Scheduling for next generation WLANs: filling the gap between offered and observed data rates

In wireless networks, opportunistic scheduling is used to increase system throughput by exploiting multi-user diversity. Although recent advances have increased physical layer data rates supported in wireless local area networks (WLANs), actual throughput realized are significantly lower due to overhead. Accordingly, the frame aggregation concept is used in next generation WLANs to improve efficiency. However, with frame aggregation, traditional opportunistic schemes are no longer optimal. In this paper, we propose schedulers that take queue and channel conditions into account jointly, to maximize throughput observed at the users for next generation WLANs. We also extend this work to design two schedulers that perform block scheduling for maximizing network throughput over multiple transmission sequences. For these schedulers, which make decisions over long time durations, we model the system using queueing theory and determine users' temporal access proportions according to this model. Through detailed simulations, we show that all our proposed algorithms offer significant throughput improvement, better fairness, and much lower delay compared with traditional opportunistic schedulers, facilitating the practical use of the evolving standard for next generation wireless networks

Opportunistic scheduling for next generation wireless LANs /

The multiuser diversity phenomenon has been exploited via opportunistic scheduling for increasing system throughput in wireless networks recently. Frame aggregation which increases the MAC efficiency is another method in enhancing system throughput. Opportunistic scheduling has been employed jointly with frame aggregation in order to maximize the system throughput in this work. The use of existing opportunistic schemes has been shown not to be optimal when frame aggregation is used, as applied in the IEEE 802.1 In Wireless Local Area Network standard. New scheduling approaches which combine channel states with queue states have been proposed with the aim of maximizing the total network throughput. In addition to schedulers which select transmitted users according to instantaneous scheduling metrics, schedulers which maximize throughput over larger time intervals have been proposed. These schedulers utilize results obtained from the queuing model developed for 802.1 In throughout this thesis. The proposed new algorithms are shown to offer significant improvement in network throughput over non-opportunistic and greedy schedulers through detailed simulations. The developed algorithms also provide a good compromise between throughput and fairness. The effect of incorporating relaying in schedulers applied for frame aggregation systems has also been analyzed

Quality-of-information aware transmission policies with time-varying links

Abstract—We consider Quality-of-Information (QoI) aware transmission policies in the presence of time-varying links in a mobile ad hoc network. QoI, tailored for military tactical networks, is defined by a set of attributes relevant to the application. Time-varying nature of links in practical networks leads to uncertainty in evaluating QoI utility to be delivered to end users. This delivered-QoI utility is a function of both attributes provided by the source input, as a result of observing certain events, and the channel induced attributes that impact the QoI obtained at the destination. The goal of this paper is to attain the maximum QoI output utility, termed as Operational Information Content Capacity (OICC) of a network. First, for a single link, we demonstrate that the optimal decision structure for transmission is threshold-based. Next, we consider multihop relay networks. For the basic model of a two-hop relay network, we propose transmission scheduling and link activation schemes based on approximate dynamic programming methods. Furthermore, we exploit time-variations of links by opportunistic scheduling by employing buffers at the relay node. We demonstrate that significant gains in QoI output utility are gained by opportunistic scheduling algorithms. I

Opportunistic scheduling with frame aggregation for next generation wireless LANs

The multiuser diversity phenomenon is exploited via opportunistic scheduling for increasing system throughput in wireless networks. Another method to enhance system throughput is through frame aggregation, which increases MAC efficiency. We consider opportunistic scheduling jointly with frame aggregation. In this paper, we argue that existing opportunistic schemes are not optimal when frame aggregation is used. We propose new scheduling approaches that combine channel states with queue states. Through detailed simulations, we show that our new algorithms offer significant improvement in network throughput over non-opportunistic and greedy schedulers. Our algorithms also provide a good compromise between throughput and fairness

Opportunistic scheduling for next generation wireless local area networks

(The final version of the chapter does not involve an abstract. Introduction is provided below.) Wireless access has been increasingly popular recently due to portability and low cost of wireless terminals and equipment. The emerging technologies for wireless local area networks (WLANs) are defined by the IEEE 802.11n standard, where physical layer data rates exceeding 200 Mbps are provisioned with multiple input multiple output antenna techniques. However, actual throughput to be experienced by WLAN users is considerably lower than the provided physical layer data rates, despite the link efficiency is enhanced via the frame aggregation concept of 802.11n. In a multi user communication system, scheduling is the mechanism that determines which user should transmit/receive data in a given time interval. Opportunistic scheduling algorithms maximize system throughput by making use of the channel variations and multi user diversity. The main idea is favouring users that are experiencing the most desirable channel conditions at each scheduling instant, i.e. riding the peaks. While maximizing capacity, such greedy algorithms may cause some users to experience unacceptable delays and unfairness, unless the users are highly mobile. In order to remedy this problem, we combine aggregation and opportunistic scheduling approaches to further enhance the throughput of next generation WLANs. We argue that aggregation can dramatically change the scheduling scenario: A user with a good channel and a long queue may offer a higher throughput than a user with better channel conditions but shorter queue. Hence, the statement that always selecting the user with the best channel maximizes throughput is not valid anymore. In this work, we first present our queue aware scheduling scheme that take into account the instantaneous channel capacities and queue sizes simultaneously, named as Aggregate Opportunistic Scheduling (AOS). Detailed simulations results indicate that our proposed algorithm offers significant gains in total system throughput, by up to 53%, as compared to opportunistic schedulers while permitting relatively fair access. We also improve AOS with the principle of relayed transmissions and show the improvements of opportunistic relaying. Later on, we propose another scheduler, which aims to maximize the network throughput over a long time scale. For this purpose, we estimate the statistical evolution of queue states and model the 802.11n MAC transmissions using queuing theory by extending the bulk service model. Utilizing the outcomes of the queuing model, we design Predictive Scheduling with time-domain Waterfilling (P-WF) algorithm. P-WF further improves the performance of our queue aware schedulers, as the throughput is maximized by applying the water filling solution to time allocations. This chapter includes an overview of existing literature on opportunistic scheduling for wireless networks in general and presents our proposed algorithms with comparative detailed performance analysis as they are applied into the next generation WLANs

Operational Information Content Sum Capacity: Formulation and Examples

Abstract—This paper considers Quality-of-Information (QoI) aware rate allocation policies for multiple access channels. QoI is a recently introduced composite metric which is impacted by a number of attributes including accuracy and timeliness of delivery of information communicated from the source(s) to the destination(s), and as such differs from traditional qualityof-service metrics considered to date. The focus of this work is defining the Operational Information Content Sum Capacity (OICC-S) of a network, as the set of QoI-vectors supported which maximize sum utility of the system. This utility is defined as a function of the QoI attributes provided by the source input, as well as the channel induced attributes that impact the QoI delivered to the destination(s). Optimum rate allocation to maximize the output sum utility and achieve OICC-S of the network is provided, and demonstrated to differ from the solution that provides maximum throughput

Quality of information aware scheduling in task processing networks

Abstract—We investigate Quality of Information (QoI) aware scheduling in task processing networks. Specifically, we consider the scenario where a network sequentially receives tasks from an end user, utilizes its resources to process them, and sends back its response. The utility derived by the end user from this response depends on both the accuracy and the freshness of the information. There is often a trade-off between these two attributes and we present a model that quantifies this dependence. Using dynamic programming and optimal stopping theory, we characterize the optimal scheduling policy that maximizes the time average utility delivered by the network. We show that for many scenarios of practical interest, the optimal policy has a simple threshold structure. We also propose a method to approximately compute the threshold in closed-form. This work takes a step towards incorporating application aware objectives in making optimal scheduling decisions