496 research outputs found
Achieving Optimal Throughput and Near-Optimal Asymptotic Delay Performance in Multi-Channel Wireless Networks with Low Complexity: A Practical Greedy Scheduling Policy
In this paper, we focus on the scheduling problem in multi-channel wireless
networks, e.g., the downlink of a single cell in fourth generation (4G)
OFDM-based cellular networks. Our goal is to design practical scheduling
policies that can achieve provably good performance in terms of both throughput
and delay, at a low complexity. While a class of -complexity
hybrid scheduling policies are recently developed to guarantee both
rate-function delay optimality (in the many-channel many-user asymptotic
regime) and throughput optimality (in the general non-asymptotic setting),
their practical complexity is typically high. To address this issue, we develop
a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a
\lower complexity , and rigorously prove that D-SSG not only achieves
throughput optimality, but also guarantees near-optimal asymptotic delay
performance. Specifically, we show that the rate-function attained by D-SSG for
any delay-violation threshold , is no smaller than the maximum achievable
rate-function by any scheduling policy for threshold . Thus, we are able
to achieve a reduction in complexity (from of the hybrid
policies to ) with a minimal drop in the delay performance. More
importantly, in practice, D-SSG generally has a substantially lower complexity
than the hybrid policies that typically have a large constant factor hidden in
the notation. Finally, we conduct numerical simulations to validate
our theoretical results in various scenarios. The simulation results show that
D-SSG not only guarantees a near-optimal rate-function, but also empirically is
virtually indistinguishable from delay-optimal policies.Comment: Accepted for publication by the IEEE/ACM Transactions on Networking,
February 2014. A preliminary version of this work was presented at IEEE
INFOCOM 2013, Turin, Italy, April 201
Resource management in QoS-aware wireless cellular networks
2011 Summer.Includes bibliographical references.Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost
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