Search

This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.

This letter proposes a novel censor-based scheme for cooperative spectrum sensing on Cognitive Radio Sensor Networks. A Takagi-Sugeno's fuzzy system is proposed to make the decision on the presence of the licensed user's signal based on the observed energy at each cognitive sensor node. The local spectrum sensing results are aggregated to make the final sensing decision at the fusion center after being censored to reduce transmission energy and reporting time. Simulation results show that significant improvement of the spectrum sensing accuracy, and saving energy as well as reporting time are achieved by our scheme.

Cooperation is an attractive approach to improving the spectrum sensing performance of cognitive systems experiencing deep shadowing and fading. In this letter, an efficient weight-based cooperative spectrum sensing scheme is proposed. Simulation results show that the proposed scheme has better accuracy than “AND,” “OR,” and “half-voting” combination schemes and has similar spectrum sensing accuracy but with lower computational and communication complexity in comparison to the “optimal data fusion” rule.

Spectrum sensing is a fundamental function for cognitive radio network to protect transmission of primary system. Cooperative spectrum sensing, which can help increasing sensing performance, is regarded as one of the most promising methods in realizing a reliable cognitive network. In such cooperation system, however the communication resources such as sensing time delay, control channel bandwidth and consumption energy for reporting the cognitive radio node's sensing results to the fusion center may become extremely huge when the number of cognitive users is large. In this paper, we propose an ordered sequential cooperative spectrum sensing scheme in which the local sensing data will be sent according to its reliability order to the fusion center. In proposed scheme, the sequential fusion process is sequentially conducted based on Dempster Shafer theory of evidence's combination of the reported sensing results. Above all, the proposed scheme is highly feasible due to the proposed two ordered sequential reporting methods. From simulation results, it is shown that the proposed technique not only keeps the same sensing performance of non-sequential fusion scheme but also extremely reduces the reporting resource requirements.

Spectrum sensing is a key technology within Cognitive Radio (CR) systems. Cooperative spectrum sensing using a distributed model provides improved detection for the primary user, which opens the CR system to a new security threat. This threat is the decrease of the cooperative sensing performance due to the spectrum sensing data falsification which is generated from malicious users. Our proposed scheme, based on robust statistics, utilizes only available past sensing nodes' received power data for estimating the distribution parameters of the primary signal presence and absence hypotheses. These estimated parameters are used to perform the Dempster-Shafer theory of evidence data fusion which causes the elimination of malicious users. Furthermore, in order to enhance performance, a node's reliability weight is supplemented along with the data fusion scheme. Simulation results indicate that our proposed scheme can provide a powerful capability in eliminating malicious users as well as a high gain of data fusion under various cases of channel condition.

In wireless sensor networks (WSNs), localization using the received signal strength (RSS) method is famous for easy adaptation and low cost where measuring the distance between sensor nodes. However, in real localization systems, the RSS is strongly affected by many surrounding factors and tends to be unstable, so that it degrades accuracy in distance measurement. In this paper, we propose the angle-referred calibration based RSS method where angle relation between sensor nodes is used to perform the calibration for better performance in distance measurement. As a result, the proposed scheme shows that it can provide high precision.

In this paper, we propose a sequential type-based detection scheme for wireless sensor networks in the case of spatially and temporally identically and independently distributed observations. First, we investigate the optimal sequential detection rule of the proposed scheme, and then with the motivation of reducing the computational complexity of the optimal detection rule, we consider an approximation scheme and derive a suboptimal detection rule. We also compare the performances of the type-based sequential detection scheme with those of the non-sequential type-based detection scheme in terms of both average number of observations and total energy consumption, and determine the region of individual node power where the proposed scheme outperforms the non-sequential scheme. In addition, we show that the approximated detection rule provides the similar results as the optimal detection rule with a significant reduction of the computational complexity, which makes the approximated detection rule useful for real-time applications.

In this letter, we present a procedure to analyze the delay distribution of data traffic in CDMA systems supporting voice and delay-tolerant data services with a finite buffer. The queueing method using a buffer for a delay-tolerant traffic can be used to improve the system utilization or the availability of system resources. Under the first-come and first-serve (FCFS) service discipline, we present a numerical procedure for the formation of delay distribution that is defined as the probability that a new data call get a service within the maximum tolerable delay requirement, based on a two-dimensional Markov model.

The Erlang capacity of multi-service multi-access systems supporting several different radio access technologies was analyzed and compared according to two different operation methods: the separate and common operation methods, by simultaneously considering the link capacity limit per sector as well as channel element (CE) limit in a base station (BS). In a numerical example with GSM-like and WCDMA-like sub-systems, it is shown that we can get up to 60% Erlang capacity improvement through the common operation method using a near optimum so-called service-based user assignment scheme when there is no CE limit in BS. Even with the worst-case assignment scheme, we can still get about 15% capacity improvement over the separate operation method. However, a limited number of CEs in BS reduces the capacity gains of multi-service multi-access systems in both the common operation and separate operation. In order to fully extract the Erlang capacity of multi-service multi-access systems, an efficient method is needed in order to select a proper number of CE in BS while minimizing the equipment cost.