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This paper considers a compressive sensing (CS) approach for hyperspectral data acquisition, which results in a practical compression ratio substantially higher than the state-of-the-art. Applying simultaneous low-rank and joint-sparse (L&S) model to the hyperspectral data, we propose a novel algorithm to joint reconstruction of hyperspectral data based on loopy belief propagation that enables the exploitation of both structured sparsity and amplitude correlations in the data. Experimental results with real hyperspectral datasets demonstrate that the proposed algorithm outperforms the state-of-the-art CS-based solutions with substantial reductions in reconstruction error.

Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink in heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink-downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band (LTE-U). Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as an optimization problem which jointly incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of echo state networks is proposed using which the small base stations autonomously choose their optimal bands allocation strategies while having only limited information on the network's and users' states. Simulation results show that the proposed approach yields significant gains, in terms of total rate, that reach up to 41% and 54%, respectively, compared to Q-learning and nearest neighbor algorithms. The results also show that ESN significantly improves convergence time of up to 17% compared to Q-learning.

Wireless sensor networks (WSNs) are widely deployed in many fields nowadays. However, the limited energy storage ability of sensor nodes causes great detrimental effect on the performance of the WSNs. Energy harvesting has been proved as one of the promising ways to replenish the energy constrained sensors for prolonging WSNs lifetime. Another exacerbated problem introduced by non-uniform energy distribution and nonoptimal energy management in WSNs also causes great challenge to WSNs. For realizing the efficient energy utilization and improving the performance of energy harvesting enabled WSNs, two transmission management schemes, Markov decision based packets aggregation allocation (MDPA) and partial Kalman filter with best-match aggregation (PKBPA), are proposed respectively in this paper. MDPA focuses on the node's own energy capabilities to decide the packets number for aggregation, while PKBPA decides the number of packets to be aggregated and transmitted based on the estimated energy state information of the node and its next hop. Simulation results show that the PKBPA scheme outperforms other schemes, especially when the energy is insufficient for the packets transmission.

The Fourier spectrum at a fractional period is often examined when extracting features from biological sequences and time series. It reflects the inner information structure of the sequences. A fractional period is not uncommon in time series. A typical example is the 3.6 period in protein sequences, which determines the $\alpha$-helix secondary structure. Computing the spectrum of a fractional period offers a high-resolution insight into a time series. It has thus become an important approach in genomic analysis. However, computing Fourier spectra of fractional periods by the traditional Fourier transform is computationally expensive. In this paper, we present a novel, fast algorithm for directly computing the fractional period spectrum (FPS) of time series. The algorithm is based on the periodic distribution of signal strength at periodic positions of the time series. We provide theoretical analysis, deduction, and special techniques for reducing the computational costs of the algorithm. The analysis of the computational complexity of the algorithm shows that the algorithm is much faster than traditional Fourier transform. Our algorithm can be applied directly in computing fractional periods in time series from a broad of research fields. The computer programs of the FPS algorithm are available at https://github.com/cyinbox/FPS., Comment: 16 pages, one figure

Numerical encoding plays an important role in DNA sequence analysis via computational methods, in which numerical values are associated with corresponding symbolic characters. After numerical representation, digital signal processing methods can be exploited to analyze DNA sequences. To reflect the biological properties of the original sequence, it is vital that the representation is one-to-one. Chaos Game Representation (CGR) is an iterative mapping technique that assigns each nucleotide in a DNA sequence to a respective position on the plane that allows the depiction of the DNA sequence in the form of image. Using CGR, a biological sequence can be transformed one-to-one to a numerical sequence that preserves the main features of the original sequence. In this research, we propose to encode DNA sequences by considering 2D CGR coordinates as complex numbers, and apply digital signal processing methods to analyze their evolutionary relationship. Computational experiments indicate that this approach gives comparable results to the state-of-the-art multiple sequence alignment method, Clustal Omega, and is significantly faster. The MATLAB code for our method can be accessed from: www.mathworks.com/matlabcentral/fileexchange/57152

In this paper, we consider the reconstruction of a high-dimensional seismic volume with randomly missing traces. Seismic data in the frequency-space domain are represented via a high-order tensor. Applying the parallel matrix factorization model to the underlying seismic tensor, we propose an iterative approximate message passing (AMP) approach to seismic data interpolation based on loopy belief propagation. In particular, we extend the bilinear generalized AMP (BiG-AMP) approach to incorporate parallel low-rank matrix factorizations by using a "turbo" framework, enabling iterative message passing between the subgraphs of the allmode unfoldings of the seismic tensor. The computational complexity of our algorithmic framework is low and scales linearly with the data size. Simulation results with synthetic seismic data suggest that the proposed algorithm yields better reconstruction performances relative to existing methods.

We study the connectivity of overlaid wireless networks where two users can communicate if the signal-to-interference ratio is larger than a threshold subject to an outage constraint. By using percolation theory, we first specify a 2-dimensional connectivity region defined as the set of density pairs—the density of secondary users and the density of primary users— within which the secondary network is percolated. Several interesting properties of this region are also revealed. Our work provides a new perspective for better understanding of the connectivity of large-scale overlaid networks.

Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink of heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink-downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band (LTE-U). Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as a noncooperative game that incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of echo state networks is proposed using which the small base stations autonomously choose their optimal bands allocation strategies while having only limited information on the network's and users' states. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution which constitutes a mixed strategy Nash equilibrium for the studied game. Simulation results show that the proposed approach yields significant gains, in terms of the sum-rate of the 50th percentile of users, that reach up to 60% and 78%, respectively, compared to Q-learning and Q-learning without decoupling. The results also show that ESN significantly provides a considerable reduction of information exchange for the wireless network., Accepted in the IEEE Transactions on Wireless Communications

In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network (CRAN) is studied. In the considered model, the network can leverage human-centric information such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal user-UAV association, optimal locations of the UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 40% and 61% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared to a benchmark algorithm without caching and a benchmark solution without UAVs.

The pilot design problem in large-scale multi-input-multioutput orthogonal frequency division multiplexing (MIMO-OFDM) system is investigated from the perspective of compressed sensing (CS). According to the CS theory, the success probability of estimation is dependent on the mutual coherence of the reconstruction matrix. Specifically, the smaller the mutual coherence is, the higher the success probability is. Based on this conclusion, this paper proposes a pilot design algorithm based on alternating projection and obtains a nonorthogonal pilot pattern. Simulation results show that applying the proposed pattern gives the better performance compared to applying conventional orthogonal one in terms of normalized mean square error (NMSE) of the channel estimate. Moreover, the bit error rate (BER) performance of the large-scale MIMO-OFDM system is improved.

Consider a wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier, which is an emerging network scenario with the advancement of cognitive radio technologies. The primary tier consists of randomly distributed legacy nodes of density $n$, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density $m=n^\beta$ with $\beta\geq 2$, which can only access the spectrum opportunistically to limit the interference to the primary tier. Based on the assumption that the secondary tier is allowed to route the packets for the primary tier, we investigate the throughput and delay scaling laws of the two tiers in the following two scenarios: i) the primary and secondary nodes are all static; ii) the primary nodes are static while the secondary nodes are mobile. With the proposed protocols for the two tiers, we show that the primary tier can achieve a per-node throughput scaling of $\lambda_p(n)=\Theta(1/\log n)$ in the above two scenarios. In the associated delay analysis for the first scenario, we show that the primary tier can achieve a delay scaling of $D_p(n)=\Theta(\sqrt{n^\beta\log n}\lambda_p(n))$ with $\lambda_p(n)=O(1/\log n)$. In the second scenario, with two mobility models considered for the secondary nodes: an i.i.d. mobility model and a random walk model, we show that the primary tier can achieve delay scaling laws of $\Theta(1)$ and $\Theta(1/S)$, respectively, where $S$ is the random walk step size. The throughput and delay scaling laws for the secondary tier are also established, which are the same as those for a stand-alone network., Comment: 13 pages, double-column, 6 figures, accepted for publication in JSAC 2011

This article puts forward a new solution to the bound of the outage probability and transmission capacity of Ad-hoc networks. For the proofs of the upper and lower bounds are too complex, a much easier way is introduced to get the same results, and by using Taylor series, the asymptotic bound is derived. By comparing with the simulation results, we found that the asymptotic bound is sufficient accurate when the network parameters are selected properly, and is tighter than the upper and lower bounds.

We study the scaling laws for the throughputs and delays of two coexisting wireless networks that operate in the same geographic region. The primary network consists of Poisson distributed legacy users of density n, and the secondary network consists of Poisson distributed cognitive users of density m, with m >n. The primary users have a higher priority to access the spectrum without particular considerations for the secondary users, while the secondary users have to act conservatively in order to limit the interference to the primary users. With a practical assumption that the secondary users only know the locations of the primary transmitters (not the primary receivers), we first show that both networks can achieve the same throughput scaling law as what Gupta and Kumar (IEEE Trans. Inf. Theory, vol. 46, no. 2, pp. 388-404, Mar. 2000) established for a standalone wireless network if proper transmission schemes are deployed, where a certain throughput is achievable for each individual secondary user (i.e., zero outage) with high probability. By using a fluid model, we also show that both networks can achieve the same delay-throughput tradeoff as the optimal one established by El Gamal et al. (IEEE Trans. Inf. Theory, vol. 52, no. 6, pp. 2568-2592, Jun. 2006) for a standalone wireless network.

A genome space is a moduli space of genomes. In this space, each point corresponds to a genome. The natural distance between two points in the genome space reflects the biological distance between these two genomes. Currently, there is no method to represent genomes by a point in a space without losing biological information. Here, we propose a new graphical representation for DNA sequences. The breakthrough of the subject is that we can construct the moment vectors from DNA sequences using this new graphical method and prove that the correspondence between moment vectors and DNA sequences is one-to-one. Using these moment vectors, we have constructed a novel genome space as a subspace in R N . It allows us to show that the SARS-CoV is most closely related to a coronavirus from the palm civet not from a bird as initially suspected, and the newly discovered human coronavirus HCoV-HKU1 is more closely related to SARS than to any other known member of group 2 coronavirus. Furthermore, we reconstructed the phylogenetic tree for 34 lentiviruses (including human immunodeficiency virus) based on their whole genome sequences. Our genome space will provide a new powerful tool for analyzing the classification of genomes and their phylogenetic relationships. Refereed/Peer-reviewed

To meet the need of data-intensive continuous monitoring, recent research on energy-efficient data gathering in wireless sensor networks (WSNs) has explored the use of compressive sensing (CS) to exploit both spatial and temporal correlations in data. However, the performance of CS-based data gathering methods has been limited since most work only considers the structured sparsity of natural signals and ignores the amplitude correlations of the signals. In this paper, the structured sparsity and the amplitude correlations are considered jointly by assuming the spatially and temporally correlated data satisfies simultaneous low-rank and joint-sparse (L&S) structure. Using a structured L&S factorization, we then propose an iterative approximate message passing (AMP) algorithm for joint CS reconstruction of the data, where the low-rankness and sparsity pattern decoding phase is formulated as a variant of the principal component analysis (PCA) problem and employs the bilinear generalized approximate message passing algorithm (BiG-AMP) to perform denoising effectively. In addition, a rank selection strategy and an expectation-maximization (EM) estimation strategy that adaptively tune model parameters are described. Simulation results with synthetic and real data suggest that the proposed algorithm outperforms the state-of-the-art reconstruction algorithms with substantial reduction in complexity.

Traditional handover algorithms are based on received signal strength indicator (RSSI) and traffic load intensity in orthogonal frequency division multiple access (OFDMA) systems, in order to guarantee all UEs' Quality of Service (QoS) and handover success ratio, a transceiver is activated even if there is only one low date rate activated user in its coverage area. In this paper, we propose an energy efficient handover algorithm combined coordinated multiple-points (CoMP) with OFDMA in a cellular systems, energy efficiency is introduced into the handover algorithm as an important parameter. The idea of the design is to enable adjacent cells to shutdown partial transceivers in some timeslots in low traffic conditions by concentrating the activated edge UEs to less cells. All activated UEs are classified into edge users and center users according to their distance to base stations, the analysis indicates that there exist an optimal edge distance corresponding to the maximum energy efficiency under different load for the proposed algorithm. Furthermore, a practical and low-complexity scheme that only considers the edge users is put forward. The edge users are identified by the reference signal received power (RSRP). Simulation results show that it can obtain up to 60% energy reduction at low traffic scenario.

The performance of wireless powered communication networks (WPCNs) with transmitters (TEs) self-sustained by opportunistically harvesting radio-frequency (RF) energy from nearby power beacons (PBs) is investigated. A ``harvest-then-transmit'' protocol is considered, under which a TE first harvest the wireless energy from its nearest PB and then send information to its associated receiver (RE) with the energy stored in the battery. A variable power transmission mode is proposed that the TE is allowed to transmit as long as its harvested energy is larger than a predefined transmission threshold. The transmission probability of the TEs is derived. The coverage probability with the TEs non-fully charged and fully charged, respectively, are characterized. By using numerical analysis, we study the tradeoff between energy harvesting and data transmission for different system parameters, such as energy harvest-ratio, transmission threshold, and PB density. Simulation results are provided to validate our theoretical analysis.

In this paper, we investigate mode selection for coordinated multi-point (CoMP) transmission in downlink LTE-A networks, where inter-cluster interference exists. The proposed scheme selects the single-cell (SC) or coordinated beamforming (CB) transmission mode according to interference level to maximize the weighted sum rate with fairness consideration. To formulate the optimization problem, an approximated closed-form expression for the achievable rate is derived by modeling the probability distributions of signal and interference powers as Gamma distributions. Since the original optimization problem is a combinatorial and non-convex one with high complexity, a low-complexity and sub-optimal algorithm is proposed, which first performs coordinated cell selection and then transmission mode selection. Simulation results show that the closed-form approximation of the achievable rate is very tight and the proposed mode selection scheme can improve the average system throughput by 13% and the 10th percentile user throughput by 10% compared with the existing scheme.

Connectivity is crucial to ensure information availability and survivability for wireless networks. In this paper, we propose a unified framework to study the connectivity of wireless networks under a general type of interference attack, which can address diverse applications including Cognitive Radio, Jamming attack and shadowing effect. By considering the primary users, jammers and deep fading as unified Interferers, we investigate a 3-dimensional connectivity region, defined as the set of key system parameters — the density of users, the density of Interferers and the interference range of Interferers — with which the network is connected. Further we study the impact of the Interferers' settings on node isolation probability, which is a fundamental local connectivity metric. Through percolation theory, the sufficient and necessary conditions for global connectivity are also derived. Our study is supported by simulation results.

A novel clustering method is proposed to classify genes and genomes. For a given DNA sequence, a binary indicator sequence of each nucleotide is constructed, and Discrete Fourier Transform is applied on these four sequences to attain respective power spectra. Mathematical moments are built from these spectra, and multidimensional vectors of real numbers are constructed from these moments. Cluster analysis is then performed in order to determine the evolutionary relationship between DNA sequences. The novelty of this method is that sequences with different lengths can be compared easily via the use of power spectra and moments. Experimental results on various datasets show that the proposed method provides an efficient tool to classify genes and genomes. It not only gives comparable results but also is remarkably faster than other multiple sequence alignment and alignment-free methods., Highlights • We propose to use Fourier power spectrum to cluster genes and genomes. • We construct mathematical moments from the power spectrum. • We perform phylogenetic analysis of genes and genomes based on moments.

The performance of large-scale cognitive radio (CR) networks with secondary users self-sustained by opportunistically harvesting radio-frequency (RF) energy from nearby primary transmissions is investigated. Using an advanced RF energy harvester, a secondary user is assumed to be able to collect ambient primary RF energy as long as it lies inside the harvesting zone of an active primary transmitter (PT). A variable power (VP) transmission mode is proposed, and a simple energy-based opportunistic spectrum access (OSA) strategy is considered, under which a secondary transmitter (ST) is allowed to transmit if its harvested energy is larger than a predefined transmission threshold and it is outside the guard zones of all active PTs. The transmission probability of the STs is derived. The coverage probabilities and the throughputs of the primary and the secondary networks, respectively, are characterized. The throughput can be increased by as much as 29%. Simulation results are provided to validate our analysis.

Identification of protein coding regions in genomic sequences is a significant problem in bioinformatics. A significant number of techniques for identifying coding regions in genomic sequences are based on Fourier representation for the sequence of nucleotides and spectral analysis methods in order to detect protein coding regions. These methods usually consider 3-base periodicity signal. In this paper, we propose a method that analyzes the distribution of nucleotides at six positions (hexamer distribution). We present the six-base nucleotide distribution (SBND) algorithm and implementation results on genomic sequences of Drosophila Melanogaster, Rat, Cow and also from H. sapiens. The performance is compared with one of the best methods available in the literature and the results establish the viability of the proposed approach.

Periodic elements play important roles in genomic structures and functions, yet some complex periodic elements in genomes are difficult to detect by conventional methods such as digital signal processing and statistical analysis. We propose a periodic power spectrum (PPS) method for analyzing periodicities of DNA sequences. The PPS method employs periodic nucleotide distributions of DNA sequences and directly calculates power spectra at specific periodicities. The magnitude of a PPS reflects the strength of a signal on periodic positions. In comparison with Fourier transform, the PPS method avoids spectral leakage, and reduces background noise that appears high in Fourier power spectrum. Thus, the PPS method can effectively capture hidden periodicities in DNA sequences. Using a sliding window approach, the PPS method can precisely locate periodic regions in DNA sequences. We apply the PPS method for detection of hidden periodicities in different genome elements, including exons, microsatellite DNA sequences, and whole genomes. The results show that the PPS method can minimize the impact of spectral leakage and thus capture true hidden periodicities in genomes. In addition, performance tests indicate that the PPS method is more effective and efficient than a fast Fourier transform. The computational complexity of the PPS algorithm is [Formula: see text]. Therefore, the PPS method may have a broad range of applications in genomic analysis. The MATLAB programs for implementing the PPS method are available from MATLAB Central ( http://www.mathworks.com/matlabcentral/fileexchange/55298 ).

In this article, an adaptive radio resource allocation algorithm applied to multiple traffic orthogonal frequency division multiple access (OFDMA) system is proposed, which distributes subcarriers and bits among users according to their different quality of service requirements and traffic type. By classifying and prioritizing the users based on their traffic characteristic and ensuring resource for higher priority users, the new scheme decreases tremendously the outage probability of the users requiring a real-time transmission without impact on the spectrum efficiency of system, as well as the outage probability of data users is not increased compared with the radio resource allocation methods published.

The 3-base periodicity, identified as a pronounced peak at the frequency N/3 (N is the length of the DNA sequence) of the Fourier power spectrum of protein coding regions, is used as a marker in gene-finding algorithms to distinguish protein coding regions (exons) and noncoding regions (introns) of genomes. In this paper, we reveal the explanation of this phenomenon which results from a nonuniform distribution of nucleotides in the three coding positions. There is a linear correlation between the nucleotide distributions in the three codon positions and the power spectrum at the frequency N/3. Furthermore, this study indicates the relationship between the length of a DNA sequence and the variance of nucleotide distributions and the average Fourier power spectrum, which is the noise signal in gene-finding methods. The results presented in this paper provide an efficient way to compute the Fourier power spectrum at N/3 and the noise signal in gene-finding methods by calculating the nucleotide distributions in the three codon positions.

We construct a virus database called VirusDB (http://yaulab.math.tsinghua.edu.cn/VirusDB/) and an online inquiry system to serve people who are interested in viral classification and prediction. The database stores all viral genomes, their corresponding natural vectors, and the classification information of the single/multiple-segmented viral reference sequences downloaded from National Center for Biotechnology Information. The online inquiry system serves the purpose of computing natural vectors and their distances based on submitted genomes, providing an online interface for accessing and using the database for viral classification and prediction, and back-end processes for automatic and manual updating of database content to synchronize with GenBank. Submitted genomes data in FASTA format will be carried out and the prediction results with 5 closest neighbors and their classifications will be returned by email. Considering the one-to-one correspondence between sequence and natural vector, time efficiency, and high accuracy, natural vector is a significant advance compared with alignment methods, which makes VirusDB a useful database in further research. Refereed/Peer-reviewed

This paper studies the opportunistic spectrum access (OSA) of secondary users in large-scale overlay cognitive radio (CR) networks. Particularly, a two-phase carrier sensing based protocol, namely the primary receiver assisted carrier sensing (PRA-CS) protocol, is investigated. Under the PRA-CS protocol, a secondary transmitter (ST) is allowed to transmit only if it satisfies the interference constraint at all the active primary receivers (PRs) and has the minimum back-off timer among its secondary contenders. It is worth noting that under the PRA-CS protocol, due to the fact that the activation of STs relies on the spatial realizations of both the primary and secondary networks, even the first order moment measure (average density) of the point process formed by the active STs can not be exactly characterized. To tackle this new difficulty, approximations are made on the conditional distributions of the eligible STs as well as the active STs given a typical primary/secondary receiver (PR/SR) activated at the origin. Based on such approximations, the coverage (transmission non-outage) performance of the primary/secondary network under the proposed PRA-CS protocol is characterized. Simulations are provided to validate our analysis.

Alignment-free sequence analysis approaches provide important alternatives over multiple sequence alignment (MSA) in biological sequence analysis because alignment-free approaches have low computation complexity and are not dependent on high level of sequence identity, however, most of the existing alignment-free methods do not employ true full information content of sequences and thus can not accurately reveal similarities and differences among DNA sequences. We present a novel alignment-free computational method for sequence analysis based on Ramanujan-Fourier transform (RFT), in which complete information of DNA sequences is retained. We represent DNA sequences as four binary indicator sequences and apply RFT on the indicator sequences to convert them into frequency domain. The Euclidean distance of the complete RFT coefficients of DNA sequences are used as similarity measure. To address the different lengths in Euclidean space of RFT coefficients, we pad zeros to short DNA binary sequences so that the binary sequences equal the longest length in the comparison sequence data. Thus, the DNA sequences are compared in the same dimensional frequency space without information loss. We demonstrate the usefulness of the proposed method by presenting experimental results on hierarchical clustering of genes and genomes. The proposed method opens a new channel to biological sequence analysis, classification, and structural module identification., Ramanujan-Fourier transform and DNA sequences

Multiple sequence alignment (MSA) is a prominent method for classification of DNA sequences, yet it is hampered with inherent limitations in computational complexity. Alignment-free methods have been developed over past decade for more efficient comparison and classification of DNA sequences than MSA. However, most alignment-free methods may lose structural and functional information of DNA sequences because they are based on feature extractions. Therefore, they may not fully reflect the actual differences among DNA sequences. Alignment-free methods with information conservation are needed for more accurate comparison and classification of DNA sequences. We propose a new alignment-free similarity measure of DNA sequences using the Discrete Fourier Transform (DFT). In this method, we map DNA sequences into four binary indicator sequences and apply DFT to the indicator sequences to transform them into frequency domain. The Euclidean distance of full DFT power spectra of the DNA sequences is used as similarity distance metric. To compare the DFT power spectra of DNA sequences with different lengths, we propose an even scaling method to extend shorter DFT power spectra to equal the longest length of the sequences compared. After the DFT power spectra are evenly scaled, the DNA sequences are compared in the same DFT frequency space dimensionality. We assess the accuracy of the similarity metric in hierarchical clustering using simulated DNA and virus sequences. The results demonstrate that the DFT based method is an effective and accurate measure of DNA sequence similarity.

We study the transmission capacity of two overlaid wireless ad hoc networks, where a primary network and a secondary network operate in the same geographic region and share the same spectrum. The primary network has a high priority to access the spectrum and is oblivious of the presence of the secondary network while the secondary network limits its interference to the primary network by controlling the density of its transmitters. Assuming the primary transmitter nodes are distributed as a Poisson point process and the secondary transmitter nodes are distributed as a Matern cluster process, we present an upper and a lower bounds for the transmission capacity of the primary network and that of the secondary network. Numerical results show that the transmission capacity of the primary network and that of the secondary network have a small increment due to the inhomogeneity of the secondary network.

We investigate the energy efficiency in the downlink of a two-tier heterogeneous cellular network with quality of service consideration. By modeling the distributions of the macrocell and small cell base stations follow two independent homogeneous Poisson point processes, we derive the analytical expressions of the mean achievable rate and the energy efficiency with the assumption that a mobile user connects to the closest BS. In addition, we evaluate the energy efficiency subject to a constraint of the blocking probability. Considering the dynamic variation of the traffic load, we use the numerical results to show that the optimum value of energy efficiency can be reached at determined deployment densities of the macrocells and the small cells, and the blocking probability has a dramatic impact on the optimal base station deployment.

This paper studies the opportunistic spectrum access (OSA) of secondary users in a large-scale overlay cognitive radio network. Particularly, a threshold-based protocol is investigated, where the secondary transmitter is allowed to access the spectrum only if the maximum signal power of the received beacons transmitted by the primary receivers is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocol, the concept of spatial opportunity is introduced and evaluated by applying tools from stochastic geometry. Due to the dependency between the realizations of the active primary and secondary users, an exact calculation of the coverage probabilities of the primary and secondary networks is infeasible. To tackle this difficulty, approximation is made on the conditional distribution of the active secondary transmitters given a typical primary/secondary receiver activated at the origin. Based on this approximation, the coverage performance of the primary/secondary network under the proposed OSA protocol is characterized. To our best knowledge, this paper is the first attempt of using stochastic geometry to evaluate the performance of the threshold-based opportunistic spectrum access in large-scale cognitive radio networks.

This paper studies the opportunistic spectrum access (OSA) of secondary users in a large-scale overlay cognitive radio (CR) network. Particularly, a threshold-based protocol is investigated, where the secondary transmitter is allowed to access the spectrum only if the maximum signal power of the received pilots sent from the primary transmitters is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocol, the concept of spatial opportunity is introduced and evaluated by applying tools from stochastic geometry. Due to the interaction between the primary and secondary users under the proposed OSA protocol, an exact calculation of the coverage probabilities of the primary and secondary networks is infeasible. To tackle this difficulty, approximation is made on the distribution of the active secondary transmitters. Based on this approximation, the coverage performance of the overlay CR network under the proposed OSA protocol is characterized. Simulation results are provided to validate our analysis.

We study the secrecy broadcast transmission capacity of a broadcast wireless ad hoc network of legitimate nodes coexisted with a correlated network of eavesdroppers. We define the secrecy broadcast transmission capacity as the product among the spatial density of broadcast transmissions without secrecy outage, the average number of neighbors of a broadcasting node, and the secrecy rate. By modeling the correlation between the legitimate nodes and the eavesdroppers as a spatial bivariate Poisson process, we derive the average number of eavesdropping neighbors that can cause secrecy outage and characterize the secrecy broadcast transmission capacity over general fading and Rayleigh fading channels. Numerical results show that in comparison to the case that without correlation, the correlation between the legitimate nodes and the eavesdroppers degrades the secrecy broadcast transmission capacity.

This paper considers the benefit of joint beamforming across multiple cells in a MIMO (multiple-input multiple-output) system, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. This paper focuses on the criteria of minimizing the total weighted transmitted power across the base-stations subject to signal-to- interference-and-noise-ratio (SINR) constraints at the remote users. The proposed algorithm is based on uplink-downlink duality to the multicell setting. Simulations reveal that our algorithm provides appreciable performance improvements as compared to the conventional per-cell optimized one.

Cognitive Radio networks which consist of primary and secondary networks are widely considered efficient systems to solve the serious problem of spectrum scarcity. The data transmission of secondary network is subject to the spectrum opportunity provided by primary network. In this paper, we investigate expected density of progress of secondary network with three routing strategies which are farthest receiver, random receiver and nearest receiver rooting strategies based on selection region. Numerical results show that the best performance can be achieved with nearest receiver routing strategy, and a maximum of expected density of progress could be got by setting an optimal transmission probability with that routing strategy.

In this paper, based on the DDNS(Dynamic Domain Name Service) and port mapping technology, a method about server port mapping and server-side gateway dynamic DNS is introduced, which can be applied in Data Collecting System based on the Internet of Things. In this way, the external sensor-side gateway can access the intranet server which has a dynamic IP address.

In this project, we focused on large complex networks and mainly investigated the following major issues: 1) We studied the overlaid complex networking systems where a primary network tier is coexisting with an opportunistic network tier. With stochastic geometry tools, we first showed that both tiers could achieve the same throughput scaling and then showed that the primary throughput scaling can be improved by allowing the opportunistic tier to help with relaying primary traffic. 2) We then studied the enabling techniques for primary vs. opportunistic coexisting network systems, where we mainly investigated how the opportunistic network senses the primary spectrum usage and detect primary occupancy in a distributed fashion, for which we established the respective asymptotic convergence results based on the Random Dynamic System theories. 3) We finally studied the related resource allocation strategies in large random networks, where we redefine the related transmission capacity concepts to quantify the system performance.

In this paper, we study the expected density of progress for wireless ad hoc networks with Nakagami-m fading. The expected density of progress is defined as expectation of the product between the number of simultaneous successful transmission per unit area and the distance towards the destination. By considering three next hop receiver (RX) selection strategies, i.e., nearest RX selection strategy, random RX selection strategy and furthest RX selection strategy, we derive the closed-form expressions to the expected density of progress. Numerical results show that, when the terminal density is small, the expected density of progress with nearest RX selection strategy is nearly the same as that with furthest RX selection strategy, and the expected density of progress with random RX selection strategy is the lowest; when the terminal density is larger, the nearest RX selection strategy has the largest expected density of progress, and furthest RX selection strategy has the smallest expected density of progress.

In this paper, we extend the diversity and multiplexing tradeoff (DMT) analysis from point-to-point channels to cellular systems to evaluate the impact of inter-cell interference on the system reliability and efficiency. Fundamental tradeoff among diversity order, multiplexing gain and inter-cell interference intensity is characterized to reveal the capability of multiple antennas in cellular systems. And the detrimental effects of the inter-cell interference on the system performance of diversity and multiplexing is presented and analyzed., Comment: Published in WCSP 2011

We study the effect of channel inversion based power control on the transmission capacities of two coexisting wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, where the SR network limits its interference to the PR network by carefully controlling its node density. Considering a general power-law wireless channels with path-loss exponent a > 2 and small-scale Rayleigh fading, by using stochastic geometry theory, we derive the transmission capacities for both of the two networks and quantify their tradeoff via asymptotic analysis. Our results show that when the transmitter densities for both of the PR and SR networks are vanishingly small, with power control the sum transmission capacity of the two networks (i.e., the overall spectrum efficiency per unit area) will be increased compared to that without power control, which is different from the recent result observed by Weber et al in [1] for a single network case when the transmitter density is relatively large.1

In this paper, we study the channel estimation algorithms for the downlink of 3GPP LTE systems. By using computer simulation, the pilot signal assisted channel estimation algorithms based on least square (LS) and linear minimum mean square error (LMMSE) criteria, together with channel interpolation based on piecewise-linear interpolation and DFT based interpolation are studied. By analyzing simulation results in terms of Mean Square Error (MSE) and un-coded Bit Error Rate (BER), we conclude that the channel frequency responses of pilot tones are estimated by using LS estimator, and the channel frequency responses of data tones are interpolated by DFT based interpolation method is appropriate for the downlink of 3GPP LTE systems.

Recursive expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. In addition, we modify the conventional Viterbi algorithm used in space-time encoder under the MIMO system, and give the recursive metric equation. The modified Viterbi algorithm can be used in the receiver when the MIMO channel information has been estimated. Compared with the conventional decoding algorithm, the improved algorithm can be used in the every receiver respectively. It is shown that the improved algorithm is also an optimal algorithm, however, its metric convergence speed is faster than the standard Viterbi algorithm.

In this paper, we propose a p-weighted noise reduction algorithm to improve the accuracy of channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. A SNR and speed adaptive weighting factor p is introduced, which can efficiently reduce the noise. After that, an exact expression of the optimal value of p under piecewise linear interpolation in time domain are derived, as well as a more practical near-optimization of p. Simulation results show that p-weighted algorithm obtains a SNR gain at low SNR and maintains the variation of channel at high SNR. It achieves the optimal tradeoff between noise reduction and maintenance of channel variation.

We propose a selection region based multi-hop routing protocol for random mobile ad hoc networks, where the selection region is defined by two parameters: a reference distance and a selection angle. At each hop, a relay is chosen as the nearest node to the transmitter that is located within the selection region. By assuming that the relay nodes are randomly placed, we derive an upper bound for the optimum reference distance to maximize the expected density of progress and investigate the relationship between the optimum selection angle and the optimum reference distance. We also note that the optimized expected density of progress scales as $\Theta(\sqrt{\lambda})$, which matches the prior results in the literature. Compared with the spatial-reuse multi-hop protocol in \cite{Baccelli:Aloha} recently proposed by Baccelli \emph{et al.}, in our new protocol the amount of nodes involved and the calculation complexity for each relay selection are reduced significantly, which is attractive for energy-limited wireless ad hoc networks (e.g., wireless sensor networks)., Comment: 5 pages, 8 figures, conference

We propose a optimal power allocation algorithm for Distributed Sensing networks under the assumption of a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. Each node computes a local statistic and communicates it to the fusion center over rayleigh fading wireless channels. At the fusion, the linear minimum mean square error (LMMSE) is used. Sensor networks in which energy is a limited resource, our goal is to minimize the distortion under certain power constraints. In this paper, for a given power constraints, equal power transmission strategy and the optimal power allocation strategy is considered. In the later, we first discuss the problem with only a sum power constraint, and then discuss the general case with both sum and individual power constraints. Finally, we demonstrate the applicability of our results through numerical examples. Result shows that the optimal distributed estimation algorithm for homogeneous sensor networks achieves the power gain by turning off sensors that with bad channels and bad observation quality.