Admission Control In Networks Research Articles

Inference and performance aware multi-channel scheduling and routing scheme with call admission control in wireless mesh networks

As wireless mesh networks have been deployed in the urban area, dynamic channel allocation is more challengeable because co-located wireless mediums are likely to be tuned to the same channels and may interfere with communication. Further, existing model doesn’t consider reutilizing routing resource. This paper presents an efficient scheduling and routing model considering an external interference and routing resource reutilization for multi-channel wireless mesh networks. Each mesh node uses a novel technique to estimate the usage status of all the channels within its interference range and assigns channels dynamically to radios to minimize interference within the mesh network and between the mesh network and collocated wireless networks. Simulation results demonstrate that our proposed algorithm can improve the network throughput, reduce packet failure rate and consume more energy while transmission substantially compared to other algorithms by removing the interference overestimation problem and assigning channels aggressively with priority.

A survey on network admission control solution in 6LoWPAN using cryptographic mechanism

This paper introduces a robust network admission control (NAC) solution tailored specifically for 6LoWPAN Wireless Sensor Networks (WSN), aiming to enhance the overall security infrastructure. By implementing stringent measures, this solution not only safeguards legitimate nodes but also fortifies the network against potential security threats originating from unauthorized nodes attempting to communicate with both legitimate nodes and the Internet. The solution enhances security through innovative features for administrative nodes, including node presence detection and robust authentication. It incorporates data filtering to swiftly identify and discard unauthorized frames, strengthening network resilience. Established protocols like Neighbor Discovery and DSDV facilitate seamless integration. This strategic utilization minimizes the reliance on additional control messages, thereby optimizing the network's efficiency and resource utilization. The solution utilizes cryptographic methods, particularly the XOR algorithm, to enhance data security in wireless sensor networks. This algorithm ensures authenticity, integrity, and freshness of transmitted data frames, providing robust protection against manipulation and bolstering network security. The proposed network admission control solution addresses security risks in 6LoWPAN WSN and integrates advanced features for a strong and efficient overall security infrastructure in wireless sensor networks.

RadiOrchestra: Proactive Management of Millimeter-Wave Self-Backhauled Small Cells via Joint Optimization of Beamforming, User Association, Rate Selection, and Admission Control

Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense self-backhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.

Joint QoS Aware Admission Control and Power Allocation in NOMA Downlink Networks

In this work, we talk about the problem of joint power allocation and user association based on quality-of-service for non-orthogonal multiple access (NOMA) to downlink networks. The problem is especially difficult due to its non-convex form and the large number of optimization variables, which are solved using two different nature-inspired algorithms with low complexity. We investigate the effect of different network parameters on increasing users. Numerical results show that, for a growing number of users, the problem is becoming increasingly difficult, which indicates the increasing network resources required to solve it. The results of the simulations show that using evolutionary algorithms is a fast and effective way to solve this kind of problem. Moreover, the NOMA advantage over OMA becomes clear as the number of users increases. Evolutionary techniques outperform randomly generated solutions, as expected.

Two-layered blockchain-based admission control for secure UAV networks

The frequent replacement requirement of UAVs for recharging outputs an extreme number of messaging for admission control of end-users. There are many studies that try to optimize the network capacity in an energy-efficient manner. However, they do not consider the security of data and control channels, which is the urgent requirement of 5G. Blockchain handles secure systems. However, the high numbered transactions in blockchain may cause bottlenecks while considering computational delay and throughput of end-user. In UAVs, a high percentage of battery is consumed for computational tasks instead of communication tasks. Therefore, to handle security by considering the computational needs, this paper proposes a novel two-layered blockchain-based admission control in UAV networks. It has two layers named as side-chain for UAVs and off-chain for end-users building in a decentralized manner. In the former one, the data bearer of end-users is protected by the side-chain of each transaction which is signed by admitted end-users and UAVs. Here, each transaction includes a unique transaction id, nonce value, IMSI of the user, RNTI of UAV, signed data, and hash value. In the latter one, the off-chain is built by signing admission contracts between users in a decentralized manner and the Merkle tree is constructed for the off-chain where the root hash is only stored in a side-chain. Then, the number of transactions that should be validated is extremely decreased. Moreover, PoS is used for the validation of transactions where the validator is selected randomly instead of the competition requirement as in PoW. A novel blockchain-based admission control algorithm isolates readmission control of end-users to 5G core-side during UAV replacement. According to performance evaluation, the proposed approach serves end-users by 64.2% better QoS than the conventional one during UAV replacement. Moreover, it keeps edge delay in acceptable range by using Merkle tree and PoS.

IFACS-Q3S-- A New Admission Control System for 5G Wireless Networks Based on Fuzzy Logic and Its Performance Evaluation

In the authors' previous work, they proposed an integrated fuzzy-based admission control system (IFACS) for admission control in 5G wireless networks. In this paper, they present a new system by considering service level agreement (SLA) as a new parameter. They call this system IFACS-Q3S. They also implemented a new scheme for slice overloading cost (SOC), called fuzzy-based scheme for SOC (FSSOC). The SOC is used as a new input for the IFACS-Q3S system. The other input parameters for IFACS-Q3S are quality of service (QoS), slice priority (SP), and SLA. From simulation results, they conclude that the considered parameters have different effects on the acceptance decision. The increase of QoS, SP, and SLA caused an increase in the AD value, whereas the increase in SOC resulted in a decrease in the AD value. For SOC 0.3, when the QoS value is 0.1 and the SP value is 0.1, in the case when SLA is increased from 0.1 to 0.5 and 0.5 to 0.9, the AD is increased by 5% and 11%, respectively. On the other side, when the SLA value is 0.9, we see that AD is decreased 14% by increasing the SOC values from 0.3 to 0.8.

Delay-Guaranteeing Admission Control for Time-Sensitive Networking Using the Credit-Based Shaper

With real-time communication being a key part of the fourth industrial revolution, the need for Quality of Service (QoS) in industrial networks is gaining increasing importance. Time-Sensitive Networking (TSN) faces this need, for example, by introducing new scheduling mechanisms. The Credit-Based Shaper (CBS) has been introduced to TSN to offer low delays for multiple traffic classes by applying rate limitations. Currently, flows are reserved decentrally in CBS networks using a Stream Reservation Protocol (SRP). In contrast, the new TSN standard IEEE 802.1Qcc allows for a centralized architecture to favor short reconfiguration latencies. However, no online admission control scheme which offers safe delay bounds has been proposed for this central architecture. To close this gap, we propose two models for admission control in TSN networks using CBS. Both models offer deadline-guaranteeing flow allocation, including routing and prioritization of flows, and configure forwarding devices while eliminating packet loss. Our models utilize the mathematical framework of Network Calculus to calculate worst-case flow delays and buffer sizes. We show how our models allow for more reservations than the decentralized standard approach by improved resource utilization. We validate our models both in synthetic and industrial network scenarios. Additionally, we compare the effects and parameters of our two models, providing guidance on when to choose them.

Admission Control and Virtual Network Embedding in 5G Networks: A Deep Reinforcement-Learning Approach

Fifth-generation (5G) networks are already available in major urban areas and are expected to bring a major transformation to citizens’ lives. 5G services, such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (mMTC), require a network infrastructure capable of supporting stringent requirements in terms of latency and bandwidth demands; as such, it must be highly dynamic and flexible. Network slicing is a key enabler technology that can provide dynamic and flexible characteristics to 5G network architecture. A network slice (NS) can be defined as a partition of network and IT resources, that is, network links and nodes capacity dedicated to a specific set of service demands. As a result, different NSs can coexist over the same physical infrastructure network and can be used to dynamically and flexibly deploy the aforementioned 5G services. However, to efficiently implement NSs with different requirements, communication service providers (CSPs) that own the physical infrastructure network must adopt sophisticated techniques for admission control and resource allocation of NSs. In this paper, we present a novel framework for admission control and resource allocation of 5G NSs in metro-core networks. Specifically, our framework is based on a deep reinforcement learning (DRL) algorithm called Advantage Actor Critic (A2C), which performs admission control, i.e. it is capable of learning which slice to admit based on the availability of the physical network resources. Then, given the diversity of requirements for each 5G service, we propose different resource allocation algorithms based on integer linear programming (ILP) and heuristics to treat each service accordingly. Results show that our proposed framework can increase the number of admitted NSs with respect to the case in which the admission control is disabled by improving the resource allocation performance.

Resource Allocation, Trajectory Optimization, and Admission Control in UAV-Based Wireless Networks

In this letter, we study the resource allocation and trajectory optimization for multi-UAV based wireless networks. Our design maximizes the number of admitted users while satisfying their data transmission demands, which formulates a mixed-integer nonlinear problem. To tackle its difficulty, we first introduce soft admission variables and propose an iterative algorithm to solve this admission maximization problem. Each iteration comprises two steps, namely soft admission maximization and user removal. Our method guarantees that the number of admitted users increases over iterations. Numerical results show that our algorithm outperforms the conventional approach based on block coordinate ascent and mixed-integer linear programming.

Multi-Objective Optimization of Joint Power and Admission Control in Cognitive Radio Networks Using Enhanced Swarm Intelligence

The problem of joint power and admission control (JPAC) is a critical issue encountered in underlay cognitive radio networks (CRNs). Moving forward towards the realization of Fifth Generation (5G) and beyond, where optimization is envisioned to take place in multiple performance dimensions, it is crucially desirable to achieve high sum throughput with low power consumption. In this work, a multi-objective JPAC optimization problem that jointly maximizes the sum throughput and minimizes power consumption in underlay CRNs is formulated. An enhanced swarm intelligence algorithm has been developed by hybridizing two new enhanced Particle Swarm Optimization (PSO) variants, namely two-phase PSO (TPPSO) and diversity global position binary PSO (DGP-BPSO) variants employed to optimize the multi-objective JPAC problem. The performance of the enhanced swarm intelligence algorithm in terms of convergence speed and stability, while optimizing both the sum throughput and power consumption, is investigated under three different operational scenarios defined by their single objective priorities, which translate to sum throughput and power consumption preferences. Simulation results have proven the effectiveness of the enhanced swarm intelligence algorithm in achieving high sum throughput and low power consumption under the three operational scenarios when the network includes an arbitrary number of primary and secondary users. Comparing the hybrid SPSO approach and the proposed approach, the proposed scheme has shown its effectiveness in increasing the sum throughput to 7%, 16%, and 31% under the multimedia, balanced and power saving operational scenarios, respectively. In addition, the proposed approach is more power efficient as it can provide additional power savings of 3.58 W, 2.48 W, and 1.6741 W under the aforementioned operational scenarios, respectively.

Call Admission Control Optimization in 5G in Downlink Single-Cell MISO System

The main goal ofNew Radio 5G (NR) mobile technology is to support three generic service categories, each with very specific requirements. The first category is enhanced Mobile Broadband (eMBB), the second category relates to massive Machine-Type Communications (mMTC), and the third category relates to ultra-Reliable Low Latency Communications (URLLC). The slicing of the radio part of 5G network access network has greatly contributed to the emergence of these three categories of service with different qualities of service. This division therefore enabled the network to reserve the necessary resources for each category of services, orthogonally, and according to the performance required. In this article, we have dealt with the problem of Call Admission Control (CAC) in 5G networks where we have considered the case of the only two categories eMBB and uRLLC, which their users are served by a single cell. We calculated the maximum eMBB users admitted into the system with guaranteed data rate, while allocating power, bandwidth, and beamforming directions to all uRLLC users whose latency requirements and reliability are always guaranteed. We only considered the downlink communication, and we used the case of the multiple-input single-output (MISO) system. This CAC problem is formulated as a minimization problem l0 which is known as NP-hard problem. We therefore chose to use Sequential Convex Programming (SCP) to find a suboptimal solution to the problem.

Efficient power and admission control in multi-cell cognitive radio networks employing hybrid users

A recent class of cognitive user that can emerge and change the future of cognitive radio networks (CRNs) is hybrid user (HU). HU is an advanced primary user that is armed with cognitive functions to benefit greatly from CRN. In this paper, we aim to propose a framework to employ this type of users in CRNs. In this regard, we introduce a modified multi-cell CRN (MMCRN) based on HUs. In conventional MCRN (CMCRN), each user connects to its own base station (BS); however, in MMCRN, it is possible for HUs to exploit the secondary network. To construct the MMCRN, a BS assignment algorithm is proposed to assign the HUs to proper BSs. Afterwards, in an infeasible MMCRN, the problem of maximizing the number of admitted SUs is studied and a novel algorithm is presented. Furthermore, sum-throughput maximization of SUs is investigated in a feasible MMCRN. Finally, we modify our schemes considering channel gain uncertainty and propose algorithms that are robust against channel gain fluctuation. It is theoretically presented that our proposed schemes can significantly improve the efficiency of the network. The extensive simulations also verify the superior efficiency of MMCRN compared to CMCRN.

Effect of Slice Priority for admission control in 5G Wireless Networks: A comparison study for two Fuzzy-based systems considering Software-Defined-Networks

The Fifth Generation (5G) networks are expected to be flexible to satisfy demands of high-quality services such as high speed, low latencies and enhanced reliability from customers. Also, the rapidly increasing amount of user devices and high user’s requests becomes a problem. Thus, the Software-Defined Network (SDN) will be the key function for efficient management and control. To deal with these problems, we propose a Fuzzy-based SDN approach. This paper presents and compares two Fuzzy-based Systems for Admission Control (FBSAC) in 5G wireless networks: FBSAC1 and FBSAC2. The FBSAC1 considers for admission control decision three parameters: Grade of Service (GS), User Request Delay Time (URDT) and Network Slice Size (NSS). In FBSAC2, we consider as an additional parameter the Slice Priority (SP). So, FBSAC2 has four input parameters. The simulation results show that the FBSAC2 is more complex than FBSAC1, but it has a better performance for admission control.

5G NB‐IoT: Efficient network call admission control in cellular networks

SummaryThe International Telecommunications Union defines in its IMT‐2020 recommendations three types of use of 5G services: mMTC (massive Machine‐type Communications), eMBB (enhanced Mobile Broadband), and uRLLC (ultra‐Reliable Low Latency Communications). The mMTC service allows a considerable number of machines and devices to communicate while guaranteeing a good quality of service. The eMBB service allows very high data throughput, even at the cell border. The uRLLC service is used for ultra‐reliable communication for critical needs requiring very low latency. These services are provided separately in a given cell. However, the number of connected objects is starting to increase rapidly as well as the bit rates and energy consumption. The 5G network must make it possible to provide access to a vast number of users of its different service categories. Call admission control (CAC) techniques focus more on availability in terms of bit rate and coverage. In this article, we suggest an algorithm for modeling CAC in an area served by the three categories of services in a 5G access network, mainly based on minimum energy consumption. This technique will allow connected objects that consume low energy to connect to the network with an adequate quality of service and enable the development of the Internet of Things.

Joint Long-Term Admission Control and Beamforming in Green Downlink Networks: Offline and Online Approaches

Admission control is an effective solution to managing a wireless network with a large user set. It dynamically rejects users in bad conditions, and thereby guarantees high quality-of-service (QoS) for the others. Unfortunately, a frequently-varying admissible user set requests remarkable power to continually re-establish the transmission link. Hence, we explore the stability of admissible user set, and formulate a joint long-term admission control and beamforming problem for a network with one multi-antenna base station (BS) and multiple single-antenna users. We consider the downlink transmission in a time period containing multiple time slices. By jointly optimizing the admissible users and the BS transmit beamformers in different time slices, we aim to minimize the total power cost, including not only the power for QoS guarantee, but also the power for user status switching. Due to the NP-hardness of the problem, we develop two (offline and online) algorithms to find some efficient suboptimal solutions. The offline algorithm requires all channel state information (CSI), and solves the admissible users and beamformers in different time slices in one shot, based on successive upper-bound minimization (SUM). To support real-time data transmission, we further design an alternating direction method of multipliers (ADMM)-based online algorithm, which outputs the admissible users and beamformers in different time slices successively, utilizing the previous admissible user set, the actual value of current CSI, and the distribution of future CSI. Simulations validate the performance of the two algorithms, and show that the online algorithm is an efficient practical alternative to the offline algorithm.

MRCSC: A cross-layer algorithm for joint Multicast Routing, Channel selection, Scheduling, and Call admission control in multi-cell multi-channel multi-radio cognitive radio wireless networks

This paper addresses the problem of joint multicast routing, channel selection, scheduling, and call admission control in multi-cell multi-channel multi-radio cognitive radio wireless mesh networks. The applied model relies on the QoS sessions, where each one is expressed with a bandwidth requirement. We propose a cross-layer algorithm named “Multicast Routing, Channel selection, Scheduling, and Call admission control (MRCSC)”. The proposed scheme, considering wireless broadcast advantage, channel/radio diversity, and spatial reuse, uses both intra- and inter-cell relaying to create multicast trees with a specific bandwidth. MRCSC considers both inter-flow and intra-flow interferences. It adopts an efficient collision-free mechanism to greedily schedule different multicast transmission in the network. As an advantage, considering the multi-radio technology, MRCSC allows the nodes to simultaneously send/receive data on different available non-overlapping channels. Numerical results of our comprehensive simulations confirm the efficiency of the MRCSC algorithm in terms of the number of transmissions, stability, and multicast period.

Load Management, Power and Admission Control in Downlink Cellular OFDMA Networks

We present a resource management framework for load-coupled downlink cellular OFDMA networks considering the load factor of an individual base station (BS) per resource block (RB), i.e., the number of adjacent sub-carriers (SCs), as the variable of interest in the resource management problem. The load factor of a BS per RB, which corresponds to the fraction of active SCs in the BS per RB, is an indicator of the level of resource consumption, and it affects the interference caused to that RB reused in other BSs, and thereby, results in a load-coupled OFDMA system. We first propose two distributed schemes to minimize: (i) the total load factor of the BSs (which would in turn increase the number of supportable users in the system), and (ii) the total downlink transmit power level of the BSs. Then, we derive the necessary and sufficient conditions for checking the feasibility of given target-rate requirements (also referred to as demand vector) for users. Accordingly, an iterative and distributed scheme is proposed to check the feasibility of a given demand vector. Next, for a priority-based load-coupled network, we propose a priority-based gradual removal algorithm to support the maximal number of low-priority users while satisfying the demands of the high-priority users. To evaluate the performance of our proposed schemes for resource management and admission control in load-coupled OFDMA networks, the theoretical investigations are complemented with Monte Carlo simulations.

EMSC: a joint multicast routing, scheduling, and call admission control in multi-radio multi-channel WMNs

This paper deals with the problem of joint multicast routing, scheduling, and call admission control in multiradio multi-channel wireless mesh networks. To heuristically solve this problem, we propose a cross-layer algorithm named “extended MIMCR with scheduling and call admission control phases (EMSC)”. Our model relies on the on-demand quality of service (QoS) multicast sessions, where each admitted session creates a unique tree with a required bandwidth. The proposed scheme extends the MIMCR algorithm to fairly schedule multiple non-interfering transmissions in the same time slot. It also exploits a call admission control mechanism to protect the QoS requirements of the multicast traffics. EMSC reduces the number of occupied time slots, with consideration of spatial reuse, both Intra-flow and Inter-flow interferences, and selecting the minimum-interference minimum-cost paths. This subsequently leads to better radio resource utilization and increases the network throughput. Simulation results show that the proposed algorithm outperforms the other algorithms and improves the network performance.

Admission Control and Network Slicing for Multi-Numerology 5G Wireless Networks

This letter studies the admission control and network slicing design for 5G New Radio (5G-NR) systems in which the total bandwidth is sliced to support the enhanced mobile broadband (eMBB) and ultra reliable and low latency communication (URLLC) services. We allow traffic from the eMBB bandwidth part to be overflowed to the URLLC bandwidth part in a controlled manner. We develop a mathematical framework to analyze the blocking probabilities of both eMBB and URLLC services based on which the network slicing and admission control is jointly optimized to minimize the blocking probability of the eMBB traffic subject to the blocking probability constraint for the URLLC traffic. An efficient iterative algorithm is proposed to deal with the underlying problem.

Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

Existing virtual network admission control algorithms targeting high utilization of data center infrastructure are computationally expensive or provide poor performance. In particular, existing algorithms have in common that they are oblivious to the past, i.e., requests are handled in a fire-and-forget manner, not taking into account information from previously solved instances. This can be inefficient and misses out on a basic optimization opportunity: as for any network optimization algorithm that faces repeating problem instances, it may be beneficial to learn from network states and the outcome of acceptance decisions of the past. In this paper, we propose Ismael, a machine learning framework for predicting the acceptance of virtual clusters, one of the most common virtual network abstractions in data centers. Ismael can be configured with, and learn from, different existing algorithms by combining fixed-size feature representations for graphs with a convolutional neural network or a fully connected deep neural network. We report on extensive simulations, which demonstrate that it is possible to mimic existing, computationally intensive admission control algorithms with an accuracy of up to 94 %, while significantly reducing runtime.