On Optimum Causal Cognitive Spectrum Reutilization Strategy

Abstract

In this paper we study opportunistic transmission strategies for cognitive radios (CR) in which causal noisy observation from a primary user(s) (PU) state is available. PU is assumed to be operating in a slotted manner, according to a two-state Markov model. The objective is to maximize utilization ratio (UR), i.e., relative number of the PU-idle slots that are used by CR, subject to interference ratio (IR), i.e., relative number of the PU-active slots that are used by CR, below a certain level. We introduce an a-posteriori LLR-based cognitive transmission strategy and show that this strategy is optimum in the sense of maximizing UR given a certain maximum allowed IR. Two methods for calculating threshold for this strategy in practical situations are presented. One of them performs well in higher SNRs but might have too large IR at low SNRs and low PU activity levels, and the other is proven to never violate the allowed IR at the price of a reduced UR. In addition, an upper-bound for the UR of any CR strategy operating in the presence of Markovian PU is presented. Simulation results have shown a more than 116% improvement in UR at SNR of −3dB and IR level of 10% with PU state estimation. Thus, this opportunistic CR mechanism possesses a high potential in practical scenarios in which there exists no information about true states of PU.

Spectrum Sensing Using a Hidden Bivariate Markov Model

“…By varying the value of γ we obtain a receiver operating characteristic (ROC) curve as shown in the next section. The detection scheme (23) can be couched in terms of the log-likelihood ratio (LLR), as was done in [13] for a detector based on the standard HMM, by setting γ = e θLLR /(1+e θLLR ), where θ LLR is the threshold against which the LLR is compared.…”

“…The adequacy of such a model was questioned in [17]. In a recent paper, optimality of a log-likelihood detector based on a standard HMM was studied [13]. A review of HMMs can be found in [18].…”

An inter-frame dynamic double-threshold energy detection for spectrum sensing in cognitive radios

“…On the other hand, the frame length of the SU is much shorter than both the average durations of the PU's ON and OFF states [20,21], which implies that the PU's activities over successive sensing frames are not independent, especially between adjacent frames. Furthermore, the PU stays at the same state with a high probability during the whole sensing frame [22]. These properties can be exploited to improve the sensing performance of the ED schemes.…”

“…In [22], the received energy sequence over successive sensing frames is modeled as a continuous hidden Markov chain, and the final decision is made according to the combined observations from all previous sensing frames. However, this scheme needs to perform simultaneously spectrum sensing and data transmission and hence suffers from implementation difficulties due to self-interference [23].…”

“…However, this scheme needs to perform simultaneously spectrum sensing and data transmission and hence suffers from implementation difficulties due to self-interference [23]. In [24] and [25], the PU activity is modeled as a Markov chain, and the final decision is also based on history observations but using different combining rules from that of [22]. However, in the schemes in [24] and [25], each sensing frame is further divided into many short slots for either dynamic spectrum sensing or data transmission.…”