Opportunistic Scheduling for Next Generation Wireless Local Area Networks

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 O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg