Joint space-time trellis decoding and channel estimation in correlated fading channels

This letter addresses the issue of joint space-time trellis decoding and channel estimation in time-varying fading channels that are spatially and temporally correlated. A recursive space-time receiver which incorporates per-survivor processing (PSP) and Kalman filtering into the Viterbi algorithm is proposed. This approach generalizes existing work to the correlated fading channel case. The channel time-evolution is modeled by a multichannel autoregressive process, and a bank of Kalman filters is used to track the channel variations. Computer simulation results show that a performance close to the maximum likelihood receiver with perfect channel state information (CSI) can be obtained. The effects of the spatial correlation on the performance of a receiver that assumes independent fading channels are examined.

MMSE precoder for unitary space–time codes in correlated time-varying channels

This letter addresses the problem of the design of a precoder for multiple transmit antenna communication systems with spatially and temporally correlated fading channels. By using the asymptotic (high signal-to-noise ratio) mean-square error of the channel estimates, the letter derives a precoder for unitary space-time codes that can exploit the spatiotemporal correlation in the time-varying fading channels. Simulation results illustrate that significant performance gains can be achieved by using the new precoder.

Precoder design for multi-antenna systems with temporally and spatially correlated fading channels

This paper addresses the problem of the design of a precoder for multiple transmit antenna communication systems with spatially and temporally correlated fading channels. Using the theories of matrix differential calculus, the paper derives a precoder for unitary space-time codes that can exploit the spatio-temporal correlation in the time-varying fading channels. The design criterion is based on minimizing the mean square error of the channel estimates. Computer simulation results show that a significant performance gain can be achieved by using the designed precoder.

Differential encoding technique for multi-antenna systems with correlated Rayleigh fading channels

Differential space-time modulation (DSTM) techniques developed for multi-antenna systems allow the receiver to detect the transmitted signal without the knowledge of the fading channels. It can be viewed as an extension of differential phase-shift keying (DPSK) in single antenna systems. In this paper, we derived the pairwise error probability upper bound of differential space-time coded systems with spatially correlated Rayleigh fading channels. Based on the performance analysis, we develop a novel DSTM scheme which can exploit the spatial correlation in the fading channels. It is found that by carefully designing the initial transmitted signal matrix, the performance of the differential space-time coded systems can be significantly improved.

Depressed Na+-K+-ATPase activity in skeletal muscle at fatigue is correlated with increased Na+-K+-ATPase mRNA expression following intense exercise

We investigated whether depressed muscle Na⁺-K⁺-ATPase activity with exercise reflected a loss of Na⁺-K⁺-ATPase units, the time course of its recovery postexercise, and whether this depressed activity was related to increased Na⁺-K⁺-ATPase isoform gene expression. Fifteen subjects performed fatiguing, knee extensor exercise at ~40% maximal work output per contraction. A vastus lateralis muscle biopsy was taken at rest, fatigue, 3 h, and 24 h postexercise and analyzed for maximal Na⁺-K⁺-ATPase activity via 3-O-methylfluorescein phosphatase (3-O-MFPase) activity, Na⁺-K⁺-ATPase content via [3H]ouabain binding sites, and Na⁺-K⁺-ATPase α1-, α2-, α3-, ß1-, ß2- and ß3-isoform mRNA expression by real-time RT-PCR. Exercise [352 (SD 267) s] did not affect [³H]ouabain binding sites but decreased 3-O-MFPase activity by 10.7 (SD 8)% (P < 0.05), which had recovered by 3 h postexercise, without further change at 24 h. Exercise elevated α1-isoform mRNA by 1.5-fold at fatigue (P < 0.05). This increase was inversely correlated with the percent change in 3-O-MFPase activity from rest to fatigue (%Δ3-O-MFPaserest-fatigue) (r = –0.60, P < 0.05). The average postexercise (fatigue, 3 h, 24 h) {alpha}1-isoform mRNA was increased 1.4-fold (P < 0.05) and approached a significant inverse correlation with %Δ3-O-MFPaserest-fatigue (r = –0.56, P = 0.08). Exercise elevated α2-isoform mRNA at fatigue 2.5-fold (P < 0.05), which was inversely correlated with %Δ3-O-MFPaserest-fatigue (r = –0.60, P = 0.05). The average postexercise α2-isoform mRNA was increased 2.2-fold (P < 0.05) and was inversely correlated with the %Δ3-O-MFPaserest-fatigue (r = –0.68, P < 0.05). Nonsignificant correlations were found between %Δ3-O-MFPaserest-fatigue and other isoforms. Thus acute exercise transiently decreased Na^+-K⁺-ATPase activity, which was correlated with increased Na⁺-K⁺-ATPase gene expression. This suggests a possible signal-transduction role for depressed muscle Na⁺-K⁺-ATPase activity with exercise.

Analysis on relationship between extreme pathways and correlated reaction sets

Background: Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets.

Results: Correlated reaction sets are identified for E. coli core, red blood cell and Saccharomyces cerevisiae metabolic networks respectively. All extreme pathways are enumerated for the former two metabolic networks. As for Saccharomyces cerevisiae metabolic network, because of the large scale, we get a set of extreme pathways by sampling the whole extreme pathway space. In most cases, an extreme pathway covers a correlated reaction set in an 'all or none' manner, which means either all reactions in a correlated reaction set or none is used by some extreme pathway. In rare cases, besides the 'all or none' manner, a correlated reaction set may be fully covered by combination of a few extreme pathways with related function, which may bring redundancy and flexibility to improve the survivability of a cell. In a word, extreme pathways show strong complementary relationship on usage of reactions in the same correlated reaction set.

Conclusion: Both extreme pathways and correlated reaction sets are derived from the topology information of metabolic networks. The strong relationship between correlated reaction sets and extreme pathways suggests a possible mechanism: as a controllable unit, an extreme pathway is regulated by its corresponding correlated reaction sets, and a correlated reaction set is further regulated by the organism's regulatory network.

Blind separation of mutually correlated sources using precoders

This paper studies the problem of blind source separation (BSS) from instantaneous mixtures with the assumption that the source signals are mutually correlated.We propose a novel approach to BSS by using precoders in transmitters.We show that if the precoders are properly designed, some cross-correlation coefficients of the coded signals can be forced to be zero at certain time lags. Then, the unique correlation properties of the coded signals can be exploited in receiver to achieve source separation. Based on the proposed precoders, a subspace-based algorithm is derived for the blind separation of mutually correlated sources. The effectiveness of the algorithm is illustrated by simulation examples.

Classification of correlated internet traffic flows

A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.