Channel allocation under uncertain primary users for delay sensitive secondary users

Air quality has always been a hot issue of concern to the people, the environmental protection department and the government. Among the massive air quality data, abnormal data can interfere with subsequent experiments and analysis. Therefore, it is necessary to detect abnormal data to improve the accuracy of the data. However, traditional air outlier detection methods require at least one year's data to make inferences about air quality. This paper firstly analyzes the characteristics of air quality big data, and then proposes a framework based on Bayesian non-parametric clustering, namely Dirichlet Process (DP) clustering framework, to realize the outlier detection of air quality. The framework optimizes Gaussian mixture model into infinite Gaussian mixture model according to the results of data analysis, and uses neural network to cluster the data processed by infinite Gaussian mixture model, which effectively improves the clustering accuracy and avoids the need of collecting a large number of training data.

Delay issue is a challenge in multi-hop cognitive radio networking because of the dynamic change of channels available to the cognitive radio nodes. To address this challenge, we propose a channel allocation and rate allocation scheme with low complexity in this paper. Our scheme is flexible so that it can react to the dynamic change of channel availability, and it can minimize the delay by considering the primary user activities. First, we model the primary users' activity, channel availability and the interference among the cognitive radio nodes in a cognitive radio network environment, and show how to implement a channel allocation algorithm and a rate allocation scheme in multi-hop cognitive radio networks. Second, we formulate an optimization problem to minimize the end-to-end delay of the network. We consider the channel availability constraint and the primary user activities jointly, and analyze the end-to-end delay in multi-hop cognitive radio networks. In order to reduce the computing complexity, we propose a graph coloring based channel allocation and rate selection algorithm using gradient descent method. Furthermore, we show the performance of our schemes and compare them with existing schemes for different scenarios of channel availability and number of channels through simulations. Our work brings insights on how to make rate selection and channel allocation in multi-hop cognitive radio networks.

To create a wireless backbone network, channel allocation algorithms in mesh networks have been actively researched these days, but they still have the problem of scalability. When they operate in a centralized manner, they are not able to guarantee the upper-bound of the channel allocation time. This paper considers a new channel allocation algorithm that works under the environments of directional antennas. It allocates channels in a distributed manner and also adaptively by reflecting the traffic demand. To do so, we reconstruct the network into several groups to apply 2P MAC protocol, remove the interference caused by the directional antennas, and allocate a channel to each link without resorting to a central manager. Through simulation work, we prove that our distributed and adaptive channel allocation (DACA) achieves good performance, in terms of packet drop and channel allocation delay, compared with the other competitive channel allocation algorithms.

This paper presents a novel statistical method that can classify given audio events into known classes or recognize them as an unknown class. We propose a nested infinite Gaussian mixture model (iGMM) to represent varied audio events in real environment. One of the main problems of conventional classification methods is that we need to specify a fixed number of classes in advance. Therefore, all audio events are forced to be classified into known classes. To solve the problem, the proposed method formulates a infinite Gaussian mixture model (iGMM) in which the number of classes are allowed to increase without bound. Another problem is that the complexity of each audio event is different. Then, the nested iGMM using nonparametric Bayesian approach is applied to adjust the needed dimension of each audio model. Experimental results show the effectiveness for these two problems to represent the given audio events.

Adaptive channel and power allocation of downlink multi-user MC-CDMA systems

In this work we present and apply a new Hyperspectral image classification method based on Dirichlet Process Gaussian mixture models (also known as infinite Gaussian mixture models - IGMMs). Since this approach is a non-parametric Bayesian method, we circumvent the problem of model selection which is unavoidable and often difficult when employing traditional parametric Gaussian mixture models (GMM). In order to infer model parameters from observations, we adopt a Gibbs Sampling method to sample the posterior distributions of these parameters. In the preprocessing step, we use Local Fisher's Discriminant Analysis (LFDA) for dimension reduction since we expect it to preserve the multi-modal, non-Gaussian structure of the hyperspectral data. We compared our proposed IGMM based classification method to the existing state-of-the-art classification methods using popular hyperspectral imagery datasets. To simulate challenging real-world sensing environment, we mix every pixel with background and study the performance of these classification approaches. Our experiments show that the proposed LFDA-IGMM method and GMM method have almost the same performance for most cases, given “pure” data - with mixed pixels, LFDA-IGMM outperformed GMM for sub-pixel classification. Both methods outperformed the other commonly used classification approaches when the training sample size is relatively large. Also, the IGMM based method is less sensitive to pixel mixing than other methods.

Cognitive radio enabled wireless sensor network is capable of reducing the spectrum scarcity problem of the wireless networks. Looking at the scarcity of available bandwidth, and the high growth in the number of communication devices in recent times, cognitive radio technology has proven to be a promising technology for the days to come. The application of Game Theory in cognitive radio networks has been visible in recent research works. However, only limited literature is available in which possibilities of applying the game-theory based approaches for the challenging task of channel assignment in cognitive radio wireless sensor are available in the literature. It is understood that the crux of the solution to the problem of scheming games for allocation of the channel is centered on the selection of the utility function in order to increase the efficiency of the channel allocation algorithm. Accordingly, the study regarding the influence of several utility functions on the performance of the corresponding channel allocation algorithm is important. Such a study enables designers to arrive at the optimal utility function to be used in game-theory based channel allocation algorithms, and the same is explored to the best extent, in this paper. The detailed procedure of allocating channels to all the contending nodes through game-based channel allocation has been discussed in this paper. Moreover, the performance of six different utility functions proposed which can be used for channel allocation using game theory has been evaluated for respective performances through MATLAB-based simulations.

SummaryCognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bio‐inspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy‐based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm.

This paper considers the primary user activity or the subchannel availability in optimally distributing the available resources for an orthogonal frequency-division multiple-access (OFDMA) cognitive radio multicast network. For this purpose, a risk-return model is presented, and a general rate-loss function, which gives a reduction in the attainable throughput whenever primary users reoccupy the temporarily accessible subchannels, is introduced. Taking the maximization of the expected sum rate of secondary multicast groups as the design objective, an efficient joint subcarrier and power-allocation scheme is proposed. Specifically, the design problem is solved via a dual optimization method under constraints on the tolerable interference thresholds at individual primary user's frequency bands. It is shown that as the number of subcarriers gets large (which is often the case in practice), the dual-domain solution becomes globally optimum with regard to the primal problem. More attractively, the ?practically optimal? performance of this approach is achieved with a substantially lower complexity, which is only linear in the total number of subcarriers as opposed to exponential complexity typically required by a direct search method. Our proposed design is valid for unicast and multicast transmissions and is applicable for a wide range of rate-loss functions, among which, the linear function is a special case. The superiority of the dual scheme is thoroughly verified by numerical examples.

Cognitive radio is an optimistic technology to implement the concept of dynamic spectrum access and provide a flexible way to share the spectrum among the primary and secondary users. In this paper, a game theoretic-based model is presented using the concept of Nash Equilibrium for spectrum sharing. In this model, interference and number of radios on each link are considered as parameters for designing the game. An algorithm for channel allocation among the users is also presented. From the simulation analysis, it is observed that the system performs satisfactorily in terms of network utilization. Also, the Taguichi method is applied and an analysis of variance (ANOVA) is performed, proving that the design parameters taken into consideration in our proposed method are impactful.

For decades, along with more and more countries paying attention to the exploitation of marine resources, underwater wireless sensor networks (UWSNs) have become a promising technology in monitoring marine environment and exploring the oceans. Due to the characteristics of water medium, acoustic communication is regard as the best choice for UWSNs. However, some marine mammals also communicate bu using acoustic signal and the frequencies of the natural acoustic systems are heavily overlapped with the frequencies used by underwater acoustic networks (UAN). As an improvement of our previous work VMOPR (a virtual void-based marine organism protection routing protocol), in this paper, we propose an environment friendly protection routing protocol with channel allocation for UWSNs aming to avoid the harmful interference with marine mammals. Considering the restriction of noise and practical link capacity, a channel allocation algorithm is designed to achieve the maximum utilization of limited frequencies. Simulation results show that the proposed scheme outperforms the counterparts EEIAR and VBF protocol in terms of throughput and energy efficiency when taking marine mammals and transmission constraints into account.

The authors consider the problem of dynamic channel allocation in cellular networks. Each cell can use any channel, subject to the interference constraints. Channel allocation algorithms are executed by the network switch in a centralized way. The authors show how to design and use objective functions aimed at proper channel allocation and improvement of network performance. As a figure of merit of network performance, they consider the blocking probability in the network as a whole, and the maximum blocking probability in any particular cell of the network ("hot-spot" in the network). They designed three specific channel allocation policies, based on three different objective functions. Compared with two other benchmark policies, the approach shows significant improvement. Performance analysis of various channel allocation policies is virtually impossible without simulations, which are prohibitively time-consuming in the case of small blocking probabilities. The authors propose an original approximate method based on a short simulation and an analytic approximation. The method exhibits good accuracy and significant improvement in efficiency.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We discuss the wireless channel allocation problem for data dissemination in mobile computing systems. Methods for accessing data through broadcast and on-demand channels are described. We provide analytical models and cost formulae for the exclusive broadcast channels and the exclusive on-demand channels and propose a dynamic channel allocation algorithm for optimizing system performance. Our performance evaluation shows that dynamic channel allocation significantly improves system performance and the channel allocation algorithm gives us the optimal solution for various system parameter settings.

Unsupervised speech processing methods are essential for applications ranging from zero-resource speech technology to modelling child language acquisition. One challenging problem is discovering the word inventory of the language: the lexicon. Lexical clustering is the task of grouping unlabelled acoustic word tokens according to type. We propose a novel lexical clustering model: variable-length word segments are embedded in a fixed-dimensional acoustic space in which clustering is then performed. We evaluate several clustering algorithms and find that the best methods produce clusters with wide variation in sizes, as observed in natural language. The best probabilistic approach is an infinite Gaussian mixture model (IGMM), which automatically chooses the number of clusters. Performance is comparable to that of non-probabilistic Chinese Whispers and average-linkage hierarchical clustering. We conclude that IGMM clustering of fixed-dimensional embeddings holds promise as the lexical clustering component in unsupervised speech processing systems.

All users in Cognitive radio networks (CRN) try to occupy the vacant channels sharing with the primary users and other users in CRN under limited channel utilization. Each selfish user can independently choose his strategy to maximize his own payoff at the Nash equilibrium (NE) state. In this work, we analyze the channel allocation problem of multiple CR users in the presence of interference of Primary transmitter (PT) and other Secondary users (SUs) over Rayleigh fading channel in overlay and underlay mode. Many policies for channel allocation are utilized to analyze the performances of CRN at NE state in term of normalized cumulative total throughput while the analysis results and comparisons are confirmed by our simulations.