42 research outputs found
Spatial CSMA: A Distributed Scheduling Algorithm for the SIR Model with Time-varying Channels
Recent work has shown that adaptive CSMA algorithms can achieve throughput
optimality. However, these adaptive CSMA algorithms assume a rather simplistic
model for the wireless medium. Specifically, the interference is typically
modelled by a conflict graph, and the channels are assumed to be static. In
this work, we propose a distributed and adaptive CSMA algorithm under a more
realistic signal-to-interference ratio (SIR) based interference model, with
time-varying channels. We prove that our algorithm is throughput optimal under
this generalized model. Further, we augment our proposed algorithm by using a
parallel update technique. Numerical results show that our algorithm
outperforms the conflict graph based algorithms, in terms of supportable
throughput and the rate of convergence to steady-state.Comment: This work has been presented at National Conference on Communication,
2015, held at IIT Bombay, Mumbai, Indi
A Tractable Approach to Coverage and Rate in Cellular Networks
Cellular networks are usually modeled by placing the base stations on a grid,
with mobile users either randomly scattered or placed deterministically. These
models have been used extensively but suffer from being both highly idealized
and not very tractable, so complex system-level simulations are used to
evaluate coverage/outage probability and rate. More tractable models have long
been desirable. We develop new general models for the multi-cell
signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under
very general assumptions, the resulting expressions for the downlink SINR CCDF
(equivalent to the coverage probability) involve quickly computable integrals,
and in some practical special cases can be simplified to common integrals
(e.g., the Q-function) or even to simple closed-form expressions. We also
derive the mean rate, and then the coverage gain (and mean rate loss) from
static frequency reuse. We compare our coverage predictions to the grid model
and an actual base station deployment, and observe that the proposed model is
pessimistic (a lower bound on coverage) whereas the grid model is optimistic,
and that both are about equally accurate. In addition to being more tractable,
the proposed model may better capture the increasingly opportunistic and dense
placement of base stations in future networks.Comment: Submitted to IEEE Transactions on Communication