is used (see Section III-B).

One MLP

MLP Ensemble

Fig. 6. Increased Classification Quality due to MPL Ensemble with Optimal Parametrization

ACKNOWLEDGMENT 10

20

30

40

50

60

70

Training Sequence Length Fig. 5. Classification Results with varying Training Sequence Length

V. C LASSIFICATION E FFICIENCY OF M OBILE N ETWORK C LASSIFIER As a result summary, the overall efficiency of a Dynamic Link Classifier is shown (rf. Fig.6). Both, the single Multilayer perceptron as well as the ensemble of three MPs are parametrized as proposed in the previous sections. With a performance gain of nearly 10%, the ensemble beats the single MP classifier. The accuracy is better than 95% with 25 samples evaluated for each classification, which validates the useful trade-off between accuracy and delay. VI. C ONCLUSION In this paper, the authors presented a study of a dynamic networks’ fingerprinting classification using monotone multilayer perceptron neuronal networks. Its was part of an overall performance engineering approach. The classification can be used for an active queue management to enhance the quality of service for a next generation public safety communication system based on an IP overlay network. This network combines heterogeneous communication networks and technologies to increase the overall systems performance and coverage. It could be shown, that with the given parametrization, an ensemble of multi-layer perceptrons lead to satisfactory classification probability. This setup is light enough to have a chance to be implemented in future systems as well as also be enhanced for more sophisticated scenarios.

This work was partially funded by the German Federal Ministry of Education and Research (BMBF) in the project ANCHORS (13N12204). R EFERENCES [1] S. Subik and C. Wietfeld, “Integrated PMR-Broadband-IP Network for Secure Realtime Multimedia Information Sharing,” in IEEE International Conference on Technologies for Homeland Security (HST). IEEE, Nov 2011. ˇ [2] S. Subik, S. Rohde, T. Weber, and C. Wietfeld, “SPIDER: Enabling Interoperable Information Sharing between Public Institutions for Efficient Disaster Recovery and Response,” in IEEE International Conference on Technologies for Homeland Security, Nov 2010, pp. 190 –196. [3] S. Subik, B. Nieh¨ofer, and C. Wietfeld, “Adaptive Multiplexing Gateway for Mission Critical Communications over High Latency IP-based Satellite Backhaul Links,” in IEEE International Conference on Technologies for Homeland Security, Boston, USA, Nov 2012. [4] B. Nieh¨ofer, S. Subik, and C. Wietfeld, “The CNI Open Source Satellite Simulator based on OMNeT++,” in 6th International OMNeT++ Workshop, Cannes, France, Mar 2013. [5] J. Liu and Z. Niu, “An adaptive receiver buffer adjust algorithm for voice amp;amp; video on ip applications,” in Communications, 2005 AsiaPacific Conference on, Oct 2005, pp. 669 –673. [6] K. Stoeckigt and H. Vu, “Voip capacity: Analysis, improvements, and limits in ieee 802.11 wireless lan,” Vehicular Technology, IEEE Transactions on, vol. 59, no. 9, pp. 4553 –4563, nov. 2010. [7] C.-C. Wu, K.-T. Chen, C.-Y. Huang, and C.-L. Lei, “An empirical evaluation of voip playout buffer dimensioning in skype, google talk, and msn messenger,” in Proceedings of the 18th international workshop on Network and operating systems support for digital audio and video, ser. NOSSDAV ’09. New York, NY, USA: ACM, 2009, pp. 97–102. [Online]. Available: http://doi.acm.org/10.1145/1542245.1542268 [8] D. E. Rumelhart, G. E. Hintont, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. [9] R. Lippmann, “An introduction to computing with neural nets,” ASSP Magazine, IEEE, vol. 4, no. 2, pp. 4–22, Apr.