TY - GEN
T1 - Channel-aware distributed scheduling for ad-hoc communications with capture
AU - Ge, Weiyan
AU - Zhang, Junshan
AU - Wieselthier, Jeffrey E.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - In this paper, we consider contention-based ad-hoc networks, where links use mini-slots to contend for the channel (i.e., channel probing), and followed by data transmission over a longer duration, as in CSMA. We investigate channel-aware distributed scheduling under the multipacket reception (MPR) model, which involves a joint process of channel probing and distributed scheduling. In this study, we formulate channelaware distributed scheduling as a team game, with the objective being to maximize the overall network throughput. We use optimal stopping theory to tackle this problem, and show that the optimal policy for distributed scheduling has a threshold structure. Observing that the network throughput depends heavily on the contention probabilities of links, we then generalize the study to jointly optimize the rate thresholds and the contention probabilities, and propose a two-stage algorithm for computing the optimal threshold-probability pair by invoking fractional optimization and convex programming.
AB - In this paper, we consider contention-based ad-hoc networks, where links use mini-slots to contend for the channel (i.e., channel probing), and followed by data transmission over a longer duration, as in CSMA. We investigate channel-aware distributed scheduling under the multipacket reception (MPR) model, which involves a joint process of channel probing and distributed scheduling. In this study, we formulate channelaware distributed scheduling as a team game, with the objective being to maximize the overall network throughput. We use optimal stopping theory to tackle this problem, and show that the optimal policy for distributed scheduling has a threshold structure. Observing that the network throughput depends heavily on the contention probabilities of links, we then generalize the study to jointly optimize the rate thresholds and the contention probabilities, and propose a two-stage algorithm for computing the optimal threshold-probability pair by invoking fractional optimization and convex programming.
UR - http://www.scopus.com/inward/record.url?scp=50249185658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50249185658&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2007.4487209
DO - 10.1109/ACSSC.2007.4487209
M3 - Conference contribution
AN - SCOPUS:50249185658
SN - 9781424421107
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 260
EP - 264
BT - Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
T2 - 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Y2 - 4 November 2007 through 7 November 2007
ER -