In this paper, we evaluate the voice service capacity of the TDD OFDMA system based on IEEE 802.16d/e. Voice traffic must be transmitted in real time with very small delay when we use packet transmission networks. Voice over IP (VoIP) services can be provided in this wireless packet network as Unsolicited Granted Service (UGS) or Real Time Polling Service (rtPS) class. As UGS, a fixed amount of bandwidth can be allocated to transmit the voice traffic for the duration of a call. On the other hand, as rtPS, bandwidth is allocated variably as needed to transmit the talk spurts using polling process. In this analysis, we also take into account the delay limit as one of the main Quality of Service (QoS) requirements of the voice services. The capacities of VoIP services for the classes with different delay limits are derived and compared in this paper. Simulation results show that the voice capacity is larger when we treat the VoIP service as rtPS than that as UGS.

This paper proposes a combined resource management and scheduling scheme for multi-channel wireless systems based on the knowledge of the channel quality information. In multi-channel wireless systems, such as MIMO, SDMA, or OFDMA systems, the quality of individual channel changes with time in a fairly independent manner, and is very much user-dependent. Consequently, the overall system performance or capacity heavily depends not only on the time (i.e., the scheduling scheme) but also the way (i. e. the radio resource management scheme) that channels are allocated to users. Unlike the most existing scheduling and/or RRM schemes for wireless systems, our approach employs multiple schedulers, each of which is responsible for allocating a single channel (or a channel group) to different users. In other words, the proposed scheme carries out the RRM and scheduling at the same time. Further, we introduce the notion of request-based scheduling as a way to extend the advantage of opportunistic scheduling to channel axis. Numerical experiments demonstrate that a significant performance improvement can be achieved by the parallel scheduling scheme, and the improvement may even be magnified via request-based scheduling.

We propose a hierarchical packet scheduling technique to closely approximate a hierarchical extension of the generalized processor sharing model, hierarchical generalized processor sharing (H-GPS). Our approach is to undertake the tasks of service guarantee and hierarchical link sharing in an independent manner so that each task best serves its own objective. For tight and harmonized integration of the two independent service functions into a single packet scheduling algorithm, we introduce two novel concepts of distributed virtual time and service credit, and develop a packet version of H-GPS called hierarchical fair queuing (HFQ). We demonstrate the layer-independent performance of the HFQ algorithm through simulation results.

With the advent of high-speed networks, a single link will carry hundreds or even thousands of applications. This results in a very natural application of the central limit theorem, to model the network traffic by a Gaussian stochastic processes. We study the tail probability P({Q>x}) of a queueing system when the input process is assumed to be a very general class of Gaussian processes which includes a large class of self similar or other types of long-range dependent Gaussian processes. For example, past work on fractional Brownian motion, and variations therein, are but a small subset of the work presented in this paper. This study is based on extreme value theory and we show that log P({Q>x})+m/sub x//2 grows at most on the order of logx, where m/sub x/ corresponds to the reciprocal of the maximum (normalized) variance of a Gaussian process directly related to the aggregate input process. The result is considerably stronger than the existing results in the literature based on large deviation theory, and we theoretically show that this improvement can be quite important in characterizing the asymptotic behavior of P({Q>x}). Through numerical examples, we also demonstrate that exp[-m/sub x//2] provides a very accurate estimate for a variety of long-range and short-range dependent input processes over the entire buffer range.

We study P({Q>x}), the tail of the steady state queue length distribution at a high-speed multiplexer. The tail probability distribution P({Q>x}) is a fundamental measure of network congestion and thus important for the efficient design and control of networks. In particular, we focus on the case when the aggregate traffic to the multiplexer can be characterized by a stationary Gaussian process. In our approach, a multiplexer is modeled by a fluid queue serving a large number of input processes. We propose two asymptotic upper bounds for P({Q>x}), and provide several numerical examples to illustrate the tightness of these bounds. We also use these bounds to study important properties of the tail probability. Further, we apply these bounds for a large number of non-Gaussian input sources, and validate their performance via simulations. We have conducted our simulation study using importance sampling in order to improve its reliability and to effectively capture rare events. Our analytical study is based on extreme value theory, and therefore different from the approaches using traditional Markovian and large deviations techniques.

In this paper, we study P(/spl Qscr/>x), the tail of the steady-state queue length distribution at a high-speed multiplexer. In particular, we focus on the case when the aggregate traffic to the multiplexer can be characterized by a stationary Gaussian process. We provide two asymptotic upper bounds for the tail probability and an asymptotic result that emphasizes the importance of the dominant time scale and the maximum variance. One of our bounds is in a single-exponential form and can be used to calculate an upper bound to the asymptotic constant. However, we show that this bound, being of a single-exponential form, may not accurately capture the tail probability. Our asymptotic result on the importance of the maximum variance and our extensive numerical study on a known lower bound motivate the development of our second asymptotic upper bound. This bound is expressed in terms of the maximum variance of a Gaussian process, and enables the accurate estimation of the tail probability over a wide range of queue lengths. We apply our results to Gaussian as well as multiplexed non-Gaussian input sources, and validate their performance via simulations. Wherever possible, we have conducted our simulation study using importance sampling in order to improve its reliability and to effectively capture rare events. Our analytical study is based on extreme value theory, and therefore different from the approaches using traditional Markovian and large deviations techniques.

In this paper, we develop a simple analytical technique to determine P({Q>q}), the tail of the queue length distribution, at an ATM multiplexer. The ATM multiplexer is modeled as a fluid queue serving a large number of independent sources. Our method is based on the central limit theorem and the maximum variance approximation, and enables us to avoid the state explosion problem. The approach is quite general and not limited by a Markovian framework. We apply our analytical method to study the buffer behavior for various traffic sources such as multiplexed homogeneous and heterogeneous Markov modulated sources, sources that are correlated at multiple time scales, sources whose autocorrelation function exhibits heavy (sub-exponential) tail behavior, and sources generated from real MPEG-encoded video sequences.