TY - GEN
T1 - Capacity of wireless systems under distributed scheduling of time-dependent users
AU - Shmuel, Ori
AU - Cohen, Asaf
AU - Gurewitz, Omer
N1 - Publisher Copyright: © Copyright 2015 IEEE All rights reserved.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Consider the problem of a multiple access channel with a large number of users K. While several multiuser coding techniques exist, in practical scenarios, not all users can be scheduled simultaneously and choosing the most suitable user to transmit can boost network performance dramatically. This is the essence of multi-user diversity. Although the problem has been studied in various time-independent scenarios, capacity scaling laws and algorithms for time-dependent channels (e.g., Markov channels) remains relatively unexplored. In this work, we consider the Gilber-Elliott Channel as a model for a simple time-dependent channel, and derive the expected capacity under centralized scheduling. We show that the capacity scaling law is O (σg √2 log K + μg), where σg and μg are channel parameters during the good channel state. In addition, a distributed algorithm for this scenario is suggested along with it's capacity analysis. The expected capacity under distributed scheduling scales (in K) the same as under centralized scheduling, hence, there is no loss in optimality due to the distributed algorithm. The analysis uses tools from Extreme Value Theory and Point Process Approximation.
AB - Consider the problem of a multiple access channel with a large number of users K. While several multiuser coding techniques exist, in practical scenarios, not all users can be scheduled simultaneously and choosing the most suitable user to transmit can boost network performance dramatically. This is the essence of multi-user diversity. Although the problem has been studied in various time-independent scenarios, capacity scaling laws and algorithms for time-dependent channels (e.g., Markov channels) remains relatively unexplored. In this work, we consider the Gilber-Elliott Channel as a model for a simple time-dependent channel, and derive the expected capacity under centralized scheduling. We show that the capacity scaling law is O (σg √2 log K + μg), where σg and μg are channel parameters during the good channel state. In addition, a distributed algorithm for this scenario is suggested along with it's capacity analysis. The expected capacity under distributed scheduling scales (in K) the same as under centralized scheduling, hence, there is no loss in optimality due to the distributed algorithm. The analysis uses tools from Extreme Value Theory and Point Process Approximation.
UR - http://www.scopus.com/inward/record.url?scp=84941243697&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/EEEI.2014.7005819
DO - https://doi.org/10.1109/EEEI.2014.7005819
M3 - Conference contribution
T3 - 2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
BT - 2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
T2 - 2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
Y2 - 3 December 2014 through 5 December 2014
ER -