Call Admission Control Problem Research Articles

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.

Call Admission Control With Inter-Network Cooperation for Cognitive Heterogeneous Networks

In this paper, a call admission control algorithm based on inter-network cooperation is proposed via a Stackelberg game framework for cognitive heterogeneous networks. The call admission control problem is subject to the variable bandwidth rate traffic, network service selection, feasible subchannel allocation, and call blocking probability. The call admission control algorithm is based on spectrum price at primary heterogeneous networks and subchannel allocation price and network selection at cognitive heterogeneous networks. In order to determine the call blocking probability, a probability upper bound of exceeding the maximum admission number for secondary mobile terminals (MTs) is analyzed based on ${\mathrm{M}}/{\mathrm{M}}/\infty $ model. Then, the subchannel allocation price and network selection are designed via the dual decomposition method, and the vacant spectrum price is determined with Bertrand game theory. Finally, a call admission control algorithm is proposed. Simulation results demonstrate that the proposed algorithm not only improves quality of service at each secondary MT, but also reduces the call blocking probability for cognitive heterogeneous networks.

Call Admission Control Using Neural Network and SVM

Mdash This research work introduces and conditionally look over a software based method for Call Admission Control using Neural Network and SVM. In this paper, Call Admission Control scheme using Neural Network and SVM is proposed for better QoS. THE rising demand for mobile communication services is increasing the importance of efficient use of the limited bandwidth and frequency spectrum. In recent years, considerable efforts have focused on the Channel Allocation and Call Admission Control (CAC) problems and many schemes that range from static to dynamic strategies have been proposed in the literature. Call Admission Control is a provisioning strategy used to limit the number of call connections into the networks in order to reduce the network congestion and provide the desired Quality of Service (QoS) to users in service. Traditional CAC schemes that mainly focus on the tradeoff between new call blocking probability and handoff call blocking probability cannot solve the problem of congestion in wireless networks. To overcome the problems arises due to traditional CAC schemes we propose a new CAC using hybrid technique i.e. SVM and Neural network.

A lexicographic approach to constrained MDP admission control

ABSTRACTThis paper proposes a reinforcement learning-based lexicographic approach to the call admission control problem in communication networks. The admission control problem is modelled as a multi-constrained Markov decision process. To overcome the problems of the standard approaches to the solution of constrained Markov decision processes, based on the linear programming formulation or on a Lagrangian approach, a multi-constraint lexicographic approach is defined, and an online implementation based on reinforcement learning techniques is proposed. Simulations validate the proposed approach.

Handover Reduction via Joint Bandwidth Allocation and CAC in Randomly Distributed HCNs

For heterogeneous cellular networks (HCNs) with different cell size levels, the connection of high-mobility user equipment (UE) is a problem due to its short residence time in micro cell. In the former investigations, high-mobility UEs are always distributed to macro base stations (BSs) with reserved bandwidth, which is not an optimal solution to reduce handovers indeed. In this paper, a joint bandwidth allocation and call admission control (CAC) strategy is proposed to reduce handovers, that some high-mobility UEs are distributed to macro BSs for larger residence time, while the other high-mobility UEs are distributed to micro BSs for larger bandwidth. A scenario of randomly distributed HCNs with shared bandwidth and reserved bandwidth is considered. The joint bandwidth allocation and CAC problem is formulated based on the stochastic geometry modeling. A two-stage iteration method is given to obtain the optimal solutions. This work may shed some light for future extension and study.

Optimality Conditions for Coordinate-Convex Policies in CAC With Nonlinear Feasibility Boundaries

Optimality conditions for Call Admission Control (CAC) problems with nonlinearly constrained feasibility regions and K classes of users are derived. The adopted model is a generalized stochastic knapsack, with exponentially distributed interarrival times of the objects. Call admission strategies are restricted to the family of Coordinate-Convex (CC) policies. For K = 2 classes of users, both general structural properties of the optimal CC policies and structural properties that depend on the revenue ratio are investigated. Then, the analysis is extended to the case K > 2. The theoretical results are exploited to narrow the set of admissible solutions to the associated knapsack problem, i.e., the set of CC policies to which an optimal one belongs. With respect to results available in the literature, less restrictive conditions on the optimality of the complete-sharing policy are obtained. To illustrate the role played by the theoretical results on the combinatorial CAC problem, simulation results are presented, which show how the number of candidate optimal CC policies dramatically decreases as the derived optimality conditions are imposed.

OFDMA 무선통신시스템의 호접속 제어를 위한 SMDP 기반 최적화모형

This study addresses the call admission control(CAC) problem for OFDMA wireless communication systems in which both subcarriers and power should be considered together as the system resources. To lessen the exccessive allocation of radio resources for protecting handoff calls, the proposed CAC allows the less data rate than their requirements to handoff calls. The CAC problem is formulated as a semi-Markov decision process(SMDP) with constraints on the blocking probabilities of handoff calls. Some extensive experiments are conducted to show the usefulness of the proposed CAC model.

Immune optimization algorithm for solving joint call admission control problem in next-generation wireless network

The integration of radio access networks with different radio access technologies (RATs) is one of the remarkable characteristics of the next-generation wireless networks (NGWNs). In NGWN, the users with multi-network interface terminals should be able to select independently radio access network to obtain the best service. Therefore, joint call admission control (JCAC) schemes are required to select the most appropriate radio access network (RAN) for incoming calls. We propose an immune algorithm-based JCAC (IA-JCAC) scheme with users centric in order to enhance user's satisfaction. However, JCAC algorithms with users centric can lead to highly unbalanced traffic load among the available RANs in NGWN because users act independently, and most of them may prefer to be connected through a particular RAN. Highly unbalanced traffic load in NGWN will result in high overall call blocking/dropping probability and poor radio result utilization. To solve this problem, we employ dynamic pricing for balancing traffic load among available RANs in heterogeneous wireless networks where users' preferences are considered in decision-making on RAT selection. The proposed IA-based JCAC scheme is compared with another scheme that does not use the dynamic pricing on the performance. The simulation result shows the effectiveness of the proposed IA-JCAC scheme is improved significantly.

Integrating a partial model into model free reinforcement learning

In reinforcement learning an agent uses online feedback from the environment in order to adaptively select an effective policy. Model free approaches address this task by directly mapping environmental states to actions, while model based methods attempt to construct a model of the environment, followed by a selection of optimal actions based on that model. Given the complementary advantages of both approaches, we suggest a novel procedure which augments a model free algorithm with a partial model. The resulting hybrid algorithm switches between a model based and a model free mode, depending on the current state and the agent's knowledge. Our method relies on a novel definition for a partially known model, and an estimator that incorporates such knowledge in order to reduce uncertainty in stochastic approximation iterations. We prove that such an approach leads to improved policy evaluation whenever environmental knowledge is available, without compromising performance when such knowledge is absent. Numerical simulations demonstrate the effectiveness of the approach on policy gradient and Q-learning algorithms, and its usefulness in solving a call admission control problem.

CAC with Nonlinearly-Constrained Feasibility Regions

Two criteria are proposed to characterize and improve suboptimal coordinate-convex (c.c.) policies in Call Admission Control (CAC) problems with nonlinearly-constrained feasibility regions. Then, a structural property of the optimal c.c. policies is derived. This is expressed in terms of constraints on the relative positions of successive corner points.

Optimal Priority-based Call Admission Control Scheme For QoS Provisioning in Heterogeneous Wireless Networks

Next generation wireless networks will support a diverse set of access technologies. And the demanding breed of multimedia applications will even more considerably require Quality of Service (QoS) support. This paper proposes an optimal prority-based Call Admission Control (CAC) scheme in heterogeneous wireless networks, i.e., the generalized semi-Markov Decision Process based CAC (GCAC). Considering the limitations of the traditional Markov decision process for optimal CAC policy, we characterize the CAC problem through a GSMDP formulation that characterize s the network behaviors in the heterogeneous system more exactly. And the proposed scheme can guarantee the QoS constrains both in the physical and network layer. Numerical examples illustrate that the performance in the GCAC scheme is significantly better than that in the conventioinal GC scheme and an optimal CAC schme based on MDP.

Optimal rate allocation and QoS-sensitive admission control in wireless integrated networks

The problem of Call Admission Control and rate allocation in loosely coupled wireless integrated networks is investigated. The related Radio Resource Management schemes were introduced to improve network performance in wireless integrated networks. However, these schemes did not reflect the independence and competitiveness of loosely coupled wireless integrated networks. Furthermore, given that users have different requirements for price and Quality of Service (QoS), they are able to select a network according to their preference. We consider a scenario with two competitive wireless networks, namely Universal Mobile Telecommunications System cellular networks and Wireless Local Area Networks. Users generate two types of traffic with different QoS requirements: real-time and non-real-time. We propose a scheme that exploits a mathematical model for the control of call admission and adopt a noncooperative game theory-based approach to address the rate allocation problem. The purpose is to maximize the revenue of the network providers while guaranteeing a level of QoS according to user needs. Simulation results show that the proposed scheme provides better network performance with respect to packet loss rate, packet delay time, and call-blocking probability than other schemes when the data rates are allocated to each call at the point that maximizes the revenue of network providers. We further demonstrate that a Nash equilibrium always exists for the considered games.

Algorithms for Deterministic Call Admission Control of Pre-stored VBR Video Streams

We examine the problem of accepting a new request for a pre-stored VBR video stream that has been smoothed using any of the smoothing algorithms found in the literature. The output of these algorithms is a piecewise constant-rate schedule for a Variable Bit-Rate (VBR) stream. The schedule guarantees that the decoder buffer does not overflow or underflow. The problem addressed in this paper is the determination of the minimal time displacement of each new requested VBR stream so that it can be accommodated by the network and/or the video server without overbooking the committed traffic. We prove that this call-admission control problem for multiple requested VBR streams is NP-complete and inapproximable within a constant factor, by reducing it from the VERTEX COLOR problem. We also present a deterministic morphology-sensitive algorithm that calculates the minimal time displacement of a VBR stream request. The complexity of the proposed algorithm along with the experimental results we provide indicate that the proposed algorithm is suitable for real-time determination of the time displacement parameter during the call admission phase.

Policy Gradient SMDP for Resource Allocation and Routing in Integrated Services Networks

In this paper, we solve the call admission control (CAC) and routing problem in an integrated network that handles several classes of calls of different values and with different resource requirements. The problem of maximizing the average reward (or cost) of admitted calls per unit time is naturally formulated as a semi-Markov Decision Process (SMDP) problem, but is too complex to allow for an exact solution. Thus in this paper, a policy gradient algorithm, together with a decomposition approach, is proposed to find the dynamic (state-dependent) optimal CAC and routing policy among a parameterized policy space. To implement that gradient algorithm, we approximate the gradient of the average reward. Then, we present a simulation-based algorithm to estimate the approximate gradient of the average reward (called GSMDP algorithm), using only a single sample path of the underlying Markov chain for the SMDP of CAC and routing problem. The algorithm enhances performance in terms of convergence speed, rejection probability, robustness to the changing arrival statistics and an overall received average revenue. The experimental simulations will compare our method's performance with other existing methods and show the robustness of our method.

Call Admission Control Optimization in WiMAX Networks

Worldwide interoperability for microwave access (WiMAX) is a promising technology for last-mile Internet access, particularly in the areas where wired infrastructures are not available. In a WiMAX network, call admission control (CAC) is deployed to effectively control different traffic loads and prevent the network from being overloaded. In this paper, we propose a framework of a 2-D CAC to accommodate various features of WiMAX networks. Specifically, we decompose the 2-D uplink and downlink WiMAX CAC problem into two independent 1-D CAC problems and formulate the 1-D CAC optimization, in which the demands of service providers and subscribers are jointly taken into account. To solve the optimization problem, we develop a utility- and fairness-constrained optimal revenue policy, as well as its corresponding approximation algorithm. Simulation results are presented to demonstrate the effectiveness of the proposed WiMAX CAC approach.

Dynamic Multiple-Threshold Call Admission Control Based on Optimized Genetic Algorithm in Wireless/Mobile Networks

Due to the dynamics of topology and resources, Call Admission Control (CAC) plays a significant role for increasing resource utilization ratio and guaranteeing users' QoS requirements in wireless/mobile networks. In this paper, a dynamic multi-threshold CAC scheme is proposed to serve multi-class service in a wireless/mobile network. The thresholds are renewed at the beginning of each time interval to react to the changing mobility rate and network load. To find suitable thresholds, a reward-penalty model is designed, which provides different priorities between different service classes and call types through different reward/penalty policies according to network load and average call arrival rate. To speed up the running time of CAC, an Optimized Genetic Algorithm (OGA) is presented, whose components such as encoding, population initialization, fitness function and mutation etc., are all optimized in terms of the traits of the CAC problem. The simulation demonstrates that the proposed CAC scheme outperforms the similar schemes, which means the optimization is realized. Finally, the simulation shows the efficiency of OGA.

A Game Theoretic Framework for Fair-Efficient Threshold Parameters Selection in Call Admission Control for CDMA Mobile Multimedia Systems

While efficient use of network resources is an important control objective of call admission control (CAC), the issue of fairness among services should also be taken into account. Game theory provides a suitable framework for formulating such fair and efficient CAC problem. Thus, in this paper, a game theoretic framework for selecting fair-efficient threshold parameters of CAC for the asymmetrical traffic case in CDMA mobile multimedia systems is proposed. For the cooperative game, the arbitration schemes for the interpersonal comparisons of utility and the bargaining problem, including the Nash, Raiffa, and modified Thomson solutions, are investigated. Furthermore, since CAC should be simple and flexible to provide a fast response to diverse QoS call requests during a connection setup, this paper also applies the concept of load factor to the previous Jeon and Jeong's CAC scheme and proposes an approximation approach to reduce the computational complexity (proposed throughput-based CAC scheme). From the numerical results, the proposed throughput-based CAC scheme shows a comparable performance to the previous Jeon and Jeong's CAC scheme while achieving lower computational complexity. All the solutions attain the fairness by satisfying their different fairness senses and efficiency by the Pareto optimality.

Optimizing Admission Control for Multiservice Wireless Networks With Bandwidth Asymmetry Between Uplink and Downlink

Next-generation wireless networks need to support multiclass services with asymmetric bandwidth allocation between uplink and downlink to match the asymmetric traffic load brought by some data services. For the design of call admission control (CAC) policy in such networks, how to decrease the average system cost is a key issue. In this paper, we study the optimal admission policy for minimizing the system cost. We consider the CAC problem as a decision process. By modeling the admission control problem into a Markov decision process (MDP) and analyzing the corresponding value function, we obtain some monotonicity properties of the optimal policy. These properties suggest that the optimal admission control policy for the bandwidth asymmetry wireless networks should have a threshold structure and the threshold specified for a class of calls may change with the system state. Due to the prohibitively high complexity for computing the thresholds in a system with large state-space, we propose a heuristic CAC policy called call-rate-based dynamic threshold (CRDT) policy to approximate the theoretical optimal policy based on the insights we obtain from the modeling and the analytical study on the properties of the optimal policy. The CRDT policy is efficient and can be easily implemented. The numerical results show that the performance of average system cost of the proposed CRDT policy is close to that of the optimal policy from the MDP model and is better than that of some known existing CAC schemes, including those performing well in bandwidth asymmetry wireless networks

Fair-Efficient Guard Bandwidth Coefficients Selection in Call Admission Control for Mobile Multimedia Communications Using Framework of Game Theory

Call admission control (CAC) plays a significant role in providing the efficient use of the limited bandwidth and the desired quality-of-service (QoS) in mobile multimedia communications. As efficiency is an important performance issue for CAC in the mobile networks with multimedia services, the concept of fairness among services should also be considered. Game theory provides an appropriate framework for formulating such fair and efficient CAC problem. Thus, in this paper, a framework based on game theory (both of noncooperative and cooperative games) is proposed to select fair-efficient guard bandwidth coefficients of the CAC scheme for the asymmetrical traffic case in mobile multimedia communications. The proposed game theoretic framework provides fairness and efficiency in the aspects of bandwidth utilization and QoS for multiple classes of traffic, and also guarantees the proper priority mechanism. Call classes are viewed as the players of a game. Utility function of the player is defined to be of two types, the bandwidth utilization and the weighted sum of new call accepting probability and handoff succeeding probability. The numerical results show that, for both types of the utility function, there is a unique equilibrium point of the noncooperative game for any given offered load. For the cooperative game, the arbitration schemes for the interpersonal comparisons of utility and the bargaining problem are investigated. The results also indicate that, for both types of the utility function, the Nash solution with the origin (0,0) as the starting point of the bargaining problem can achieve higher total utility than the previous CAC scheme while at the same time providing fairness by satisfying a set of fairness axioms. Since the Nash solution is determined from the domain of the Pareto boundary, the way to generate the Pareto boundary is also provided. Therefore, the Nash solution can be obtained easily.

Optimal Call Admission Control on a Single Link With a GPS Scheduler

The problem of call admission control (CAC) is considered for leaky bucket constrained sessions with deterministic service guarantees (zero loss and finite delay bound) served by a generalized processor sharing scheduler at a single node in the presence of best effort traffic. Based on an optimization process, a CAC algorithm capable of determining the (unique) optimal solution is derived. The derived algorithm is also applicable, under a slight modification, in a system where the best effort traffic is absent and is capable of guaranteeing that if it does not find a solution to the CAC problem, then a solution does not exist. The numerical results indicate that the CAC algorithm can achieve a significant improvement on bandwidth utilization as compared to a (deterministic) effective bandwidth-based CAC scheme.