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.

Fifth-generation millimeter wave (mmWave) systems rely on sub-connected (SC) hybrid beamforming architecture due to low power consumption and reduced hardware complexity. In SC architecture, each RF chain is connected to a subset of antennas which limits the associated beamforming gain. Thus, designing beam patterns for SC mmWave systems that can effectively serve multiple users with high beamforming gains becomes a challenging task. Considering a clustered user geometry to model multiple users in close proximity, this work proposes a beam pattern multiplication-based precoder design to improve the overall spectral efficiency performance in mmWave systems. Simulation results demonstrate the effectiveness of the proposed design for SC mmWave systems in terms of high beamforming gain, improved spectral and energy efficiency.

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.

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.

The progression of Sixth-Generation (6G) services requires networks to meet stringent Quality of Service (QoS) and Quality of Experience (QoE) standards, demanding superior performance from all network segments. Selecting the most suitable path that meets application demands across multiple network segments is a key strategy for achieving these objectives. Traditional and modern path selection methods focus on QoS metrics such as bandwidth and latency to ensure efficient and reliable data delivery. However, as 6G introduces more interactive applications like Virtual Reality (VR) and Augmented Reality (AR), prioritizing user experience alongside QoS metrics becomes more crucial. This paper presents a Service-Aware Optimal Path Selection (SOPS) algorithm designed to select an optimal End-to-End (E2E) path for each application by minimizing a global cost function that incorporates both QoS and QoE parameters. The global optimality of the selected E2E path is proven by demonstrating the closed and convex nature of the underlying cost functions. Moreover, our proposed distributed cost function ensures optimal E2E paths formed from selected network segment-specific paths. Extensive simulations show that SOPS improves QoS and QoE when compared with other state-of-the-art routing algorithms. SOPS enhances QoS by improving reliability by 0.84% and reducing packet delay by 5.55%. An improvement of up to 99.65% is seen in the connection acceptance ratio, particularly for diverse application requirements. Significant improvements in QoE are observed across Hypertext Transfer Protocol (HTTP) and video applications, with throughput increases of 10.04% and 7.99%, jitter reductions of 86.25% and 2.49%, and delay improvements of 77.08% and 0.69%, respectively.

Puncturing ongoing enhanced Mobile Broadband (eMBB) traffic to accommodate the stringent delay and reliability requirements of ultrareliable low-latency communication (URLLC) Internet of Things (IoT) traffic has attracted a lot of attention recently. These puncturing decisions are based on the channel quality indicator (CQI) as seen by the eMBB/URLLC users, and any error/delay in the estimation/observation of the CQI is likely to adversely impact the reliability of URLLC-IoT traffic. Moreover, each puncturing decision incurs a signaling overhead in terms of the downlink control information for the URLLC-IoT devices, as well as puncturing indication for eMBB devices. In this work, we propose a static puncturing scheme which does not rely on CQI information. The reliability of URLLC-IoT transmissions is maintained by designing the system for lowest supported CQI. The deterministic nature of the proposed technique will allow a base station to schedule the eMBB traffic in such a way that system design goals like overall fairness and other Quality of Experience parameters for eMBB users can be optimized. The amount of URLLC-IoT traffic that can be served reliably at any given time is always limited, and we quantify this limit for the proposed scheme by numerically evaluating the maximum URLLC-IoT arrival rate that can be supported for any chosen level of reliability. Furthermore, we show that our puncturing scheme provides a deterministic and contained impact on eMBB throughput when compared to other schemes that puncture eMBB traffic greedily with respect to URLLC-IoT CQI or eMBB sum-throughput.

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.

Fifth-generation millimeter wave (mmWave) systems deploy spatial beam search using synchronization signal (SS) blocks for initial access (IA). Technique based on narrow beam sweeping procedure completed in a single SS burst is optimum for idle users that have arrived before IA. With a maximum IA duration of 5ms specified by 3GPP Release 15, the probability of user arrival during IA is non-trivial. For these users, existing search procedures will result in significant discovery delays and low detection probability. This work considers an alternative scenario wherein the user arrives after the commencement of IA. In such cases, a single SS burst may not suffice and one must consider repeating the SS burst to enhance user detection probability and reduce IA delay. Repetition of SS burst brings the trade-off between half-power beamwidth (HPBW) and the number of SS blocks per burst i.e., large HPBW supports a smaller number of SS blocks per burst and vice-versa. To achieve high accuracy of angular position with SS burst repetition, a grating-based search has been proposed which utilizes antenna element spacing greater than half-wavelength. This paper presents an IA delay, location error and detection analysis for both scenarios; with and without SS burst repetition. Simulation results demonstrate the enhanced performance of the proposed grating search technique with SS burst repetition in terms of user detection, reduced IA delay, and low angular position error.

Massive multiple-input multiple-output (MIMO) systems operating in the millimeter-wave (mmWave) frequency band support extremely high data rates. One of the major shortcoming of severe path losses in these systems is addressed by the employment of large phased array antennas with highly directional beams. Owing to the precise nature of the beams, identification of the most suitable beam for link establishment between the base station (BS) and the user equipment (UE) becomes an extremely challenging and time-consuming task. The existing two-level phased array approach is based on an exhaustive search that requires a high number of beam sweeps. In this work, a novel hierarchical fast beam search approach based on a two-level phased array is proposed for improved UE discovery in mmWave massive MIMO systems. The proposed approach leverages suitable antenna spacing and radiation pattern multiplication at two-levels to yield refined beams. The hierarchical implementation via grouped antenna units results in a refinement of the beamspace at the digital level based on the best beam direction evaluated at the analog subarray level. Extensive simulation results validate the superior performance of the proposed algorithm in terms of mean position error and the total number of beam sweeps. The investigation of misdetection probability will be considered in our future work.

In Band Full Duplex (IBFD) is a promising technology to double Spectral Efficiency (SE). Due to practical limitations in realizing IBFD User Equipments (UEs), Hybrid IBFD Cellular Network (HICN), composed of an IBFD Base Station (BS) and legacy Half Duplex (HD) UEs, is preferred. Optimum resource allocation algorithm for HICNs has been of great interest, and it primarily relies on UE-to-UE Channel State Information (CSI). Though computational and signaling intensive approaches are used in literature to obtain this CSI, we introduce a location-aware technique without these overheads at UEs. Using proposed idea of frequency sharing within UE groups, we formulate a constrained optimization problem for HICNs to maximize sum-SE. Based on multiple constraints imposed, frequency sharing possibilities amongst UEs are captured in the adjacency matrix, which lays the foundation for UE grouping. An exhaustive search from adjacency matrix identifies the theoreticallybestUE groups to achieve maximum frequency sharing. For practical realizations, a computationally efficient algorithm to identifyoptimalgroups of two and/or three UEs is derived. We propose anovelcorrelation approach to avoid expensive resource allocations in every scheduling interval. Simulation results reveal median sum-SE of 89% over HD systems, thus making the proposed solution far more attractive.

Demonstrated Spectral Efficiency (SE) gain by In Band Full Duplex (IBFD) makes it an attractive radio technology. Owing to inherent complexities in IBFD, Hybrid IBFD Cellular Network (HICN) is opted in practice because it limits IBFD capability only to Base Station and continues with legacy Half Duplex (HD) User Equipments (UEs). Since sharing frequencies within UE groups maximizes sum-SE in a HICN, we formulate a grouped UEs maximization problem for achieving maximum frequency sharing. Unlike heuristic search methods developed in literature, in this paper, we build a mathematical framework for UE grouping. Three grouping algorithms are analytically derived in closed-forms with varying performance and time complexity trade-offs. The optimal algorithm provides benchmarking on these performance measures. Using structural advantages of a HICN, we derive reduced search-optimal algorithm that reduces time complexity while guaranteeing optimal grouping, whereas near-optimal algorithm achieves close-to-optimal grouping with substantially reduced time complexity for real-time cellular systems. We further introduce the concept of ineligible UEs, which uniformly reduces time complexity of all three algorithms without impacting their grouping performance. Extensive simulations reveal that the proposed near-optimal algorithm achieved a median sum-SE of 93.5% of theoretically doubling maximum over legacy HD systems, thus emerging as a prospective solution.

Zigbee is lenient in a few security policies as a result, there are certain security vulnerabilities in Zigbee, as identified by current research. This paper addresses problem of identifying and detecting attacks on the Zigbee network, especially using spoofed devices. Various parameters of the Zigbee (802.15.4) stack are analysed and from the analysis, most effective parameters for identifying and detecting the attack by spoofed devices are understood. Identified parameters are used to form a unique fingerprint of devices. The idea is to fingerprint the devices that are in the network and also devices that were previously in the network and form a database of white-listed devices. If a newly joining device does not adhere to these fingerprints it can be prevented from joining the network until further authentication or manual intervention.

Flows in software defined networking (SDN) can pass through multiple domains having separate controllers. A domain is interpreted as part of the network topology under a SDN controller. This necessitates negotiation between various domains, to meet the QoS requirements of flows. These QoS negotiations may be static or dynamic. The SDN domains may be wired or wireless. Though, there have been efforts in the past to define inter-domain QoS negotiations for wired networks using SDN, to the best our knowledge, not much headway has been made when both wireless and wired domains are involved. In this paper, we consider a case where flows connect devices passing through a wired and a wireless domain having separate SDN controllers, and propose a novel QoS negotiation mechanism between them. First, we define a generic mechanism to map QoS parameters from one domain to the other. We then, define utilities for both the domains based on the QoS parameters of flows, and model our proposed mechanism as a mixed integer program. We observe that the problem is NP-complete, and propose a branch and bound-based algorithm to maximize the utility of the wireless domain, and evaluate the same for the wired part. Results indicate, as the resources provided to the flows in the wired domain varies dynamically, the wireless domain accordingly modifies the flows, satisfying their minimum/maximum QoS requirements under different scenarios. Conversely, this also means that resources in wired domain get adjusted, when wireless counterpart changes flow parameters based on wireless channel conditions. Further, the results reveal that smaller granularity of increment/decrement steps of QoS parameters satisfy flow requirements efficiently.

Machine Type Communication (MTC) is becoming an integral part of the Long Term Evolution - Advanced (LTE-A) cellular network. Challenges arise when some of the MTC devices, due to the nature of their applications, are deployed in low signal locations. As per 3GPP requirements, there is a need for additional coverage enhancement up to 20 dB in comparison with LTE category 1 UE for MTC devices. In the previous works reported till now, Repetition Coding is proposed as an effective technique to achieve the required coverage enhancements at cost of longer decoding time. In low signal conditions where many repetitions are required to build the SNR needed, the decoding delay may be unacceptable. For a LTE-A MTC UE, Physical Broadcast CHannel (PBCH) decoding has a very important role and fast, efficient decoding of PBCH will help to improve the device performance. In this paper, we propose to use well established technique called Reduced Search (RS) Viterbi to improve PBCH decoding performance without compromising the time-to-decode. RS Viterbi technique utilizes a priori knowledge of transmitted bits to reduce the size and complexity of trellis, which in turn also reduces probability of choosing incorrect path, i.e., error. Up to 2.2 dB SNR gain is seen in simulation using the RS Viterbi decoding against the conventional Viterbi decoding, which will contribute in improving the sensitivity of MTC devices for better reachability.