TY - JOUR

T1 - Power control and asymptotic throughput analysis for the distributed cognitive uplink

AU - Nekouei, Ehsan

AU - Inaltekin, Hazer

AU - Dey, Subhrakanti

PY - 2014/1

Y1 - 2014/1

N2 - This paper studies optimum power control and sum-rate scaling laws for
the distributed cognitive uplink. It is first shown that the optimum
distributed power control policy is in the form of a threshold based
water-filling power control. Each secondary user executes the derived
power control policy in a distributed fashion by using local knowledge
of its direct and interference channel gains such that the resulting
aggregate (average) interference does not disrupt primary's
communication. Then, the tight sum-rate scaling laws are derived as a
function of the number of secondary users N under the optimum
distributed power control policy. The fading models considered to derive
sum-rate scaling laws are general enough to include Rayleigh, Rician
and Nakagami fading models as special cases. When transmissions of
secondary users are limited by both transmission and interference power
constraints, it is shown that the secondary network sum-rate scales
according to 1/en
h
log log (N), where n_h is a parameter obtained from the distribution of
direct channel power gains. For the case of transmissions limited only
by interference constraints, on the other hand, the secondary network
sum-rate scales according to 1/eγ
g
log (N), where γ
g
is a parameter obtained from the distribution of interference channel
power gains. These results indicate that the distributed cognitive
uplink is able to achieve throughput scaling behavior similar to that of
the centralized cognitive uplink up to a pre-log multiplier 1/e, whilst
primary's quality-of-service requirements are met. The factor 1/e can
be interpreted as the cost of distributed implementation of the
cognitive uplink.

AB - This paper studies optimum power control and sum-rate scaling laws for
the distributed cognitive uplink. It is first shown that the optimum
distributed power control policy is in the form of a threshold based
water-filling power control. Each secondary user executes the derived
power control policy in a distributed fashion by using local knowledge
of its direct and interference channel gains such that the resulting
aggregate (average) interference does not disrupt primary's
communication. Then, the tight sum-rate scaling laws are derived as a
function of the number of secondary users N under the optimum
distributed power control policy. The fading models considered to derive
sum-rate scaling laws are general enough to include Rayleigh, Rician
and Nakagami fading models as special cases. When transmissions of
secondary users are limited by both transmission and interference power
constraints, it is shown that the secondary network sum-rate scales
according to 1/en
h
log log (N), where n_h is a parameter obtained from the distribution of
direct channel power gains. For the case of transmissions limited only
by interference constraints, on the other hand, the secondary network
sum-rate scales according to 1/eγ
g
log (N), where γ
g
is a parameter obtained from the distribution of interference channel
power gains. These results indicate that the distributed cognitive
uplink is able to achieve throughput scaling behavior similar to that of
the centralized cognitive uplink up to a pre-log multiplier 1/e, whilst
primary's quality-of-service requirements are met. The factor 1/e can
be interpreted as the cost of distributed implementation of the
cognitive uplink.

UR - http://www.scopus.com/inward/record.url?scp=84893812690&partnerID=8YFLogxK

U2 - 10.1109/TCOMM.2013.112413.130510

DO - 10.1109/TCOMM.2013.112413.130510

M3 - Article

AN - SCOPUS:84893812690

SN - 0090-6778

VL - 62

SP - 41

EP - 58

JO - IEEE Transactions on Communications

JF - IEEE Transactions on Communications

IS - 1

ER -