Local Hard Decisions Research Articles

A Quantization-Based Multibit Data Fusion Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks.

Spectrum sensing remains a challenge in the context of cognitive radio networks (CRNs). Compared with traditional single-user sensing, cooperative spectrum sensing (CSS) exploits multiuser diversity to overcome channel fading, shadowing, and hidden terminal problems, which can effectively enhance the sensing performance and protect licensed users from harmful interference. However, for a large number of sensing nodes that need high bandwidth of the control channel for data transmitting, CSS increases cooperative overhead. To address this problem, we investigated the soft decision fusion strategy under a limited bandwidth of the control channel and proposed a simple quantization-based multibit data soft fusion rule for CSS for its simple structure and easily implementation. Under the quantization-based sensing strategy, each cooperative secondary user (SU) adopts an energy detector for local spectrum sensing. Each SU transmits quantized multibit data that sends local sensing information, instead of forwarding local one-bit hard decision results or original observation statistics, to the fusion center (FC). Furthermore, the closed-form expressions of the quantization levels and the quantization thresholds are analytically derived. Simulation results indicate that the detection performance of the proposed method approaches that of the conventional soft fusion rule with less cooperative overhead and outperforms the hard decision rules. Extensive simulations also show that multibit quantization fusion achieves a desirable tradeoff between the sensing performance and the control channel overhead for CSS.

Collaborative Fusion for Distributed Target Classification Using Evidence Theory in IOT Environment

As an efficient strategy, collaborative fusion can promote the classification performance while decreasing data transmission energy consumption and bandwidth requirements. In practice, the appropriate reliability assessment plays an essential rule in the fusion process. In this paper, we mainly concentrate on the classification problem of distributed target in Internet of Things scenarios, and an effective collaborative fusion method in terms of internal reliability and relative reliability evaluation is proposed. The inner reliability reveals the potential classes of the target in accordance with the local hard decision made by distinct sensor. The relative reliability reflects the credibility of the soft decision represented by belief function. These two reliability measures are complementary with each other. In our proposed fusion method, the inner reliability is applied to transfer the local hard decision into rational soft decision, and the relative reliability is utilized to decrease the influence of conflicting soft decisions by making full use of the evidential discounting operation. The discounted soft decisions are related to the combination rule of Dempster–Shafer for the final target classification. Results of experiment show that compared with the traditional fusion method, this method has the better fusion performance.

Convex Optimization of Distributed Cooperative Detection in Multi-Receiver Molecular Communication

In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication (MC) system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N-out-of-K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance.

Concept-to-Speech generation with knowledge sharing for acoustic modelling and utterance filtering

A Concept-to-Speech (CTS) system converts the conceptual representation of a sentence-to-be-spoken into speech. While some CTS systems consist of independently built text generation and Text-to-Speech (TTS) modules, the majority of the existing CTS systems enhance the connection between these two modules with a prosodic prediction module that utilizes linguistic knowledge from the text generator to predict prosodic features for TTS generation. However, knowledge embodied within the individual modules has the potential to be shared in more ways. This paper describes knowledge sharing for acoustic modelling and utterance filtering in a Mandarin CTS system. First, syntactic information generated by the text generator is propagated to a hidden Markov model (HMM) based acoustic model within the TTS module and replaces the symbolic prosodic phrasing features therein. Our experimental results show that this approach alleviates the local hard-decision problem in automatic prosodic phrasing for Mandarin CTS systems and achieves a comparable performance to the traditional approach without explicit prosodic phrasing. Second, the acoustic features of multiple synthetic utterances expressing the same input concept are utilized to evaluate the utterance candidates. With this ‘post-processing’ mechanism, our CTS system is able to filter out inferior synthetic utterances and find an acceptable candidate to express the input concept.

Double threshold‐based cooperative spectrum sensing for a cognitive radio network with improved energy detectors

Performance of cooperative spectrum sensing in a cognitive radio (CR) network is investigated where each CR node uses an improved energy detector (IED) to sense the primary user (PU), and makes a local decision regarding the presence of PU using double thresholds (DTHs). The local hard decisions are combined at fusion centre (FC) to obtain the global decision. The advantage of a DTH-based system over a single threshold based one is, a CR node can opt for no decision when a decision variable lies in the fuzzy zone between two thresholds. Such censoring reduces the transmission overhead between CR and FC without significantly affecting the receiver operating characteristics. In this study, the performance of the abovementioned CR network has been assessed in terms of the average number of normalised transmitted sensing bits (k nor), the total error probability (P e,n ) and optimal number of CR users that ensures minimum Pe,n . It was observed that k nor increases as the signal power raise factor of IED increases or failed sensing probability decreases. Further, the agility of the network improves as the PU death rate increases. Impact of reporting channel on the sensing performance has also been indicated.

Fuzzy likelihood ratio test for cooperative spectrum sensing in cognitive radio

Efficient and reliable spectrum sensing is an essential requirement in cognitive radio networks. One challenge faced in the spectrum sensing is the existence of the noise power uncertainty. This paper proposes a cooperative spectrum sensing scheme using fuzzy set theory to mitigate the noise power uncertainty. In this scheme, the noise power uncertainty in each Secondary User (SU) is modeled as a Fuzzy Hypothesis Test (FHT). We deploy the likelihood ratio test on the FHT to derive a fuzzy energy detector with a threshold that depends on the noise power uncertainty bound. The fusion center combines the received local hard decisions from the SUs and makes a final decision to detect the absence/presence of a Primary User (PU). We compare the performance of the proposed algorithm with some classical threshold-based energy detection schemes using receiver operating characteristic and detection probability versus the signal to noise ratio curves via Monte Carlo simulations. The proposed algorithm outperforms the cooperative spectrum sensing with a bi-thresholds energy detector and a simple energy detector.

Cooperative blind spectrum sensing using autocorrelation matrix

To improve the spectrum sensing performance in cognitive radios, a scheme of cooperative blind spectrum sensing based on autocorrelation matrix is proposed. The test statistic is extracted from the autocorrelation matrix of the received signal samples and a bi-threshold hybrid decision scheme is designed for local spectrum sensing. The cognitive radio base station makes a credibility fusion based on the local soft decisions and then takes global fusion combining with the local hard decisions. The proposed method is blind in the sense since it requires no apriori knowledge of the signal and the noise power. Theoretical analysis and computer simulation results show that the proposed method can enhance the spectrum sensing capability.

Optimal Distributed Detection in Clustered Wireless Sensor Networks

In a clustered, multihop sensor network, a large number of inexpensive, geographically-distributed sensor nodes each use their observations of the environment to make local hard decisions about whether an event has occurred. Each node then transmits its local decision over one or more wireless hops to the clusterhead (CH). When all local decisions have been gathered by the CH, it fuses them into a final hard decision about the event. Two sources of error affect the CH's final decision: 1) local decision errors made by the sensor nodes because of noisy measurements or unreliable sensors and 2) bit errors affecting each hop on the wireless communication channel. We show that if both of these sources of error are considered, then the optimal decision at the CH is obtained by thresholding a weighted sum of the nodes' local decisions or, equivalently, computing a weighted order statistic of these decisions. The optimal weights are shown to be functions of the bit error probability of the channel and the ring from which the local decision originated. We determine the error probability of this optimal fusion algorithm, the effect of adding more nodes or rings to the cluster, and the tradeoffs involving the energy consumed in the network, the decision error probability, and the time to reach a decision. This paper thus provides tools to determine the effect of measurement and communication errors on other tradeoffs in the design of clustered sensor networks.

Distributed multitarget classification in wireless sensor networks

We study distributed strategies for classification of multiple targets in a wireless sensor network. The maximum number of targets is known a priori but the actual number of distinct targets present in any given event is assumed unknown. The target signals are modeled as zero-mean Gaussian processes with distinct temporal power spectral densities, and it is assumed that the noise-corrupted node measurements are spatially independent. The proposed classifiers have a simple distributed architecture: local hard decisions from each node are communicated over noisy links to a manager node which optimally fuses them to make the final decision. A natural strategy for local hard decisions is to use the optimal local classifier. A key problem with the optimal local classifier is that the number of hypotheses increases exponentially with the maximum number of targets. We propose two suboptimal (mixture density and Gaussian) local classifiers that are based on a natural but coarser repartitioning of the hypothesis space, resulting in linear complexity with the number of targets. We show that exponentially decreasing probability of error with the number of nodes can be guaranteed with an arbitrarily small but nonvanishing communication power per node. Numerical results based on real data demonstrate the remarkable practical advantage of decision fusion: an acceptably small probability of error can be attained by fusing a moderate number of unreliable local decisions. Furthermore, the performance of the suboptimal mixture density classifier is comparable to that of the optimal local classifier, making it an attractive choice in practice.