Channel quality prediction based on Bayesian inference in cognitive radio networks

Abstract

The problem of channel quality prediction in cognitive radio networks is investigated in this paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov Model (NSHMM), which captures the fact that the channel state transition probability is a function of the time interval the primary user has stayed in the current state. Then the model parameters, which carry the information about the expected duration of the channel states and the spectrum sensing accuracy (detection accuracy and false alarm probability) of the SU, are estimated via Bayesian inference with Gibbs sampling. Finally, the estimated NSHMM parameters are employed to design a channel quality metric according to the predicted channel idle duration and spectrum sensing accuracy. Extensive simulation study has been performed to investigate the effectiveness of our design. The results indicate that channel ranking based on the proposed channel quality prediction mechanism captures the idle state duration of the channel and the spectrum sensing accuracy of the SUs, and provides more high quality transmission opportunities and higher successful transmission rates at shorter spectrum waiting times for dynamic spectrum access.

Achievable transmission capacity of cognitive radio networks with cooperative relaying

“…In our simulation, the change of the relay position is quantified by β, the angle between the relay-PR line and the PT-PR line. This implies that our previous numerical study ( Figures 4,5,6,7 and 8) corresponds to the case of β = 0. As indicated by (14) and Figure 2 in Section 5, the feasible location region of the relay is determined by the interferences from other SUs and the transmit powers; more specifically, it is determined by the interference ratio keeping the ST1-ST2 line perpendicular to the PT-PR line as shown in Figure 3.…”

“…Therefore, we can get the upper bound of the power ratio γ u ps when f 2 = 0. Furthermore, based on the observation that the SU's capacity decreases when the power ratio γ ps increases (see Equation 6), we can estimate the maximum achievable transmission capacity by substituting γ l ps into Equation 6, i.e.,…”

“…Both academic and regulatory bodies have focused on dynamic spectrum access aiming at enhancing the utilization of the spectrum resource. Cognitive radio [3][4][5][6], with the capability to flexibly adapt its parameters, has been proposed as the enabling technology for unlicensed secondary users (SUs) to dynamically access the licensed spectrum owned by legacy primary users (PUs) on a negotiated or an opportunistic basis. As a fundamental problem, the achievable transmission capacity of a cognitive radio network over the underlay spectrum sharing model has been extensively studied [7][8][9], in which the secondary users are allowed to access the channel only if their transmissions do not cause intolerable interference at the primary receiver.…”

A Review on Cognitive Radio for Next Generation Cellular Network and its Challenges

“…In CRN, secondary users are permitted to use the primary users channel without any interference. However, the channel quality may differ significantly and the good quality channel drastically decreases the spectrum efficiency for secondary user (Xing et al, 2013). In OFDM system, CP is added between the information to avoid the ISI (Inter Symbol Interference).…”

Distributed back-pressure scheduling with opportunistic routing in cognitive radio networks

“…Cognitive radio networking has been extensively studied in recent years [1][2][3][4][5][6]. The cognitive radio (CR) technique allows unlicensed users (secondary users (SUs)) to opportunistically access the licensed spectrum without interfering with licensed users (primary users (PUs)) to exploit the under-utilized portion of the spectrum [7][8][9].…”