On throughput optimality with delayed network-state information

Abstract-We study the fundamental network capacity of a multi-user wireless downlink under two assumptions: (1) Channels are not explicitly measured and thus instantaneous states are unknown, (2) Channels are modeled as ON/OFF Markov chains. This is an important network model to explore because channel probing may be costly or infeasible in some contexts. In this case, we can use channel memory with ACK/NACK feedback from previous transmissions to improve network throughput. Computing in closed form the capacity region of this network is difficult because it involves solving a high dimension partially observed Markov decision problem. Instead, in this paper we construct an inner and outer bound on the capacity region, showing that the bound is tight when the number of users is large and the traffic is symmetric. For the case of heterogeneous traffic and any number of users, we propose a simple queue-dependent policy that can stabilize the network with any data rates strictly within the inner capacity bound. The stability analysis uses a novel frame-based Lyapunov drift argument. The outer-bound analysis uses stochastic coupling and state aggregation to bound the performance of a restless bandit problem using a related multi-armed bandit system. Our results are useful in cognitive radio networks, opportunistic scheduling with delayed/uncertain channel state information, and restless bandit problems.Index Terms-stochastic network optimization, Markovian channels, delayed channel state information (CSI), partially observable Markov decision process (POMDP), cognitive radio, restless bandit, opportunistic spectrum access, queueing theory, Lyapunov analysis.

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A general class of throughput optimal routing policies in multi-hop wireless networks

Naghshvar

Javidi

2009

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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This paper considers the problem of throughput optimal routing/scheduling in a multi-hop constrained queueing network with random connectivity whose special case includes opportunistic multi-hop wireless networks and input-queued switch fabrics. The main challenge in the design of throughput optimal routing policies is closely related to identifying appropriate and universal Lyapunov functions with negative expected drift. The few well-known throughput optimal policies in the literature are constructed using simple quadratic or exponential Lyapunov functions of the queue backlogs and as such they seek to balance the queue backlogs across network independent of the topology.By considering a class of continuous, differentiable, and piece-wise quadratic Lyapunov functions, this paper provides a large class of throughput optimal routing policies. The proposed class of Lyapunov functions allow for the routing policy to control the traffic along short paths for a large portion of state-space while ensuring a negative expected drift. This structure enables the design of a large class of routing policies. In particular, and in addition to recovering the throughput optimality of the well known backpressure routing policy, an opportunistic routing policy with congestion diversity is proved to be throughput optimal.

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On throughput optimality with delayed network-state information

Cited by 12 publications

References 26 publications

Scheduling with pairwise XORing of packets under statistical overhearing information and feedback

Scheduling with pairwise XORing of packets under statistical overhearing information and feedback

Exploiting channel memory for multiuser wireless scheduling without channel measurement: Capacity regions and algorithms

A general class of throughput optimal routing policies in multi-hop wireless networks

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