Direct smartphone-to-satellite connectivity is currently under investigation in both 5G Advanced and 6G to extend the support of basic communication services to the most remote geographical areas. While current satellite connectivity systems are focused on offering a global service for emergency and text messaging, their support for other extended data services, such as multimedia services, remains limited due to the challenging wireless conditions of non-terrestrial networks (NTN). These limitations stem from the significantly longer propagation distances between the device and the satellite resulting in poor link budgets and high latencies. To address these challenges and improve the reliability of data transmission in satellite networks, leveraging repeated transmissions of transport blocks with both low-rate modulation schemes and incremental redundancy can be key. In this paper, the focus is on voice services. Particularly the problem of optimally selecting the modulation coding scheme and the number of scheduled repetitions to maximize the probability of successful voice packet reception within a fixed end-to-end latency. The feasibility of supporting voice calls over 5G-NTN under different practical conditions is then evaluated for the optimized scheduler. Link-level simulations are performed based on a 3GPP 5G-NTN model, integrating 5G NR waveforms, LDPC coding, and HARQ with incremental redundancy. Numerical results suggest that high-quality voice calls can be supported in 5G-NTN for a wide range of altitudes and elevation angles, even at 2,000 km satellite altitude, which is the highest altitude considered for LEO satellites. Moreover, numerical results indicate that HARQ retransmissions are unnecessary for supporting voice transmission, as proactively scheduling a large number of repetitions is sufficient. However, using both scheduled repetitions and HARQ retransmissions is more efficient in terms of radio resources and power consumption.

Next-generation mobile networks will rely on their autonomous operation. Virtual Network Functions empowered by Artificial Intelligence (AI) and Machine Learning (ML) can adapt to varying environments that encompass both network conditions and the cloud platform executing them. In this view, it becomes paramount tounderstand whyAI/ML algorithms made a decision, to be able to reason upon those decisions and, eventually, take further decisions related toe.g., network orchestration. In this paper, we present ATHENA, an ML-based radio resource scheduler for virtualized Radio Access Network (RAN) system. Our real-software implementation shows that the proposed ML-based approach can outperform the baseline solution. We discuss how additional re-orchestration actions can be taken by analyzing our scheduling decisions and learning from the past.

Due to the diversity of mobile services and rising user expectations, mobile network management has changed its focus from Quality of Service (QoS) to Quality of Experience (QoE). As a consequence, classical network optimization procedures must be updated accordingly. One of these optimization procedures is Mobility Robustness Optimization (MRO), whose aim is to improve HandOver (HO) performance by reducing HO failures. In this work, a novel QoE-aware MRO algorithm is proposed considering a multi-service scenario. Unlike previous approaches, whose aim is to increase successful handover rates, the optimization aim in this work is two-folded: to improve cell edge QoE while improving successful handover rates in the whole network. For this purpose, the handover trigger point, defined by the pair of HO control parameters HO margin and Time to Trigger, are tuned on a per-adjacency basis according to QoE and HO failure measurements. Method assessment is based on a dynamic system-level simulator implementing a realistic LTE scenario with multiple services. Results show that the proposed QoE-aware MRO algorithm improves cell edge QoE throughout the network while increasing the percentage of successful handovers compared to traditional approaches.

Automatic Configuration Management (CM) parameter change assessment, the so-called Self-Organizing Network (SON) verification, is an important enabler for stable and high-quality modern mobile communication networks. However, it also presents a new set of challenges. While improving network stability and resolving unexpected conflicts caused by parallel configuration changes, SON verification can have trouble coping with very dynamic networks that exhibit frequent topology changes. Such changes can be, for example, due to energy saving features which try to maximize the energy efficiency of a mobile network by adapting the topology to the network traffic. The experimental system presented in this paper demonstrates a new paradigm for handling such changes. It extends the currently available SON verification principles by providing a solution for the assessment of the impact of topology changes and for correcting them, in case the changes lead to degraded performance. The system includes a wide variety of visualization capabilities, including the various network states, user behavior, and performance statistics.

Diagnosis functionality as a key component for automated Network Management (NM) systems allows rapid, machine-level interpretation of acquired data. In existing work, network diagnosis has focused on building “point solutions” using configuration and performance management, alarm, and topology information from one network. While the use of automated anomaly detection and diagnosis techniques within a single network improves operational efficiency, the knowledge learned by running these techniques across different networks that are managed by the same operator can be further maximized when that knowledge is shared. This paper presents a novel diagnosis cloud framework that enables the extraction and transfer of knowledge from one network to another. It also presents use cases and requirements. We present the implementation details of the diagnosis cloud framework for two specific types of models: topic models and Markov Logic Networks (MLNs). For each, we describe methods for assessing the quality of the local model, ranking models, adapting models to a new network, and performing detection and diagnosis. We performed experiments for the diagnosis cloud framework using real cellular network datasets. Our experiments demonstrate the feasibility of sharing topic models and MLNs.

In today's Self-Organizing Networks (SONs) we differentiate between closed-loop functions, which have a predefined absolute goal, and such that form an action plan that achieves a high expected utility. Both function types perform changes to Configuration Management (CM) parameters, but only the second type may re-adapt the action plan in order to maximize the utility. A SON verification approach is one member of this particular function class. It is seen as a special type of anomaly detection that divides the network into sets of cells, triggers an anomaly detection algorithm for those sets, and finally generates CM undo actions for the abnormally performing cells. Unfortunately, one of the challenges verification strategies are facing are network topology changes. Typically, cells are switched on or off when energy saving features are enabled. However, enabling or disabling cells can negatively influence a verification mechanism which may create a suboptimal action plan or even blame certain CM changes that actually did not harm performance. In order to overcome this issue, we present an approach that is based on Steiner trees. In graph theory, a Steiner tree is a Minimum Spanning Tree (MST) whose costs can be reduced by adding additional vertexes to the graph. We use this tree to filter out anomalies caused by topology adjustments and such induced by other CM changes. In this paper, we also evaluate the proposed solution in several scenarios. First, in a simulation study we evaluate the functions that are used to build the Steiner tree. Second, we show how it positively affects the network performance when having concurrent CM and topology changes.

The verification of Configuration Management (CM) changes has become an important step in the operation of a mobile Self-Organizing Network (SON). Typically, a verification mechanism operates in three phases. At first, it partitions the network into verification areas, then it triggers an anomaly detection algorithm for those areas, and finally generates CM undo requests for the abnormally performing ones. Those requests set the CM parameters to a previous stable state. However, verification areas may overlap and share anomalous cells which results in a verification collision. As a consequence, the verification mechanism is not able to simultaneously deploy the undo requests since there is an uncertainty which to execute and which to potentially omit. In such a case, it has to serialize the deployment process and resolve the collisions. This procedure, though, can be negatively impacted if unnecessary collisions are processed, since they might delay the execution of the queued CM undo requests. To overcome this issue, we propose an approach for changing the size of the verification areas with respect to the detected collisions. We achieve our goal by using a Minimum Spanning Tree (MST)-based clustering approach that is able to group similarly behaving cells together. Based on the group they have been assigned to, we remove cells from a verification area and prevent false positive collisions from being further processed. Furthermore, we evaluate the proposed solution in two different scenarios. First, we highlight its benefits by applying it on CM and Performance Management (PM) data collected from a real Long Term Evolution (LTE) network. Second, in a simulation study we show how it positively affects the network performance after eliminating the false positives.

Self-Organising Networks (SON) are a key enabler for the automation of managing and operating a cellular Radio Access Network (RAN). The purpose of SON management is thereby to jointly instrument and configure a multitude of independent SON functions according to objectives defined by the Mobile Network Operator (MNO). Existing approaches such as Policy-based SON Management (PBSM) use action policies for decision making. Utility-based SON Management (UBSM), instead, uses utility function policies, where the utility reflects the degree of satisfaction of an objective for a certain network state. The demonstrator described in this paper represents a graphical frontend for the simulation environment that allows to compare the effectiveness of UBSM with an unmanaged SON implementation.

The verification of configuration management (CM) changes is an essential operation in a mobile self-organizing network (SON). Usually, a verification approach operates in three steps: it divides the network into verification areas, triggers an anomaly detection algorithm for those areas, and finally generates CM undo requests for the abnormally performing ones. Those requests set CM parameters to a previous stable state. However, the successful completion of this process can be quite challenging, since there are factors that might negatively impact its outcome. For instance, if a temporal degradation occurs during the optimization of a cell, a verification mechanism may wrongly assume that it is anomalous, and interrupt the optimization process. Furthermore, a verification strategy experiences difficulties when it faces verification collisions, i.e., conflicting undo requests that cannot be simultaneously deployed. At first, it has to determine whether the collision is a false positive one. Then, it has to resolve it by finding out which requests have the highest probability of restoring the network performance. In addition, it needs to consider the time that is given for rolling back CM changes. In this paper, we contribute to the area of SON verification by providing a solution that addresses those problems. Our approach makes use of constraint optimization techniques to resolve collisions as well as find the appropriate order for deploying undo requests. In addition, we use a minimum spanning tree-based clustering technique to eliminate false positive collisions. Further, we evaluate our solution in a simulation study in which we show its positive effect on the network performance.

Increased demand for higher user throughput has led to deployment of multi-layer networks commonly called heterogeneous networks (Hetnets). Therein, small cells are deployed alongside traditional macro cells, in many cases on the same spectrum. Such scenarios complicate the configuration of network parameters such as the Physical Cell Identity (PCI). A number of approaches have as such been proposed to automate the allocation of PCIs in such scenarios. These approaches seek to address the two conflicting objectives for PCI assignment in a hetnet scenario: 1) the need for optimal performance by avoiding conflicts, against 2) the requirement to separate the different layers and avoid any need to share knowledge among the layers. However, as the density of small cells increases evolving the Hetnets into what are called Ultra Dense Networks (UDN), these approaches reach their limits. In this paper, we study the performance of the current PCI allocation strategies in such UDN scenarios and evaluate their break down points. Our results show that these strategies do not adequately address PCI allocation for the UDN scenario. Specifically, we observe that PCI assignment in one layer requires knowledge of the assignments in the other layer, otherwise the consequence is a very high count of PCI confusions.

The verification of Configuration Management (CM) changes is an important step in the operation of a Self-Organizing Network (SON). In order to perform its tasks, a verification mechanism makes use of an observation and a correction time window. In the first window it assesses the impact of deployed CM changes by monitoring the network's Performance Management (PM) data. Furthermore, it partitions the network in one or more verification areas, detects anomalies within them, and generates CM undo requests, each having the purpose to set CM parameters to some previous state. In the second window it deploys those requests to the network. However, two or more verification areas might be overlapping and share anomalous cells. As a consequence, we have verification collisions preventing two or more generated CM undo requests to be deployed at same time. Thereby, the verification mechanism might not be able to deploy all generated CM undo actions for the given correction window. In this paper, we propose a method that makes use of constraint optimization techniques to identify which requests can be merged together in order to meet the time requirement. We achieve our goal by using constraint softening based on so-called performance rating values of the requests. We evaluate our method in two different scenarios. First, we highlight the need for handling verification collisions by observing CM and PM data of a real Long Term Evolution (LTE) network. Second, a simulation study shows the ability of our method to keep the network performance at a high level.

In a mobile Self-Organizing Network (SON) a coordinator is necessary to avoid the execution of conflicting SON function instances. Typically, such a coordinator bases its decision to accept or reject a network parameter change request on a rule set that considers only known conflicts. Moreover, it does not observe the impact of approved changes on the network. For this reason, SON verification approaches have been specified to assess the impact of deployed configuration changes and identify those that are causing an undesired network behavior. Similarly to anomaly detection techniques, a SON verification mechanism has a mathematical model that specifies how the network behavior should look like and defines any behavior that significantly deviates form the expectations as abnormal. Furthermore, the outcome is a corrective action, also called an undo action, that sets network parameters to some previous configuration. The question that often remains unanswered is how conflicting undo actions should be scheduled. A SON coordinator does not have the knowledge to resolve them and may, therefore, prevent such from being deployed. In this paper we present a scheduling approach of such undo actions that uses minimum graph coloring in order to identify the sets of cells whose configuration can be safely rolled back. Our evaluation is split in two parts. In the first part we highlight the importance of our approach by observing a real Long Term Evolution (LTE) network. The second part is based on simulation data in which we show the ability of our method to keep the performance of the network at a high level.

The operation of a Self-Organizing Network (SON) such that it achieves the objectives of a cellular network operator is a complex task. The previously presented SON objective manager enables the automatic management of SON functions based on formalized operator objectives on network Key Performance Indicators (KPIs). However, this approach initially did not consider SON coordination which resolves runtime conflicts between concurrently executed SON functions. We present an objective-driven SON coordination which adapts the SON objective manager approach to SON function conflict resolution. Therefore, the SON objective manager concept is based on multiattribute utility theory and the conflict resolution is modeled as a constraint optimization problem. As a result, this approach coordinates the execution of SON functions such that the operator objectives are satisfied as quickly as possible.

The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we build on our previous work [19] and study the feasibility of an operational deployment of an adaptive ensemble-method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to determine cell-performance status. Our results, generated using real cellular network data, show that the computational overhead and the detection delay are sufficiently low for practical use of our methods to perform cell anomaly detection in operational networks.

The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we present Dynamic Cell Anomaly Detection (DCAD), a tool that implements an adaptive ensemble method for modeling cell behavior [5], [6]. DCAD uses Key Performance Indicators (KPIs) from real cellular networks to determine cell-performance status; enables KPI data exploration; visualizes anomalies; reduces the time required for successful detection of anomalies; and accepts user input.

The Self-Organizing Network (SON) paradigm enables automated management of next generation mobile communication networks by introducing a set of autonomous functions that jointly achieve self-configuration, self-optimization, and self-healing. Each SON function is driven by a specific objective to optimize one or more Key Performance Indicators (KPIs). If a function encounters situations in which the given targets cannot be achieved, it can produce undesired behavior, resulting in an impaired SON. This paper presents the idea of SON Operational Troubleshooting function and outlines a concept for the automatic detection, analysis, and mitigation of operational problems.

The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. This paper focuses on the problem of cell anomaly detection, addressing partial and complete degradations in cell-service performance, and it proposes an adaptive ensemble method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to determine cell-performance status and is able to cope with legitimate system changes (i.e., concept drift). The results, generated using real cellular network data, suggest that the proposed ensemble method automatically and significantly improves the detection quality over univariate and multivariate methods, while using intrinsic system knowledge to enhance performance.

Self-Organizing Mobile Networks contain a potentially large number of SON function instances that need to be coordinated in order to achieve system-level operational goals. Many SON functions (and thus their coordination) will be realized in a centralized way (C-SON) due to the required integration with a (centralized) legacy OAM system. In such systems, many configuration requests ("batches") are treated in regular time intervals For SON functions, this may lead to undesired effects (monopolization by high priority functions and frequent rollbacks of configuration actions). This paper proposes a novel coordination approach combining an output buffer of pending configuration requests with dynamic priorities for different SON function types. A simulated network scenario exhibiting a specific optimization problem (closure of a coverage hole) is used to evaluate the proposed method. The method is able to avoid the undesired effects and approximate legacy workflows in network optimization while still keeping the autonomous characteristic of SON functions.

Policy-based management is a flexible approach for the management of networks as policies make context-sensitive and automated decisions. For their effective development it is desired to specify policies at a high level of abstraction initially and to refine them until they are represented in a machine-executable way. We present an approach that uses models to specify event-condition-action (ECA) policies at different abstraction layers and that uses model transformations to refine them in an automated way. A relational algebra is used to formally validate the models and define the semantics of the refinement process. One benefit of the approach is the automated policy refinement at runtime. Changes at the high-level models are automatically reflected in their low-level implementation through refinement. This allows to manage a system at a high level of abstraction. The approach is applied to the network management domain and demonstrated with policies for physical cell identification (PCI) in a mobile network. It can also be applied to other domains and supports a flexible number of abstraction layers.

Future wireless networks (LTE and beyond) will experience a continuous growth regarding the number of network elements with increasingly complex interrelations between the configuration of multiple network elements (NEs). A related trend is the seamless integration of multiple radio technologies into a single heterogeneous wireless network. Both developments increase network management complexity and require new management concepts with a very high degree of automation such as Self-Organizing Network (SON) concepts, which are currently discussed in the network operator (NGMN), research, and standardization (3GPP) communities. SON functions have to be coordinated and supervised in an automated way in order to enable a stable system operation with tight control over the system behavior by the network operator together with a high degree of automation. Based on a detailed analysis of the requirements for the coordination, a policy-based approach to realize the coordination-related decision making based on the network configuration and SON function context is presented. Results for two use cases (fully automatic hardware to site mapping and coverage & capacity optimization) are presented to show the applicability of the developed approach to diverse SON use cases.

Relays are mobile network base stations, which connect to the network via an in-band wireless backhaul link instead of using a dedicated wired or microwave backhaul link as regular base stations do. Connecting through a wireless backhaul raises difficulties when it comes to accessing the operator's Operation, Administration and Maintenance (OAM) System, since prior to the appearance of relay nodes, wireless access was reserved solely for user equipments and not used by network elements at all. However, after deploying a relay node, establishing an initial OAM access is essential in any kind of configuration, particularly when considering an automated configuration process known as self-configuration in principle. Self-configuration is gaining more importance for regular base stations as well as relays as automation of OAM processes is seen as a major contributor to reduce complexity and cost in network operation. In this paper, we introduce a conceptual separation of the initial configuration phase and the operational phase and present a detailed concept for automatic connectivity establishment to the OAM system. The transition from configuration to operational phase is also covered. The results have been verified by an event-driven packet based simulator and the proposed method has been accepted by the 3rdGeneration Partnership Project (3GPP) as the baseline solution for configuring relays in the next generation radio access technology, Long Term Evolution (LTE).

One of the key requirements that come with the introduction of next generation mobile networks is the automatic provisioning and configuration of new network elements. This targets to reduce on the one hand the time required between the introduction of new hardware until it is fully functional and on the other hand the overall configuration overhead. Hardware should be setup without requiring any preconfiguration and without manual configuration after the on-site installation. A required enabler is the automatic acquisition of the site informa- tion where a network element has been set up at. Only if this is possible further autoconfiguration steps can be successfully executed. Automatic site identification provides the means to assess the location and the information required to map location information to a site defined by network planning. An analysis shows that depending on the network setup and the deployment scenario automatic hardware to site mapping shows different degrees of complexity that need to be handled. In addition to the analysis of deployment scenarios and their individual site identification and mapping solutions a combined solution that is capable to perform site identification and hard- ware to site mapping based on the context of the new Network Element is proposed.

Through the introduction of Self-Organizing Net- works (SON) into mobile networks, a potentially large number of SON functions are available. These SON functions automatically perfom management actions. There will be SON functions for many aspects of fault, configuration, accounting, performance, and security (FCAPS) management. The functions are executed based on monitored network behavior, which may lead to several functions being active concurrently in the same network area. Simultaneous execution of different functions with contradicting goals may lead to oscillating function execution and service degra- dation in the worst case. Therefore, SON function coordination is indispensable for SON-enabled networks in order to align the executed SON funtions and thus assure that they take full effect (improved performance and fault handling). Coordination mechanisms need to be developed and verified before they are deployed into the network. For this purpose, an experimental sys- tem realizes SON function coordination based on flexible policy- based decisions. Coverage and Capacity Optimization (CCO) is presented as use case to demonstrate succesful coordination of multiple independent SON functions.

Future wireless networks will experience a continuous growth regarding the number of network elements with increasingly complex interrelations between the configuration of multiple network elements. Another trend is the seamless integration of multiple radio technologies into a single wireless network. Both developments increase network management complexity and require new management concepts with a very high degree of automation. For this purpose, a novel management approach based on a combination of workflow and policy technologies is presented. The goal is to simplify and automate management tasks in mobile networks in order to raise the state of self-organization while the network still remains under control of the operator. The approach provides the means for a dynamic system to automatically adapt to context changes. Moreover, an experimental system is presented for the purposes of concept validation and evaluation along with a real world use case.

The trend for future mobile networks is to move away from Network Elements (NEs) delivered with specially tailored configurations towards off-the-shelf products. The configurations of NEs are automatically created with respect to their context including information on location and configuration of neighboring NEs. To minimize time-consuming and error-prone human interaction, automatic behavior is required for all stages of a NE's life cycle. The possibility to pre-assess the effects of configuration changes is inevitable in order to avoid service degradation caused by unnecessary reconfigurations. Graph coloring-based Physical Cell ID (PCID) assignment for LTE networks was introduced previously. The foundation on graph coloring theory allowed to transfer knowledge from this domain to the task of PCID assignment in order to pre-asses if an assignment is possible and how many PCIDs are required. Now the focus lies on adaptations of the basic approach to satisfy additional operator requirements such as safety margins. Those adaptations should provide equally good results in terms of used PCIDs with only minimal impact on costs and operation and maintenance tasks. Variations of the basic PCID assignment approach are discussed to address other types of problems.

In second and third generation cellular networks the rollout of base stations needs to be conscientiously prepared, because their radio configuration should match with the coverage provided by existing cells in their neighborhood. The detailed radio planning for every new cell requires considerable human resources and time. This paper proposes a framework for 3G Long Term Evolution (LTE) that can perform a dynamic radio configuration of a base station when powered on, which is adaptive to the current network topology context and does not require human intervention. This function enables the deployment of base stations and new cells in an ad hoc manner, reduces OPEX and contributes to the concept of a self-configuring network.

In access networks, the roll-out of new network elements (NE) or changes to the NE HW and SW cause considerable overhead. The total number of NE is significant and is increasing for new radio access technologies like Long Term Evolution (LTE) due to the decreasing cell size. Furthermore for network scenarios like femto access points / home NEs conventional network deployment and management approaches where the network is fully planned and NEs are tightly managed cannot be followed any more. Furthermore the increased security requirements by operators for such network deployments have to be observed. An auto-connectivity scheme which incorporates the NE's security setup is proposed which tries to balance the trade-off between automation (avoiding any manual intervention) and security. This is achieved by shifting manufacturer and operator activities to a preparation (rather than the actual roll-out) phase and eliminating any interaction between them as much as possible. The NE is delivered only with an ldquooff-theshelfrdquo software & configuration installation. Only at the point in time when the NE is placed on site, the NE hardware-to-site mapping happening is executed. Together with mutual authentication between NE and the Operation, Administration and Maintenance (OAM) system it is possible to enable a very flexible and secure roll-out process.

Autoconfiguration of the radio parameters is a key feature for next generation mobile networks. Especially for LTE the NGMN Forum has brought it up as a major requirement. It is indispensable that algorithms used for autoconfiguration terminate quickly and do not cause infinite iterative reconfigurations within the network.

In a "managed network" where the location / point of attachment of network elements (NE) are entirely planned and the NEs are tightly controlled by an element management system, the roll-out of new NEs or changes to the NE HW and SW cause considerable overhead. The overhead is due in large part due to the concurrent processes at the network operator, NE factory and roll-out field service which require extensive interaction. An autoconfiguration method is proposed which is able to decouple the individual roll-out processes, i.e., changes in the NE configuration and association of NE with certain locations can be changed at any time in the roll-out process without incurring additional overhead. This is achieved by an autoconfiguration support system under control of the operator where all data relevant to a to-be-rolled-out NE is stored. When the NE is booting it can retrieve the required configuration and SW which is needed to fulfil the required network function at its point of attachment to the network. Finally it is shown that in a managed network it is advantageous to use the geo-location of a network element as its key identifier in network planning and rollout, rather than a hardware identifier as usually employed in DHCP-based autoconfiguration. This is the case because in a "managed network" it is important that autoconfiguration support is provided for a certain network function at a certain location, rather than autoconfiguration of a certain hardware which is attached at an arbitrary location.

Real Time Performance Management (RTPM) is expected to be a key features offuture developments of mobile networks. Although it is already requested by operators of mobile networks only basic approaches have been realized. Implementing access to real time performance data in a mobile network, with a large number of manageable nodes, leads to a huge resource consumption. Not only network bandwidth but also processing resources. Current PM schemes can hardly be extended to support real time performance management. We present a policy based approach that is capable to deal with challenges imposed by the request for real time performance data. Typical workflows are identified and analysed. Based on these analyses a policy based performance management system is introduced that allows a balanced configuration which does not exceed available resources but still satisfies the information requirements of the human operators.

"Best effort" packet-switched networks, like the Internet, do not offer a reliable transmission of packets to applications with real-time constraints such as voice. Thus, the loss of packets impairs the application-level utility. For voice this utility impairment is twofold: on one hand, even short bursts of lost packets may decrease significantly the ability of the receiver to conceal the packet loss and the speech signal playout is interrupted. On the other hand, some packets may be particular sensitive to loss as they carry more important information in terms of user perception than other packets.We first develop an end-to-end model based on loss run-lengths with which we can describe the loss distribution within a flow. These packet-level metrics are then linked to user-level objective speech quality metrics. Using this framework, we find that for low-compressing sample-based codecs (PCM) with loss concealment isolated packet losses can be concealed well, whereas burst losses have a higher perceptual impact. For high-compressing frame-based codecs (G.729) on one hand the impact of loss is amplified through error propagation caused by the decoder filter memories, though on the other hand such coding schemes help to perform loss concealment by extrapolation of decoder state. Contrary to sample-based codecs we show that the concealment performance may "break" at transitions within the speech signal however.We then propose mechanisms which differentiate between packets within a voice data flow to minimize the impact of packet loss. We designate these methods as "intra-flow" loss recovery and control. At the end-to-end level, identification of packets sensitive to loss (sender) as well as loss concealment (receiver) takes place. Hop-by-hop support schemes then allow to (statistically) trade the loss of one packet, which is considered more important, against another one of the same flow which is of lower importance. As both packets require the same cost in terms of network transmission, a gain in user perception is obtainable. We show that significant speech quality improvements can bem achieved and additional data and delay overhead can be avoided while still maintaining a network service which is virtually identical to best effort in the long term.

Multimedia flows are inherently inhomogenous, i.e. packets within a flow are of different importance for actual user perception. When transmitting such flows with real-time constraints in networks like the Internet which offer no reliability of transmission, some packet loss is inevitable. The perceptual impact of these losses is then amplified by the arbitrary distribution of packet losses within the flow which impairs the reconstruction of application data units (ADUs) at the receiver, as well as the performance of end-to-end loss recovery mechanisms. To control the loss distribution within a flow ("intra-flow" QoS) typically filtering higher-layer information within the network is proposed, which is both expensive in terms of resources, as well as undesirable with regard to network security. We compare two novel queue management algorithms which improve the intra-flow QoS without higher-layer filtering. The first algorithm is called DiffRED (differential random early detection). It differentiates between packets marked by the sender as either more or less eligible to be dropped in comparison to unmarked packets without keeping any per-flow state. The other algorithm called PLoP (predictive loss pattern) operates without per-packet marking, yet keeping partial per-flow state. We introduce simple metrics to describe the loss process of individual flows and present simulation results with voice as foreground traffic using the proposed methods in a multi-hop topology. We find that both algorithms do not have a significant impact on the background traffic. For the given scenario algorithms using packet marking are found to be superior because for the foreground traffic a high probability for short bursts with potentially high perceptual impact can be traded against a higher probability for isolated losses as well as higher (but acceptable) probability for very long loss bursts.

End-to-end QoS can be improved by exploiting knowledge about the flow structure to influence the flow within the network. This leads to a graceful degradation under congestion ("intra-flow" QoS). Typically this is accomplished by filtering higher-layer information within the network, which is both expensive in terms of resources, as well as undesirable with regard to network security. In this paper, we present a queue management algorithm called DiffRED (differential random early detection) that allows us to enhance intra-flow QoS without higher-layer filtering. The algorithm differentiates between packets marked by the sender as either more or less eligible to be dropped in comparison to unmarked packets. This gives the application some control over the packet loss process and thus enhances the performance of available end-to-end loss recovery mechanisms, end-to-end fairness and finally the perceived quality. The algorithm helps to bridge the huge gap between the current "best-effort" Internet and full deployment of inter-flow service differentiation. We introduce simple metrics to describe the loss process of individual flows and present simulation results for a voice service using the proposed scheme. We demonstrate how the algorithm provides a significant intra-flow QoS enhancement and evaluate the impact on conventional traffic.