TY - GEN
T1 - Exploiting geometry in the SINRk model
AU - Aschner, Rom
AU - Citovsky, Gui
AU - Katz, Matthew J.
N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2015.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - We introduce the SINRk model, which is a practical version of the SINR model. In the SINRk model, in order to determine whether s’s signal is received at c, where s is a sender and c is a receiver, one only considers the k most significant senders w.r.t. to c (other than s). Assuming uniform power, these are the k closest senders to c (other than s). Under this model, we consider the well-studied scheduling problem: Given a set L of sender-receiver requests, find a partition of L into a minimum number of subsets (rounds), such that in each subset all requests can be satisfied simultaneously. We present an O(1)- approximation algorithm for the scheduling problem (under the SINRk model). For comparison, the best known approximation ratio under the SINR model is O(log n). We also present an O(1)-approximation algorithm for the maximum capacity problem (i.e., for the single round problem), obtaining a constant of approximation which is considerably better than those obtained under the SINR model. Finally, for the special case where k = 1, we present a PTAS for the maximum capacity problem. Our algorithms are based on geometric analysis of the SINRk model.
AB - We introduce the SINRk model, which is a practical version of the SINR model. In the SINRk model, in order to determine whether s’s signal is received at c, where s is a sender and c is a receiver, one only considers the k most significant senders w.r.t. to c (other than s). Assuming uniform power, these are the k closest senders to c (other than s). Under this model, we consider the well-studied scheduling problem: Given a set L of sender-receiver requests, find a partition of L into a minimum number of subsets (rounds), such that in each subset all requests can be satisfied simultaneously. We present an O(1)- approximation algorithm for the scheduling problem (under the SINRk model). For comparison, the best known approximation ratio under the SINR model is O(log n). We also present an O(1)-approximation algorithm for the maximum capacity problem (i.e., for the single round problem), obtaining a constant of approximation which is considerably better than those obtained under the SINR model. Finally, for the special case where k = 1, we present a PTAS for the maximum capacity problem. Our algorithms are based on geometric analysis of the SINRk model.
UR - http://www.scopus.com/inward/record.url?scp=84927782200&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-46018-4_8
DO - 10.1007/978-3-662-46018-4_8
M3 - Conference contribution
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 125
EP - 135
BT - Algorithms for Sensor Systems - 10th International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics, ALGOSENSORS 2014, Revised Selected Papers
A2 - Gao, Jie
A2 - Efrat, Alon
A2 - Fekete, Sándor P.
A2 - Zhang, Yanyong
PB - Springer Verlag
T2 - 10th International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics, ALGOSENSORS 2014
Y2 - 12 September 2014 through 12 September 2014
ER -