Queue Overflow Probability Research Articles

Queue-Aware Opportunistic Interference Alignment in Downlink MU-MIMO Cellular Systems

Recently, attention has been paid to the integration of opportunistic communications, whether based on opportunistic user selection (OUS) or opportunistic antenna selection, with interference alignment (IA) in order to improve the performance of wireless networks. In OUS, users that have the best operational conditions are usually selected. However, fairness among users is another important aspect that should be considered in scheduling users. In this paper, a queue-aware two-stage opportunistic IA (OIA) algorithm is proposed for the downlink multicell multiuser multiple-input-multiple-output system. In the first stage, inter-cell interference is eliminated using one pair of precoding/postcoding matrices. Whereas in the second stage, two user selection polices are proposed namely, capacity-based selection (CBS) and queue-based scheduling (QBS), to select a group of users and minimize the inter-user interference among them using another pair of precoding/postcoding matrices. In the QBS-OIA case, a joint scheduling, resource allocation, and IA optimization problems are formulated, and a low complexity heuristic is proposed to solve it. Comparisons are conducted with other OIA algorithms in terms of achieved sum rate, achieved degrees-of-freedom (DoFs), number of served users, queue overflow probability, and computational complexity. Simulations show that the two proposed CBS-OIA and QBS-OIA algorithms outperform other schemes in terms of sum rate and DoFs. Moreover, the proposed QBS-OIA is capable of serving more users, in some cases, and achieves lower overflow probability with much reduced complexity on the expense of achieving a bit lower sum rate than the CBS-OIA in some cases.

A Study of Scheduling Algorithms to Maintain Small Overflow Probability in Cellular Networks with a Single Cell

Wireless scheduling algorithms for the download of a single cell that can maximize the asymptotic decay rate of the queue-overflow probability as the overflow threshold approaches infinity. We first derive an upper bound on the decay rate of the queue-overflow probability over all scheduling policies. Specifically, we focus on the class of “α - algorithms,” the base station picks the user for service at each time that has the largest product of the transmission rate multiplied by the backlog raised to the power α. The α-algorithms arbitrarily achieve the highest decay rate of the queue-overflow probability. We design a scheduling algorithm that is both close to optimal in terms of the asymptotic decay rate of the overflow probability and to maintain small queue-overflow probabilities over queue-length ranges of practical interest.

Delay analysis and optimality of the renewal access protocol

For many years, the IEEE 802.11 distributed coordination function (DCF) has been widely used as a dominant medium access control (MAC) protocol in wireless networks and a large number of works have been done for analyzing and improving its performance. In our earlier work, as a substitute of the IEEE 802.11 DCF, a simple MAC protocol, called the renewal access protocol (RAP), is proposed. The RAP adopts all of the legacy 802.11 standard but the backoff stage feature. Each terminal selects its backoff counter value from a fixed sized window according to a priori given selection distribution in the RAP, regardless of the packet transmission result. It is shown that, if a Poisson distribution is used as the selection distribution, then the resulting RAP achieves high short-term fairness as well as optimal throughput. In this work, we analyze the relation between delay performance and the selection distribution of the RAP. With the help of effective bandwidth theory, we derive the conditions for the selection distribution of the RAP that optimizes the queue overflow probability. We also construct the delay optimal selection distribution satisfying the optimal conditions for throughput and delay. However, we show that the use of the delay optimal selection distribution results in an extremely slow convergence to steady state compared with that of the Poisson selection distribution. Moreover, we show that the Poisson selection distribution provides near-optimal delay performance. Therefore, we conclude that the use of a Poisson selection distribution is still recommended even from the delay perspective.

Buffer-aware adaptive resource allocation scheme in LTE transmission systems

Dynamic resource allocation scheme is a key component of 3GPP long-term evolution (LTE) for satisfying quality-of-service (QoS) requirement as well as improving the system throughput. In this paper, a buffer-aware adaptive resource allocation scheme for LTE downlink transmission is proposed for improving the overall system throughput while guaranteeing the statistic QoS and keeping certain fairness among users. Specifically, the priorities of the users’ data queues in the base station are ranked by their remaining life time or their queue overflow probability which is estimated by applying large deviation principle. An online measurement based algorithm which requires no statistical knowledge of the network conditions uses the queue priorities to dynamically allocate the resource blocks (RBs) for avoiding buffer overflow and providing statistic QoS guarantee. The simulation results show that the proposed algorithm improves the throughput and fairness while considerably reducing the average bit loss rate.

On the Queue-Overflow Probability of Wireless Systems: A New Approach Combining Large Deviations With Lyapunov Functions

In this paper, we study the queue-overflow probability of wireless scheduling algorithms. In wireless networks operated under queue-length-based scheduling algorithms, there often exists a tight coupling between the service-rate process, the system backlog process, the arrival process, and the stochastic process governing channel variations. Although one can use sample-path large-deviation techniques to form an estimate of the queue-overflow probability, the formulation leads to a difficult multidimensional calculus-of-variations problem. In this paper, we present a new technique to address this complexity issue. Using ideas from the Lyapunov function approach in control theory, this technique maps the complex multidimensional calculus-of-variations problem to a 1-D calculus-of-variations problem, and the latter is often much easier to solve. Further, under appropriate conditions, we show that when a scheduling algorithm minimizes the drift of a Lyapunov function at each point of every fluid sample path, the algorithm will be optimal in the sense that it maximizes the asymptotic decay rate of the probability that the Lyapunov function value exceeds a given threshold. We believe that these results can potentially be used to study the queue-overflow probability of a large class of wireless scheduling algorithms and to design new scheduling algorithms with optimal overflow probabilities.

On the queue-overflow probabilities of a class of distributed scheduling algorithms

In this paper, we are interested in using large-deviations theory to characterize the asymptotic decay-rate of the queue-overflow probability for distributed wireless scheduling algorithms, as the overflow threshold approaches infinity. We consider ad hoc wireless networks where each link interferes with a given set of other links, and we focus on a distributed scheduling algorithm called Q-SCHED, which is introduced by Gupta et al. First, we derive a lower bound on the asymptotic decay rate of the queue-overflow probability for Q-SCHED. We then present an upper bound on the decay rate for all possible algorithms operating on the same network. Finally, using these bounds, we are able to conclude that, subject to a given constraint on the asymptotic decay rate of the queue-overflow probability, Q-SCHED can support a provable fraction of the offered loads achievable by any algorithms.

On Wireless Scheduling Algorithms for Minimizing the Queue-Overflow Probability

In this paper, we are interested in wireless scheduling algorithms for the downlink of a single cell that can minimize the queue-overflow probability. Specifically, in a large-deviation setting, we are interested in algorithms that maximize the asymptotic decay rate of the queue-overflow probability, as the queue-overflow threshold approaches infinity. We first derive an upper bound on the decay rate of the queue-overflow probability over all scheduling policies. We then focus on a class of scheduling algorithms collectively referred to as the α-algorithms. For a given α ≥ 1, the α-algorithm picks the user for service at each time that has the largest product of the transmission rate multiplied by the backlog raised to the power α. We show that when the overflow metric is appropriately modified, the minimum-cost-to-overflow under the α-algorithm can be achieved by a simple linear path, and it can be written as the solution of a vector-optimization problem. Using this structural property, we then show that when α approaches infinity, the α-algorithms asymptotically achieve the largest decay rate of the queue-overflow probability. Finally, this result enables us to design scheduling algorithms that are both close to optimal in terms of the asymptotic decay rate of the overflow probability and empirically shown to maintain small queue-overflow probabilities over queue-length ranges of practical interest.

On the Elasticity of Marking Functions in an Integrated Network

Much of the research on network modeling and analysis has focused on the design of end controllers and network algorithms with the objective of stability and convergence of the transmission rate. However, a network is typically composed of a mixture of both controlled elastic flows and uncontrolled real-time flows. In this paper, we study the effects of marking elasticity (which characterizes how aggressively the marking function responds to congestion) on queue overflow probability for uncontrolled real-time flows. First, we derive lower and upper bounds on the queue overflow probability at a router of a single bottleneck system. Using this, we quantify the trade-off between stability for controlled flows and queue overflow probability for uncontrolled real-time flows as a function of marking elasticity. Next, we compare the capacity required at a router with only FIFO scheduling versus a router with priority scheduling (priority given to the real-time flows) for supporting a given queue overflow probability. We quantify the scheduling-gain of priority scheduling over FIFO scheduling, as a function of marking elasticity. We show that this scheduling gain decreases with more elastic marking functions.

A Large Deviations Analysis of Scheduling in Wireless Networks

In this correspondence, we consider a cellular network consisting of a base station and N receivers. The channel states of the receivers are assumed to be identical and independent of each other. The goal is to compare the throughput of two different scheduling policies (a queue-length-based (QLB) policy and a greedy policy) given an upper bound on the queue overflow probability or the delay violation probability. We consider a multistate channel model, where each channel is assumed to be in one of L states. Given an upper bound on the queue overflow probability or an upper bound on the delay violation probability, we show that the total network throughput of the (QLB) policy is no less than the throughput of the greedy policy for all N. We also obtain a lower bound on the throughput of the (QLB) policy. For sufficiently large N, the lower bound is shown to be tight, strictly increasing with N, and strictly larger than the throughput of the greedy policy. Further, for a simple multistate channel model-ON-OFF channel, we prove that the lower bound is tight for all N

Hybrid technique for analysis of multiplexed power-law traffic in broadband networks

Recent research has shown that the simulation of traffic in many broadband network scenarios involves excessive time consumption, owing to the power-law nature of much of this traffic. As an alternative, analysis will often yield systems of equations that are prohibitively complex to solve. Thus, there is growing interest in combinations of simulation and analysis, i.e. hybrid solutions. Here, a hybrid technique is developed for the evaluation of queue state and overflow probabilities when buffering multiple ON/OFF sources, where ON and OFF periods are Pareto distributed. It is shown that this technique allows these probabilities to be predicted accurately, while avoiding insoluble systems of equations and excessively long simulations.

Buffer management control in data transport network node

The application of analytical queuing theory results in behaviour analysis of a distributed computer network or mobile data system (data transport network). It belongs to the preferred method in comparison to the simulation method. The use of analytical methods allows us to calculate effectively various values of parameters in equilibrium, including the total input intensity of data units to every node of transport network even for the more realistic models than the M/M/1 systems. However, these results are derived assuming an infinite buffer in size at a given node. For practical application, we need to project the concrete number of buffers in every node. This paper describes the method of buffer management control for each decomposed network's node of a data transport network in two real cases. For this purpose, the linear dependence between buffer memory size and input queue size at each node of a data transport network was used. For these two real statistical distributions of incoming data units closed expressions are derived, enabling to calculate the required queue size for both queue size limitation methods (assumption of the unlimited and limited queue sizes). For practical use, a very efficient way of computing queue overflow probabilities was developed.