Sixth Generation (6G) networks are envisioned to unify 5G services, offering new service classes with stringent data rate, connectivity, and latency requirements. End-to-End (E2E) network slicing orchestration is essential to ensure optimum resource provisioning for services while upholding service quality standards. E2E slicing for a large number of slices hosting diverse services across multiple domains is a complex problem, for which highly centralized resource management approaches have been proposed in literature. With increasing types of services in 6G, the demand for slices will expand, making centralized solutions less practical due to scalability and responsiveness issues. To address this challenge, this paper proposes a novel hierarchical Multi-Domain Resource Management (MDRM) framework that translates global slice-hosted service requirements into requirements per network domain. MDRM operates in two phases; in the first phase, a centralized cross-domain resource optimization model is proposed to estimate user transmission parameters. Leveraging this information, the framework deploys a distributed and probabilistic open radio access network resource management strategy in the second phase that combinatorially groups and manages service requests. The two-phase approach allows the system to be highly responsive to address a large volume of 6G requests while significantly improving user service quality and experience. Extensive simulations show that MDRM enhances resource provisioning efficiency by up to 66.48%, thereby boosting slice throughput by up to 52.63% while minimizing decision delay by 57.87% compared to state-of-the-art algorithms. Additionally, MDRM enhances user service experience by up to 4.44 times compared to existing algorithms.

The evolution of Fifth Generation (5G) and beyond networks aims to support a diverse array of devices, each with distinct Quality of Service (QoS) requirements, particularly in terms of latency, packet loss, and reliability. Efficient utilization of both licensed and unlicensed spectrum is essential to meet these demands. To address this, 3GPP standards have introduced the Multi-Access solution to enhance spectrum usage and accommodate increasing number of QoS-sensitive devices. This paper investigates the mathematical formulation of a joint optimization problem that aims to satisfy latency and throughput requirements; explicitly considering the QoS demands of services and the capacity constraints of 3GPP and non-3GPP access systems, modeled via queuing theory. We prove the correctness and convexity of the objective function, ensuring that the feasible solution region is convex and the derived scheduling policy is globally optimal. Simulations with realistic network settings further validate the framework's efficiency under static load conditions.

Fifth-generation systems with a sub-connected (SC) hybrid beamforming architecture are highly beneficial due to their low complexity and high energy efficiency. The fixed number of antenna elements per subarray renders array gain per data stream limited for SC architecture. Enhancing beamforming gain in this architecture is a challenging problem. This work introduces a novel hybrid precoder designed to enhance spectral efficiency in a multi-user SC hybrid beamforming system by leveraging spatial information about users. An initial beam training approach is employed to gather users’ spatial information, which is further used to form user batches. Each batch is assigned a single coarse steering angle and can have multiple refined steering angles. These angles are used to design the analog and digital beamforming vectors for the proposed hybrid precoder. Users within the same batch employ beam pattern multiplication to efficiently utilize a higher number of antenna elements per data stream, leading to enhanced beamforming gains. The analytical beamforming gain in the presence of user batches has been derived and a relationship between spatial location, user batch formation, and achievable beamforming gain is established. It is observed that the average beamforming gain is maximized when the number of antenna elements per subarray approaches the total number of radio frequency chains. Simulation results demonstrate a significant improvement in beamforming gain and validate the superiority of the proposed design over the conventional SC approach in terms of high beamforming gain and improved spectral efficiency.

As networks advance toward the Sixth Generation (6G), management of high-speed and ubiquitous connectivity poses major challenges in meeting diverse Service Level Agreements (SLAs). The Zero Touch Network (ZTN) framework has been proposed to automate and optimize network management tasks. It ensures SLAs are met effectively even during dynamic network conditions. Though, ZTN literature proposes closed-loop control, methods for implementing such a mechanism remain largely unexplored. This paper proposes a novel two-stage closed-loop control for ZTN to optimize the network continuously. First, an XGBoosted Bidirectional Long Short Term Memory (BiLSTM) model is trained to predict the network state (in terms of bandwidth). In the second stage, the Q-learning algorithm selects actions based on the predicted network state to optimize Quality of Service (QoS) parameters. By selecting appropriate actions, it serves the applications perpetually within the available resource limits in a closed loop. Considering the scenario of network congestion, with available bandwidth as state and traffic shaping options as an action for mitigation, results show that the proposed closed-loop mechanism can adjust to changing network conditions. Simulation results show that the proposed mechanism achieves 95 % accuracy in matching the actual network state by selecting the appropriate action based on the predicted state.

Extreme device connectivity and high energy efficiency are top requirements for massive machine type communication (mMTC), which can be satisfied by cell free Massive MIMO (CF-mMIMO) proposed in 6G. Grant-Free Random Access (GFRA) is an attractive access scheme for MTC traffic characterized by random short data transmissions. In CF-mMIMO systems configured with limited number of pilots chances of pilot collisions increase rapidly with increase in the number of MTC devices resulting in degradation of communication performance. In this paper we propose modified GFRA protocol with collision resolution for CF-mMIMO (GFRA-CR- CFM). The proposed protocol consists of two solution components; dominant AP-based pilot assignment and introduction of a new control signal for collision resolution to separate resources between colliding and non-colliding users of the same AP. The simulation results show that GFRA-CR-CFM achieves more than 20% improvement for mean per-user throughput performance compared to GFRA. The 95th percentile likelihood per user throughput performance of GFRA-CR-CFM is significantly improved by more than 4X times compared to GFRA.

The transition to Sixth Generation (6G) networks presents challenges in managing quality of service (QoS) of diverse applications and achieving Service Level Agreements (SLAs) under varying network conditions. Hence, network management must be automated with the help of Machine Learning (ML) and Artificial Intelligence (AI) to achieve real-time requirements. Zero touch network (ZTN) is one of the frameworks to automate network management with mechanisms such as closed loop control to ensure that the goals are met perpetually. Intent- Based Networking (IBN) specifies the user intents with diverse network requirements or goals which are then translated into specific network configurations and actions. This paper presents a novel architecture for integrating IBN and ZTN to serve the intent goals. Users provides the intent in the form of natural language, e.g., English, which is then translated using natural language processing (NLP) techniques (e.g., retrieval augmented generation (RAG)) into Network Intent LanguagE (Nile). The Nile intent is then passed on to the BiLSTM and Q-learning based ZTN closed loop framework as a goal which maintains the intent under varying network conditions. Thus, the proposed architecture can work autonomously to ensure the network performance goal is met by just specifying the user intent in English. The integrated architecture is also implemented on a testbed using OpenAirInterface (OAI). Additionally, to evaluate the architecture, an optimization problem is formulated which evaluated with Monte Carlo simulations. Results demonstrate how ZTN can help achieve the bandwidth goals autonomously set by user intent. The simulation and the testbed results are compared and they show similar trend. Mean Opinion Score (MOS) for Quality of Experience (QoE) is also measured to indicate the user satisfaction of the intent.

The rapid expansion of cellular networks and rising demand for high-quality services require efficient and autonomous network management solutions. Zero Touch Network (ZTN) management has emerged as a key approach to automating network operations, minimizing manual intervention, and improving service reliability. Digital Twin (DT) creates a virtual representation of the physical network in realtime, allowing continuous monitoring, predictive analytics, and intelligent decision-making by simulating what-if scenarios. This paper integrates DT with ZTN proactive bandwidth management in end-to-end (E2E) next-generation networks. The integrated architecture applies Few-Shot Learning (FSL) to a memoryaugmented Bidirectional Long Short Term Memory (BiLSTM) model to predict a new network state to augment the known and trained states. Using Q-learning, it determines the optimal action (e.g. traffic shaping) under varying network conditions such that user Quality of Service (QoS) requirements are met. Three scenarios have been considered: 1) normal ZTN operation with closed-loop control, 2) a what-if scenario of DT, and 3) network state unknown to DT. The simulation results show that the network can adapt to underlying changing conditions. In addition, DT-assisted ZTN achieves better performance than the other techniques.

Fifth Generation (5G) advanced and beyond communication systems aim to support a large volume of applications with diverse Quality of Service (QoS) requirements. With limited spectrum resources, 5G and Wireless Local Area Network (WLAN) multi-access scheduling is one of the proposed solutions that efficiently utilizes licensed and unlicensed frequency bands. While such heterogeneous systems help serve many users, they also present significant challenges in packet scheduling due to the varying serving capacities of the licensed vs unlicensed bands, measured in terms of latency and through-put. The existing scheduling algorithms typically try to reduce resource costs, latency, or congestion without any specific focus on the unique QoS requirements of different 5G applications. In this paper, we propose an Adaptive Latency and Throughput Aware Multi Access Scheduler (ALTAMAS) that makes the scheduling decisions based on application QoS requirements and time-varying channel characteristics. ALTAMAS utilizes the Goal programming-based Multi-Objective Optimization (MOOP) method to optimize the scheduling decision. Extensive simulations conducted to verify the veracity of the scheduler show that ALTAMAS can meet the QoS requirements of all the applications (measured in terms of throughput and latency) up to link utilization of 90 %.

Designing efficient routing protocols for Uncrewed Aerial Vehicle (UAV)-assisted communication presents significant challenges due to rapidly changing topology, limited battery capacity, and dynamic network conditions.such as energy consumption, link quality, or latency but often overlook the necessity of an integrated approach considering a broader range of factors. This paper introduces the Improved Q-learning-based Multi-hop Routing (IQMR) algorithm that facilitates energy-efficient, and reliable data transmission in UAV-assisted communication. IQMR achieves this by selecting the optimal next-hop node to ensure efficient energy utilization, reliable packet delivery through collision avoidance, and adaptive network reorganization to maintain connectivity without relying on predefined UAV paths. To the best of our knowledge, IQMR is the first to employ a multi-objective framework that captures the inter-dependencies between network parameters and UAV operational states while leveraging $Q(\lambda)$ learning to make routing decisions, ensuring reliable communication in dynamic environments. Results show that IQMR demonstrates a 36.35% improvement in energy efficiency and a 32.05% increase in data throughput over existing methods.

Low-altitude platform (LAP)-based aerial cells require frequent replacement due to periodic and aperiodic triggers, like low battery, variations in capacity demands, and mechanical malfunctions while in service. During a replacement event, the ongoing data sessions for all connected users should be transferred from the source LAP to the target LAP without interruptions. Protocols based on user handover and link management between the source and target LAPs are slow due to the overheads of signaling, causing a degradation in user data throughput during replacement. Recent literature on aerial cell cloning (ACC) proposes a much faster protocol by replicating link and location parameters from source to target LAP, but with the limitation that it is directly proportional to the number of user contexts. As a result, the latency and energy consumption for replacement are higher with increased users. Another recent scheme, dual active protocol stack (DAPS) handover, does not apply to aerial cell replacement scenarios due to high packet forwarding overhead. This paper, at the outset, proposes a novel low overhead aerial dual active protocol stack (aDAPS) handover with reduced packet forwarding, applicable to aerial cell replacement in ultra-reliability and low latency (URLLC) scenarios. Next, it proposes a novel adaptive aerial cell replacement (ACeR) mechanism. ACeR assists the network in selecting an appropriate session transfer protocol for each user based on predicted data traffic volume, traffic characteristics, and the trigger for the replacement. ACeR jointly optimizes the latency and energy optimality of the network. The system is modeled mathematically, and extensive system-level simulation results show that the proposed ACeR mechanism is efficient. ACeR reduces network energy consumption by up to 34.5% and around 14% to 18% on an average compared to the fixed protocol used during replacement in the available literature

Unmanned Aerial Vehicle (UAV) assisted communication is gaining prominence as a vital solution for establishing effective emergency communication during disaster management operations. UAVs are essential for enhancing and expanding communication systems, acting as relays to boost data transmission to ground stations, extend network coverage, and provide connectivity. However, the dynamic and resource-limited nature of aerial networks necessitates robust routing mechanisms to facilitate seamless data dissemination. While existing Q-learning-based routing protocols are adaptive to changing network conditions and resilient to failures, they often lead to suboptimal network-wide decisions due to UAVs operating independently, each maximizing its gains. This paper proposes a novel Coordinated Q-learning-based Multi-hop Routing (CQMR) algorithm for multi-UAV networks. To the best of our knowledge, this is the first time a routing algorithm introduces UAV coordination for data routing through utility function approximation with a message-passing scheme, enabling the selection of globally optimal joint actions. This novel approach meticulously considers a comprehensive set of parameters for data routing, including minimizing the expected number of hops to the destination, monitoring energy usage, maintaining network connectivity, preventing UAV collisions, and supporting adaptive network reorganization. This integrated consideration of multiple factors positions the proposed solution as superior to existing work, offering a uniquely robust and highly effective strategy for UAV-assisted communication in dynamic, resource-constrained environments, such as emergency scenarios. CQMR builds upon and extends the Improved Q-learning-based Multi-hop Routing (IQMR) algorithm, demonstrating a 12.47% increase in energy efficiency and a 13.34% higher success rate in data transmission compared to IQMR while requiring 40% fewer hops to reach the destination.

Upcoming 6G networks are expected to serve several mission-critical cyber-physical systems (CPS) applications that demand timely and fresh control updates. Different from traditional quality of service (QoS) metrics, Age of Information (AoI) measures the freshness of information for CPS applications. To efficiently serve such applications, cell-free massive multipleinput multiple-output (CF-mMIMO) has emerged as a better alternative to traditional cellular architectures. To the best of our knowledge, the time-average power minimization problem has not been addressed so far in AoI-constrained CF-mMIMO networks. In this context, we aim to minimize the time-average power consumed in CF-mMIMO networks with maximum tolerable average AoI constraints, along with communication QoS and per-transmitter power budget constraints. We propose a novel iterative scheduling and precoding algorithm to achieve our objective. Detailed analytical modeling and resulting simulation illustrate the performance of our algorithm as a function of a penalty parameter, which show the average AoI of all users are within constraint limits. We also show that the penalty parameter allows to dynamically prioritize between the time-averages of power consumed and AoI as per performance requirements.

Fifth-generation millimeter wave (mmWave) systems rely on sub-connected (SC) hybrid beamforming architecture due to low power consumption and reduced hardware complexity. These systems require directional beamforming to establish communication between the base station (BS) and user equipment (UE). Typically, the communication between BS and UE occurs in two phases. In the first namely initial access (IA) phase, BS utilizes spatial beam search using synchronization signal (SS) blocks for link establishment and is followed by data transmission in the second phase. As per the 3GPP standard for 5G NR systems, existing approaches utilize a maximum IA duration of 5ms corresponding to the transmission of 64 SS blocks for beam search. The maximum transmission of SS blocks results in a significant delay in data transmission to the user. This work proposes an optimized value of the number of SS block transmissions to achieve a higher sum-rate in an SC hybrid mmWave system. Simulation results demonstrate that an optimized value of SS block transmission results in faster user discovery and yields more time for data transmission. This in turn results in an improved performance of the SC hybrid beamforming systems in terms of enhanced sum-rate capacity and reduced initial beam acquisition time.

The rehabilitation is a most important phase in recovery of motor movements in the body. Using advanced robotic systems in the field of rehabilitation brings effective therapy in recuperation treatment. In this paper we propose a novel Electromyography (EMG) signal augmented, upper and lower limb integrated, Internet of Things (IoT) enabled, wheel chair based exoskeleton system called XoRehab. The proposed system is designed for patients affected with hemiplegic, paraplegia and tetraplegia. Existing systems as per literature survey are, for either for upper or lower limbs, with limited degrees of freedom. A system with integrated upper and lower limb mechanisms can help in reducing the patients’ movement for treatment from one system to another. The addition of EMG signals helps assess the recovery status. Additionally, providing remote access using IoT framework enables home-based rehabilitation. The system prototype has been successfully developed and experiments for various muscle movements have been conducted. The photograph of the developed system and the plots of the muscle movements captured by EMG signals, have been included as results.

Multi-layer complex networks (MLCN) appears in various domains, such as, transportation, supply chains, etc. Failures in MLCN can lead to major disruptions in systems. Several research have focussed on different kinds of failures, such as, cascades, their reasons and ways to avoid them. This paper considers failures in a specific type of MLCN where the lower layer provides services to the higher layer without cross layer interaction, typical of a computer network. A three layer MLCN is constructed with the same set of nodes where each layer has different characteristics, the bottom most layer is Erdos-Renyi (ER) random graph with shortest path hop count among the nodes as gaussian, the middle layer is ER graph with higher number of edges from the previous, and the top most layer is scale free graph with even higher number of edges. Both edge and node failures are considered. Failures happen with decreasing order of centralities of edges and nodes in static batch mode and when the centralities change dynamically with progressive failures. Emergent pattern of three key parameters, namely, average shortest path length (ASPL), total shortest path count (TSPC) and total number of edges (TNE) for all the three layers after node or edge failures are studied. Extensive simulations show that all but one parameters show definite degrading patterns. Surprising, ASPL for the middle layer starts showing a chaotic behavior beyond a certain point for all types of failures.

In the 6G wireless network, there may be a communication architecture with three levels of systems: user equipment (UE), non-terrestrial aerial low altitude platform (LAP), and terrestrial base station with mobile edge computing (MEC). Co-inference is the intelligent sharing of the multiple computing layers in the AI/ML model amongst the UE, LAP, and MEC. Computing, storage, and power shared between the above systems for co-inference will bring several system-level advantages. Furthermore, it optimizes the required data traffic bandwidth, energy consumption, and end-to-end latency. The available literature has analyzed the optimal split point of the AI/ML model between UE and MEC (one wireless link). However, to the best of our knowledge, there is no study on the AI/ML split model in the case of UE, LAP, and MEC architectures with two wireless links (UE to LAP and LAP to MEC). In this paper, for the first time, we propose a novel device-edge co-inference with a LAP-based aerial cell having computing power. We present a novel Aerial Cell-Assisted Device-Edge co-inference ModEl (ACADEME) algorithm that optimally assigns layers to compute to UE, LAP, and MEC according to two wireless link characteristics to minimize power consumption and latency of inference. Through mathematical modelling and simulations, we show that the proposed coinference substantially reduces latency, i.e., by 47% through the selection of the two optimal split points of the AI/ML model, one each at the UE and the LAP-based aerial cell, respectively.

Open source RISC-V based SoC is an essential component required for the widespread development and deployment of secure and privacy enhanced IoT edge devices. PicoRV32 has been identified to be the suitable core for the development of RISC-V based SoC. The security features required by aSoC that is used as a component of an edge device are encryption-decryption, authentication, authorization, digital signatures, etc. In this paper, the light weight authenticated encryption-decryption module Ascon -128, which is the NIST finalist, has been integrated with PicoRV32 and implemented on FPGA. This is an important step towards developing a secure RISC-V SoC. The relative performance evaluation has been carried out by integrating PicoRV32 with AES GCM-128 as well. Since Ascon has been chosen over AES GCM-128 for edge devices, it is crucial to see that the advantages are maintained in the implementation even after integration with PicoRV32 using AXI4-lite interconnect, as a proof of concept. The post implementation results show that Ascon -128 needs resources much lesser than that of AES GCM-128 in the range of operating frequencies between 50 MHz to 600 MHz. Its maximum operating frequency is however lower by about 20% as compared to AES GCM, while maintaining its power advantage of around 3%.

Optimum network slice resource allocation for multiple User Equipment (UE) requesting several services concurrently in a scalable manner is a complex open research problem. Each network slice is designed to support distinct applications with corresponding Service Level Agreements (SLAs) across multiple UEs. The distribution of network resources across slices has been a challenge to achieve the SLAs under limited bandwidth and power budget constraints. This is referred to as inter-slice resource allocation, where a UE may simultaneously request multiple services hosted across slices. When a UE is assigned a slice, the slice shares the available resources across new and existing UEs without violating the SLA. This is referred to as intra-slice scalability. While inter-slice resource allocation has been addressed in the literature, further throughput gains can be achieved by improving intra-slice scalability. This paper presents a novel Probabilistic Intra-slice Resource Service Scheduling (PRSS) method. PRSS algorithm works in two stages. In the first stage, the service throughput is estimated using a multinomial probabilistic model, followed by dynamic conditional resource estimation in the second stage. For newly requested services, the resources are re-estimated and reconfigured across prior inter and intra-slice services. The proposed two-stage method allows the system to be highly adaptive with respect to the number of UEs and their requested services hosted across slices. Analytical and simulation results show that by using PRSS, the number of services served with a better experience in terms of service quality, i.e., throughput is enhanced by 20% to 75% compared to state-of-the-art schedulers.

Unprecedented numbers of wireless devices are increasingly connecting, either autonomously or manually, to get various types of services via mobile communication systems. So far, majority of the efforts in the mobile communication system has been focused on delivering secure communication once connected, whereas security related to bootstrapping those connections are relatively less explored. A wireless device can connect to a cell without assessing base station's legitimacy. This opens way for the attackers to create room for rogue devices to enter a communication network and turn the environment malicious. False Base Station (FBS) attacks are one such example and its potential to cause active and passive impacts are an alarming concern, worldwide. In this paper, we present a new solution for mitigating FBS attacks and have simulated this experiment using OpenAirInterface (OAI) 5G codebase. In contrast to ECDSA, we propose a verification mechanism using ECCSI, an identity-based cryptography (IBC), and a novel mechanism to intelligently derive the keys on the fly and distribute the cryptographic parameters to the User Equipment (UE), which helps in determining an authentic cell during its initial connection phase. We also ensure a significant reduction in the number of cryptographic parameters required and allow law enforcement agencies to perform lawful interception without depending on the Mobile Network Operators (MNOs).