Integrated Sensing and Communications (ISAC) enhances traditional mobile network capabilities by enabling the detection of passive, non-connected objects. Latency-sensitive vehicular applications such as Augmented Reality (AR), Virtual Reality (VR), and High Definition Maps (HD Maps) can be integrated with ISAC to improve the utilization of limited wireless resources. The Configuration Grant (CG) mechanism, in 3GPP Release 16, reduces signaling overhead in Uplink (UL) by preassigning resources to UEs (vehicles). However, employing CG for ISAC can lead to incorrect assignment of transmission slots due to the aperiodic nature of ISAC sensing traffic. To address this issue, we propose a CG allocation scheme that models the interarrival times of aperiodic traffic using a Weibull distribution. A probability distribution model, implemented and evaluated using the NS-3 5G-LENA CG module, assists the radio resource scheduler by analyzing sensing arrivals within a configuration window to predict future bursts and proactively reserve UL resources for UEs (vehicles). This prediction-driven allocation significantly improves Packet Delivery Ratio (PDR) and spectral efficiency in vehicular scenarios.

Emerging latency-critical applications such as cloud gaming and industrial automation demand agile and ultra-low-latency service delivery, which traditional network appliances struggle to support. Network Function Virtualization (NFV) addresses this by chaining Virtual Network Functions (VNFs) into Service Function Chains (SFCs). Parallelized SFCs (PSFCs) reduce service delay by executing independent VNFs concurrently, but introduce significant copy/merge and buffering overheads due to synchronization delays across branches. Moreover, dynamic PSFC arrival rates complicate efficient VNF placement decisions. This paper presents RL-PARETO, a hybrid deep reinforcement learning approach that adaptively orchestrates parallel VNFs while minimizing parallelization overheads and satisfying SLA constraints. RL-PARETO uses a graph transformer encoder with dual pointer-network heads to jointly generate PSFC partitions and VNF placements in a single pass. Training integrates Proximal Policy Optimization (PPO) for stable exploration with a Double-DQN critic for efficient value estimation. A fallback heuristic ensures feasible deployments under resource constraints. Extensive evaluations across diverse network topologies demonstrate that RL-PARETO achieves up to $10 \%$ higher acceptance rate and $15 \%$ reduction in merge buffer overhead, while maintaining robust performance under dynamic conditions.

Next-Generation Radio Access Networks (NG-RANs) aim to facilitate high data rates, low-latency applications, and dense mobile connectivity — benefit from the integration of Artificial Intelligence and Machine Learning (AI/ML) to enhance performance and efficiency. Nevertheless, the dynamic service demands within NG-RAN (namely Open RAN) lead to AI/ML performance degradation known as drift, resulting in violations of Service Level Agreements (SLA) and issues like over-or under-provisioning of resources. Detecting and adapting to drift becomes crucial to meet the diverse requirements of intelligent networks. Due to frequent retraining, the existing threshold and classifier-based approaches have potential disadvantages such as SLA violations and resource inefficiency. This paper introduces a novel approach that exploits the Generative Adversarial Network (GAN) architecture to determine the drift and anomaly. The proposed approach is evaluated for a throughput prediction use case over a real-time dataset and compared to the threshold and classifier-based approaches. The results show that the proposed approach outperforms the threshold and classifier-based approaches.

Network intrusion detection (NID) aims to identify unusual network traffic patterns (distribution shifts) that require NID systems to evolve continuously. While prior art emphasizes fully supervised annotated data-intensive continual learning methods for NID, semi-supervised continual learning (SSCL) methods require only limited annotated data. However, the inherent class imbalance (CI) in network traffic can significantly impact the performance of SSCL approaches. Previous approaches to tackle CI issues require storing a subset of labeled training samples from all past tasks in the memory for an extended duration, potentially raising privacy concerns. The proposed Semisupervised Privacy-preserving Intrusion detection with Drift-aware continual LEaRning (SPIDER) is a novel method that combines gradient projection memory (GPM) with SSCL to handle CI effectively without the requirement to store labeled samples from all of the previous tasks. We assess SPIDER’s performance against baselines on six intrusion detection benchmarks formed over a short period and the Anoshift benchmark spanning ten years, which includes natural distribution shifts. Additionally, we validate our approach on standard continual learning image classification benchmarks known for frequent distribution shifts compared to NID benchmarks. SPIDER achieves comparable performance to fully supervised and semisupervised baseline methods, while requiring a maximum of 20% annotated data and reducing the total training time by 2X.

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.

Enabling widespread adoption of resource-intensive vehicular applications such as Extended Reality (XR) and High Definition map (HD Map) necessitates further enhancements in 5G, which is anticipated with 5G-Advanced. These applications, sensitive to latency, prompt researchers to propose offloading vehicles' complex computations to nearby edge clouds, aiming to minimize latency and meeting the Quality-of-Service (QoS) demands of these applications. However, the uncertainties arising from spatio-temporal factors due to vehicle mobility and the dynamic nature of application behaviour pose significant challenges in deciding the efficient offloading decision for minimizing latency. To tackle this challenge, this paper introduces a crosslayer framework that bridges the Radio Access Network (RAN) scheduler with the Mobile Edge Computing (MEC) scheduler. The proposed framework facilitates the exchange of vehicle ranks and channel condition information between schedulers, strategically aimed at reducing Head-Of-Line (HOL) delay for efficient computational offloading. Furthermore, the MAC layer incorporates the prediction of the Buffer Status Report (BSR) using Machine Learning (ML) to further reduce the queuing delay experienced by the offloading jobs of the vehicles in uplink. Simulation results using the NS-3 gym demonstrate that the proposed cross-layer framework achieves a higher Offloading Success Rate (OSR) than the state-of-the-art QoS scheduler by effectively reducing HOL delay for HD Map vehicular application.

New Radio (NR) Sidelink was first introduced by 3GPP in Rel. 16 to meet service requirements of advanced Vehicle-to-Everything (V2X) use cases. The mode-2 of NR-V2X enables direct vehicle-to-vehicle communication through distributed resource scheduling, specifically Semi-Persistent Scheduling (SPS). The focus of Intelligent Transport Systems (ITS) till now has been predominantly on ground vehicles and infrastructure, however the anticipated future scenario where airspace becomes as crowded as ground space by the mid-2030s necessitates a robust and efficient cooperation between aerial vehicles (AVs) and terrestrial/ground vehicles (GVs). There is also an ongoing discussion in the 3GPP Technical Specification Group Radio Access Network (TSG RAN) on supporting aerial communications in AVs by using NR-sidelink. This paper is the first to study extensively the performance of Cooperative Awareness Messages (CAM) and Decentralized Environmental Notification Messages (DENM) in a unified setup, involving systems of aerial vehicles (AVs) and systems of ground vehicles (GVs) in mode-2 using NR Sidelink. The SPS at Medium Access Control (MAC) layer schedules CAM and DENM packets. We conduct a comparative analysis of CAM performance for AVs versus GVs using a 3D geographical dataset of Washington DC, evaluating Packet Reception Ratio (PRR) and Packet Inter-Reception (PIR). Furthermore, we enhance the reachability of DENM among GVs by integrating AVs using the NR sidelink UE (User Equipment)-to-UE relay feature introduced in Rel. 17/18. Finally, we propose a congestion-aware multi-hop DENM dissemination protocol with a novel relay selection mechanism to keep congestion below the maximum Channel Busy Ratio (CBR) limit, thereby improving reception node coverage by 17% compared to state-of-the-art NR-V2X DENM dissemination mechanisms.

Beyond fifth-generation (B5G) networks (namely 6G) aim to support high data rates, low-latency applications, and massive machine communications. Integrating Artificial Intelligence (AI) and Machine Learning (ML) models are essential for addressing the network’s increasing complexity and dynamic nature. However, dynamic service demands of B5G cause the AI/ML models performance degradation, resulting in violations of Service Level Agreements (SLA), over-or under-provisioning of resources, etc. To address the performance degradation of the AI/ML models, retraining is essential. Existing threshold and periodic retraining approaches have potential disadvantages such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. This paper presents a novel algorithm that predicts when to retrain AI/ML models using an unsupervised classifier. The proposed predictive approach is evaluated for a Quality of Service (QoS) prediction use case on the Open RAN Software Community (OSC) platform and compared to the threshold approach. The results show that the proposed predictive approach outperforms the threshold approach.

Intrusion detection is a form of anomalous activity detection in communication network traffic. Continual learning (CL) approaches to the intrusion detection task accumulate old knowledge while adapting to the latest threat knowledge. Previous works have shown the effectiveness of memory replay-based CL approaches for this task. In this work, we present two novel contributions to improve the performance of CL-based network intrusion detection in the context of class imbalance and scalability. First, we extend class balancing reservoir sampling (CBRS), a memory-based CL method, to address the problems of severe class imbalance for large datasets. Second, we propose a novel approach titled perturbation assistance for parameter approximation (PAPA) based on the Gaussian mixture model to reduce the number of \textit{virtual stochastic gradient descent (SGD) parameter} computations needed to discover maximally interfering samples for CL. We demonstrate that the proposed approaches perform remarkably better than the baselines on standard intrusion detection benchmarks created over shorter periods (KDDCUP'99, NSL-KDD, CICIDS-2017/2018, UNSW-NB15, and CTU-13) and a longer period with distribution shift (AnoShift). We also validated proposed approaches on standard continual learning benchmarks (SVHN, CIFAR-10/100, and CLEAR-10/100) and anomaly detection benchmarks (SMAP, SMD, and MSL). Further, the proposed PAPA approach significantly lowers the number of virtual SGD update operations, thus resulting in training time savings in the range of 12 to 40\% compared to the maximally interfered samples retrieval algorithm.

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%.

Vehicular applications such as Augmented Reality (AR), Virtual Reality (VR), and High Definition Map (HD Map) are known for their latency-sensitive traits. But, dynamic scheduling at the MAC layer incurs significant signalling overhead (in terms of Scheduling Requests (SRs) in Uplink (UL)), leading to non-negligible latency in 5G NR. To address this issue, 5G NR introduces Configuration Grant (CG) for UL transmission, which pre-allocates radio resources to UEs (vehicles), thereby reducing signalling overhead between a vehicle and the Base Station (gNB). However, the high-speed mobility of vehicles results in rapid changes in channel conditions. Employing CG in a vehicular scenario can lead to incorrect assignment of transmission parameters (e.g., Modulation and Coding Scheme (MCS)), thereby adversely impacting the vehicles’ Packet Delivery Ratio (PDR). To address this issue, this paper proposes a CG allocation algorithm that utilizes a Machine Learning (ML)-driven approach to predict the future MCS of vehicles. A data-driven ML model, derived from a real-world dataset, assists the radio resource scheduler and is evaluated using the NS-3 5G-LENA CG module. The ML-assisted CG allocation algorithm demonstrates significant improvements in terms of PDR and spectrum usage efficiency in vehicular scenarios.

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.

To reduce the processing delay from the sequentially running virtual network functions (VNFs) in a service function chain (SFC), network function parallelism (NFP) is introduced that allows VNFs of the SFC to run in parallel. Existing NFP solutions only focused on improving parallelism benefits without paying much attention to resource utilization while deploying VNFs of SFCs. We take advantage of resource-delay dependency to propose a flexible and efficient parallelized SFC placement mechanism called FlexSFC which determines the optimal SFC placement while reducing resource usage and meeting end-to-end delay guarantees of the SFCs deployed. Initial results show that FlexSFC guarantees the end-to-end delay requirement with better resource utilization and SFC acceptance rate than the state-of-the-art approaches.

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.

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.

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models.

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.

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.

To meet the rising data-offloading demands, IEEE 802.11-based WiFi networks have undergone consistent densification. The unlicensed spectrum has also been harnessed through LTE-WiFi coexistence. However, in dense and ultradense networks (DNs/UDNs), the network capacity is even more adversely impacted by the endemic interference. Yet, the precise nature of Capacity Interference Relationship (CIR) in DNs/UDNs and LTE-WiFi coexistence remains to be studied. Densification also exacerbates the challenges to network optimization. The conventional approaches to simplify the complex SINR-Capacity constraints lead to high convergence times in DN/UDN optimization. We investigate the CIR in dense and ultra-dense WiFi (IEEE 802. 11a) and LTE-WiFi (LTULAA) networks through real-time experiments. We then subject the empirical data to linear and polynomial regression to determine the nature of CIR and demonstrate that strong linear correlations may exist. We also study the impact of predictor variables, topology, and radio access technology on CIR. Most importantly, we propose CIRNO, a CIR-inspired network optimization approach, wherein the empirically determined CIR equation replaces the theoretically assumed SINR-Capacity constraints in optimization formulations. We evaluate CIRNO by implementing three recent works on optimization. We demonstrate the relevance of CIR and CIRNO in DNs/UDNs through a significant reduction in convergence times (by over 50%) while maintaining high accuracy (over 95%). To the best of our knowledge, this is the first work to statistically analyze CIR in DNs/UDNs and LTE-WiFi heterogeneous networks (HetNets) and to use CIR regression equations in network optimization.

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.

A multitude of Channel Assignment (CA) schemes have created a paradox of plenty, making CA selection for Wireless Mesh Networks (WMNs) an onerous task. CA performance prediction (CAPP) metrics are novel tools that address the problem of appropriate CA selection. However, most CAPP metrics depend upon a variety of factors such as the WMN topology, the type of CA scheme, and connectedness of the underlying graph. In this work, we propose an improved Channel Assignment Link-Weight Metric (iCALM) that is independent of these constraints. To the best of our knowledge, iCALM is the first universal CAPP metric for WMNs. To evaluate iCALM, we design two WMN topologies that conform to the attributes of real-world mesh network deployments, and run rigorous simulations in ns-3. We compare iCALM to four existing CAPP metrics, and demonstrate that it performs exceedingly well, regardless of the CA type, and the WMN layout.

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.

Cellular heterogeneous networks (HetNets) are going to be one of the key enablers for 5G. Downlink/Uplink decoupling (DUDe) is a concept in which a mobile device is connected with Macro cell for downlink communication and with small cell for uplink communication in LTE/LTE-A HetNets. It improves uplink data rate, reduces power consumption of devices, balances load between Macro cell and small cells. Due to incorporation of DUDe, a mobile device has to perform separate uplink and downlink handovers unlike traditional handovers in coupled LTE networks. In this paper, we propose various handover schemes for DUDe LTE networks. Apart from this, we have mathematically analysed the received SINR by small cells taken part in decoupling, with respect to a device moving in decoupling regions of these small cells, in multiple cell interference scenario. Simulation results show the signaling impact of DUDe in handovers, increased uplink SINR, decreased power consumption of devices in both single small cell and multiple small cell scenarios.

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.

Femto cells a.k.a. Low Power Nodes (LPNs) are used to improve indoor data rates as well as to reduce traffic load on macro Base Stations (BSs) in LTE cellular networks. These LPNs are deployed inside office buildings and residential apartment complexes to provide high data rates to indoor Users. With high SINR (Signal-to-Interference plus Noise Ratio) the users experience good throughput, but the SINR decreases significantly because of interference and obstacles such as building walls, present in the communication path. So, efficient placement of Femtos in buildings while considering Macro-Femto interference is very crucial for attaining desirable SINR. At the same time, minimizing the power leakage in order to improve the signal strength of outdoor users in a high interference (HIZone) around the building area is important. In our work, we have considered obstacles (walls, floors) and interference between Macro and Femto BSs. To be fair to both indoor and outdoor users, we designed an efficient placement and power control SON (Self organizing Network) algorithm which optimally places Femtos and dynamically adjusts the transmission power of Femtos based on the occupancy of Macro users in the HIZone. To do this, we solve two Mixed Integer Programming (MIP) methods namely: Minimize number of Femtos (MinNF) method which guarantees threshold SINR (SINRTh) -2dB for all indoor users and optimal Femto power (OptFP) allocation method which guarantees SINRTh(- 4 dB) for indoor users with the Macro users SINR degradation as lesser than 2dB.

In Wireless Local Area Networks (WLANs), providing seamless mobility and balancing load among Access Points (APs) are challenging issues due to simple signal strength based association and hand-off mechanisms employed at wireless clients. Extensions to Software Defined Networking (SDN) framework for wireless networks could help to address theses issues in an efficient and cost-effective manner with a central view of WLAN at the SDN controller. In this work, we propose a novel load-aware hand-off algorithm for SDN based WLAN systems which considers traffic load of APs in addition to received signal strength at wireless clients to solve load imbalance among APs and offer seamless mobility. We implemented the proposed algorithm on a small-scale prototype testbed and obtained improved network throughput for mobile clients as well as static clients compared to legacy hand-off algorithms used in WLANs.

Femto cells have been integrated into 4G Long Term Evolution (LTE) cellular network architecture to efficiently address the coverage and capacity issues faced in indoors and at hotspots. Though spectral efficiency increases through frequency reuse one at Femtos, it could lead to co-tier interference and cause higher interference for cell edge User Equipments (UEs). This problem is more severe in enterprise and hotspot Femto deployments due to dense placement of Femtos. Existing co-tier interference management techniques do not solve this problem completely. Hence, in this paper, we propose a Variable Radius (VR) algorithm which dynamically increases or decreases the cell edge/non-cell edge region of Femtos and efficiently allocates the radio resources among cell edge/non-cell edge region of Femtos so that the co-tier interference between neighboring Femtos can be avoided. We implemented the proposed VR algorithm on top of Proportional Fair (PF) scheduling algorithm in NS-3 simulator. In our experiments, for 90 UEs the proposed technique (VR + PF) achieved 29% and 38% improvement in average throughput for static and mobile scenarios, respectively when compared to classic PF algorithm without any interference management.

IEEE has introduced a standard on vertical handover known as Media Independent Handover (IEEE 802.21 MIH) which aims to achieve end user mobility by assisting the vertical handover across heterogeneous wireless technologies thus improving end user experience. However, the current MIH standard lacks context-aware architecture which is needed to adapt to the changing network dynamics and mechanisms to distribute load uniformly in the network. In this paper, we propose an enhanced architecture to IEEE 802.21 Media Independent Handover (MIH) framework in heterogeneous wireless networks. In the enhanced MIH framework, we add in a new network element known as Handover Agent(HA) for simplifying handover overhead at mobile nodes and offering more seamless vertical handover to mobile nodes. The proposed architecture addresses several challenges involved in unified heterogeneous network system such as context-aware handover, load balancing and signaling overhead. We also present comparative mathematical analysis of our proposed architecture with respect to the current MIH standard.

We propose a novel power saving strategy called Sleep during Neighbor-Addressed Frame (SNAF) for improving energy efficiency of IEEE 802.11 based wireless networks. IEEE 802.11 (Wi-Fi) radios that employ SNAF mode can turn OFF their wireless transceivers (i.e., put radios in sleep mode) within specific periods of neighbor-addressed frames while they are being received. The sleep duration of transceivers is easy to determine with no loss of packet's critical control information. The proposed SNAF mode operation does not have any negative effect on network throughput and even complements Power Saving Mode (PSM) available in 802.11 standard. We further propose GreenFrame format for next generation wireless networks. In experiments conducted in wireless LAN scenarios, we observed savings as much as 57.8% when we implement SNAF mode in 802.11 standard and up to 49.5% when we implement SNAF mode in 802.11 PSM.

In this paper, we investigate the machine learning based strategies for dynamic channel selection in Cognitive Access Points (CogAPs) of WLANs. We employ Multi-layer Feedforward Neural Network (MFNN) models that utilize historical traffic information from network environment for learning the influence of spatio-temporal-spectral factors on the network and then predicting future traffic loads on each of the channels. Based on the future traffic loads, CogAP chooses the best channel for serving wireless clients. An important factor is the time scale of traffic prediction. We construct three kinds of traffic predictors that predict traffic at different time scales: MLP (Minute Level Prediction), MILP (Minute Interval Level Prediction), and HLP (Hourly Level Prediction) schemes and study their prediction accuracy. Experiment results show that MFNN predictors perform better than traditional autoregressive models in terms of prediction accuracy. In addition to accurate prediction, another factor that influences the design of cognitive network channel selection is the impact of channel selection strategy on the transport layer performance. We, therefore, conduct performance studies on the TCP throughput achieved on the above mentioned cognitive channel selection strategies. The MFNN predictors will also help CogAP to find and switch to the optimal channel, leading to a higher and more sustained throughput.

Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios that apply cognition only at the physical layer. Designing a cognitive network is challenging since learning the relationship between network protocol parameters in an automated fashion is very complex. We propose to use Bayesian Network (BN) models for creating a representation of the dependence relationships among network protocol parameters. BN is a unique tool for modeling the network protocol stack as it not only learns the probabilistic dependence of network protocol parameters but also provides an opportunity to tune some of the cognitive network parameters to achieve desired performance. To the best of our knowledge, this is the first work to explore the use of BNs for cognitive networks. Creating a BN model for network parameters involves the following steps: sampling the network protocol parameters (Observe), learning the structure of the BN and its parameters from the data (Learn), using a Bayesian Network inference engine (Plan and Decide) to make decisions, and finally effecting the decisions (Act). We have proved the feasibility of achieving a BN-based cognitive network system using the ns-3 simulation platform. From the early results obtained from our cognitive network approach, we provide interesting insights on predicting the network behavior, including the performance of the TCP throughput inference engine based on other observed parameters.

In this paper, we present an application of the Cognitive Networking paradigm to the problem of development of autonomous Cognitive Access Point (CogAP) for small scale wireless network environments such as Wi-Fi hotspots and home networks. In these environments we typically use only one AP per service provider/residence for providing wireless services to the users. However, note that larger number of APs from multiple service providers/residences vie for bandwidth in any geographic region. Here we can reduce the cost of autonomic network control by equipping the same AP with a cognitive functionality. We first present architecture of our autonomous CogAP. Then we introduce our algorithmic solution, in which a Neural Network-based traffic predictor makes use of historical traffic traces to learn network traffic conditions and predicts traffic loads on each of 802.11 b/g channels. The cognitive decision engine makes use of traffic forecasts to dynamically decide which channel is best for CogAP to operate on for serving its clients. One of the challenges in autonomous cognitive decision making is the computation resource constraints in today's embedded APs. We have built a prototype CogAP device using cognitive software modules and off-the-self hardware components. We carried out performance evaluation of the proposed CogAP system by conducting experimental measurements on our testbed platform; the obtained results show that the proposed CogAP is effective in achieving performance enhancements with respect to state-of-the-art channel selection strategies.

In this paper, we present an application of the cognitive networking paradigm to the problem of dynamic channel selection in infrastructured wireless networks. We first discuss some of the key challenges associated with the cognitive control of wireless networks. Then we introduce our solution, in which a Neural Network-based cognitive engine learns how environmental measurements and the status of the network affect the performance experienced on different channels, and can therefore dynamically select the channel which is expected to yield the best performance for the mobile users. We carry out performance evaluation of the proposed system by experimental measurements on a testbed implementation; the obtained results show that the proposed cognitive engine is effective in achieving performance enhancements with respect to state-of-the-art channel selection strategies.

Traffic sensing and characterization is an important building block of cognitive networking systems; however, it is very challenging in multi-channel multi-radio wireless networks. The contributions of this paper include the following: (i) a discussion of packet sampling for traffic sensing in multi-channel wireless networks, (ii) a comparison of various time-based sampling strategies using the Kullback-Leibler Divergence (KLD) measure, (iii) a study of the effect of the sampling parameters on the accuracy of the sampling strategies, (iv) the proposal of a new metric (Traffic Intensity) which estimates the busyness of channels by taking into consideration not only the successfully received packets but also corrupt or broken packets, and (v) some preliminary results on the characterization of a campus 802.11 network environment in a spatio-temporal fashion.

Wireless network characterization is an important task in next generation wireless networks. In order to achieve efficient wireless network characterization, accurate sampling strategies are required. The relative performance of different sampling strategies for assessing various wireless network traffic metrics is significant due to the complexity and expense involved in the collection, storage, and analysis of all the traffic generated in the wireless medium. Since the spectrum used for most wireless networks, especially those based on IEEE 802.11 standards, is divided into several channels, the existing count-based sampling methods demand continuous capture on each channel for selecting the desired packets of interest. Continuous capturing makes the cost of monitoring infrastructure very expensive and hence count-based sampling methods are not scalable. However, the time-based sampling methods which were considered inaccurate in wired network characterization, appear to offer a cost-effective and scalable solution by reducing the cost of resources necessary to accurately characterize the wireless medium. For example, the use of time-based sampling enable us to make use of a single wireless interface for accurately sampling multiple channels. However, in order to achieve this, we need to identify the right set of parameters for time-based sampling. This paper presents a study of the performance of various time-based sampling methods in answering questions related to their use in wireless network traffic characterization. We simulate time-based sampling traces at a variety of granularities using a complete packet trace (i.e., parent population) captured in a campus wireless network environment that aggregates traffic from a large number of nodes. From our analysis using Chi-square test, we found that the timer-driven time-based sampling is more accurate than count-driven time-based sampling for both systematic and stratified sampling schemes.

Providing real-time voice support over multihop ad hoc wireless networks is a challenging task. To make a voice application feasible over ad hoc wireless networks, the perceived voice quality must be improved while not significantly increasing the packet overhead. We suggest packet-level media-dependent adaptive forward error correction (FEC) at the application layer in tandem with multipath transport for improving the voice quality. Since adaptive FEC masks packet losses in the network, at the medium access control (MAC) layer, we avoid retransmissions in order to reduce the control overhead and end-to-end delay. Further, we exploit the combined strengths of layered coding and multiple description (MD) coding for supporting error resilient voice communication in ad hoc wireless networks. We propose an efficient packetization scheme in which the important sub-stream of the voice stream is protected adaptively with FEC depending on the loss rate present in the network and is transmitted over two maximally node-disjoint paths. Our scheme achieves significant gains in terms of reduced frame loss rate, reduced control overhead, and minimum end-to-end delay and almost doubles the perceived voice quality compared to the existing approaches.

In this paper, we address the problem of voice multicasting in ad hoc wireless networks. The unique characteristics of voice traffic (viz. small packet size, high packet rate, and soft realtime nature) make conventional multicasting protocols perform quite poorly, hence warranting application-centric approaches in order to increase robustness to packet losses and lower the overhead due to high packet rate. By exploiting the path diversity and the error resilience properties of multiple description coding (MDC), we propose a robust voice multicast routing (RVMR) protocol. Our protocol uses a novel path based Steiner tree heuristic to reduce the number of forwarders in each tree, and constructs two trees in parallel with reduced number of common nodes among them. Moreover, unlike other on-demand multicast protocols, RVMR specifically attempts to reduce the periodic (non on-demand) control traffic. We propose several optimizations for reducing the overhead while transmitting high packet rate voice traffic in ad hoc networks. We extensively evaluate RVMR in the NS-2 simulation framework and show that it out-performs existing single-tree and two-tree multicasting protocols.

Providing real-time voice multicasting over multi-hop ad hoc wireless networks is a challenging task. The unique characteristics of voice traffic (viz. small packet size, high packet rate, and soft real-time nature) make conventional multicasting protocols perform quite poorly, hence warranting application centric approaches in order to provide robustness against packet losses and lower the overhead due to high packet rate. In this paper, we first show that the optimal voice multicasting tree (OVMT) problem is NP-complete and then propose a localized distributed heuristic for minimum number of transmissions (LDMT). By incorporating LDMT in ADMR protocol, extensive simulations are done in NS-2 framework to measure the performance of LDMT for voice applications. We observed that LDMT reduces the redundant transmissions in transmitting voice packets from the source to all multicast receivers (thus reducing the overall voice traffic considerably), thereby making it suitable for voice multicasting in AWNs.

In this paper, we address the problem of video multicasting in ad hoc wireless networks. The salient characteristics of video traffic make conventional multicasting protocols perform quite poorly, hence warranting application-centric approaches in order to increase robustness to packet losses and lower the overhead. By exploiting the path-diversity and the error resilience properties of multiple description coding (MDC), we propose a robust demand-driven video multicast routing (RDVMR) protocol. Our protocol uses a novel path based Steiner tree heuristic to reduce the number of forwarders in each tree, and constructs multiple trees in parallel with reduced number of common nodes among them. Moreover, unlike other on-demand multicast protocols, RDVMR specifically attempts to reduce the periodic (non on-demand) control traffic. We extensively evaluate RDVMR in the NS2 simulation framework and show that it outperforms existing single-tree and two-tree multicasting protocols.

The issue of providing Quality of Service (QoS) guarantees in an Ad~hoc wireless network is a very challenging problem. In this paper, we make the following contributions: (i) analytically derive bounds for the end-to-end call acceptance rate using existing queueing theory methods, (ii) study the impact of the routing scheme on the end-to-end call acceptance rate, and (iii) propose a differentiated services scheme for deterministically providing QoS guarantees.Unlike existing studies which analyze the transport capacity, we focus on the end-to-end call acceptance. The framework that we assume is that of a TDMA-based Ad~hoc wireless network. The routing scheme employed influences the end-to-end call acceptance of the network. The metrics that we consider are the call acceptance probability and the system saturation probability (i.e., the probability that the network is in a state in which every new call is rejected). We derive general bounds on the call acceptance and the system saturation for the case of differentiated-classes of users in the network. These bounds indicate the number of calls of the highest priority class that can be admitted into the network.Simulation studies were carried out to study the effect of load, hopcount, and the influence of the routing protocol on the call acceptance. The increase in the call acceptance rate with the introduction of load-balancing highlights the importance of load-balancing in enhancing the system performance. From these studies, we arrive at the following results: (i) load-balancing leads to significant improvement in the end-to-end call acceptance rate, and is an important factor in attaining the maximum end-to-end call acceptance rate in a given network and (ii) it is indeed possible to provide deterministic QoS guarantees for a designated set of nodes which are characterized by "deterministic guarantee limit".

In this paper, we address the issue of providing multimedia traffic support in asynchronous ad hoc wireless networks. Since multimedia traffic has stringent bounds on end-to-end delay, we present a resource reservation for transmitting in such traffic. The existing asynchronous MAC protocols such as RTMAC (B.S. and C. Siva Ram Murthy, August 2002) and MACA/PR (C.R. Lin and M. Gerla, March 1999) when used for multimedia traffic provide inefficient utilization of network resources and affect call acceptance ratio and call drop ratio of multimedia traffic severely. Hence in this work, we modify the RTMAC protocol for supporting multimedia traffic so that it overcomes these limitations and improves packet delivery ratio and end-to-end delay of such traffic. The core concept of this protocol is a novel slot allocation strategy for efficient utilization of the available bandwidth for carrying multimedia traffic and best-effort traffic. Extensive simulations were performed to assess the performance of the protocol under varying network conditions. The simulations clearly indicate the gains in using such a slot allocation strategy for carrying multimedia traffic.

The issue of providing QoS guarantees in an ad hoc wireless network is a challenging problem. Irrespective of the nature of the routing and reservation protocol used in the QoS scheme, there is an inherent limitation on the kind of QoS guarantees that can be provided. Unlike existing studies which analyze the transport capacity, we focus on the achievable capacity. The framework that we assume is that of a TDMA-based network. In this paper, we investigate the achievable capacity and the influence of routing protocols on it. The metrics that we consider are the call acceptance probability and the system saturation probability. We derive general bounds for the case of multiple-classes of users in the network. These bounds indicate the number of calls of the highest priority class that can be admitted into the network. Simulation studies were performed to study the effect of load, hopcount, and the routing protocol on the call acceptance. The increase of the call acceptance with the introduction of load-balancing highlights the importance of load-balancing in enhancing the system performance.