Spectrum sensing based on three-state model to accomplish all-level fairness for co-existing multiple cognitive radio networks

Abstract

Spectrum sensing plays a critical role in cognitive radio networks (CRNs). The majority of spectrum sensing algorithms aim to detect the existence of a signal on a channel, i.e., they classify a channel into either busy or idle state, referred to as a two-state sensing model in this paper. While this model works properly when there is only one CRN accessing a channel, it significantly limits the potential and fairness of spectrum access when there are multiple co-existing CRNs. This is because if the secondary users (SUs) from one CRN are accessing a channel, SUs from other CRNs would detect the channel as busy and hence be starved. In this paper, we propose a three-state sensing model that distinguishes the channel into three states: idle, occupied by a primary user, or occupied by a secondary user. This model effectively addresses the fairness concern of the two-state sensing model, and resolves the starvation problem of multiple co-existing CRNs. To accurately detect each state of the three, we develop a two-stage detection procedure. In the first stage, energy detection is employed to identify whether a channel is idle or occupied. If the channel is occupied, the received signal is further analyzed at the second stage to determine whether the signal originates from a primary user or an SU. For the second stage, we design a statistical model and use it for distance estimation. For detection performance, false alarm and miss detection probabilities are theoretically analyzed. Furthermore, we thoroughly analyze the performance of throughput and fairness for the three-state sensing model compared with the two-state sensing model. In terms of fairness, we define a novel performance metric called all-level fairness for all(ALFA) to characterize fairness among CRNs. Extensive simulations are carried out under various scenarios to evaluate the three-state sensing model and verify the aforementioned theoretical analysis.

Advanced High-order Hidden Bivariate Markov Model Based Spectrum Prediction

“…As presented in Section 3, transient state probability P B→I and P I→B are calculated in Eqs. (4) and (5). The values of P B→I and P I→B can be altered by adjusting parameters of µ B and µ I , to simulate a various spectrum environment.…”

“…Specifically, time is divided into slots which consist of a small portion of sensing period, followed by a relative long period for data transmission. SUs conduct regular sensing and update the sensing result every time slot, which leads to high computational complexity [5]. To speed up the sensing process and save more time for transmission, spectrum prediction is a favorable technology.…”

Quality-of-service-aware weighted-fair medium access control protocol for coexisting cognitive radio networks

“…However, two-state sensing model (idle or busy) fails to provide fair channel usage to the SUs of different CRNs [13]. In this work, we use three-state sensing model [14], as of Eq. 1, where each busy state is further divided into state 1 (accessed by PU) and state 2 (accessed by SU), using a distance-based estimation technique.…”

“…Each WF-MAC SU has a spectrum sensing module that periodically senses the channel and informs BS through CCC to define channel states using collaboration module. Spectrum sensing and collaborative channel state determination techniques are well studied in the literature [12][13][14]; we have adopted [14]. If an SU has data to transmit, then its Data Generation Module requests the BS for channel information vector (CIV).…”

“…Furthermore, a smart settlement between channel sharing and channel switching has been designed to avoid repeated interference and starvation events over a specific channel. We are using a three-state sensing model [14] to evade false interpretation of channel usage. The contributions of this work are summarized as follows:…”