The impending threat posed by quantum-capable adversaries necessitates a secure and practical transition of Public Key Infrastructures (PKIs) to support post-quantum cryptography (PQC). This paper addresses the multifaceted challenges of integrating PQC into existing PKI ecosystems by examining novel cryptographic combiners and hybrid certificate designs that can provide quantum-resistant security. We assess the performance, compatibility, and security of these hybrid certificates across standard communication protocols such as TLS, considering variations in root and intermediate certification paths. In addition, we introduce key management procedures that cover signature generation, validation, and lifecycle considerations under both software and hardware constraints. Beyond X.509, alternative trust models and certificate mechanisms are also analyzed for specialized domains, including IoT, firmware signing, and smart cards.

The increasing deployment of generative Artificial Intelligence (AI) systems, particularly large language and multimodal foundation models, presents unprecedented challenges in software engineering. Current development pipelines face issues such as bias propagation, evolving model integration, security vulnerabilities, and lack of explainability, especially in compliance with AI regulations. This paper presents a comprehensive framework for engineering bias-free and auto-evolving generative AI systems, addressing key technical and regulatory challenges through modular software design, hardware-aware optimisation, and human-in-the-loop reinforcement learning. We propose a reference architecture that integrates fairness-aware orchestration, explainability mechanisms, and resilience against prompt and dataset poisoning.

The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology offers a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This article provides a comprehensive classification and categorization of blockchain-based network–sharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmos) is assessed for selected scenarios through extensive evaluations. This article also identifies gaps and opportunities in blockchain–based network sharing solutions and outlines future research directions.

The evolution of Artificial Intelligence (AI) has reached a critical point, where agentic AI systems demonstrate strong capabilities in goal formulation and planning but remain difficult to deploy in real-world settings due to their limited grounding in physical environments. These limitations arise from the challenges of partial observability, actuation uncertainty, and strict resource constraints that characterize the physical world. This survey argues that the Artificial Intelligence of Things (AIoT) provides the necessary foundation to embed agentic intelligence into such environments by enabling continuous interaction between sensing, reasoning, and action. We analyze the synergy between goal-driven agentic AI and distributed AIoT infrastructures and present a unified taxonomy of AIoT-enabled agentic architectures, highlighting trade-offs across centralized, edge-native, and hybrid deployment models. The survey further examines key enabling technologies, including edge intelligence, semantic communication, digital twins, and trust mechanisms, and discusses how they integrate into cognitive control loops. Through representative applications in smart cities, industrial automation, healthcare, and energy systems, we show how this convergence moves automation beyond rule-based behavior toward context-aware autonomy. Finally, we identify open challenges related to long-horizon safety, resource-aware intelligence, and ethical governance, and outline research directions toward robust, trustworthy, and socially embedded autonomous systems.

This paper explores the role of multivariate probabilistic forecasting in improving O-RAN operations, focusing on network sustainability aspects. A comprehensive analysis of its potential benefits and challenges, as well as its integration into the O-RAN architecture are described. The paper first presents an overview of the O-RAN architecture and components, followed by an examination of power consumption models relevant to O-RAN deployments and the challenges associated with traditional deterministic models in resource allocation. We then examine the performance of several state-of-the-art probabilistic multivariate forecasting techniques namely, Gaussian Process Vector Autoregression (GPVAR), Temporal Fusion Transformer (TFT) and non-probabilistic multivariate technique namely, Multivariate Long-Short Term Memory (LSTM) and explain their implementation details and provide their evaluations. The simulation results show the effectiveness of these techniques in predicting Physical Resource Block (PRB) utilization and optimizing resource allocation. In particular, significant energy savings – around 20-30%– are achieved, depending on the percentile of the used probabilistic forecasting techniques. The benefits of considering probabilistic forecasting techniques compared to multi-variate LSTM are also analyzed. Our results emphasize the potential of probabilistic forecasting to improve energy efficiency and sustainability in O-RAN operations.

One of the most critical components of the Internet that an attacker could exploit is the DNS (Domain Name System) protocol and infrastructure. Researchers have been constantly developing methods to detect and defend against the attacks against DNS, specifically DNS flooding attacks. However, most solutions discard packets for defensive approaches, which can cause legitimate packets to be dropped, making them highly dependable on detection strategies. In this paper, we propose MTDNS, a resilient MTD-based approach that employs Moving Target Defense techniques through Software Defined Networking (SDN) switches to redirect traffic to alternate DNS servers that are dynamically created and run under the Network Function Virtualization (NFV) framework. The proposed approach is implemented in a testbed environment by running our DNS servers as separate Virtual Network Functions, NFV Manager, SDN switches, and an SDN Controller. The experimental result shows that the MTDNS approach achieves a much higher success rate in resolving DNS queries and significantly reduces average latency even if there is a DNS flooding attack.

With its disaggregated and open interfaces, the Open Radio Access Network (O-RAN) architecture promises flexibility, innovation, and cost-effectiveness for future wireless networks. However, the increased reliance on software and open interfaces also introduces new security challenges. As the threat of quantum computing looms, the traditional cryptographic algorithms used in O-RAN will become vulnerable to potential attacks. This paper proposes the integration of Post-Quantum Cryptography (PQC) into O-RAN to enhance its security against post-quantum adversaries. We discuss the specific vulnerabilities of current O-RAN security mechanisms to quantum attacks and identify key areas where PQC can be applied, such as key exchange, authentication, and data protection. We present a framework for evaluating the suitability of different PQC algorithms for O-RAN and outline potential challenges and implementation considerations. By proactively integrating PQC into O-RAN, we aim to ensure the long-term security and resilience of this emerging network architecture in the era of quantum computing.

In this paper, we present an innovative federated learning (FL) approach that utilizes Kolmogorov-Arnold Networks (KANs) for classification tasks. By utilizing the adaptive activation capabilities of KANs in a federated framework, we aim to improve classification capabilities while preserving privacy. The study evaluates the performance of federated KANs (F-KANs) compared to traditional federated Multi-Layer Perceptrons (F-MLPs) on classification task. The results show that the F-KANs model significantly outperforms the F-MLP model in terms of accuracy, precision, recall, F1 score and stability, and achieves better performance, paving the way for more efficient and privacy-preserving predictive analytics.

The convergence of connectivity, cloud and compute, the so-called “3 C network”, is a turning point that can promote industrial innovation, digital sovereignty and sustainability. In this paper, we present a comprehensive framework for implementing large-scale telco-edge cloud use cases that can integrate heterogeneous computing and communication resources across device, edge and cloud layers. We explore an architecture that incorporates AI-driven orchestration, lightweight virtualisation and privacy protection, tailored to meet stringent latency, energy and mobility requirements. The proposed approach supports open, multi-supplier and interoperable implementations. By considering governance models, security by design and industrial use cases, this paper provides a foundational blueprint for the transition to a federated, scalable digital infrastructure that can support next-generation applications in key vertical sectors.

The DNS (Domain Name System) protocol has been in use since the early days of the Internet. Although DNS as a de facto networking protocol had no security considerations in its early years, there have been many security enhancements, such as DNSSec (Domain Name System Security Extensions), DoT (DNS over Transport Layer Security), DoH (DNS over HTTPS) and DoQ (DNS over QUIC). With all these security improvements, it is not yet clear what resource-constrained Internet-of-Things (IoT) devices should be used for robustness. In this paper, we investigate different DNS security approaches using an edge DNS resolver implemented as a Virtual Network Function (VNF) to replicate the impact of the protocol from an IoT perspective and compare their performances under different conditions. We present our results for cache-based and non-cached responses and evaluate the corresponding security benefits. Our results and framework can greatly help consumers, manufacturers, and the research community decide and implement their DNS protocols depending on the given dynamic network conditions and enable robust Internet access via DNS for different devices.

The advent of quantum computing threatens the security of classical public-key cryptographic systems, prompting the transition to post-quantum cryptography (PQC). While PQC has been analyzed in theory, its performance in practical wireless communication environments remains underexplored. This paper presents a detailed implementation and performance evaluation of NIST-selected PQC algorithms in user equipment (UE) to UE communications over 5G networks. Using a full 5G emulation stack (Open5GS and UERANSIM) and PQC-enabled TLS 1.3 via BoringSSL and liboqs, we examine key encapsulation mechanisms and digital signature schemes across realistic network conditions. We evaluate performance based on handshake latency, CPU and memory usage, bandwidth, and retransmission rates, under varying cryptographic configurations and client loads. Our findings show that ML-KEM with ML-DSA offers the best efficiency for latency-sensitive applications, while SPHINCS+ and HQC combinations incur higher computational and transmission overheads, making them unsuitable for security-critical but time-sensitive 5G scenarios.

Security mechanisms and Service Level Guarantees (SLGs) often operate in tension within communication systems, where stronger security protocols introduce overhead, processing delays, and encryption-related latency that can hinder the ability to meet predefined SLGs. This challenge is particularly critical in Multi-Access Edge Computing (MEC), where service migration across edge nodes can significantly impact system performance. Ensuring the security of migrating services while maintaining low-latency communication is vital to preserving service continuity and avoiding disruptions. In this paper, we introduce a novel security framework for MEC-enabled gNodeBs that supports secure and seamless service migration. Central to our framework is an adaptive security optimization model that dynamically adjusts the security level of the migration channel based on real-time bandwidth utilization. This approach maintains the continuity of service without compromising the available bandwidth, thereby upholding the required SLGs while minimizing the impact of security-related overhead.

The inherent virtualization characteristics of 5G, 6G, and subsequent generations (xG) facilitate the modularity of components and their interfaces, leading to an increase in the number of stakeholders and necessitating more complex administrative relationships. In this demonstration, we present an open-source Decentralized Application (DApp) integrated with smart contracts deployed on a live Polygon testnet to support trustworthy Federated Learning (FL). FL enables clients to conduct local training and updates, while a central aggregation server processes these inputs for global FL training. We introduce a blockchain-based reputation system within such FL model to facilitate collaborative training of Machine Learning (ML) models. The deployed smart contracts on the Layer 2 Polygon blockchain calculate and store reputation scores on-chain for each FL client based on their contribution. This demonstration presents a visualization of the blockchain network parameters during the execution of blockchain-enabled smart contracts. The DApp offers an interactive interface for client registration, performance parameter submission, and reputation score calculation. We use Alchemy's dashboard for real-time monitoring of blockchain transactions and smart contract interactions. The open-source implementation is publicly available11https://github.com/farhanajaved/bc-fl-demo-l2 and the video of the demo is available at22https://youtu.be/B9J1gxjoKdM.

The virtualization and softwarization of 5G/6G mobile networks have enabled the deployment and orchestration of cloud-native network and application functions. The deployment of these functions is crucial, as the placement of data plane elements (i.e., User Plane Function (UPF)) and vertical services can significantly impact the overall user latency. However, in multi-slice edge scenarios, characterized by users with distinct levels of criticality, the problem of UPF and application placement is becoming increasingly complex due to i) the various costs involved and ii) the limited computational resources at the edge. In this paper, the problem of joint UPF and application placement for a multi-slice user scenario is studied, taking into account multiple cost components that influence the placement decision, including service migration, traffic forwarding, server activation and processing costs. To tackle this problem, we introduce a Joint UPF and Application Reinforcement Learning-based (JUAP-RL) algorithm, which decides the UPF and application deployment location and coordinates the placement stages. Extensive experiments have shown that JUAP-RL demonstrates up to 17% gain in terms of user acceptance ratio and up to 23.4% reduction in provisioning cost compared to baseline schemes.

Resource provisioning plays a crucial role in effective resource management. As we move into the 6G era, technologies such as Open Radio Access Network (O-RAN) offer the opportunity to develop intelligent and interoperable cutting-edge solutions for qualitative management of the latest communication system. Previous works have mostly used single-point forecasts like Long-Short Term Memory (LSTM) for predicting resource requirements, which presents decision-makers with the problem of making informed decisions about resource allocation. On the other hand, probability-based forecasting techniques such as DeepAR, Transformer and Simple-Feed-Forward (SFF) offer new dimensions to the predictions by quantifying their uncertainties. This work shows the comprehensive comparison of single-point and probabilistic estimators and evaluates their effectiveness in predicting the actual number of Physical Resource Blocks (PRBs) needed in the context of O-RAN, especially for multi-tenant use cases. The results show the superiority of the probabilistic model in terms of various evaluation metrics. DeepAR achieves the highest accuracy, outperforming single-point and other probabilistic estimators. Based on these findings, a novel approach named Dynamic Percentile Adjustment Approach (DYNp) algorithm is proposed, which utilizes probabilistic forecasting for adaptive resource allocation. After extensive analysis, the numerical results show that the DYNp algorithm for DeepAR predictions reduces the Service Level Agreement (SLA) violation to 8% and the over-provisioning to 0.509 by dynamic percentile adaption. DYNp approach ensures that resources are allocated by efficiently handling over-and under-provisioning, making it suitable for real-time scenarios in O-RAN environments.

The industrial sector’s digital transformation under Industry 4.0 necessitates robust, scalable, and secure communication frameworks. 6G technology, particularly its nonpublic network (NPN) configurations, offers promising solutions for industrial cyber-physical systems (ICPS). However, security and privacy remain significant challenges, especially concerning identity management in these networks. This paper proposes the integration of blockchain-based Self-Sovereign Identity (SSI) within 6G NPNs as a novel approach to enhance security and data privacy. We explore how this combination can provide decentralized identity management, reduce reliance on centralized authorities, and increase trustworthiness within industrial networks. The paper also examines real-world scenarios and provides a detailed analysis of deployment models, highlighting the potential benefits and challenges of integrating blockchain-based SSI into 6G networks.

This paper deals with the problem of energy consumption minimization in Open RAN cell-free (CF) massive Multiple-Input Multiple-Output (mMIMO) systems under minimum per-user signal-to-noise-plus-interference ratio (SINR) constraints. Considering that several access points (APs) are deployed with multiple antennas, and they jointly serve multiple users on the same time-frequency resources, we design the precoding vectors that minimize the system power consumption, while preserving a minimum SINR for each user. We use a simple, yet representative, power consumption model, which consists of a fixed term that models the power consumption due to activation of the AP and a variable one that depends on the transmitted power. The mentioned problem boils down to a binary-constrained quadratic optimization problem, which is strongly non-convex. In order to solve this problem, we resort to a novel approach, which is based on the penalized convex-concave procedure. The proposed approach can be implemented in an O-RAN cell-free mMIMO system as an xApp in the near-real time RIC (RAN intelligent Controller). Numerical results show the potential of this approach for dealing with joint precoding optimization and AP selection.

This tutorial deals with the integration of data engineering with network management and orchestration in telecommunication networks. It provides participants with a comprehensive insight into the use of data engineering to improve the efficiency and performance of telecommunication systems, especially through the use of Artificial Intelligence (AI)/ Machine Learning (ML) technologies in network infrastructures. Practical applications are also demonstrated using relevant case studies to illustrate the implementation of these concepts.

In this paper, we propose an innovative integration of Quantum Key Distribution (QKD) and Blockchain-based Self-Sovereign Identity (SSI) within the Open RAN (O-RAN) framework for 6G networks to address the critical need for enhanced security and robust identity management. We first present a general architecture that takes a multi-layered approach and is carefully designed to leverage the different capabilities of quantum security and blockchain technology. The architecture ensures seamless and secure operation across different layers of the O-RAN, focusing on the Distributed Identity Management (DIM) and Management & Orchestration layers, and explains the interactions between these layers to improve the security and operational efficiency of the network. We also investigate detailed case studies and applications that demonstrate the practicality and transformative potential of integrating QKD-secured blockchain identity management systems in real-world 6G scenarios. We also address the inherent challenges and limitations of such integration and propose viable solutions to overcome them. Finally, we provide insights into future research and implementation directions and highlight the critical role of quantum-secured blockchain systems in the evolution of telecommunication networks toward a more secure, decentralized, and user-centric paradigm.

This paper presents a next-generation architecture that focuses on the advancement of edge computing and Internet of Things (IoT) technologies in the context of the automotive supply value chain for electric vehicles (EVs). First, we outline the general architecture design, the specific layers and their goals. Based on the principles of the proposed architecture, we also give a use case for improving the traceability, monitoring and efficiency of EV battery transportation using innovative approaches in federated data spaces, AI-powered inference and orchestration of a multi-objective computational continuum. The automotive supply chain use case is presented with potential Key Performance Indicators (KPIs) while emphasizing the potential impact on operational efficiency, cost reduction and sustainability. By addressing the current limitations in distributed intelligence, data governance, and cross-domain interoperability, we emphasize the importance of real-time data processing, dynamic field governance, and energy-efficient machine learning in the context of the electric vehicle supply chain. At the end of the paper, a discussion and comparative analysis highlights the advances over existing technologies and frameworks and identifies future directions to further improve innovations and applications in this area.

The transition to sustainable Open Radio Access Network (O-RAN) architectures brings new challenges for resource management, especially in predicting the utilization of Physical Resource Block (PRB)s. In this paper, we propose a novel approach to characterize the PRB load using probabilistic forecasting techniques. First, we provide background information on the $O-R A N$ architecture and components and emphasize the importance of energy/power consumption models for sustainable implementations. The problem statement highlights the need for accurate PRB load prediction to optimize resource allocation and power efficiency. We then investigate probabilistic forecasting techniques, including Simple-Feed-Forward (SFF), DeepAR, and Transformers, and discuss their likelihood model assumptions. The simulation results show that DeepAR estimators predict the PRBs with less uncertainty and effectively capture the temporal dependencies in the dataset compared to SFF- and Transformer-based models, leading to power savings. Different percentile selections can also increase power savings, but at the cost of over-/under provisioning. At the same time, the performance of the Long-Short Term Memory (LSTM) is shown to be inferior to the probabilistic estimators with respect to all error metrics. Finally, we outline the importance of probabilistic, prediction-based characterization for sustainable O-RAN implementations and highlight avenues for future research.

The increasing demand for mobile data services has led to a need for efficient and cost-effective network sharing solutions. Blockchain technology has emerged as a promising solution for addressing the challenges associated with network sharing, such as interoperability, trust, and accountability. This paper presents a comprehensive classification and categorization of blockchain-based network sharing scenarios, highlighting their advantages and limitations. We have identified seven network sharing scenarios, ranging from centralized network sharing to fully decentralized spectrum sharing. For each scenario, the suitability of some of the selected blockchain architectures, from public, private, sidechain, and hybrid, is evaluated through extensive evaluations. We also identify gaps and opportunities of blockchain-based network sharing solution and present future research directions at the end of paper. Our analysis and results reveal that a single blockchain architecture is not suitable for all network sharing scenarios but careful analysis should be performed when selecting the suitable blockchain network in network sharing.

The H2020 Distributed management of Network Slices in beyond 5G(MonB5G) project aims to provide zero-touch management and orchestration to support network slicing at scale to reduce the management burden on mobile operators by leveraging distribution of operations along with advanced data-driven Artificial Intelligence (AI)-based mechanisms. However, while this approach shows promise and large companies with abundant data and ML expertise are developing powerful MLdriven services, a critical aspect that remains to be analyzed is its business case. The vast majority of potentially valuable ML services, such as predictive maintenance, Quality of Service (QoS) optimization, network security enhancements, remain stuck at the idea or prototype stage. This paper delves into an analysis of how the MonB5G solutions in particular the tuples (Monitoring System (MS), Analytics Engine (AE), Decision Engine (DE) and Actuator (ACT) could be applied within the network management and orchestration market while investigating various business models and value chains. Numerical results based on experimental data have also been performed to evaluate the OpEX (Operational Expenditure) benefits associated with different network management techniques, for centralized and distributed systems.

Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federate Learning (FL), on the other hand, provides a framework to update Machine Learning (ML) models without relying on explicit data exchange between the users. This paper investigates identity management and authentication for vehicle users, which are participating into FL. We propose a new approach to SSI, that is alternative to the conventional blockchain-based SSI, specifically for use in vehicular networks, which focuses on maintaining confidentiality, authenticity, and integrity of vehicle users' identities and data exchanged between the users and the aggregation server during the execution of the FL process. We also provide experimental results for distributed identity management (DIM) operations, which show that the performance of credential operations in the implemented system is generally efficient and the average times are within reasonable limits. However, there is a slight increase in presentation time, offer time, connection establishment time, and credential revocation time as the number of requests increases, indicating a slight degradation in performance for these operations.

This paper demonstrates an implementation of Federated Learning (FL) for streaming applications using cloud-native technology. Compared to a centralized management, by adopting a decentralized approach, the FL method improves convergence time, reduces communication overhead, and increases network energy efficiency. The cloud-native FL architecture presented comprises three sites, each with its own Kubernetes (K8s) cluster. The edge sites run FL Analytical Engines (AEs)/clients for local training and updates, and the central site runs the aggregation server for FL training. Some other relevant workloads deployed at the clusters are the video streaming server, the orchestrator, and monitoring components. As for the RAN, we showcase a multi-gNB setup from which we obtain monitoring data via custom sampling functions. Following the description of the testbed infrastructure and setup, this demonstration presents the real-time visualization of network parameters during FL training, and the enhancement of video streaming through proactive Central Processing Unit (CPU) scaling, made possible by the resource forecasting.

There has been a rapid increase in the number of connected vehicles with a huge amount of data exchange between these vehicles that needs to be communicated, processed and analyzed reliably and efficiently. For secure and decentralized authentication, self-sovereign identity (SSI) management in vehicular networks have attracted attention in recent years. Hierarchical deployment frameworks, on the other hand, can provide secure and efficient knowledge sharing for vehicular networks with heterogeneous and geographically distributed vehicles and infrastructure in 6G networks. In this paper, we explore the joint use of hierarchical federated learning, as a collaborative machine learning framework, and hierarchical SSI management in vehicular networks, highlighting its advantages, limitations. At the end of the paper, we also provide two illustrative use cases.

This poster presents a use case for smart contract-based inter-provider agreements and Service Level Agreement (SLA) monitoring for 6G networks. We use chainlink oracle and InterPlanetary File System (IPFS) to monitor SLA data logs. We also provide experimental evaluations of two approaches: raw data log access in IPFS and chainlink-based log access. To understand the performance and feasibility of the proposed approaches on a public blockchain, the proposed framework is deployed on the Ethereum and Polygon testnets to measure the cost and latency for both approaches. We measure the latency as well as the total cost for comparison purposes. The maximum cost observed for the first approach is ≈ 1.4 USD, and the maximum latency observed with the first approach is ≈4 seconds in the Polygon testnet and 12 ~ 14 seconds in the Ethereum testnet. However, the second approach's latency is 30 ~ 60 seconds.

The expected complexity of shared 5G/6G cloud and network infrastructures requires a functional management and orchestration (MANO) architecture to automatically provision distinct vertical services. These are mapped as generic network services (NSes) specifying their computing and networking needs: Virtual Network Functions (VNFs) and Virtual Links. We focus on a hierarchical MANO implementation where a Resource Layer orchestrator handles the configuration of the physical infrastructure and exposes an abstract view of it to the upper-layer Service Orchestrator. Two different abstraction philosophies are adopted, namely the Infrastructure Abstraction, which pre-calculates the resource allocations before advertising them, and the Connection Service Abstraction, which exposes potential connectivity services without an actual resource allocation. The resulting abstracted infrastructure becomes the input of the Service Orchestrator to make (placement) decisions fulfilling the NS demands. Thus, a novel placement algorithm is devised which, unlike previous works, can handle NSes with arbitrary VNF topology demands. The performance of the MANO functions is experimentally evaluated by dynamically creating/terminating heterogeneous NSes in terms of: NS blocking, blocked bandwidth/VNF ratio, bandwidth occupancy, and VNF distribution per cloud site. From the results, Connection Service Abstraction does attain a more efficient use of resources and in general, performs better than the Infrastructure Abstraction approach.

Slices are deployed to continuously fulfill requirements from vertical industries. Thus, the management of service level agreements (SLAs) is fundamental. Composite network services (NSs), made up of multiple nested NSs, can be exploited to build slices. Generally, slices may span multiple domains and the different nested NSs may be deployed by different providers, in what is referred to as federated NS. Previous work focused on the architectural design and workflows for the functional deployment of federated services without considering the continuous SLA management. Herein, we cover this gap by presenting a complete workflow, the required interface extensions, and the operation profiling over an experimental testbed to automatically scale nested NSs deployed in a federated domain. Evaluation results show that such scale out/in operations can be performed in tens of seconds (39/31 s, respectively), where the allocation/release of underlying resources account for most of the profiled scaling time (more than 80%).

Heterogeneity is one relevant characteristic of next generation mobile networks. This term embraces not only different kind of infrastructure resources or transmission technologies (e.g., networking vs computing, wireless vs optical), but it also applies to different ways of implementing the network functions (NFs) composing the network services (NSs). This allows the definition of hybrid NSs combining different kind of components, such as physical, virtual, and cloud-native NFs. This demonstration shows the enhancements introduced in the 5Growth platform to manage physical NFs, hence increasing the capabilities of this platform to cope with more heterogeneous hybrid NSs in multi-site scenarios. In particular, we show the deployment of an NS constituted by physical, virtual, and cloud-native NFs implementing an end-to-end service covering access, mobile core and application functionalities.

Blockchain technologies are gradually being deployed in a variety of industries, including telecommunications. In this paper, due to the strict governance of telecommunication infrastructure, we propose a permissioned distributed ledger (PDL)-based blockchain supported architecture for a network management and orchestration platform. The work focuses on creating a trusted environment for both Cloud Service Providers (CSPs) and Mobile Network Operators (MNOs) for managing the lifecycle of network services (e.g., instantiation, scaling, termination, etc.) in a multi-cloud environment. We also validate our proposed approach with an experimental scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, time to write, and transactions per second) of different service orchestrator (SO)-related instantiation metrics. Our evaluation results show that the values for the service instantiation time and the corresponding BCN metrics can be completely different, suggesting that some logs arrive very quickly and generate a high transaction load, while others take longer and generate a low number of transactions. As a solution, at the end of the paper, we also provide some recommendations for appropriate optimizations during transfer of SO-related logs to BCNs and some observed challenges.

In 5G networks, heterogeneous vertical services with different requirements are rolled out over a common multi-technology infrastructure. A resource orchestrator entity automatically coordinates the operations and functions to support the service's lifecycle management (i.e., creation, update and termination). Moreover, it is essential that service needs are continuously assured even if transport network anomalies (e.g., link failures) occur. Herein, we present an implemented resource orchestrator architecture integrating monitoring capabilities to attain closed-loop operations for: i) gathering monitored information; ii) detecting transport network anomalies; and iii) triggering the required action (e.g., restoration) to keep the service continuity. When a link failure happens, several transport services may be disrupted requiring to be immediately restored. To this end, we propose a novel on-line restoration algorithm called as Global Concurrent Optimization (GCO). The GCO algorithm aims at attaining an enhanced restorability performance compared to a more traditional restoration algorithm (referred to as 1-by-1). Both algorithms are experimentally compared on top of the deployed resource orchestrator architecture. The evaluation is done upon both dynamic service arrival/departure and link failure generation using different performance metrics: the average restorability, the average network resource utilization, and the restoration computational time.

Next generation mobile networks base on the auto- mated, flexible, and dynamic orchestration of virtualised network services (NSs). These NSs are made up of Virtual Network Functions, which have hitherto mostly been implemented by means of virtual machines. The current trend is to include the use of containers, the so-called Cloud-native Network Functions, which may fit better the need of NS deployments embracing edge infrastructures having constrained resources. This ends up in the definition of NSs mixing both kinds of network functions (NFs) satisfying the needs of network operators and vertical industries and the characteristics of available infrastructures. We refer to the use of both kinds of NFs in a single NS as Hybrid NS. This demonstration presents the extensions done in the 5Growth management and orchestration platform to perform the deployment of such kind of NSs in a multi-site infrastructure, including the dynamic interconnection of the deployed NFs according to their nature.

Measuring several relevant metrics related to Network Function Virtualization (NFV) service lifecycle management in real time brings an enhanced monitoring of the operation of the network service orchestrator (SO) and the NFV infrastructure. In this demonstration, we integrate a complete data engineering pipeline in an operational management and orchestration stack (that of EU 5Growth project) to analyze lifecycle management metrics in real-time, in this case the network service instantiation time related metrics. In our demonstration, a data connection module instance continuously monitors the NFV SO log files and sends the log changes to the data ingestion layer, where log files are temporarily stored to be fetched by Apache Spark jobs. After utilizing Spark jobs to cleanse the log files and to obtain the service instantiation times, the metrics are sent back to the data ingestion layer to be transferred to the Elasticsearch (ELK) stack for data indexing and visualization purposes. Furthermore, the statistical information of network service instantiation (in total and its components) of studied metrics inside the network can also be profiled via a separate data analysis layer connected to the ELK stack.

It has become commonplace to observe frequent multiple disk failures in big data centers in which thousands of drives operate simultaneously. Disks are typically protected by replication or erasure coding to guarantee a predetermined reliability. However, in order to optimize data protection, real life disk failure trends need to be modeled appropriately. The classical approach to modeling is to estimate the probability density function of failures using nonparametric estimation techniques such as kernel density estimation (KDE). However, these techniques are suboptimal in the absence of the true underlying density function. Moreover, insufficient data may lead to overfitting. In this article, we propose to use a set of transformations to the collected failure data for almost perfect regression in the transform domain. Then, by inverse transformation, we analytically estimated the failure density through the efficient computation of moment generating functions, and hence, the density functions. Moreover, we developed a visualization platform to extract useful statistical information such as model-based mean time to failure. Our results indicate that for other heavy-tailed data, the complex Gaussian hypergeometric distribution and classical KDE approach can perform best if the overfitting problem can be avoided and the complexity burden is overtaken. On the other hand, we show that the failure distribution exhibits less complex Argus-like distribution after performing the Box-Cox transformation up to appropriate scaling and shifting operations.

In this paper, we propose a network coding (NC) enabled transmission strategy in the User Plane (UP) of mobile backhaul for networks operators. In the proposed method, NC provides robustness against the transport network failures, so that there will not be any more processing for re-transmission by the User Equipment (UE) in comparison to traditional approaches where re-transmissions are performed by UE applications. Our simulation results indicate that an average 1% loss ratio in the backhaul link creates 59.44% additional total transmission time compared to normal standard GPRS Tunneling Protocol - User Plane (GTP-U) transmission. On the other hand, applying NC at 1% and 2% rates reduces this amount to 52.99% and 56.26% respectively, which is also better than the total transmission time performance of some previously studied dynamic replication schemes as keeping bandwidth utilization at low ratios. Moreover, we also observe a trade-off between total transmission time and NC rate related to expected packet loss ratio such that minimum total transmission time is obtained when NC rate is equal to expected packet loss rate.

This paper investigates the recent-advances in intent-based technologies while concentrating on aspects related to network management and orchestration. We provide a comprehensive analysis of the standardization activities as well as platforms related to intent-based networking. At the end of the paper, we also provide some insights into challenges related to future development process on the intent-based networking design. Our survey results indicate that intent-based networking concept has not evolved further since 2015 in terms of framework, platform and tool developments. However, recent rapid advances in Natural Language Understanding (NLU) propelled by IT and cloud giants (Google, Amazon, Facebook) are expected to increase its adaption into networking and telecommunication world in the forthcoming years.

5G networks are characterized by strict latency, iitter and reliability requirements. On the other hand, a lossy backhaul scenario where high packet losses over the backhaul transmission medium can render backhaul links unavailable for service consumption. This necessities a multi-transmission scheme over backhaul using the existing resources of the operators such as base stations and core networks. This can enhance reliability for 5G backhaul networks to meet the requirements of new 5G services that have strict end-to-end latency requirements. In this paper, we propose a GPRS Tunnelling Protocol User Plane (GTP-U) Protocol Data Unit (PDU) multi-transmission scheme that achieves improved Transmission Control Protocol (TCP) latency and packet loss probability at the expense of higher resource (bandwidth) utilization compared to existing traditional systems. The scheme can use several measuring mechanisms such as continuous Two-Way Active Measurement Protocol (TWAMP) testing between core network and base stations, GTP-U user plane echo requests/replies or backhaul quality testing during evolved Packet System Radio Access Bearer (eRAB) establishment phase. This mechanism can also be applied to X2 interface between base stations to reduce the service latency and improve the backhaul link performance. Our simulation results using ns-3 indicate that by applying the proposed strategy in backhaul systems, the application level packet loss and TCP delay can be improved at the expense of the bandwidth improvement at certain drop or loss levels.

Mobile Network Operators (MNOs) are on the path towards designing flexible architectures. The main goal is to improve the end-to-end system performance responses and at the same time generate more revenue streams thanks to easier and faster service deployments. For this reason, traditional mobile network infrastructure needs to evolve towards a data center oriented infrastructure where services can be initiated on demand. Software Defined Networks (SDN) in combination with Service Function Chaining (SFC) have great potentials for MNOs in terms of flexible and scalable rich service deployment. This paper investigates the setting up an SDN-based SFC experimental implementation that is based on application of SDN concept for mobile core networks. During our experiments, we build a SDN-based Gi-LAN mobile core architecture solution using SFC and evaluate its performance compared to traditional mobile core network service. Our results validate that by dynamically adjusting defined virtual network functions as well as directions and policies of flows with Open Network Operating System (ONOS) controller, SDN-based SFC mechanism is capable of forwarding traffic data flows into each compute nodes having different virtual network functionality.

Factories of Future (FoF) is an emerging vertical sector towards 5G network evolution. Fine-grained monitoring the network performance of FoF environment can help to extract insight on the quality-of-service (QoS) of a given industrial service provided by next generation cellular technologies. However, one of the main solutins that mobile network operators (MNOs) are investing today is on data evaluation tools that can be integrated withing their network infrastructure. In this paper using the open-source analytics tools, we analyze the industrial network traffic characteristics behaviour of an operational cellular network of a MNO. Our results finds out the relationship between various key performance indicators (KPIs) and extract insights on the performance and operational aspects of a factory environment using the cellular networks data with ElasticSearch stack's data analytics capabilities.

Latency and bandwidth limitations of mobile network operators (MNOs)' backhaul networks can have negative impacts on 5G service experience. Data compression methods, on the other hand, could allow data transmission using less bandwidth and buffer sizes that can provide solutions to backhaul latency problems of MNOs. Together with the combination of a quality measurement method in transmission network as well as softwarization components that allow data modification at both core network and base station (BS), an intelligent and dynamic solution can be created to improve the performance of 5G networks. In this demo work, we demonstrate how 5G BSs could be compatible with Internet Protocol (IP) payload compression using software components which do not require any changes to the existing backhaul network infrastructure. In our demonstration, a Two-Way Active Measurement Protocol (TWAMP) server continuously monitors transmission network performance and initiates compression/decompression operations on both core network and 5G BS when bandwidth and latency problems occur. After identifying that the problem has disappeared, the compression process ends. Furthermore, the amount of instant traffic inside the network and the gains achieved within the proposed system can also monitored via dashboards.

Providing high quality and uninterrupted network service is becoming crucial for service providers. In this paper, we present an approach to quantify and indicate service quality based on the topology state transitions from the perspective of network service provider. Building our model as a Finite State Machine (FSM), we show novel application of machine learning (ML) classification algorithms to classify appropriate states for undefined input alphabets in FSM. In other words, we implement ML algorithms to extract both service states and possible root causes of service degradation only from measured certain Key Performance Indicator (KPI) values that are observed directly through network elements. We have implemented our network topology state classification approach using the dataset obtained in Graphical Network Simulator 3 (GNS3) simulation environment, and performed measurements to evaluate classification accuracy of different algorithms. Additionally, we have identified priority of relevant KPIs impacting the service quality. Our results indicate that network topology state changes can be classified up to 88% accuracy and F1 scores using ensemble learning methods such as Gradient Boosting Classifiers.

In this paper, we develop a framework to estimate network flow length distributions in terms of the number of packets. We model the network flow length data as a three-way array with day-of-week, hour-of-day, and flow length as entities where we observe a count. In a high-speed network, only a sampled version of such an array can be observed and reconstructing the true flow statistics from fewer observations becomes a computational problem. We formulate the sampling process as matrix multiplication so that any sampling method can be used in our framework as long as its sampling probabilities are written in matrix form. We demonstrate our framework on a high-volume real-world data set collected from a mobile network provider with a random packet sampling and a flow-based packet sampling methods. We show that modeling the network data as a tensor improves estimations of the true flow length histogram in both sampling methods.

Mobile Network Operators (MNOs) are eager to learn more about complaint behaviour of their subscribers. In this demo, we study topic modeling approach for extracting relevant problems experienced by subscribers of MNOs in Turkey and visualize the topic distributions using LDAvis data analytics tool. For building topic models using Latent Dirchlet Allocation (LDA), we have built customer complaint text dataset of subscriber complaints for each MNOs from Turkey's largest customer complaint website. The proposed analysis tool can be used as customer complaint analysis service by MNOs in Turkey to gain more insight. We have also validated our generated topic model using another dataset obtained from Turkey's largest online community website. Our results indicate similar and dissimilar topics of complaints as well as some of the distinctive problems of MNOs in Turkey based on their subscriber's experiences and feedback.

The exponential increase of mobile data traffic pushes mobile operators to seek more efficient heterogeneous communication techniques. In this study, multi-user extension methods for multiple attribute decision making algorithms for network-assisted data offloading in heterogeneous wireless networks are developed and performance evaluations are performed in the presence of Device-to-Device (D2D) connections. Evaluations are carried out using simulations to point out the metrics and factors influencing data offloading in heterogeneous networks. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions. Additionally, up to 67% increase in user satisfaction can be achieved when D2D density is kept 68% under a heavy load scenario. The simulation results also indicate the existence of optimal D2D densities in heterogeneous networks depending on the total number of users and available network capacity.

Mobile Network Operators (MNOs) are investigating new business opportunities and planning to launch new revenue generating services in the context of 5G deployments. In order to achieve this, MNOs are willing to open up new services and applications to end users as well as vertical industries such as automotive, health, entertainment in order to obtain new revenues. Thanks to recent advances in virtualization technologies, network slicing is one promising approach that can be utilized by MNOs for generating new services and applications that are tailored to demands of end users and vertical industries. Although there have been many works on network slicing applied to mobile network infrastructure, data analytic approach using real world network dataset that investigates how many network slices are needed for each MNOs is still missing. In this paper, we propose a methodology to extract the number of network slices specific to each MNOs via applying clustering on Key Parameter Indicators (KPIs) of major telecommunication operators. Our large scale data analysis results indicate that the number of network slices may differ depending on network quality of MNOs. For some MNOs up to five different network slices can be obtained which correspond to launching different services and applications on each selected network slice.

Mobile Network Operators (MNOs) are evolving towards becoming data-driven, while delivering capacity to collect and analyze data. This can help in enhancing user experiences while empowering the operation workforce and building new business models. Mobile traffic demands of users can give insights to MNOs to plan, decide and act depending on network conditions. In this paper, we investigate the behaviour of Istanbul residents using the cellular network traffic activity over spatial and temporal dimensions via exploratory factor analysis (EFA) using a major MNO's cellular network traffic data in Turkey. Our results reveal various time and spatial patterns for Istanbul residents such as morning and evening commuting factors, business and residential factors as well as nightlife and weekend afternoon factors as the most prominent cultural behaviour. The analysis results also demonstrate interesting findings such as tunnels and transportation paths selected by Istanbul residents may differ during morning rush work hour compared to evening rush after-work hour.

Mobile OTT video services are consumed by users of Mobile Network Operators (MNOs). Hence, MNOs are willing to understand the Quality of Experience (QoE) that is perceived by mobile end-users. For this reason, Customer Experience Management (CEM) tools are traditionally utilized inside mobile network infrastructure. In this paper, we propose a new quality-of-experience (QoE) measurement methodology using only call detail records (CDRs) of MNO. The proposed method is used to measure the mobile customer experience of OTT video services offered to MNO's users with Adaptive Bitrate (ABR) based protocols. Through evaluations on real network sites in Turkey, we show that our proposed methodologies' measured average throughput (a Key Quality Indicator (KQI)) exhibits similar behaviour with observed average throughput (a Key Performance Indicator (KPI)) of base station over an observation period of one-month.

In recent years, service providers, such as mobile operators providing wireless services, collected location data in enormous extent with the increase of the usages of mobile phones. Vertical businesses, such as banks, may want to use this location information for their own scenarios. However, service providers cannot directly provide these private data to the vertical businesses because of the privacy and legal issues. In this demo, we show how privacy preserving solutions can be utilized using such location-based queries without revealing each organization's sensitive data. In our demonstration, we used partially homomorphic cryptosystem in our protocols and showed practicality and feasibility of our proposed solution.

Radio Access Network (RAN) sharing and Cloud-RAN (C-RAN) are two major candidates for next generation mobile networks. RAN sharing ensures efficient usage of network equipments among multiple mobile network operators (MNOs) and C-RAN benefits installation, evolution, management and performance improvements. Similarly, Software-Defined Networking (SDN) concept provides many features including hardware abstraction, programmable networking and centralized policy control. One of the main benefits that can be used along with these features is virtualization of RAN and core/backhaul networks to ensure network sharing among MNOs and efficient usage of the network equipments. In this work, we propose SDN-based C-RAN architecture including RAN controller integrated to virtualization controller that is crucial for core/backhaul network sharing towards next generation cellular network. In proposed architecture, eNodeB functions are shifted to the top of C-RAN controller as a consequence of separating baseband units from remote radio heads (RRHs). We further provide RRH assignment based load balancing algorithm that is executed at the top of the controller and allows sharing of RRHs among multiple MNOs. We evaluate its performance using traditional RRH distribution as benchmark and simulation results reveal that our proposed algorithm outperforms traditional distribution in terms of average number of connected user equipments to RRHs.

In this paper, we propose a novel cellular network architecture including network virtualization controller for mobile core and backhaul sharing. Software-Defined Networking (SDN) based network virtualization is applied into Evolved Packet System (EPS) architecture of Long Term Evolution (LTE) networks. After virtualization of all evolved Node-Bs (eNodeBs) associated with different Mobile Operators (MOs) as a consequence of mobile core and backhaul sharing, the performances of eNodeB assignment mechanisms with the use of quality-of-service (QoS)-aware and QoS-unaware scheduling algorithms are investigated and compared with currently deployed static eNodeB distributions through Monte-Carlo simulations. Jain's fairness index, Shannon capacity and satisfied-MO-ratio are considered as the key performance indicators (KPIs). The results reveal that our proposed architecture outperforms the currently deployed network architecture as depending on proper scheduler selection.

In order to cope with the challenges of increasing user bandwidth demands as well as create new revenues by offering innovative services and applications, Mobile Network Operators (MNOs) are willing to increase their networks' capabilities by making it more flexible, programmable and agile. MNOs are also seeking new technologies to benefit from recent advances in cloud for rapid deployments and elastically scaling services that cloud providers are mostly benefiting today. On one hand, Software-Defined Networking (SDN) concept can be helpful for enabling network infrastructure sharing/slicing and elasticity for “softwarization” of network elements. On the other hand, machine learning and game-theoretical concepts can also be utilized to address network management and orchestration needs of services and applications and improve network infrastructure's operational needs. In that regard, joint utilization of machine learning, game theoretical approaches and SDN concepts for network slicing can be beneficial to MNOs as well as infrastructure providers. In this paper, we utilize regret-matching based learning approach for efficient Radio Remote Head (RRH) assignments among MNOs in software-defined based cloud radio access network (C-RAN). Using game-theoretical approach, we demonstrate convergence of RRH allocations to mixed strategy Nash equilibrium and present significant performance improvements compared to traditional assignment approach.

Radio Access Network (RAN) sharing that ensures efficient usage of network equipments among multiple mobile network operators (MNOs) and Cloud-RAN (C-RAN) benefiting installation, evolution, management and performance improvements are two major candidates towards the next generation mobile networks. In addition to them, Software-Defined Networking (SDN) paradigm provides many features including hardware abstraction, programmable networking and centralized policy control. One of the main benefits that can be used along with these features is dynamic virtualization of RAN in order to ensure network sharing among multiple MNOs and efficient usage of the RAN equipments such as remote radio heads (RRHs). In this work, we provide a use case study of SDN-based shared RAN infrastructures for channel-aware remote radio head (RRH) assignment to multiple MNOs benefiting from global view of the network in order to provide better received signal strength levels. We propose two assignment mechanisms and compare the performance of them with traditional RRH distribution. The Monte-Carlo simulation results reveal the proposed methods' performance advantages.

In this demo, we demonstrate the integration of radio access network (RAN)/Software-Defined Networking (SDN) controller with a connectivity management platform designed for mobile wireless networks. This is an architecture designed throughout the EU Celtic-Plus project SIGMONA1. OpenDaylight based RAN/SDN controller and the application server are capable of collecting infrastructure and client related parameters from OpenFlow enabled switches and Android based phones respectively. The decision on the best access network selection is computed at the application server using a Multiple Attribute Decision Making (MADM) algorithm and instructed back to Android-based mobile client for execution of access network selection.

The immense development in mobile devices and the proliferation of ubiquitous cloud-based services have led to a surge in the utilization of remote data centers. These dramatic advances in wireless based services induce a huge traffic flow within the network. However, consequent high load on the network devices may lead to faults and thus an adverse decrease in the user experience. On the other hand, balancing the aggregated multi-flow traffic on ports of network switches is a challenging issue. This work presents a load balancing algorithm implemented in OpenDaylight controller for managing the load on the switch ports by utilizing Software-Defined Networking (SDN) architecture. The switch acts a connector which aggregates traffic from wireless access nodes. An experiment design is constructed with hardware-based OpenFlow-enabled switch and connected wireless hosts. According to the load on the ports, the developed scheme dynamically load-balances the incoming traffic towards the backhaul connection. It uses OpenFlow for gathering statistics and flow rule installation on the switch in an adaptive manner.

Telecom Networks produce huge amount of daily alarm logs. These alarms usually arrive from different regions and network equipments of mobile operators at different times. In a typical network operator, Network Operations Centers (NOCs) constantly monitor those alarms in a central location and try to fix issues raised by intelligent warning systems by performing a trouble ticketing based management system. In order to automate rule findings, different sequential rule mining algorithms can be exploited. However, the number of sequential rules and alarm correlations that can be generated by using these algorithms can overwhelm the NOC administrators since some of those rules are neither utilized nor reduced appropriately by the non-customized sequential rule mining algorithms. Therefore, additional efficient and intelligent rule identification techniques need to be developed depending on the characteristic of the data. In this paper, two new metrics that is inspired from document classification approaches are proposed in order to increase the accuracy of the sequential alarm rules. This approach utilizes new definition of identifying transactions as alarm features and clustering the alarms by their occurrences in built transactions. Experimental evaluations demonstrate that up to 61% accuracy improvements can be achieved through utilizing the proposed appropriate metrics compared to a sequential rule mining algorithm.

Integration of offloading technologies into mobile network operator's infrastructures that provide heterogeneous access services is a challenging task for mobile operators. A connectivity management platform is a key element for heterogeneous mobile network operators in order to enable optimal offloading. In this study, development and integration of a connectivity management platform that uses a novel multiple attribute decision making algorithms for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. The proposed platform collects several terminal and network level attributes via infrastructure and client Application Programming Interfaces (APIs) and decides the best network access technology to connect for requested users. Through experimentation, we provide details on the platform integration with service provider's network and sensitivity analysis of the multiple attribute decision making algorithm.

Several thousands of alarm events are arriving into the network operation center of today's mobile service providers per day. On the other hand, managing this huge amount of information is getting much more difficult as the size of the network infrastructure gets larger. The demo presented in this paper proposes a novel scalable architecture for alarm/event data analyses where alarms are captured, processed and visualized with appropriate notifications in real time. Alarm rules are pre-registered offline into the system through root cause analysis module. The proposed system includes novel methods for streaming data analytics with Complex Event Processing (CEP) using platforms such as Apache Kafka and Storm. The presented proof-of-concept demo helps data center and network operators to monitor continuous alarm streams in order to predict the upcoming alarms and potential network failures.

Monitoring network statistics is important for the maintenance and infrastructure planning for the network service providers. In this demonstration, we will showcase an initial analysis of a general purpose network monitoring platform for high speed mobile networks. The developed platform is the basis for performing complex real-time analysis such as application usage behaviour, security analysis, infrastructure planning. We have used the platform for real-time flow size and length monitoring with packet sampling.

In this paper, we focus on the radio resource management problem for the Orthogonal Frequency Division Multiple Access (OFDMA)-based mobile relay-enhanced heterogenous cellular networks. We combine mobile relaying and data offloading scenarios to increase the capacity of the system and cope with the mobile data traffic volume that is increased by the number of wireless subscribers accessing mobile data services. We propose network interface selection, relay selection and resource allocation solutions for this scenario and show effect of relaying and data offloading on the system capacity and on the ratio of satisfied users.

We present a distributed joint power control and transmit beamforming selection scheme for multiple antenna wireless ad hoc networks. Under the total network power minimization criterion, a joint iterative beamforming and power control algorithm is proposed to reduce mutual interference at each node. Total network transmit power is minimized while ensuring a constant received signal-to-interference and noise (SINR) at each receiver. First, transmit beamformers are selected from a predefined codebook to minimize the total power in a cooperative fashion. We also study the interference impaired network as a noncooperative beamforming game. By selecting transmit beamformers judiciously and performing power control, convergence of noncooperative beamformer games are guaranteed throughout the iterations. The noncooperative distributed algorithm is compared with centralized and cooperative solutions through simulation results.

In this paper, we present the code design structure of unitary and non-unitary differential space-frequency group codes (DSFCs) for multiple-input multiple-output (MIMO)- orthogonal frequency division multiplexing (OFDM) systems based on optimal coherent space-frequency (SF) group codes. Under the assumption that the transmitter knows only the delay profile of the channel, a differential transmission rule incorporated with subcarrier allocation is obtained that allows data to be sent without channel estimates at the transmitter or receiver. The differential encoding/decoding is performed in the frequency domain within each single OFDM symbol. Therefore, proposed DSFCs can be successfully decoded even for a rapidly fading channel which may change independently from one OFDM symbol to another. Unitary and nonunitary DSFCs, that are constructed based on design criteria, are compared with recently published techniques in the literature and shown to inherit coding gain of optimal coherent SF codes. Due to design structure and energy constraints, our nonunitary DSFCs do not blow up or diminish during the differential encoding.