Blocking Probability Constraint Research Articles

Low Complexity Online Radio Access Technology Selection Algorithm in LTE-WiFi HetNet

In an offload-capable Long Term Evolution (LTE)- Wireless Fidelity (WiFi) Heterogeneous Network (HetNet), we consider the problem of maximization of the total system throughput under voice user blocking probability constraint. The optimal policy is threshold in nature. However, computation of optimal policy requires the knowledge of the statistics of system dynamics, viz., arrival processes of voice and data users, which may be difficult to obtain in reality. Motivated by the Post-Decision State (PDS) framework to learn the optimal policy under unknown statistics of system dynamics, we propose, in this paper, an online Radio Access Technology (RAT) selection algorithm using Relative Value Iteration Algorithm (RVIA). However, the convergence speed of this algorithm can be further improved if the underlying threshold structure of the optimal policy can be exploited. To this end, we propose a novel structure-aware online RAT selection algorithm which reduces the feasible policy space, thereby offering lesser storage and computational complexity and faster convergence. This algorithm provides a novel framework for designing online learning algorithms for other problems and hence is of independent interest. We prove that both the algorithms converge to the optimal policy. Simulation results demonstrate that the proposed algorithms converge faster than a traditional scheme. Also, the proposed schemes perform better than other benchmark algorithms under realistic network scenarios.

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.

Performance analysis of flexible reuse in cellular networks

Flexible frequency reuse schemes namely fractional frequency reuse (FFR) and soft frequency reuse (SFR) are suggested in the literature to improve the cell edge throughput over unity frequency reuse in orthogonal frequency division multiple access networks. In this study, the authors focus on real time (RT) traffic for which blocking probability and Erlang load are the important performance metrics. Both FFR and SFR have signal-tointerference-plus noise-ratio (SINR) threshold and power ratio as important design parameters, whereas FFR has a bandwidth partitioning ratio as an additional parameter. They investigate the cell capacity and the cell edge performance in terms of Erlang load with blocking probability constraint brought about by the suitable choice of these parameters. They also analyze the fairness aspect which is a critical indicator of user satisfaction. They give a complete comparison between FFR and SFR for the cell edge as well as total cell for both RT and BE traffics. For RT traffic, with proper choice of the parameters, the cell edge and the total cell performance in FFR and SFR is improved by a notable gain over the reference scheme. For BE traffic, both schemes provide significant gains at cell edge over reuse one and reuse three schemes.

Design based routing and wavelength assignment in WDM networks using link-based multiplexing gain

Design based routing (DBR) is originally proposed for wavelength division multiplexing (WDM) networks with the objective to minimize the total link cost subject to the constraints based on traffic demands in terms of the maximum acceptable end-to-end blocking probability. In DBR, optimized routing is computed off-line based on prior information on traffic demand statistics, and are utilized for on-line routing of dynamic lightpath requests. However, the integer linear programming (ILP) formulation of DBR allocates, on each link, dedicated portions of the link capacity to utilizing source–destination (s–d) pairs without considering the effects of statistical multiplexing. The lack of consideration of the multiplexing gain on transmission links leads to an unnecessary limitation on the applicability of DBR in networks with high traffic loads from a large number of s–d pairs. In such traffic scenarios, the ILP problem of DBR can become infeasible even though the traffic can still be supported without violating the blocking probability constraint. In this paper, we provide an alternative approach to DBR, referred to as link-based DBR (LDBR), by considering the multiplexing gain on each link. Unlike the ILP formulation for DBR, which imposes constraints on end-to-end blocking probabilities, the blocking probability constraints for LDBR are imposed on individual links with the end-to-end blocking probabilities estimated using the union bound. Compared to DBR, LDBR is attractive when individual links carry traffic from a large number of s–d pairs since there are a lot of opportunities for utilizing the multiplexing gain. Simulation results demonstrate that, subject to the same end-to-end blocking probability constraints, the proposed LDBR can extend the applicability of DBR to higher traffic loads when there are a lot of active s–d pairs, which are expected in typical backbone networks.

An efficient dynamic multicast traffic-grooming algorithm for WDM networks

With the growth of multi-granularity multicast applications, there comes into being a huge gap between the bandwidth of a wavelength provided and a multicast traffic required in the wavelength division multiplexing (WDM) networks. The dynamic multicast traffic-grooming is an effective way for WDM networks to improve the wavelength utilization and decrease the traffic blocking probability. A novel switching node architecture with the multicast switching matrix and traffic-grooming fabric is studied in the paper. Then, an efficient dynamic multicast traffic-grooming algorithm is proposed for the architecture. According to the ratio of network available grooming port number to network transceiver number, the proposed algorithm estimates whether the traffic-grooming port is a scarce resource for input traffic and chooses the appropriate grooming strategy. If the traffic-grooming port is scarce, the minimized use grooming port strategy is designed for the coming traffic. On the contrary, the minimized use node transceiver strategy is applied for the coming traffic. Simulation results show that the proposed algorithm can groom traffic efficiently with low blocking probability and high network throughput constraint by limiting number of node transceivers and grooming ports.

Deployment and management of SDR cloud computing resources: problem definition and fundamental limits

Software-defined radio (SDR) describes radio transceivers implemented in software that executes on general-purpose hardware. SDR combined with cloud computing technology will reshape the wireless access infrastructure, enabling computing resource sharing and centralized digital-signal processing (DSP). SDR clouds have different constraints than general-purpose grids or clouds: real-time response to user session requests and real-time execution of the corresponding DSP chains. This article addresses the SDR cloud computing resource management problem. We show that the maximum traffic load that a single resource allocator (RA) can handle is limited. It is a function of the RA complexity and the call setup delay and user blocking probability constraints. We derive the RA capacity analytically and provide numerical examples. The analysis demonstrates the fundamental tradeoffs between short call setup delays (few processors) and low blocking probability (many processors). The simulation results demonstrate the feasibility of a distributed resource management and the necessity of adapting the processor assignment to RAs according to the given traffic load distribution. These results provide new insights and guidelines for designing data centers and distributed resource management methods for SDR clouds.

QoS-Aware and Energy-Efficient Resource Management in OFDMA Femtocells

We consider the joint resource allocation and admission control problem for Orthogonal Frequency-Division Multiple Access (OFDMA)-based femtocell networks. We assume that Macrocell User Equipments (MUEs) can establish connections with Femtocell Base Stations (FBSs) to mitigate the excessive cross-tier interference and achieve better throughput. A cross-layer design model is considered where multiband opportunistic scheduling at the Medium Access Control (MAC) layer and admission control at the network layer working at different time-scales are assumed. We assume that both MUEs and Femtocell User Equipments (FUEs) have minimum average rate constraints, which depend on their geographical locations and their application requirements. In addition, blocking probability constraints are imposed on each FUE so that the connections from MUEs only result in controllable performance degradation for FUEs. We present an optimal design for the admission control problem by using the theory of Semi-Markov Decision Process (SMDP). Moreover, we devise a novel distributed femtocell power adaptation algorithm, which converges to the Nash equilibrium of a corresponding power adaptation game. This power adaptation algorithm reduces energy consumption for femtocells while still maintaining individual cell throughput by adapting the FBS power to the traffic load in the network. Finally, numerical results are presented to demonstrate the desirable operation of the optimal admission control solution, the significant performance gain of the proposed hybrid access strategy with respect to the closed access counterpart, and the great power saving gain achieved by the proposed power adaptation algorithm.

A Multi-objective QoS Optimization with Fuzzy Based Parameter Setting for Real-Time Multicasting

We propose a multi-objective Pareto-optimal technique using Genetic Algorithm (GA) for group communication, which determines a min-cost multicast tree satisfying end-to-end delay, jitter, packet loss rate and blocking probability constraints. The model incorporates a fuzzy-based selection technique for initialization of QoS parameter values at each instance of multicasting. The simulation results show that the proposed algorithm satisfies on-demand QoS requirements (like high availability, good load balancing and fault-tolerance) made by the hosts in varying topology and bursty data traffic in multimedia communication networks.

Link Capacity Sharing between Guaranteed- and Best Effort Services on an ATM Transmission Link under GoS Constraints

While link allocation policies in multi-rate circuit switched loss models have drawn much attention in recent years, it is still an open question how to share the link capacity between service classes in a fair manner. In particular, when an ATM link is offered calls from service classes with/without strict QoS guarantees one is interested in link capacity sharing policies that maximize throughput and keep the per-class blocking probabilities under some GoS constraints. In this paper we propose a model and associated computational technique for an ATM transmission link to which CBR/VBR and ABR classes offer calls. We also propose a simple link allocation rule which takes into account blocking probability constraints for the CBR/VBR calls and a throughput constraint for the ABR calls and attempts to minimize the blocking probability of ABR calls. Numerical examples demonstrate the effectiveness of the policy and of the applied computational technique.

Mobility modeling and traffic analysis in three-dimensional high-rise building environments

To efficiently plan future personal communications services, we need to solve various mobility/traffic problems in one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) micro- or pico-cell environments. Although many users exhibit vertical motion inside elevators in high-rise buildings, there have been no studies regarding cell planning which take into account vertical motion with elevators. We extend the previous 3-D indoor mobility modeling by considering the proper boundary conditions on each floor and vertical motions through elevators and modeling mobility in high-rise buildings in order to estimate the number of handoffs. We then propose a blocking probability model with mobility as a traffic model in 3-D indoor environments. Using this model, we can obtain the required number of channels per cell under the given blocking probability constraint. These results can be used in planning the networks of future personal communications services.

Mobility modeling and traffic analysis in three-dimensional indoor environments

To efficiently plan future personal communications services (PCSs), we need to solve various mobility/traffic problems in one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) micro or picocell environments. However, mobility/traffic problems have thus far primarily been studied in 1-D and 2-D cell structures. In this paper, we extend the previous mobility modeling from 1-D or 2-D space to 3-D indoor building environments by considering the proper boundary conditions on each floor and analytically modeling mobility in multi-story buildings to estimate the number of handoffs. We then propose a blocking probability model with mobility as a traffic model in 3-D indoor environments. Utilizing this model, we can obtain the required number of channels per cell under the given blocking probability constraint. These results can be utilized in planning the networks of future PCSs.

Mobility and traffic analyses in three-dimensional PCS environments

We need to solve various mobility/traffic problems in one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) micro or picocell environments to efficiently plan future personal communications services (PCS). However, mobility/traffic problems have thus far primarily been studied in 1-D and 2-D cell structures. We extend the previous mobility modeling from 1-D or 2-D space to 3-D unbounded indoor building environments having staircase regions by analytically modeling the mobility to estimate the number of handoffs. We also characterize the blocking probability of each cell according to the mobility by predicting the equivalent input traffic per cell. Based on the blocking probability model, we obtain the required number of channels per cell under the given blocking probability constraint. For example, 13 channels per cell are required to be assigned in order to meet the requirement that the blocking probability does not exceed 0.02 in the case where the number of radio ports (RPs) (cells) is four and 180 users are moving with a mean velocity of 2 km/h (horizontal motion) and 2/3 km/h (vertical motion) on each floor. The computer simulation results are also close to the analytical ones. These results can be utilized in the network planning of future PCS.