Incident management in telecommunications networks generates large volumes of incident management tickets (IMTs), each containing heterogeneous and often unstructured text describing service outages, performance degradations, or security issues. Accurately categorizing these IMTs into multiple impact and cause labels is essential for rapid diagnosis and resolution. However, existing rule-based and standard language-model-based approaches struggle with noisy data, overlapping categories, and limited contextual understanding. To address these challenges, we propose two complementary solutions for automated multi-label classification of IMTs. To mitigate the effects of noisy data and overlapping categories, the first solution employs an encoder-based language model (i.e., Bidirectional Encoder Representations from Transformers (BERT)) with a relevance-guided feature selection strategy that focuses on semantically meaningful attributes. To improve contextual understanding and label consistency, the second solution leverages a decoder-based large language model (i.e., Phi-3.5) enhanced with retrieval-augmented generation (RAG) and a novel probabilistic re-ranking mechanism to refine label predictions. Experimental results show that our encoder-only model achieves an F1 score of 79.20%, while our RAG-enhanced decoder model achieves 94.98%, outperforming traditional machine learning models and BERT baselines by 23.59% and 29% on average, respectively. These findings demonstrate that combining fine-tuned language models with intelligent retrieval and re-ranking significantly improves classification accuracy in incident management systems.

As 5G and beyond mobile networks evolve, their increasing complexity necessitates advanced, automated, and datadriven fault diagnosis methods. While traditional data-driven methods falter with modern network complexities, Transformer models have proven highly effective for fault diagnosis through their efficient processing of sequential and time-series data. However, these Transformer-based methods demand substantial labeled data, which is costly to obtain. To address the lack of labeled data, we propose a novel active learning (AL) approach designed for Transformer-based fault diagnosis, tailored to the time-series nature of network data. AL reduces the need for extensive labeled datasets by iteratively selecting the most informative samples for labeling. Our AL method exploits the interpretability of Transformers, using their attention weights to create dependency graphs that represent processing patterns of data points. By formulating a one-class novelty detection problem on these graphs, we identify whether an unlabeled sample is processed differently from labeled ones in the previous training cycle and designate novel samples for expert annotation. Extensive experiments on real-world datasets show that our AL method achieves higher F1-scores than state-of-the-art AL algorithms with 50% fewer labeled samples and surpasses existing methods by up to 150% in identifying samples related to unseen fault types.

Machine Learning (ML) models are increasingly employed for critical network management tasks such as traffic prediction, anomaly detection, root cause analysis, and resource allocation. A major issue in the reliability of these models is data drift, which refers to the discrepancy between training data (source domain) and test/operational data (target domain) caused by changes in network conditions and configurations. Domain adaptation, which aims to develop robust models that can generalize well across different but related domains, is a promising solution to the data drift issue. However, existing domain adaptation methods often fall short in few-shot scenarios, where target domain data is limited due to high data collection costs or restricted operational network access. Furthermore, the existing methods require the network management ML models to be frequently retrained or fine-tuned over time to adapt to the changes in data distributions, leading to high operational costs. To address these limitations, we propose a novel, model-agnostic domain adaptation approach specifically designed for few-shot scenarios and network data. In our approach, network management ML models are trained exclusively on source domain data with all the input features included, while a two-step method aligns test data samples from the target domain with the source domain during inference. The first step employs our proposed causal-inference-based feature separation (FS) method, which introduces a novel perspective by treating domain shift as soft interventions (interventions that adjust the probability distribution of features rather than making absolute changes) on a set of specific features. FS effectively separates domain-variant and domain-invariant features directly in the input space, even with limited target training data. In the second step of our approach, we propose a Generative Adversarial Network (GAN)-based reconstruction method, trained exclusively on source data, to reconstruct the domain-variant features given the domain-invariant features. During inference, the GAN model maps the domain-variant features of the target domain samples to the source domain distribution, allowing the use of domain-variant features without causing cross-domain performance degradation. Since our approach trains the network management ML models exclusively on source domain data, it eliminates the need for retraining or fine-tuning these models as network conditions or data distributions evolve over time, significantly reducing costs and operational overhead. Comprehensive evaluations on two public 5G network datasets demonstrate an average 52% improvement in mitigating data drift compared to state-of-the-art methods in terms of F1-score.

Multi-connectivity is anticipated to provide more reliable, higher data rate connections for cellular network users by leveraging all available radio resources across base stations within one or multiple radio access technology(ies) (RAT). It aims to improve user mobility through multi-RAT connections or mitigate quality of service (QoS) degradation when users connect to congested cells through load-balancing traffic among base stations and distributing the user's flow across multiple links. Although many studies have investigated the benefits of multi-connectivity across various network deployments using analytical models or simulated environments, we critically assess these reported gains, particularly regarding system throughput. We argue that multi-connectivity's advantages are primarily restricted to scenarios with a low user-to-base station ratio and that dense networks are less likely to benefit. We formulate the user-to-base station association and resource allocation within a proportional fair (PF) setting across varying user densities to examine this. Our findings demonstrate that multi-connectivity offers no superiority over the PF single-connectivity baseline in dense networks. Furthermore, in sparse networks, we show that while multi-connectivity can potentially enhance system throughput, it does not significantly improve individual users' QoS, as the PF single-connectivity scheme can offer sufficient resources to every user.

The adoption of virtualization in 5G and beyond networks enables the creation of network slices tailored to specific application requirements. While this flexibility is transformative, it introduces new challenges in slice management and orchestration (MANO). AI-based techniques are becoming essential for automated slice MANO. However, their effectiveness relies on the accuracy of network models that map VNF configurations and resource allocations to slice performance. Previous approaches, including simulations, control theory, and machine learning, face limitations such as high computational complexity, limited visibility, large data requirements, or specific use cases, e.g., in data center networks. In this work, we present vNetRunner, a framework for slice modeling using individually trained virtual network function models. We validate our framework using datasets from an open-source 5G testbed, focusing on traffic metrics such as mean delay and throughput. Our results demonstrate that vNetRunner estimates mean packet delay and throughput with Wasserstein distances of 6 ms and 0.554 Mbps, respectively, achieving execution times that are an order of magnitude faster than the state-of-the-art modeling approaches.

The integration of Network Functions Virtualization (NFV) systems into mobile edge and core networks has heightened the need for effective anomaly detection and localization methods. The complexity of NFV demands robust mechanisms for network resilience, security, and performance. Machine Learning approaches have demonstrated promising solutions in crafting adaptive and efficient mechanisms for detecting and localizing potential anomalies within NFV systems. Particularly, Unsupervised Learning (UL) methods have garnered significant attention for their potential to detect anomalies without the need for labeled data. However, UL methods are susceptible to even minor levels of anomalous samples in the training data, termed contamination, which can severely compromise their performance. This paper proposes a novel approach using the Noisy-Student technique for anomaly detection. It addresses data contamination by combining a density-estimation teacher model for pseudo-labeling with a weakly-supervised student model based on a Masked Autoencoder trained on the pseudo-labeled data. For anomaly localization, we introduce a heuristic tailored for our anomaly detection model and two Explainable Artificial Intelligence (XAI)-based approaches applicable to any detection model. Extensive experiments on three NFV datasets demonstrate superior performance, with up to a 20% improvement in anomaly detection and up to a 22% improvement in localization, in terms of F1-score.

Data-driven algorithms play a pivotal role in the automated orchestration and management of network slices in 5G and beyond networks, however, their efficacy hinges on the timely and accurate monitoring of the network and its components. To support 5G slicing, monitoring must be comprehensive and encompass network slices end-to-end (E2E). Yet, several challenges arise with E2E network slice monitoring. Firstly, existing solutions are piecemeal and cannot correlate network-wide data from multiple sources (e.g., different network segments). Secondly, different slices can have different requirements regarding Key Performance Indicators (KPIs) and monitoring granularity, which necessitates dynamic adjustments in both KPI monitoring and data collection rates in real-time to minimize network resource overhead. To address these challenges, in this paper, we present Monarch, a scalable monitoring architecture for 5G. Monarch is designed for cloud-native 5G deployments and focuses on network slice monitoring and per-slice KPI computation. We validate the proposed architecture by implementing Monarch on a 5G network slice testbed, with up to 50 network slices. We exemplify Monarch’s role in 5G network monitoring by showcasing two scenarios: monitoring KPIs at both slice and network function levels. Our evaluations demonstrate Monarch’s scalability, with the architecture adeptly handling varying numbers of slices while maintaining consistent ingestion times between 2.25 to 2.75 ms. Furthermore, we showcase the effectiveness of Monarch’s adaptive monitoring mechanism, exemplified by a simple heuristic, on a real-world 5G dataset. The adaptive monitoring mechanism significantly reduces the overhead of network slice monitoring by up to 76% while ensuring acceptable accuracy.

A pivotal attribute of 5G networks is their capability to cater to diverse application requirements. This is achieved by creating logically isolated virtual networks, or slices, with distinct service level agreements (SLAs) tailored to specific use cases. However, efficiently allocating resources to maintain slice SLA is challenging due to varying traffic and quality-of-service (QoS) requirements. Traditional peak traffic-based resource allocation leads to over-provisioning, as actual traffic rarely peaks. Additionally, the complex relationship between resource allocation and QoS in end-to-end slices spanning different network segments makes conventional optimization techniques impractical. Existing approaches in this domain use mathematical network models (e.g., queueing models) or simulations, and various optimization methods but struggle with optimality, tractability, and generalizability across different slice types. In this paper, we propose MicroOpt, a novel framework that leverages a differentiable neural network-based slice model with gradient descent for resource optimization and Lagrangian decomposition for QoS constraint satisfaction. We evaluate MicroOpt against two state-of-the-art approaches using an open-source 5G testbed with real-world traffic traces. Our results demonstrate up to 21.9% improvement in resource allocation compared to these approaches across various scenarios, including different QoS thresholds and dynamic slice traffic.

Fifth generation (5G) standalone (SA) mobile networks are rapidly gaining prominence worldwide, and becoming increasingly prevalent as the telecommunication industry standard. Most published work concerning 5G applications relies on open-source 5G radio access network (RAN) simulation and emulation tools to evaluate various concepts, algorithms, and use cases. However, these tools are not always accurate in conveying a realistic representation of real-world RAN performance and expected quality of service (QoS). This paper discusses the deployment of a 5G SA testbed supporting three different RAN scenarios of real and simulated deployments using open- source software, commercial-off-the-shelf (COTS) hardware, and software defined radios (SDRs). We experimentally evaluate the performance of these scenarios for the RAN and quantify their differences in terms of computational resource utilization, throughput, latency, coverage, and power consumption. Specifically, we explore the emulation and simulation tools’ ability to reflect realistic RAN performance and highlight the differences compared to the SDR-based deployment. Through this analysis, this paper provides insights into the performance of each approach and sheds light on the feasibility of using open- source software for 5G testing and experimentation.

The virtualization and distribution of 5G Radio Access Network (RAN) functions across radio unit (RU), distributed unit (DU), and centralized unit (CU) in conjunction with multi-access edge computing (MEC) enable the creation of network slices tailored for various applications with distinct quality of service (QoS) demands. Nonetheless, given the dynamic nature of slice requests and limited network resources, optimizing long-term revenue for infrastructure providers (InPs) through real-time admission and embedding of slice requests poses a significant challenge. Prior works have employed Deep Reinforcement Learning (DRL) to address this issue, but these approaches require re-training with the slightest topology changes due to node/link failure or overlook the joint consideration of slice admission and embedding problems. This paper proposes a novel method, utilizing multi-agent DRL and Graph Attention Networks (GATs), to overcome these limitations. Specifically, we develop topology-independent admission and slicing agents that are scalable and generalizable across diverse metropolitan networks. Results demonstrate substantial revenue gains-up to 35.2% compared to heuristics and 19.5% when compared to other DRL-based methods. Moreover, our approach showcases robust performance in different network failure scenarios and substrate networks not seen during training without the need for re-training or re-tuning. Additionally, we bring interpretability by analyzing attention maps, which enables InPs to identify network bottlenecks, increase capacity at critical nodes, and gain a clear understanding of the model decision-making process.

In the rapidly evolving landscape of 5G and its successor technologies, the Next Generation Radio Access Network (NG-RAN) stands out as a transformative pillar. Functional splitting, a core concept in NG-RAN, splits the traditional base station into distinct functional entities, notably the Distributed Unit (DU), Centralized Unit (CU) and Radio Unit (RU). With flexible functional splitting, Infrastructure Providers (InPs) can dynamically allocate RAN resources to cater to each network slice's distinct throughput and latency demand. However, the problem of optimally selecting functional splits, placement of RAN functions in DU/CU with constrained computational capacities and determining routing paths present an NP-hard challenge. The coexistence of multiple slices on shared infrastructure may necessitate slice isolation for security, performance, and operational reasons, adding another layer of complexity. To address this multifaceted problem, we formulate an Integer Linear Programming (ILP) model that seeks to maximize the InP profit considering computation, virtual machine instantiation and routing costs. Using Gurobi optimizer, we show that optimal slice admission solutions directly impact InP profit and that enhanced computational capacities can increase the number of slices admitted.

Changes are an integral part of the day-to-day operation of large telecommunications networks as they allow to keep pace with technological advancements, meet growing network demands, ensure scalability, enhance security, improve service quality, and meet customer expectations. Changing configurations, installing devices, and migrating traffic are some examples of these changes. These changes are documented by opening tickets through a ticket management system. Automation in the ticket management system is now becoming highly desirable to manage the large number of submitted tickets. An automated ticket management system supports the management of a ticket by automating several parts of a ticket's lifecycle. In this context, ticket classification problem consists in assigning an appropriate label to a ticket to be utilized in the later stages of the ticket management cycle. In this paper, we use a collection of network-change tickets from a real network operator to solve a ticket classification problem. We observe that the network-change ticket dataset is highly skewed in the number of tickets for different possible classes. We address this challenge of classification in a highly imbalanced dataset by proposing two token-prioritization strategies along with other components. We compare three variations of our proposed approach with three methods from the literature and show that the variations of the proposed approach outperform existing methods by up to 7% in terms of F1 score.

The 5G RAN functions can be virtualized and distributed across the radio unit (RU), distributed unit (DU), and centralized unit (CU) to facilitate flexible resource management. Complemented by multi-access edge computing (MEC), these components create network slices tailored for applications with diverse quality of service (QoS) requirements. However, as the requests for various slices arrive dynamically over time and the network resources are limited, it is non-trivial for an infrastructure provider (InP) to optimize its long-term revenue from real-time admission and embedding of slice requests. Prior works have leveraged Deep Reinforcement Learning (DRL) to address this problem, however, these solutions either require re-training when facing topology changes or do not consider the slice admission and embedding problems jointly. In this paper, we use multi-agent DRL and Graph Attention Networks (GATs) to address these limitations. Specifically, we propose novel topology-independent admission and slicing agents that are scalable and generalizable to large and different metropolitan networks. Results show that the proposed approach converges faster and achieves up to 35.2% and 20% gain in revenue compared to heuristics and other DRL-based approaches, respectively. Additionally, we demonstrate that our approach is generalizable to scenarios and substrate networks previously unseen during training, as it maintains superior performance without re-training or re-tuning.

Automated decision making algorithms are expected to play a key role in management and orchestration of network slices in 5G and beyond networks. State-of-the-art algorithms for automated orchestration and management tend to rely on data-driven methods which require a timely and accurate view of the network. Accurately monitoring an end-to-end (E2E) network slice requires a scalable monitoring architecture that facilitates collection and correlation of data from various network segments comprising the slice. The state-of-the-art on 5G monitoring mostly focuses on scalability, falling short in providing explicit support for network slicing and computing network slice key performance indicators (KPIs). To fill this gap, in this paper, we present MonArch, a scalable monitoring architecture for 5G, which focuses on network slice monitoring, slice KPI computation, and an application programming interface (API) for specifying slice monitoring requests. We validate the proposed architecture by implementing MonArch on a 5G testbed, and demonstrate its capability to compute a network slice KPI (e.g., slice throughput). Our evaluations show that MonArch does not significantly increase data ingestion time when scaling the number of slices and that a 5-second monitoring interval offers a good balance between monitoring overhead and accuracy.

Network slicing is a key enabler for 5G to support various applications. Slices requested by service providers (SPs) have heterogeneous quality of service (QoS) requirements, such as latency, throughput, and jitter. It is imperative that the 5G infrastructure provider (InP) allocates the right amount of resources depending on the slice’s traffic, such that the specified QoS levels are maintained during the slice’s lifetime while maximizing resource efficiency. However, there is a non-trivial relationship between the QoS and resource allocation. In this paper, this relationship is learned using a regression-based model. We also leverage a risk-constrained reinforcement learning agent that is trained offline using this model and domain randomization for dynamically scaling slice resources while maintaining the desired QoS level. Our novel approach reduces the effects of network modeling errors since it is model-free and does not require QoS metrics to be mathematically formulated in terms of traffic. In addition, it provides robustness against uncertain network conditions, generalizes to different real-world traffic patterns, and caters to various QoS metrics. The results show that the state-of-the-art approaches can lead to QoS degradation as high as 44.5% when tested on previously unseen traffic. On the other hand, our approach maintains the QoS degradation below a preset 10% threshold on such traffic, while minimizing the allocated resources. Additionally, we demonstrate that the proposed approach is robust against varying network conditions and inaccurate traffic predictions.

5G Cloud Radio Access Networks (C-RANs) facilitate new forms of flexible resource management as dynamic RAN function splitting and placement. Virtualized RAN functions can be placed at different sites in the substrate network based on resource availability and slice constraints. Due to limited resources in the substrate network and variability in revenue of slices, the Infrastructure Provider (InP) must perform network slicing in a strategic manner, and accept or reject slice-requests to maximize long-term revenue. In this paper, we propose to use multi-agent Deep Reinforcement Learning (DRL) to jointly solve the problems of network slicing and slice Admission Control (AC). Multi-agent DRL along with reward shaping is a promising choice, which is well-suited to problems where multiple distinct tasks have to be performed optimally. The proposed DRL approach can learn the dynamics of slice-request traffic and effectively address these joint problems. We compare multi-agent DRL to approaches that use: (i) simple heuristics to address the problems, and (ii) DRL to address either slicing or AC. Our results show that the proposed approach achieves up to 30% and 5.18% gain in long-term InP revenue when compared to approaches (i) and (ii), respectively. Additionally, we show that multi-agent DRL is preferable to a single-agent DRL approach for the joint problems in terms of convergence time and InP revenue. Finally, we evaluate the robustness of the trained agents in scenarios that differ from training, such as different arrival rates and real dynamic traffic patterns.

Due to the scarcity of labeled faulty data, Unsupervised Learning (UL) methods have gained great traction for anomaly detection and localization in Network Functions Virtualization (NFV) systems. In a UL approach, training is performed on only normal data for learning normal data patterns, and deviation from the norm is considered as an anomaly. However, it has been shown that even small percentages of anomalous samples in the training data (referred to as contamination) can significantly degrade the performance of UL methods. To address this issue, we propose an anomaly-detection approach based on the Noisy-Student technique, which was originally introduced for leveraging unlabeled datasets in computer-vision classification problems. Our approach not only provides robustness against training-data contamination, but also can leverage this contamination to improve anomaly-detection accuracy. Moreover, after an anomaly is detected, localization of the anomalous virtualized network functions in an unsupervised manner is a challenging task in the absence of labeled data. For anomaly localization in NFV systems, we propose to exploit existing local AI-explainability methods to achieve a high localization performance and propose our own novel AI-explainability method, specifically designed for the anomaly-localization problem in NFV, to improve the performance further. We perform a comprehensive experimental analysis on two datasets collected on different NFV testbeds and show that our proposed solutions outperform the existing methods by up to 22% in anomaly detection and up to 19% in anomaly localization in terms of F1-score.

Network slicing has been envisaged as a key enabler to satisfy diverse requirements of 5G networks, by creating multiple isolated end-to-end virtual networks dedicated to different services. An accurate view of these end-to-end 5G network slices is essential for both artificial intelligence (AI) driven slice orchestration, and data-driven automated service assurance. However, the existing open-source implementations of the 5G core do not natively support slice Key Performance Indicator (KPI) monitoring. In this demonstration, we show how to deploy a functional 5G testbed using a combination of open-source frameworks and tools, with a guide for configuring multiple network slices published on GitHub [1]. We also show the feasibility of monitoring and visualizing network slice KPIs using a representative cloud-gaming use-case.

5G Cloud Radio Access Networks (C-RANs) facilitate new forms of flexible resource management as dynamic RAN function splitting and placement. Virtualized RAN functions can be placed at different sites in the substrate network according to resource availability and slice constraints. Due to limited resource availability in the substrate network, the Infrastructure Provider (InP) must perform network slicing in a strategic manner, and accept or reject slice-requests in order to maximize long-term revenue. In this paper, we propose to use multi-agent Deep Reinforcement Learning (DRL) to jointly solve the problems of network slicing and slice Admission Control (AC). Multi-agent DRL is a promising choice since it is well-suited to problems where multiple distinct tasks have to be performed optimally. The proposed DRL approach can learn the dynamics of slice-request traffic and effectively address these joint problems. We compare multi-agent DRL to approaches that use: (i) simple heuristics to address the problems, and (ii) DRL to address either slicing or AC. Our results show that the proposed approach achieves up to 18% and 3.8% gain in long-term InP revenue when compared to approaches (i) and (ii), respectively. Additionally, we show that multi-agent DRL is preferable to a single-agent DRL approach that addresses the problems jointly. Finally, we evaluate the robustness of the trained model in terms of its ability to generalize to scenarios that deviate from training.

It is known that the achievable data rate per user can be increased when relays are deployed in wireless networks. However, the drawback of this solution is that some of the network's resources should be allocated to the relays. In this paper, we consider a two-tier network in which all users send or receive data in two hops. By applying vector quantization, we compute the relays' locations to improve network's average transmission rate. These locations are also computed analytically when the number of relays is less than six. Having determined the relays' locations, the network's average transmission rate is evaluated. Subsequently, we define the "neutrality-surface" such that the performance of any relay network operating below this surface is inferior to that of the same network without relays. Finally, we study the relative relaying gain for different network configurations.

With the co-existence of different wireless networks, which exhibit largely different bandwidth and coverage characteristics, much interest has been involved in integrating these networks to support smooth and efficient multimedia services. In this paper, we present an analytical framework for variable-bit-rate (VBR) video streaming in a two-tier wireless network with VBR channels. We derive the expected number of jitters and average buffering delay during video playback as measures of system performance. Our objective is to discover heterogeneous networking attributes that may influence the streaming performance, in terms of the tradeoff between jitter frequency and buffering delay. Through experimenting with a wide range of fixed, separate, and jointly optimal jitter-recovery buffering schemes, based on buffering delay, buffered data, and buffered playback duration, we quantify the benefit of incorporating user location information in streaming over heterogeneous wireless networks.

Channel inversion, and its minimum mean square error (MMSE) variation, are low complexity methods for space division multiple access (SDMA) in multiple input multiple output broadcast channel (MIMO-BC). As the channel matrix deviates from orthogonal, these methods result in a waste of transmit power. This paper proposes a trellis precoding method (across time and space) to improve the power efficiency. Adopting a 4-state trellis shaping method, the complexity of the proposed method, which is entirely at the transmitter side, is equivalent to the search in a trellis with 4Nstates where N is the number of transmit antennas. Numerical results are presented showing that the achievable gains, which depend on the channel realization, can be significantly higher than the traditional shaping gain which is limited to 1.53 dB.

In this paper, we consider an interworking architecture of wireless mesh backbone and propose an effective vertical handoff scheme between 802.11 and 802.16 wireless access networks. The proposed vertical handoff scheme aims at reducing handoff signaling overhead on the wireless backbone and providing a lower handoff delay to mobile nodes. The handoff signaling procedure in different scenarios is discussed. Together with call admission control, the vertical handoff scheme directs a new call request in the 802.11 network to the 802.16 network, if the admission of the new call in the 802.11 network can degrade quality-of-service (QoS) of the existing real-time traffic flows. Simulation results demonstrate the performance of the handoff scheme with respect to signaling cost, handoff delay, and QoS support.

It is known that the achievable data rate per user can be increased when relays are deployed in wireless networks. However, the drawback with this solution is that some of the network resources should be allocated to the relays. In this paper, we consider a two-tier network where all users should send/receive data in two hops (via a relay). Applying vector quantization, we approximately find the location of the relays. These approximate relays' locations are also computed analytically when the number of relays is less than six. Having the relays' locations, the network average transmission rate is evaluated in terms of a set of network parameters. Then, in the multi-dimensional space of these network parameters, we introduce the concept of neutrality-surface. The neutrality-surface is defined such that the performance of any relay network operating below this surface is inferior to that of a simple no-relay network with the same parameters. Finally, we study the relative and differential relaying gain for different network configurations.

The future-generation wireless systems will combine heterogeneous wireless access technologies to provide mobile users with seamless access to a diverse set of applications and services. The heterogeneity in this inter-technology roaming paradigm magnifies the mobility impact on system performance and user perceived service quality, necessitating novel mobility modeling and analysis approaches for performance evaluation. In this paper, we present and compare three mobility models in two-tier integrated heterogeneous wireless systems, the independence model as a naive extension of the traditional cell residence time modeling techniques for homogeneous cellular networks, the basic Coxian model which takes into consideration the correlation between the residence time within different access technologies, and the extended-Coxian model for further improved estimation accuracy. We propose a general stochastic performance analysis framework based on application session models derived from these mobility models, applying it to a 3G-WLAN integrated system as an example. Our numerical and simulation results demonstrate the general superiority of Coxian-based mobility modeling over the independence model. Furthermore, using the proposed modeling and analysis methods, we investigate the impact of different parameters on system performance metrics such as network utilization time, handoff rates, and forced termination probability, for a wide range of user applications.

In this paper, an agent based WLAN/cellular network integrated service model and relevant authentication scheme is proposed. The service model, or solution, does not require cumbersome peer-to-peer roaming agreements to provide seamless user roaming between WLAN hotspots and cellular networks, which are operated by independent wireless network service providers. Security analysis and overhead evaluation are given to demonstrate that the proposed service model and the supporting schemes are secure and effective.

In this paper, a ticket ID system is proposed for service agent based WLAN/cellular network integrated service architecture. The proposed system accelerates the authentication process for the mobile terminal in the visited network, which effectively compensates the additional cost introduced by the service agent. The design of the ticket ID system also considers the user anonymity feature of the integrated service architecture. The performance evaluation demonstrates the ticket ID system effectively reduces the overall overhead in service agent based integrated service architecture.

Abstract The integration of wireless local area network (WLAN) hotspot and the 3G cellular networks is imminently the future mode of public access networks. One of the key elements for the successful integration is vertical handoff between the two heterogeneous networks. Service disruption may occur during the vertical handoff because of the IP layer handoff activities, such as registration, binding update, routing table update, etc. In this paper, the network interface switching and registration process are proposed for the integrated WLAN/cellular network. Two types of fast vertical handoff protocols based on bicasting and non‐bicasting supporting real‐time traffic, such as voice over IP, are modeled. The performance of a bicasting based handoff scheme is analyzed and compared with that of fast handoff without bicasting. Numerical results and the simulation are given to show that packet loss rate can be reduced by the bicasting during handoff scheme without increasing bandwidth on both wireless interfaces. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we study the voice and data service provisioning in an integrated system of cellular and wireless local area networks (WLANs). To maximize the overall resource utilization of the integrated system, complementary quality of service (QoS) support capabilities of the two networks are exploited to serve voice and data traffic. As an essential resource allocation aspect, admission control can be used to properly admit voice and data calls to the overlaying cellular cells and WLANs. In this study, a generalized admission scheme is analyzed to investigate the dependence of resource utilization on admission parameters, which vary with user mobility and traffic variability. By applying an effective QoS evaluation approach, the admission parameters can be determined using a search algorithm.

Call admission control plays an important role in quality of service (QoS) provisioning in the interworking between the cellular network and wireless local area network (WLAN). Within the WLAN coverage, a service request can be admitted into the cellular network or the WLAN. Due to the heterogeneous underlying QoS support of the cellular network and WLANs, the admission of traffic in the WLAN coverage has a significant impact on QoS satisfaction and overall resource utilization, especially when multiple services are considered. A popular admission strategy (referred to as WLAN-first scheme) is to admit the incoming service requests into the WLAN whenever it is available so as to take advantage of the low cost and large bandwidth of the WLAN. In this paper, we investigate the performance of the WLAN-first scheme. It is observed that the overall resource utilization can be maximized when the admission regions for voice and data services in a cell and a WLAN are properly configured.

Next-generation wireless networking is evolving towards a multi-service heterogeneous paradigm that converges different pervasive access technologies and provides a large set of novel revenue generating applications. Hence, system complexity increases due to its embedded heterogeneity, which can not be accounted by the existing modeling and performance evaluation techniques. Consequently, the development of new modeling approaches becomes as a crucial requirement for proper system design and performance evaluation. This paper presents a novel mobility model for a two-tier integrated wireless system using a new modeling approach that accommodates the aforementioned complexity. Additionally, a novel session model is developed as an adapted version of the proposed mobility model. These models use phase-type distributions that are known to approximate any generic probability laws. Using the proposed session model, a novel generic analytical framework is developed to obtain several salient performance metrics such as network utilization times and handoff rates. Simulation and analysis results prove the proposed model validity and demonstrate the accuracy of the novel modeling approach when compared with traditional modeling techniques.

A sub-optimum a-posteriori probability (APP) detector is proposed for iterative joint detection/decoding in a multiple-input multiple-output (MIMO) wireless communication system employing an outer code. The proposed detector searches inside a given sphere in a parallel manner to simultaneously find a list of m-best points based on an additive metric. The metric is formed by combining the channel output and the a-priori information. The parallel structure of the proposed method is suitable for hardware parallelization. The radius of the sphere and the value of m are selected according to the channel condition to reduce the complexity. Numerical results are provided showing a significant reduction in the average complexity (for a similar performance and peak complexity) as compared to the best earlier known method. The proposed scheme is applied for the decoding of the rate 2, 4 times 2 MIMO code employed in the 802.16e standard