The Open Radio Access Network (O-RAN) architecture provides flexibility in the deployment of RAN, but introduces significant security vulnerabilities due to its open interfaces and disaggregated nature, expanding the attack surface for threats like DoS, DDoS, MiTM, unauthorized access, etc. Current security solutions often lack comprehensive protection. This paper proposes an O-RAN-SDP framework integrating Software Defined Perimeter (SDP) principles to establish security protection in the O-RAN with Zero-Trust deployment. By deploying an SDP Controller (SDPC) as an xApp within the NearRT RIC and embedding SDP agents in O-RAN components, our approach utilizes the Single Packet Authorization (SPA) and secure tunneling in the O-RAN-SDP architecture. This provides robust authentication, authorization, and attack surface reduction, inherently mitigating DoS/DDoS, MiTM, and zero-day vulnerability attacks across the O-RAN ecosystem.

Network slicing in fifth generation (5G) networks has become a key enabler for tailored resource allocation to meet diverse use cases. Operators can instantiate isolated 5G slices to meet various service-level agreements (SLAs) such as quality-of-service (QoS) profiles and latency constraints. Despite being isolated, slices introduce new attack surfaces in the 5G core. Traditional anomaly detection (AD) methods are supervised, centralized, and do not scale out across slices. Machine Learning (ML) models for AD require frequent retraining to account for drifts in traffic patterns. Centralized methods increase the risk of leaking user data, as network flow records containing Personally Identifiable Information (PII) leave the slice. To address these gaps, we propose an online, unsupervised federated learning (FL) framework that enables slices to continuously train a local ML model on unlabeled network flow data from core network functions (NFs) in their own slice and aggregate parameter updates in a privacy-preserving manner to form a robust global model. It integrates with the Network Data Analytics Function (NWDAF), which collects data from NFs and provides FL capabilities. This cross-slice knowledge transfer accelerates AD while preventing leakage of PII by keeping data within slices. With the help of FL, our proposed framework detects previously unseen anomalous traffic patterns in a particular slice in real time, achieving a macro F1 score of 93.83% on a test dataset containing new anomalous traffic flows. The results confirm that cross-slice online FL keeps AD robust, responsive, and privacy-compliant in sliced 5G core networks. To the best of our knowledge, this is the first FL-based AD framework that detects both new and recurring anomalous traffic patterns in heterogeneous 5G slices using unlabeled data in real time.

The deployment of 5G network slicing introduces unique capabilities, enabling multiple virtual networks to coexist on a shared physical infrastructure. However, this flexibility also opens up new security vulnerabilities, particularly when Network Functions are shared among slices. A significant challenge arises when a compromised or malicious NF within one slice impersonates another slice, leading to potential data leakage and unauthorized service access. This paper identifies a critical vulnerability in the current 5G network, where insufficient slice validation can be exploited to perform service theft and data breaches. We propose a novel solution that integrates the Network Slice Selection Function (NSSF) into the authorization workflow to perform precise slice identity verification. By leveraging the NSSF’s centralized database of slice information and access policies, the proposed mechanism introduces a two-step verification process that strengthens the security of token-based service requests. This approach mitigates the risk of cross-slice impersonation while maintaining compatibility with existing 5G architecture. The paper presents an in-depth analysis of the problem, details the proposed solution, and evaluates its effectiveness through security analysis and performance metrics. Results demonstrate that the NSSF-based validation significantly reduces fraud risks in multi-slice environments, enhancing the reliability of 5G network slicing.

360-degree video streaming is an emerging technology that immerses viewers in the dynamic environment of a video. However, streaming such videos over wireless networks faces challenges due to their substantial data demands. This research introduces an innovative approach aimed at mitigating the data requirements of 360-degree video streaming. Our proposed method leverages video inpainting to reconstruct select segments of the 360-degree video. To ensure a seamless integration of video inpainting without compromising the user's quality of experience, we devise a mechanism to identify tiles suitable for reconstruction with minimal impact on video quality. Empirical findings demonstrate that the proposed solution markedly reduces the data demand of 360-degree video streaming while maintaining satisfactory video quality.

Providing resilient and sustained service is quite challenging in the Service Based Architecture of distributed 5G Core (5GC) as multiple Network Functions (NFs) are involved to help serve the various User Service Requests (USRs) arriving in the control plane. In this regard, the continuous monitoring of individual NFs in a Closed Loop Automation (CLA) is a need of hour to keep up the robust and resilient functioning of the 5GC overall. Any unforeseen situations like the sudden failure, overload, or congestion of the NFs of the 5GC can drop the critical USRs unnecessarily. This paper proposes the proactive monitoring of the NFs of the 5GC in the control plane and utilizes it to intelligently schedule and serve the frequently arriving USRs and prioritize the critical slice service requests. Specifically, the Ford-Fulkerson algorithm popularly known as the Max-Flow problem solver is leveraged to proactively assess the NFs' performance and availability and use it effectively to serve critical service requests arriving during unexpected situations of failure and overloads. Our experiments based on the 3GPP-compliant 5G testbed show that, with the proposed solution, the native 5GC can serve 20% more predominant USRs, and the slice-supportive 5GC can serve 33% more massive Machine Type Communications (mMTC) slice USRs, and 47% more ultra Reliable Low Latency Communications (uRLLC) slice USRs while handling their respective peak traffic.

With multiple Network Functions (NFs) participating, the current Service Based Architecture (SBA) of 5G Core (5GC) is crafted stateless, scalable, robust, and reliable to cater to various service requirements. While Beyond 5G (BSG) and 6G networks aim to achieve higher resilience, studying the Consistency (C), Availability (A), and Partition (P) tolerance (CAP) theorem becomes a critical need for SBA-based SGC to evaluate its impact. In this paper, we evaluate the CAP theorem on the 3GPP-compliant non-roaming stateless SGC and show the trade-off between Consistency and Partition tolerance (CP) mode and Availability and Partition tolerance (AP) mode of functioning. Experiments prove that the SGC in CP mode serves more PDU session requests while remaining less available as compared to the SGC in AP mode.

The continuously growing demand for availing services on 5G has resulted in a massive amount of user data being stored and tracked at the 5G Core (5GC). Also, the amount of traffic arriving in the control plane demands a scalable yet highly available design of the datastore in the 5GC. This paper introduces a novel approach addressing these challenges by integrating the database sharding and replica set techniques into the Unified Data Management (UDM) and Unstructured Data Storage Function (UDSF) of 5GC. Sharding is employed to efficiently distribute and manage extensive user data, ensuring not only scalability but also resilience, and High Availability (HA). Specifically, we propose range-based and slice-specific sharding techniques, each tailored to meet specific service and slice requirements. Through a comparative analysis of these sharding methods on the 3GPP-compliant 5G testbed, the paper illuminates their distinct advantages and limitations. While the range-based sharding shows a significant improvement in UE registration completion time of approximately 29.13%, slicespecific sharding demonstrates an even higher average benefit of about 31.46%. We hope that the insights presented in this paper are quite promising in enhancing 5G infrastructure to facilitate a variety of 5G services and elevated end-user experience.

Network Functions Virtualization (NFV) offers enhanced programmability and cost-efficiency by replacing hardware middleboxes with versatile Virtual Network Functions (VNFs) on commodity servers. Key challenges in NFV orchestration involve economically placing VNFs and chaining them to meet the requirements of the Service Function Chains (SFCs). Additionally, depending on the VNF functionality, some VNFs may be shared or may not be shared among SFCs. Many existing studies have overlooked the potential benefits of placing shareable VNFs on high centrality nodes, which can boost re-usability and increase SFC acceptance ratios. Vertical scaling of VNF resources, rather than over-allocating for shareable VNF instances, can address resource under-utilization for future SFC admissions. This paper introduces a mechanism for SFC placement, considering both VNF reuse and vertical scalability when deploying SFC requests in the network. The proposed mechanism demonstrates significant improvements in SFC acceptance ratios compared to two baseline schemes, SPH-BC and SPH-VS, as well as a state-of-the-art scheme, SMA-VAA.

Being scalable, distributed by nature, and stateless by design, the Service Based Architecture (SBA) of 5G Core (5GC) has become the popular approach today, to provide reliable services to 5G users. However, the well-known Consistency (C), Availability (A), and Partition (P) tolerance (CAP) theorem places a limit on 5GC, as it typically does for any distributed system to choose between consistency and availability, whenever a partition occurs in the system. Hence, choosing between ensuring consistency for users’ state information and the availability of the service while being resilient against unforeseen sudden failures becomes a critical need for Beyond 5G (B5G) and 6G networks. In this paper, we present RECAP 5GC: a REsilience and CAP (RECAP)-aware 5GC that keeps up the High Availability (HA) service by being resilient and consistent during sudden failures of stateless functioning. To prove the feasibility and effectiveness of the proposed solution, we implement it entirely on the 3GPP-compliant control plane of the 5GC testbed between the Access and mobility Management Function (AMF) and Unstructured Data Storage Function (UDSF). Experiments on the minimal two nodes active/active cluster configuration prove that the proposed RECAP 5GC is not only resilient by dropping 47% fewer user requests than the CP mode and 86% fewer user requests than the AP mode but also 1.6% additionally available than the CP mode while remaining consistent on the user’s state and information.

Autonomous vehicles require precise and reliable data for critical functions like localization and navigation. High-Definition (HD) maps are essential, segmented into vari-ous layers (each with varying roles and lifespans) and verti-cally structured into 'tiles' that encapsulate these layers. Effi-ciently disseminating these tiles via Road Side Units (RSUs) in Vehicle-to-Everything (V2X) networks is challenging. An efficient dissemination method must minimize both the Turn Around Time (TAT)-the time from a tile request to receipt-and the inci-dence of schedule misses, which is when a vehicle does not receive a tile before its deadline, impacting reliability and performance. This paper introduces an Adaptive Broadcast Scheduling (ABS) scheme tailored for HD maps, factoring in layer priority, request popularity, and deadlines. Experimental studies show that ABS outperforms conventional periodic broadcast by cutting schedule misses by over 50% and reducing TAT to milliseconds.

Current advancements in the design of the service-based architecture of 5G Core (5GC) are flexible to be deployed with Network Function Virtualization (NFV) in the form of network slices. While some slices tolerate the delay and some do not, this versatility of slice services imposes challenges to Mobile Network Operators in designing a robust, scalable, and yet resilient 5GC. Therefore, in this work, we build a self-resilient 5GC that not only addresses the scheduling requirements per slice type but also yields service satisfaction. Precisely, we build it as a combination of large Integer Linear Programming based Column Generation (CG) and Artificial Intelligence based Deep Learning in a Closed Loop Automation with a Self Organizing Network paradigm. During unexpected situations of overload and failures of serving instances, the proposed CG technique in the work improves the scheduling and serving of control plane user requests by dropping 15% lesser user requests in delay-tolerant slices and 5% lesser in delay-sensitive slices, than the existing state-of-the-art solutions. With 95% learning accuracy, the Deep Learning-based Long Short Term Memory (LSTM) method predicts the availability of serving instances of the Network Functions (NFs) of the 5GC. Overall, by deriving the optimal number of serving instances for each NF of the 5GC, this blend of CG and AI in the 5GC not only contributes to efficient scaling and reconfiguration of the slices but also improves the resilience and High Availability of the 5GC. We believe that this proposed work is a highly promising solution for building Cognitive Autonomous Networks (CAN) of core side slices for upcoming Beyond 5G (B5G) and 6G in both cloud and edge deployments.

The control plane of 5G Core (5GC) is typically shared among multiple dependent network slices of the data plane. But as the number of dependent slices and services on the common and shared control plane increases, its resilience threat also increases. This paper proposes FlexCore: a 5GC that is not only flexible and scalable but also resilient to cater to various service requirements on both stateful and stateless architectures of 5GC. FlexCore is built with an eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) based SCTP load balancer hooked at the entry point of the 3GPP compliant 5GC control plane, and a set of micro-AMF instances to serve the user requests. Precisely, the FlexCore is fabricated to honor the variety of incoming user requests on the control plane as per the service requirements, like, per slice, per user, or per control procedure of users too. Experiments on a 3GPP compliant 5G testbed show that FlexCore can provide average latency reduction of up to 14% and 79% on stateful and stateless architectures, respectively, and up to 63% latency reduction for latency-critical slices on the slice-aware architecture.

The energy efficiency of 5G and beyond 5G(B5G) networks is critical for reducing the high operational expenditure (OPEX) of mobile network operators. In 5G RAN, functional split enables the disaggregation of baseband functions, which significantly increases energy efficiency but induces various challenges in the placement of baseband functions. Various recent works have focused on addressing these challenges; however, most of the solutions do not consider the delay and data rate requirements of different slices as well as different functional splits. In this work, we aim to develop an energy-efficient baseband function placement strategy that jointly considers different functional splits and network slice-specific requirements. We formulate an Integer Linear Program (ILP) based optimization model to minimize the energy consumption in the network by selecting appropriate functional split and baseband function placement options for RAN slices. We show that our proposed model outperforms the baseline strategies in providing energy efficient baseband function placement solution. To tackle the computational complexity of ILP, we also design a polynomial time heuristic algorithm that can be applied in large-scale scenarios.

As network slicing is the chief enabler for future Beyond 5G(B5G) and 6G networks, multiple tenants interoperate cost-effectively to provide a variety of slice services on a common physical infrastructure. However, this opens the doors to cross-slice disruptions with Man-in-the-Middle (MITM) attack which ultimately disrupts the slice services in the data plane. In this paper, we address such possible cross-network slice disruptions in a zero-trust and multi-tenant based 5G network by proposing different design techniques namely, secure communication and Artificial Intelligence (AI)-based anomaly detection to prevent them. Our experiments on a 5G testbed prototype show that in the secure communication method, Attribute-Based Encryption (ABE) provides higher security benefits in confidentiality and implicit authorization. However, symmetric encryption and integrity protection prevent cross-slice disruptions with less communication overhead, but with a weaker security level. On the other hand, with online learning and noise tolerance capabilities, AI-based Hierarchical Temporal Memory (HTM) can proactively detect the occurrences of the identified cross-slice disruptions.

Network Functions Virtualization (NFV) enables carriers to replace dedicated middleboxes with Virtual Network Functions (VNFs) consolidated on a few shared servers. However, the question of how (and even whether) one can achieve performance related Service Level Objectives (SLOs) with software packet processing in NFV remains open. VNF consolidation causes high variability and unpredictability in throughput and latency of VNFs deployed together. It was shown in our prior work that isolating the processor’s Last Level Cache (LLC) and limiting Memory Bandwidth (MB) directly helps in achieving performance isolation among the co-located VNFs. So, in this work, we formulate VNF placement problem with exclusive allocation of LLC and MB resources as a Mixed Integer Linear Program (MILP). Due to its hardness to solve, we also present a heuristic solution named RAVIN that enforces performance SLOs for multi-tenant NFV servers while being as much resource-efficient as possible. We demonstrate RAVIN’s effectiveness in improving resource utilization and in reducing the total number of required servers to deploy VNFs compared to state-of-the-art and baseline approaches.

Mobile operators are widely adopting Network Functions Virtualization (NFV) to get the benefits of virtualization, including ease of deployment, flexibility, and cost savings. NFV allows multiple virtualized Radio Access Networks (vRANs) to run on commodity hardware enabling joint signal processing and efficient interference management. In addition, mobile operators can run general-purpose workloads alongside vRANs to utilize spare system resources in the NFV infrastructure. In such a consolidated scenario, it is necessary to ensure that the Key Performance Indicators (KPIs) of vRAN workloads are always met. But the workload consolidation could cause high variability and unpredictability in the performance of the deployed vRANs due to contentions for shared system resources like CPU cores, Last Level Cache (LLC), etc. In order to address this problem, we present JARS – a joint allocation of radio and system resources for the NFV infrastructure – that dynamically adjusts system resources such as CPU cores and LLC-ways, and radio resources such as Physical Resource Blocks (PRBs) to ensure KPIs for vRANs and improve overall resource utilization by workload consolidation. We profile srsLTE to determine the minimal CPU core and LLC resource requirements to satisfy KPIs during different traffic loads, which is used in the JARS. Experimental studies on a prototype system show that the proposed JARS outperforms a state-of-the-art scheme by 23%.

Many IoT use cases have ultra-low latency and strong security requirements. But achieving both simultaneously is challenging. In this paper, as a use case, we consider the authentication of IoT devices for every transaction and develop a fast and secure authentication protocol. Our key idea is to leverage highly secure Physically Unclonable Functions (PUFs) and high-speed programmable switch and offload PUF-based authentication protocol to the switch. By doing so, it enables authentication of every transaction at network speed. In this paper, we demonstrate the feasibility of our idea by offloading the authentication protocol to a programmable switch with Tofino chip. Our preliminary experiments show that protocol offloading reduces authentication latency by 2-4 times and scales to a few hundred thousand IoT devices.

At the dawn of autonomous driving, vehicular communications and coordination become more vital than ever. Fast information gathering, processing and sharing creates the basis of safety and efficiency, the main promises of conceding the control of vehicles from humans to machines. In this paper we propose to deploy an information gathering and distributing system that aims exactly at minimizing the latency of delivering the essential information to the end clients. We specifically tackle the crowd-sourced maintenance of high definition maps, i.e., road maps with extremely high accuracy and environmental fidelity containing dynamic information about the traffic as well, via a federated analysis scheme, and by broadcasting those maps through a 5G network. The system is designed for minimizing the latency of information delivery: analytical models based on queuing theory and optimization are proposed, and a wide range of system parameters are evaluated in numerical simulations. We find that the latency of delivering timely high quality information to end clients can be reduced with careful dimensioning of the system. According to our measurements, high-speed 5G data connection is a must, as we reach the optimal latency by building map segments with 1km in diameter via Gb/s uplink speeds in densely populated central metropolitan settings.

Vehicular coordination and communication tasks are crucial aspects of enabling autonomous driving, guaranteeing safety and efficiency. In our present work, we explore methods for collecting and distributing information among participants by employing collaboratively-built high-definition maps that contain fine-grained contextual data. We leverage a hierarchical federated learning structure and anticipatory onboarding of the maps through a mobility-aware content caching scheme and minimize the delay of data delivery in both subsystems. We provide analytical models built on queuing theory and integer linear programming and evaluate essential system parameters in an emulation testbed. Based on our results, we conclude that we can significantly reduce the delay in delivering timely information to vehicular clients by introducing intermediary layers in the federated learning structure and by pre-loading current map tiles corresponding to vehicle paths.

Network Functions Virtualization (NFV) promises programmability and cost savings by replacing hardware middleboxes with more flexible Virtual Network Functions (VNFs) on commodity servers. But, the current virtualization technologies do not fully isolate the system resources like Last Level Cache (LLC) and Memory Bandwidth (MB); therefore, co-location of VNFs on the same commodity server causes interference effects which might severely impact the performance of VNFs in terms of throughput, latency, etc. Contention at LLC is one of the root causes of this performance degradation and it is addressed by LLC resource partitioning. But, it remains unexplored the impact of both LLC and MB on VNF performance. In this work, we investigate the importance of MB partitioning along with LLC partitioning to achieve performance isolation in NFV-based systems. Allocating these system resources among co-located VNFs to meet Service Level Agreements (SLAs) is challenging due to the dynamic nature of traffic and varying functionality of VNFs. In this work, we formulate the resource allocation problem as an Integer Linear Programming (ILP) for maximizing the number of accepted VNF requests with SLA guarantees. Since the problem is NP-hard, we present a polynomial time$\epsilon $-approximation scheme. Further, we propose a heuristic approach namedNFVPermit, a resource manager for NFV-based systems that tries to ensure performance isolation among co-located VNFs based on their current traffic rates and SLA requirements. Through extensive experiments, we show howNFVPermitoutperforms state-of-the-art and baseline approaches.

The presence of a Load Balancer (LB)s is much significant to keep up the High Availability (HA) and resilience of the scalable 5G Core (5GC). The whole system may collapse just because of inefficient LB at any NF, resulting in total disruption to the High Availability (HA) service. In this paper, we present the LOCOMOTIVE 5GC which outperforms the traditional hot standby in both HA and resilience during various dynamic conditions. LOCOMOTIVE serves 16% (at least) more user requests compared to hot standby in the control plane while handling unexpected overloaded conditions (without the failure of LB). During the failures of LB, it drops 22% lesser user requests than hot standby. With this outstanding resilience, LOCOMOTIVE even achieves 4% better availability than the hot standby in an active-active cluster configuration. To prove the feasibility of LOCOMOTIVE and to encourage further research works in the world of LBs, we developed its entire framework in a 3GPP compliant 5G test-bed system along with eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) framework.

Service function chaining of network-functions across multiple domains enables next-generation services to be offered efficiently. Multi-domain orchestrator decouples the complexities of setting up such chains across domains on behalf of the users of the service. Privacy preservation is essential in this scenario since domains do not want to expose their topology, available resources, cost, and other operational aspects. Similarly, users try to minimize the exposure of their requirements to the domains and the orchestrator. Prior work relied on abstraction and/or aggregation of information in advertisements to preserve privacy. This paper presents a novel cryptographic mechanism based on bilinear mapping that allows the domains and users to privately advertise their information without revealing anything to the orchestrator and other domains. A private set intersection is used to compute the common elements between domains and users. Security proof for the proposed flows is provided using the universally composable framework. Usage of cryptographic techniques reduces the need for abstraction and provides strong privacy guarantees quantified in the privacy metrics of the paper. The simulation results demonstrate that the performance of the proposed system is at par with other existing work while providing strong cryptographic privacy, which none of the other systems provide.

The recent adaption of virtualization technologies in the next generation mobile network enables 5G base station to be segregated into a Radio Unit (RU), a Distributed Unit (DU), and a Central Unit (CU) to support Cloud based Radio Access Networks (C-RAN). RU and DU are connected through a fronthaul link. In contrast, CU and DU are connected through a midhaul link. Although virtualization of CU gives benefits of centralization to the operators, there are other issues to be solved such as optimization of midhaul bandwidth and computing resources at edge cloud and central cloud where the DUs and CUs are deployed, respectively. In this paper, we propose a dynamic functional split selection for the DUs in 5G C-RAN by adopting to traffic heterogeneity where the midhaul bandwidth is limited. We propose an optimization problem that maximizes the centralization of the C-RAN system by operating more number of DUs on split Option-7 by changing the channel bandwidth of the DUs. The dynamic selection of split options among each CU-DU pair gives 90% centralization over the static functional split for a given midhaul bandwidth.

Network slicing is a key feature of 5G and beyond networks. Intelligent management of slices is important for reaping its highest benefits which needs further exploration. Focusing only on one goal as revenue maximization or cost minimization may not generate the highest profit for infrastructure providers in the long run. In this paper we jointly consider online admission and placement of Radio Access Network (RAN) slices with two objectives - a) maximizing revenue from accepting slices which are more profitable in the long run, and b) minimizing the cost to deploy them in Open RAN (O-RAN) enabled network by placing the slices efficiently. We formulate it as an optimization problem and propose a Deep Reinforcement Learning (DRL) based solution using Proximal Policy optimization (PPO). We compare our model with a state-of-the-art DRL based admission control solution and a greedy heuristic. We show that our proposed solution can efficiently adapt to dynamic load conditions. We also show that the proposed solution results in better performance to maximize the overall profit for infrastructure providers in comparison to the baselines.

The rapid growth in mobile network traffic and dynamic user mobility patterns have propelled network operators toward the Cloud-Radio Access Network (C-RAN) to reduce operational costs and improve service quality. C-RAN handles the traffic and mobility issues in a centralized manner by segregating the central units (CUs) from the distributed units (DUs) in a shared CU pool. The ability of C-RAN to map multiple DUs to the same CU allows optimal coverage with high multiplexing gains, using the least number of CUs. However, dynamically mapping DUs to CUs is not trivial since the network traffic and mobility patterns are difficult to predict. This paper presents a two-phase framework for an optimal city-wide C-RAN network. In the first phase, we propose to use the ConvLSTM model, which simultaneously learns the hidden spatial and temporal dependencies in a real-world dataset and makes accurate traffic forecasts for a future duration of time. In the second phase, we use the predicted traffic from the first phase to develop a proactive optimal DU-CU clustering scheme that is cost-effective and meets quality objectives. We first formulate an optimization problem, and later, to reduce the computational complexity of the optimization, we propose a lightweight heuristic algorithm. Finally, we evaluate the performance of our prediction model and the mapping scheme using a two-month real-world mobile network dataset of Milan, Italy. Based on simulation results of phase one, we observe the ConvLSTM model, when deployed in a C-RAN architecture, outperforms existing state-of-the-art prediction models with up to 26% better RMSE (Root Mean Square Error) and up to 36% better MAPE (Mean Absolute Percentage Error) values. Similarly, in phase two, our simulation results show that compared to reactive threshold-based clustering, proactive clustering can reduce the average number of active CU servers by up to 18% every 10 minutes without overloading.

Advancements in 5G and edge computing infrastructure increase the need to deploy location-based services for mission-critical applications like Vehicle to Everything (V2X) and Intelligent Transport System (ITS). In this demonstration, we showcase the location service capabilities of our 5G Core (5GC), coupled with Multi-access Edge Computing (MEC) for delay-sensitive ultra-Reliable Low Latency Communication (uRLLC) service types like V2X and ITS. We believe that this work will guide Mobile Network Operators in building a location assistance service system for emergencies and delay-critical applications.

Mobile Network Operator (MNO)s avail the benefit of providing an isolated service with the network slice in a variety of forms like enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communications (mMTC). However, they face challenges in handling situations of overload, congestion, and scaling with the arrival of unexpected control plane User Service Requests (USRs) leading to the dropping of USRs, and hence disturbing the slice’s High Availability (HA). This paper presents a novel High Availability supportive self Reliant NEtwork Slicing System (HARNESS) for 5G Core, powered by intelligent and autonomous Self Organizing Network (SON) paradigm. In HARNESS, we propose algorithms to intelligently schedule and serve the significant portion of control plane USRs for both delay tolerant and delay sensitive slices, to ensure their uninterrupted HA service provision. Along with efficient resource utilization, the HARNESS shows improvements over the traditional HA provisioning methods, in preventing the occluding of crucial control plane USRs by 60% and reducing the average response time for these USRs by 50%. We developed the HARNESS framework in a 5G test-bed system using eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) mechanism for coherent and optimized end-to-end working of it. With this additional gear, HARNESS productively contributes to achieving 3.2% better slice service HA in the minimal two nodes active/active cluster configuration.

5G network supports end-to-end logically isolated networks in the form of network slices, catering to the needs of users of various primary network services, namely enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC). Mobile Virtual Network Operators (MVNO)s often face challenges in achieving strong slice isolation and High Availability per slice during overload and scaling situations as the 5G network uses a shared environment for slices with multiple domains, especially considering a variety of services and devices. In this paper, we propose a novel Self Optimizing Network Slicing framework (SONS) leveraging Self Organizing Network by building it as an autonomous slice system in 5G network slicing management for efficient slice sharing and isolation. Precisely, we formulate a system model with Probabilistic Graphical Model (PGM) based Markov Network, building it as an Artificial Intelligence based learning framework. We propose Slice Belief Propagation based algorithms and Deep Learning based Long Short Term Memory (LSTM) methods to aid in serving user requests and reconfiguration of self optimizing slice. Our experiments on the proposed SONS framework shows improvement in serving higher number of users with uninterrupted connectivity by 80% in eMBB, 35% in uRLLC, and 52% in mMTC when compared to standard slice deployments, while handling the worst case of peak traffic in the control plane of 5G Core network.

Network slicing is one of the core features of the 5G network to meet the requirements of various network services, namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building an isolated virtual network of resources typically in a Network Function Virtualization (NFV) environment. We have built a 3GPP compliant 5G Core (5GC) testbed and enabled network slicing on it to provide eMBB, uRLLC, and mMTC services to end users. In this demonstration, we showcase the capabilities of our zero-touch framework of 5GC network slicing management and orchestration in an NFV environment with the network functions of 5GC and the applications on eMBB and uRLLC slice services. We believe that this work will guide Mobile Network Operator (MNO)s in building zero-touch autonomous network slices in 5GC along with their smart management and orchestration in closed loop automation.

Cloud Radio Access Network (C-RAN) disaggregates the functionalities of the base station in a way that some of the radio processing tasks are centralized in a virtualized computer pool of general-purpose processors (GPPs) on a cloud platform. This enables efficient utilization of the computational resources based on the spatio-temporal traffic fluctuations at cell sites. In this paper, we attempt to further reduce the computation resources by C-RAN on the cloud platform. First, we profiled the energy consumed in an OpenAirInterface (OAI) based C-RAN system using the existing Linux CPU frequency scaling governors. Based on the observations, we propose a traffic-aware compute resource tuning (CRT) scheme that reduces the energy consumption of C-RANs. The CRT scheme opportunistically lowers Modulation Coding Scheme (MCS) used while serving users by utilizing all of the available radio resources in every scheduling interval during non-peak hours. This reduction in the MCS helps in reducing energy consumption (due to usage of lower CPU clock frequency in the GPPs of the cloud platform) and fronthaul bandwidth requirements. Another benefit of the CRT scheme is its ability to work with any MAC scheduler. The extensive simulation results show how the CRT outperforms the existing frequency scaling governors in energy consumption while reducing fronthaul bandwidth requirements.

Owing to the diverse nature of traffic incidents, accepting and storing relevant data in the form of natural language is more convenient than in constrained value fields. Textual information in such cases can be rich enough for traffic incident analysis and modelling even in the absence of certain fixed set of parameters. However limited studies considered the complexity in processing such information to predict traffic incident duration. In this paper, we propose to represent the textual data from incident reports using BERT word embeddings. These text representations are then inputted into various regressors such as LSTM, XGBoost, RF and SVR to predict traffic incident duration. To demonstrate the significance of this approach, the method is compared with the state-of-the-art approach using LDA representation. Dataset used for the experiment is the Caltrans Performance Measurement System (PeMS). Result analysis indicates that the BERT- LSTM hybrid model is effective in capturing the contextual meaning of textual incident reports to predict the traffic incident duration and outperforms LDA topic modelling with MAE around 11.16 minutes.

3GPP has introduced C-V2X technology in the Rel. 14 for improving road safety. There are two modes of vehicular communication in C-V2X viz., Mode 3 and Mode 4. Since Mode 4 allows vehicles to communicate with each other even outside the cellular coverage, it is acknowledged as the baseline mode. In Mode 4, vehicles select the best available least interfered radio resource by utilising Semi-Persistent Scheduling (SPS) in a distributed fashion. Medium Access Control (MAC) parameters like pKeep (probability of keeping a radio resource) and Resource Counter (RC) affect the duration of radio resource collision, so these need to be configured carefully for efficient C-V2X communication. This paper explicates the impact of pKeep and RC on the overall performance of C-V2X system. A new algorithm called Traffic-Aware SPS (TA-SPS) is then proposed, which optimally configures these parameters according to the current density of road traffic by estimating it from periodic Cooperative Awareness Messages (CAMs) broadcasted by the vehicles. By reducing the number of resource collisions, TA-SPS increases transmission reliability of the C-V2X system. Through extensive simulation studies, we show how TA-SPS outperforms the traditional SPS in mixed road traffic scenarios.

The proliferation of 360° video content and easy availability of head-mounted devices propelled the popularity of 360° video streaming on various platforms, including smart-phones. However, the high bandwidth requirement of 360° video streaming impedes its further growth, specifically in bandwidth constraint cellular networks. Some viewport adaptive 360° video streaming methods have been proposed recently to reduce the bandwidth requirement by spatially reducing the transmitted data. However, these methods require accurate prediction of the future viewport, and a prediction error can significantly impair the user’s Quality of Experience (QoE). In this work, we took a different approach and propose a content-aware solution for tiled 360° video streaming that does not require viewport predictions. In this work, we exploit the fact that the background scene does not contain prominent details and can be transmitted at a lower bit rate without affecting the user’s QoE. The proposed approach uses the saliency map to identify prevalent tiles of the 360° video through machine learning-based classification using the Kernel Density Estimation (KDE). First, the tiles are classified based on their saliency information, and then bitrates are assigned to the tile clusters. The results show that the proposed approach reduces the bandwidth requirement by 50% while delivering the equivalent user’s QoE.

5G Mobile network will reap the benefits from key technologies like Software Defined Networking and Network Function Virtualization. Cloud Radio Access Network architecture (Cloud RAN) is proven to be a promising architecture, but fully centralized Cloud RAN imposes a great bandwidth requirement in the fronthaul link. Different functional split options for 5G RAN have been proposed which lead to a trade-off between centralization and bandwidth requirement. Functional split at different granularity such as per cell, per logical network (slice), per user, or per bearer, have been an area of interest. To explore the effect of slice granularity in adaptive splits for slices, we formulate slice centric functional split in 5G RAN as an ILP to maximize centralization of baseband processing. By varying the slice granularity from macro slicing to micro slicing, we observe how slice centric split can impact centralization benefit of the network. We show that with increasing slice granularity slice centric split can render more centralization benefit in some scenarios but a trade off exists between centralization benefit and migration cost in the network which should be carefully considered in real deployment scenario.

In today's moving world to ambitious 5G, 3GPP has defined three fundamental and promising services, namely enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC) by tuning user's needs on these services to network slicing. While the Network Function Virtualization (NFV) and Software Defined Networking (SDN) are used to enable the network slicing in the mobile network, an effective end-to-end slice management in 5G system is still a challenge to improve the network performance in terms of throughput, latency, and connectivity for each of these envisioned services. In this paper, we focus on the end-to-end network slice life cycle management of network slices on different sites using a single management and orchestration entity with a coherent proof of concept. We propose algorithms for efficiently activating, deactivating, and decommissioning the network slices, using real time status information of network slices from Network Slice Management Function (NSMF). Our results show that by adopting better strategy in these algorithms and considering learned user traffic from the past, in controlling various phases of slice life cycle, we can reduce the response time for a user request by 50%.

The recent adoption of virtualized technologies in Next Generation Radio Access Network (NG-RAN) has driven a significant impact on energy consumption by subsequently decreasing the number of active base stations. The base station (gNodeB) of 5G is segregated into cost-efficient Central Units (CU) hosted on virtual platforms and cheaper & smaller Distributed Units (DU) present at the cell sites. Multiple CUs are pooled together in a single powerful central cloud, known as CU pool. The logical connection between DU and CU can be dynamically adjusted and can potentially affect the energy consumption of the CU pool. The deployment of NG-RAN imposes strict latency requirements on the fronthaul link that connects DUs to CU. To relax these strict latency requirements, various alternate architectures such as Flexible RAN Functional Splits have been proposed by 3GPP. In this paper, we first evaluate the energy consumption of DU and CU for various functional split options using OpenAirInterface (OAI), a real-time open source software radio solution. We find that lower layer splits have high energy consumption at CU as compared to higher layer split options. We also observe the variation in energy consumption due to traffic heterogeneity. Motivated by the above study, we formulate an optimization model, Apt-RAN, that optimizes the energy consumption of the CU pool and the number of handovers, considering different functional splits. To address the computational complexity of solving the optimization model, a lightweight polynomial time heuristic algorithm is proposed. Simulation results demonstrate that our proposed model outperforms existing state-of-art schemes.

3GPP has chosen Service Based Architecture (SBA) for 5G Core (5GC). In this work, we build a prototype of SBA of 5GC from scratch using open source tools in Network Functions Virtualization (NFV) environment. In SBA, Network Functions (NFs) are designed to expose capabilities to consumers with interfaces, called Service Based Interface (SBI). To reduce the latency and the load on NFs, we use gRPC, a modern open-source Remote Procedure Call (RPC) framework, instead of REST for implementing SBI. We present a distributed service registration and discovery framework for 5GC using a software solution named Consul. We then propose usage of a Look Aside Load Balancer (LALB) for load balancing multiple NFs of 5GC. The control plane latency is reduced by routing traffic across multiple instances of Access and Mobility Management Function (AMF) via an LALB. Experimental results suggest that carefully chosen load balancing algorithms can significantly lessen the control plane latency when compared to simple random or round-robin schemes.

Service Function Chaining (SFC) is used to steer the traffic to a specific set of Network Functions (NFs) (such as load balancer, proxy, firewall, etc.) based on the type of traffic and operator policy. Handling the massive amount of user traffic envisioned in the next generation networks using traditional techniques is costly and tedious. By leveraging advanced technologies such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN), NFs can be deployed as software instances on Virtual Machines (VMs) (also called as Virtual Network Function (VNF)). Network operators widely place different types of VNFs at different locations to meet the user traffic demands. Multiple VNF instances on the same physical server compete for common resources such as network I/O bandwidth, CPU cycles, cache memory, and main memory which can lead to severe performance interference, which is ignored in existing NF selection mechanisms. However, increasing the SFC acceptance rate of SFC requests with an effective selection of required VNFs under the constraint of end-to-end latency is still an open problem. Since this problem is NP-Hard, we propose a heuristic algorithm based on dynamic programming which efficiently selects the required VNFs and steers the traffic by considering the interference effect. Results show that the proposed algorithm improves the average SFC acceptance rate by 29% as compared with existing methods.

The ambitious goals of Fifth Generation (5G) mobile networks for higher system capacity, massive number of devices and flexibility in operations demand the network architecture to be much more flexible, efficient and autonomous. The design of Radio Access Network (RAN) is undergoing architectural transformations to increase the flexibility of deployment and programmability by leveraging Network Functions Virtualization (NFV), Software Defined Networking (SDN), and Cloud Computing. Hence, efficient resource management strategies with enhanced service quality play a vital role in realizing true benefits of 5G RAN. In this work, we propose a novel and dynamic resource management framework called "KORA" for 5G Cloud Radio Access network (C-RAN) considering spatio-temporal traffic heterogeneity exhibited at Remote Radio Units (RRUs). To minimize net energy consumption in the cloud data center and to maximize the service quality to end users, we formulate an Integer Linear Programming model (ILP) for KORA that performs efficient consolidation and relocation of Control Units (CUs) in 5G C-RAN. To alleviate the computational heaviness of the ILP optimization model, we propose a light-weight heuristic algorithm that is scalable and applicable to real-world dense deployments spanning a large set of CUs. By simulations, we compare and contrast between various distinctive features of 5G C-RAN architecture under study as well as evaluate the efficacy of our proposed KORA framework. The heuristic algorithm can save 27% of relocations and 33% of GBR flows from disruption, at increased energy consumption of 6.6% in data center as compared to KORA.

The diversified use cases of next-generation mobile networks can be realized by the key concept of Network Slicing that enables mobile network operators to slice a single physical network into multiple virtual network instances optimized to specific services and business goals. Scaling of network slices is required to cope with the resources needed for peak traffic demand. In this paper, we demonstrate scaling of network slices based on the type of network slice such as enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC) in order to ensure Service Level Agreement (SLA) guarantees of the network slices with the help of our proposed Network Slicing Profiler (NSP) and Network Slice Scaling Function (NSSF) in an ETSI MANO based network slicing framework.

Recent advances in LTE operating in unlicensed spectrum (LTE-U) has grabbed a lot of attention from industry and academia. The duty cycled LTE-U is shown to be fair with Wi-Fi technology by following an ON-OFF cycle for its transmission. However, the effect of LTE-U on the video quality of Wi-Fi users has not been studied in the literature. In this work, we study the video quality performance of a Wi-Fi user in the presence of LTE-U, in a testbed system. Our results show that the parameters that contribute to the video QoE (Quality of Experience) of Wi-Fi users get adversely affected as the fraction of channel utilized by LTE-U increases, but the same is not shown to be true with another Wi-Fi. We found that poor video QoE of Wi-Fi users in the presence of LTE-U is because of a large number of packet collisions and less channel access time due to ON cycle of LTE-U.

The Licensed Assisted Access (LAA) is shown asa required technology to avoid overcrowding of the licensedbands by the increasing cellular traffic. Proposed by 3GPP,LAA uses a Listen Before Talk (LBT) and backoff mechanismsimilar to Wi-Fi. While many mathematical models have beenproposed to study the problem of the coexistence of LAAand Wi-Fi systems, few have tackled the problem of QoSprovisioning, and in particular analysed the behaviour of thevarious classes of priority available in Wi-Fi and LAA. Thispaper presents a new mathematical model to investigate theperformance of different priority classes in coexisting Wi-Fi andLAA networks. Using Discrete Time Markov Chains, we modelthe saturation throughput of all eight priority classes used byWi-Fi and LAA. The numerical results show that with the 3GPPproposed parameters, a fair coexistence between Wi-Fi and LAAcannot be achieved. Wi-Fi users in particular suffer a significantdegradation of their performance caused by the collision withLAA transmissions which has a longer duration compared toWi-Fi transmissions.

In order to meet the traffic demand from diverse next generation wireless network applications and exponentially increasing mobile subscriptions, various 5G network architectures are proposed by leveraging Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. Network slicing will be one of the 5G technologies that would support next-generation wireless applications over a shared network infrastructure. However, improper network slicing may lead to either over-provisioning or under-utilization of the underlying network infrastructure resources, especially the 5G core network. Over-provisioning of data plane components such as Serving Gateway (SGW) and Packet Data Network Gateway (PGW) can lead to higher CAPEX and OPEX to mobile operators. In this paper, we propose a novel auto-scaling approach called Bit rate Aware Auto Scaling (BAAS) that maintains a precise UE bit rate requirement in the network slices without over-provisioning of data plane resources.

Tightly coupled LTE-Wi-Fi networks have emerged as a promising solution for improving capacity and coverage of wireless networks. Different architectures which realize this integration includes LTE-Wi-Fi radio level interworking with IPSec tunnel (LWIP) and LTE-Wi-Fi Aggregation (LWA). The major issue with these architectures is that they do not exhibit expected performance when TCP is employed, i.e., TCP throughput decreases compared to using either LTE or Wi-Fi for transmission. Also, Multipath TCP (MPTCP) is inefficient while aggregating LTE and Wi-Fi links, especially when the link rates are incomparable. In this paper, we propose VIrtual congeStion control wIth Boost acknowLdgEment (VISIBLE) algorithm for LWIP networks which encompasses an efficient packet level traffic steering technique for steering Downlink traffic across LTE and Wi-Fi links of LWIP node and Boost ACK technique to reduce the number of duplicate ACKs (DUP-ACKs) delivered to the TCP sender. Unlike MPTCP, VISIBLE+LWIP uses both LTE and Wi-Fi links efficiently even if their link rates are incomparable and reduces unnecessary DUP-ACKs. We have developed VISIBLE+LWIP framework in NS-3 and compared its performance with the state-of-the-art MPTCP algorithms. We could observe that the proposed VISIBLE algorithm has doubled the throughput of basic LWIP and outperformed throughput of MPTCP by 37%. Also, it has enhanced the throughput by 30% as compared to basic LWA.

Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%.

LTE—Wi-Fi Radio Level Integration with IPsec Tunnel (LWIP) thrives as a complete solution to address the data requirement for telecom operators by effectively utilizing unlicensed spectrum. Co-located LWIP (C-LWIP) node couples Home eNodeB (HeNB) and WiFi Access Point (AP) at radio protocol stack to enable a unified control decision over both LTE and Wi-Fi links. The challenges pertaining to small cells viz., high co-tier interference and QoS provisioning can be effectively addressed by using LWIP. This paper proposes a novel Power awaRE dynamic traffic StEering (PRECISE) algorithm which regulates transmit powers of LTE and Wi-Fi of LWIP node in order to minimize interference across LWIP nodes in dense deployments. The PRECISE algorithm also does flow steering across LTE and Wi-Fi interfaces by employing Multi Attribute Decision Making (MADM) technique. It thrives on ensuring Guaranteed Bit Rate (GBR) flow requirements by dynamically controlling the transmit power across multiple LWIP nodes. Interference mitigation sub-problem and ensuring GBR sub-problem are formulated as Mixed Integer Non-Linear Programming (MINLP) problems. The proposed PRECISE algorithm improves the network throughput by 84% compared to 3GPP Rel-12 LTE Wi-Fi interworking and 48% compared to state-of-the-art a-optimal scheduler. It also has reduced the number of unsatisfied GBR flows by 35% as compared to a-optimal scheduler.

LTE-WLAN Aggregation (LWA) at Radio Access Network (RAN) level offers better performance compared to other WLAN inter-working and offloading mechanisms due to its tighter integration. In rel. 13, 3GPP standardized an LWA architecture which works at the Packet Data Convergence Protocol (PDCP) layer of LTE eNodeB and provides packet-level steering. But this architecture provides sub-optimal performance because of various delays incurred on both sender (eNodeB) and receiver (TIE) sides. To overcome this, we propose a new architecture LTE-WLAN integration at RLC Layer (LWIR) which works at the Radio Link Control (RLC) layer of LTE eNodeB. Along with this, Virtual WLAN Scheduler (VWS) which employs traffic steering scheme has been proposed. The VWS minimizes waiting time on Wi-Fi queue and thereby reduces outof-order delivery at the TIE side. Five different bearer selection schemes have also been proposed which provide efficient steering by smartly choosing a bearer to route some data onto Wi-Fi based on available bandwidth of Wi-Fi link. The VWS also contains an LTE feedback mechanism which coordinates with the LTE scheduler to ensure fairness as well as better utilization of system capacity. The evaluation considers collocated scenario in which LTE small cell (SeNB) and Wi-Fi Access Point (AP) are tightly integrated in LWIR node. We show that the proposed LWIR with VWS increases throughput up to 85% when compared to LWA based packet-level steering.

Due to spatio-temporal variation of mobile subscriber's data traffic requirements, traffic load experienced by base stations present at different cell sites exhibit highly dynamic behavior in traditional cellular systems. This non-uniform and dynamic traffic load leads to under utilization of the base station computing resources at cell sites. Cloud Radio Access Network (C-RAN) is an innovative architecture which addresses this issue and keeps the Total Cost of Ownership (TCO) under safe limit for cellular operators. In C-RAN, the baseband processing units (BBUs) are segregated from cell sites and are pooled in a central cloud data center thereby facilitating shared access for a set of Remote Radio Heads (RRHs) present at cell sites. In order to truly exploit the benefits of C-RAN, the BBU pool deployed in the cloud has to efficiently serve clusters of RRHs (i.e., many-to-one mapping between RRHs and BBUs in the BBU pool) and thereby minimizing the required number of active BBUs. In this work, potential benefits of C-RAN are studied by considering realistic traffic loads of base stations deployed in urban areas by using statistical models. We propose a lightweight and load-aware algorithm, Dynamic RRH Assignment (DRA), which achieves BBU pooling gain close to that of a well known First-Fit Decreasing (FFD) bin packing algorithm. Using extensive simulations, we show that DRA consumes only 25% of time on average compared to FFD for the case of urban cellular deployment of 1000 RRHs. DRA slightly overestimates the required number of active BBUs as compared to FFD by 1.7% and 1.4% for weekdays and weekends, respectively.

Actor–actor communication is an important part of the functioning of wireless sensor–actor networks and enables the actor nodes to take coordinated action on a given event. Owing to various reasons such as actor mobility and low actor density, the actor network tends to get partitioned. The authors propose to use the underlying sensor nodes, which are more densely deployed, to heal these partitions. In order to maximise the utilisation of the limited energy available with the sensor nodes, a new routing protocol for actor–actor communication using directional antennas on the actor nodes is proposed. The authors contribution is threefold. First, using simulations they show that the problem of partitioning in the actor networks is significant and propose an architecture with directional antennas on actor nodes and sensor bridges to heal these partitions. Second, they identify the routing problem for this architecture based on a theoretical framework and propose centralised as well as distributed solutions to it. Third, they develop a routing protocol based on the distributed solution and show, using network simulations, that the proposed protocol not only heals the network partitions successfully, but also achieves high throughput and fairness across different flows, in addition to maximising the network lifetime.

The maximum utilization of Multi Channel - Multi Radio Wireless Mesh Networks (WMNs) can be achieved only by intelligent Channel Assignment (CA) and Link Scheduling (LS). A common CA and LS may not be optimal, in terms of utilization of underlying network resources, for every traffic demand in the network. Using the best CA and LS for every traffic demand results in channel reassignments which in turn lead to traffic disruption in the network. This makes WMNs very unreliable. In this paper, we present a simple, general, and efficient framework to quantitatively evaluate a reconfiguration policy, based on the two conflicting objectives, namely maximizing network utilization and minimizing traffic disruption. Then we propose a reconfiguration algorithm called Clustered Channel Assignment Scheme (CCAS), based on clustering of similar traffic matrices. We demonstrate the effectiveness of CCAS which mainly depends on the correlation between successive traffic matrices through extensive simulation studies.

Careful Channel Assignment (CA) and Link Scheduling (LS), tuned to the traffic demand in the network, are required to efficiently utilize Multi-Channel Multi-Radio (MC-MR) Wireless Mesh Networks (WMNs). In a dynamic network, where the traffic demand keeps changing with time, we need to reconfigure the CA and LS to suit to the changing demands. But, a change in CA leads to disruption of traffic resulting in a less reliable and lossy network. In this paper, we propose a theoretical framework to evaluate the efficiency of channel reconfiguration by taking into consideration the two conflicting objectives of maximizing network throughput and minimizing the reconfiguration overhead. A channel reconfiguration scheme that takes into account the current state of the network can find a new CA with significantly less overhead caused by reconfiguration. We propose and evaluate polynomially bounded heuristic algorithms for performing demand-based and state aware channel reconfiguration. Further, in a highly dynamic network scenario, performing reconfiguration very frequently to suit every traffic demand will lead to high reconfiguration overhead. Whereas doing it less frequently will lead to underutilization of the network. Hence, we propose a scheme that employs prediction techniques to estimate the future traffic demands in order to reduce the frequency of reconfiguration considering the long term traffic demand and conduct simulation studies to evaluate this scheme.

The performance of multi-channel multi-radio (MC-MR) wireless mesh networks (WMNs) can be improved significantly with the increase in number of channels and radios. Despite the availability of multiple channels in several of the current wireless standards, only a few of them are non- overlapping and many channels are partially overlapped. In this paper, we formulate the joint channel assignment and flow allocation problem for MC-MR WMNs as a mixed integer linear program (MILP). Unlike most of the previous studies, we consider the case of using both non-overlapped and partially overlapped channels. We consider an objective of maximizing aggregate end-to-end throughput and minimizing queueing delay in the network, instead of the sum of link capacities, since the traffic characteristics of a multihop WMN are quite different from a single hop wireless network. Our formulation takes into consideration several important network parameters such as the transmission power of each node, path loss information, signal to interference plus noise ratio at a node, and frequency response of the filters used in the transmitter and receiver. We show by simulations that our MILP formulation makes efficient use of the spectrum, by providing superior channel assignments and flow allocations with the addition of partially overlapped channels, without the use of any additional spectrum.

In the deployment of wireless mesh networks (WMNs) the placement of Mesh Nodes (MNs) is an important design issue. The performance of WMNs is greatly affected by the location of the MNs. As it is difficult to place the MNs in a regular pattern in the real deployment, finding the optimal locations in the deployment environment is of much interest for the service providers. For a given possible locations for the MNs and the user density in the deployment environment, we aim to find the locations of the MNs to be used that maximizes the coverage and the connectivity of the network together. Due to high computational complexity of the exhaustive searching algorithm, an efficient local searching algorithm is proposed. Numerical results show that, the local search algorithm can give close to optimal performance with much lower time complexity than exhaustive searching.