  This paper considers cooperative spectrum sensing algorithms for Cognitive
Radios which focus on reducing the number of samples to make a reliable
detection. We develop an energy efficient detector with low detection delay
using decentralized sequential hypothesis testing. Our algorithm at the
Cognitive Radios employs an asynchronous transmission scheme which takes into
account the noise at the fusion center. We start with a distributed algorithm,
DualSPRT, in which Cognitive Radios sequentially collect the observations, make
local decisions using SPRT (Sequential Probability Ratio Test) and send them to
the fusion center. The fusion center sequentially processes these received
local decisions corrupted by noise, using an SPRT-like procedure to arrive at a
final decision. We theoretically analyse its probability of error and average
detection delay. We also asymptotically study its performance. Even though
DualSPRT performs asymptotically well, a modification at the fusion node
provides more control over the design of the algorithm parameters which then
performs better at the usual operating probabilities of error in Cognitive
Radio systems. We also analyse the modified algorithm theoretically. Later we
modify these algorithms to handle uncertainties in SNR and fading.
