TY - GEN
T1 - Distributed throughput maximization for multi-channel ALOHA networks
AU - Cohen, Kobi
AU - Leshem, Amir
PY - 2013
Y1 - 2013
N2 - We consider the problem of distributed throughput maximization for multi-channel ALOHA networks. We focus on networks containing a large number of users that transmit over a low number of channels. First, we consider the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a distributed best-response algorithm to solve the rate maximization problem, where each user updates its strategy using its local channel state information (CSI) and by monitoring the channel utilization. We then consider the case where users are not restricted by transmission probability constraints. Distributed optimization of the network throughput under uncertainty is mandatory since the transmission probabilities of other users are unknown. We propose a distributed scheme to solve the throughput optimization problem under uncertainty, where users adjust their transmission probability to maximize their rates, but maintain the desired load on the channels. We propose sequential and parallel algorithms for this purpose.
AB - We consider the problem of distributed throughput maximization for multi-channel ALOHA networks. We focus on networks containing a large number of users that transmit over a low number of channels. First, we consider the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a distributed best-response algorithm to solve the rate maximization problem, where each user updates its strategy using its local channel state information (CSI) and by monitoring the channel utilization. We then consider the case where users are not restricted by transmission probability constraints. Distributed optimization of the network throughput under uncertainty is mandatory since the transmission probabilities of other users are unknown. We propose a distributed scheme to solve the throughput optimization problem under uncertainty, where users adjust their transmission probability to maximize their rates, but maintain the desired load on the channels. We propose sequential and parallel algorithms for this purpose.
KW - Collision channels
KW - best-response dynamics
KW - distributed optimization
KW - multi-channel ALOHA
UR - http://www.scopus.com/inward/record.url?scp=84894120487&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2013.6714106
DO - 10.1109/CAMSAP.2013.6714106
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AN - SCOPUS:84894120487
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 456
EP - 459
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -