8 research outputs found
Speeding up Glauber Dynamics for Random Generation of Independent Sets
The maximum independent set (MIS) problem is a well-studied combinatorial
optimization problem that naturally arises in many applications, such as
wireless communication, information theory and statistical mechanics.
MIS problem is NP-hard, thus many results in the literature focus on fast
generation of maximal independent sets of high cardinality. One possibility is
to combine Gibbs sampling with coupling from the past arguments to detect
convergence to the stationary regime. This results in a sampling procedure with
time complexity that depends on the mixing time of the Glauber dynamics Markov
chain.
We propose an adaptive method for random event generation in the Glauber
dynamics that considers only the events that are effective in the coupling from
the past scheme, accelerating the convergence time of the Gibbs sampling
algorithm
Delay Performance and Mixing Times in Random-Access Networks
We explore the achievable delay performance in wireless random-access
networks. While relatively simple and inherently distributed in nature,
suitably designed queue-based random-access schemes provide the striking
capability to match the optimal throughput performance of centralized
scheduling mechanisms in a wide range of scenarios. The specific type of
activation rules for which throughput optimality has been established, may
however yield excessive queues and delays.
Motivated by that issue, we examine whether the poor delay performance is
inherent to the basic operation of these schemes, or caused by the specific
kind of activation rules. We derive delay lower bounds for queue-based
activation rules, which offer fundamental insight in the cause of the excessive
delays. For fixed activation rates we obtain lower bounds indicating that
delays and mixing times can grow dramatically with the load in certain
topologies as well
Delay performance in random-access grid networks
We examine the impact of torpid mixing and meta-stability issues on the delay
performance in wireless random-access networks. Focusing on regular meshes as
prototypical scenarios, we show that the mean delays in an toric
grid with normalized load are of the order . This
superlinear delay scaling is to be contrasted with the usual linear growth of
the order in conventional queueing networks. The intuitive
explanation for the poor delay characteristics is that (i) high load requires a
high activity factor, (ii) a high activity factor implies extremely slow
transitions between dominant activity states, and (iii) slow transitions cause
starvation and hence excessively long queues and delays. Our proof method
combines both renewal and conductance arguments. A critical ingredient in
quantifying the long transition times is the derivation of the communication
height of the uniformized Markov chain associated with the activity process. We
also discuss connections with Glauber dynamics, conductance and mixing times.
Our proof framework can be applied to other topologies as well, and is also
relevant for the hard-core model in statistical physics and the sampling from
independent sets using single-site update Markov chains
Optimal CSMA-based Wireless Communication with Worst-case Delay and Non-uniform Sizes
Carrier Sense Multiple Access (CSMA) protocols have been shown to reach the
full capacity region for data communication in wireless networks, with
polynomial complexity. However, current literature achieves the throughput
optimality with an exponential delay scaling with the network size, even in a
simplified scenario for transmission jobs with uniform sizes. Although CSMA
protocols with order-optimal average delay have been proposed for specific
topologies, no existing work can provide worst-case delay guarantee for each
job in general network settings, not to mention the case when the jobs have
non-uniform lengths while the throughput optimality is still targeted. In this
paper, we tackle on this issue by proposing a two-timescale CSMA-based data
communication protocol with dynamic decisions on rate control, link scheduling,
job transmission and dropping in polynomial complexity. Through rigorous
analysis, we demonstrate that the proposed protocol can achieve a throughput
utility arbitrarily close to its offline optima for jobs with non-uniform sizes
and worst-case delay guarantees, with a tradeoff of longer maximum allowable
delay
Queue-Based Random-Access Algorithms: Fluid Limits and Stability Issues
We use fluid limits to explore the (in)stability properties of wireless
networks with queue-based random-access algorithms. Queue-based random-access
schemes are simple and inherently distributed in nature, yet provide the
capability to match the optimal throughput performance of centralized
scheduling mechanisms in a wide range of scenarios. Unfortunately, the type of
activation rules for which throughput optimality has been established, may
result in excessive queue lengths and delays. The use of more
aggressive/persistent access schemes can improve the delay performance, but
does not offer any universal maximum-stability guarantees. In order to gain
qualitative insight and investigate the (in)stability properties of more
aggressive/persistent activation rules, we examine fluid limits where the
dynamics are scaled in space and time. In some situations, the fluid limits
have smooth deterministic features and maximum stability is maintained, while
in other scenarios they exhibit random oscillatory characteristics, giving rise
to major technical challenges. In the latter regime, more aggressive access
schemes continue to provide maximum stability in some networks, but may cause
instability in others. Simulation experiments are conducted to illustrate and
validate the analytical results