Intent-Aware Sequential Recommendation (ISR) predicts next-item interactions by capturing the implicit intents behind users’ historical behavior sequences. Although existing ISR methods have achieved promising performance, we argue that two smoothness issues remain insufficiently addressed. First, existing methods often overemphasize short-term intents, making it difficult to smoothly balance users’ short- and long-term preferences. Second, hard positive–negative boundaries in intent contrastive learning may introduce misleading contrastive signals and disrupt the semantic continuity of user behaviors. To tackle these issues, we propose Smoothed Intent Learning for Sequential Recommendation (SILRec) , which improves the smoothness of short- and long-term intents modeling as well as intent contrastive samples. Specifically, we design an SILRec encoder with a multi-scale exponential moving average module and a frequency-domain module to model user intents across different temporal and spectral patterns. We further introduce boundary-smoothed contrastive learning based on representation interpolation, which constructs smoother transition regions between intent representations and alleviates the rigidity of hard contrastive sample boundaries. Comprehensive experiments on 6 conventional real-world datasets with 13 competitors demonstrate the effectiveness of SILRec. We further conduct an additional evaluation on the KuaiRec short-video dataset to examine its applicability to more dynamic recommendation scenarios. Additional analyses from multiple perspectives, including controlled noise perturbation, long-tail performance, and intent visualization, provide further evidence for the proposed design. Our codes and datasets are available at https://github.com/syf1844803351/SILRec .

As queries in retrieval-augmented generation (RAG) pipelines powered by large language models (LLMs) become increasingly complex and diverse, dense retrieval models have demonstrated strong performance in semantic matching. Nevertheless, they often struggle with fine-grained retrieval tasks, where precise keyword alignment and span-level localization are required, even in cases with high lexical overlap that would intuitively suggest easier retrieval. To systematically evaluate this limitation, we introduce two targeted tasks, keyword retrieval and part-of-passage retrieval, designed to simulate practical fine-grained scenarios. Motivated by these observations, we propose LexSemBridge, a unified framework that enhances dense query representations through fine-grained, input-aware vector modulation. LexSemBridge constructs latent enhancement vectors from input tokens using three paradigms: Statistical (SLR), Learned (LLR), and Contextual (CLR), and integrates them with dense embeddings via element-wise interaction. Theoretically, we show that this modulation preserves the semantic direction while selectively amplifying discriminative dimensions. LexSemBridge operates as a plug-in without modifying the backbone encoder and naturally extends to both text and vision modalities. Extensive experiments across semantic and fine-grained retrieval tasks validate the effectiveness and generality of our approach. All code and models are publicly available at https://github.com/Jasaxion/LexSemBridge/

Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-dimensional space. We then utilized these learned features to train a classifier to categorize tumors into a high- or low-risk group of recurrence. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that CL can significantly improve classification accuracy. Specifically, our CL-based classifiers achieved an area under the receiver operating characteristic curve (AUC) greater than 0.8 for 14 types of cancer, and an AUC greater than 0.9 for 3 types of cancer. We also developed CL-based Cox (CLCox) models for predicting cancer prognosis. Our CLCox models trained with the TCGA data outperformed existing methods significantly in predicting the prognosis of 19 types of cancer under consideration. The performance of CLCox models and CL-based classifiers trained with TCGA lung and prostate cancer data were validated using the data from two independent cohorts. We also show that the CLCox model trained with the whole transcriptome significantly outperforms the Cox model trained with the 16 genes of Oncotype DX that is in clinical use for breast cancer patients. The trained models and the Python codes are publicly accessible and provide a valuable resource that will potentially find clinical applications for many types of cancer.

MOTIVATION: Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: (i) relatively small size of samples available, and (ii) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. RESULTS: In this article, we use the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further used to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. AVAILABILITY AND IMPLEMENTATION: R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. RESULTS: A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. CONCLUSIONS: Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

MOTIVATION: Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. RESULTS: In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. AVAILABILITY AND IMPLEMENTATION: The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes. RESULTS: In this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1. CONCLUSION: With its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions.

Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs) are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL), for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML) is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL) based scheme, and the QTL-directed dependency graph (QDG) method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

BACKGROUND: The Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model. RESULTS: We developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects. CONCLUSIONS: The EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.

In energy-constrained wireless networks such as sensor networks, it is important to maximize power efficiency by minimizing power consumption for a given quality of service such as the data rate; it is equally important to evenly or fairly distribute power consumption to all nodes to maximize the network life. In this paper, we develop optimal power control methods to balance the tradeoff between energy efficiency and fairness for wireless cooperative networks where several relays assist the communication of multiple source-destination pairs. Our optimal power control policy is derived in a quasi-closed form by solving a convex optimization problem with a properly chosen cost-function. We further develop novel stochastic optimization algorithms to dynamically learn the statistics of the wireless channels on-the-fly to approach the optimal power control policy. Moreover, our power control algorithm can be implemented distributedly at relays requiring each relay to know its local channel state information. Simulation results demonstrate the merits of our power control methods.

In wireless systems where half duplex transceivers are employed, most existing practical cooperative protocols achieve a spectral efficiency of 0.5 symbols per channel use (pcu). Recently a decode-and-forward protocol was developed to achieve a spectral efficiency of 2/3 symbols pcu. But there is no practical amplify-and-forward (AF) protocol that can achieve a spectral efficiency higher than 0.5 symbols pcu. In this paper, we develop a nonorthogonal AF protocol which achieves a spectral efficiency of 2/3 symbols pcu and provides almost the same bit error rate as the traditional orthogonal AF which has a spectral efficiency of 0.5 symbols pcu.

Recently developed cooperative protocol with distributed path selection provides a simple and practical means of achieving full cooperative diversity in wireless networks. While the best path selection method can significantly improve bit error rate (BER) performance, it may cause unequal power consumption among relay nodes, which may reduce the lifetime of energy-constrained networks. A path selection method under the equal power constraint has been developed for the amplifyand- forward (AF) protocol, but there is no such method for the decode-and-forward (DF) protocol. In this paper, we develop a distributed path selection method with an equal power constraint for the DF protocol. We also analyze the BER performance of our path-selection method. Numerical results demonstrate that the proposed method can guarantee equal power consumption, while achieving full diversity as the best path selection method and providing significant performance gain relative to noncooperative communication.

Stochastic simulation of biological systems has received much attention recently. A very promising stochastic simulation method is the τ-leap method, which can significantly accelerate simulation with controllable accuracy. However, all current τ-leap methods produce biased results, which can cause large simulation errors. In this paper, we analyze the expected number of reactions occurring during each leap. Relying on the analytical results, we develop an unbiased Poisson τ-leap method and an unbiased binomial τ-leap method. Simulations demonstrate that our new unbiased τ-leap method can significantly improve simulation accuracy without sacrificing simulation speed.

Obtaining a dynamically updated background reference image is an important and challenging task for video applications using background subtraction. This paper proposes a novel algorithm for online video background reconstruction. Firstly, multiple candidates of background values at each pixel are obtained by locating subintervals of stable intensity in a processing period. Then criteria based on pixel intensity distribution and local optical flows are employed to decide the most likely candidate to represent the background. For the methods utilizing the distributions of intensity values, the decision of determining the background value at a pixel position is based on the observation that the appearance time and sub- period frequency of the background is higher than non- background. An enhanced method using neighborhood optical flow information is adopted for more precise decision with slightly additional computation by identifying the events of covering and revealing of a pixel position. The experimental results show that the proposed algorithm outperforms existing adaptive mixture Gaussian background model and provides robust, efficient background image reconstruction in complex and busy environment.

Recently, cooperative communication has been proposed to provide diversity, thereby improving the performance of wireless networks. In this paper, we develop two transmission methods for cooperative networks that need to exchange no or a minimum amount of information among cooperating nodes and the source node to coordinate cooperative transmissions. We then investigate the bit error rate performance of complementary punctured convolutional (CPC) codes, when they are applied to our two cooperating transmission schemes. Our simulation results demonstrate that cooperative communication can improve the BER performance significantly, and our cooperation scheme II offers better performance than cooperation scheme I, because it uses a better node selection method, although it does not need any signaling overhead.

Hemodynamic parameters of the pulmonary circulation are important reference factors in the diagnoses and treatments of children 's cardiovascular diseases. These parameters are usually measured by the cardiac catheterization. Recently several noninvasive methods have been proposed to estimate these parameters using the ultrasound technique. In order to execute the noninvasive estimation automatically, several signal and image processing algorithms are applied to obtain the feature points of the Doppler spectrogram and ECG signal, from which hemodynamic parameters can be calculated based on empirical formulas. To combine all the processes and simplify the manipulation, a computer-based system is designed and tested for sixty children with congenital heart diseases. Experimental results show that parameters estimated using this system highly correlate to the clinic ones measured by the cardiac catheterization. For its noninvasive and automatic advantage, this system may be used as an assistant tool in the diagnoses and treatments of children's cardiovascular diseases

Within a new paradigm, where wireless user cooperation is viewed as a form of (opportunistic) multipath, we exploit the unique capabilities of direct-sequence spread spectrum transmissions in handling multipath to design a novel spectrally efficient protocol for wireless cooperative networks. We show how and why our proposed system achieves diversity without increasing bandwidth. After analyzing its performance, we deduce that user capacity can be significantly improved with respect to existing third generation cellular systems in the uplink.

We analyze the stability properties of slotted Aloha with capture for random access over fading channels with infinitely-many users. We assume that each user node knows only its own uplink channel gain, and uses this decentralized channel state information (CSI) to perform power control and/or probability control. The maximum stable throughput (MST) for a general capture model is obtained by means of drift analysis on the backlog Markov chain. We then specialize our general result to a signal-to-interference-plus-noise ratio (SINR) capture model. Our analysis shows that if the channels of all users are identical and independently distributed (i.i.d.) with finite means, the system is unstable under any kind of power and probability control mechanism that is based only on decentralized CSI.

We consider point-to-point wireless links with multiple antennas in the presence of interference, and exploit channel's spatial correlation and the temporal covariance of the interference to design multiantenna transmitters. We develop a space-time spreading scheme that maximizes average signal-to-interference-and-noise ratio, and an optimally power-loaded space-time beamforming (STBF) scheme which improves error-probability performance. In order to increase transmission rates, we combine orthogonal space-time block coding with STBF, optimize power loading across beams, and develop low-complexity receivers. Optimal training for least-squares error channel estimation, and STBF for minimum mean-square error channel estimation, are also studied. Our analytical and simulated results corroborate that STBF with optimal power loading can considerably reduce error probability and channel-estimation errors.

Relayed transmissions enable low-power communications among nodes (possibly separated by a large distance) in wireless networks. Since the capacity of general relay channels is unknown, we investigate the achievable rates of relayed transmissions over fading channels for two transmission schemes: the block Markov coded and the time-division multiplexed (TDM) transmissions. The normalized achievable minimum energy per bit required for reliable communications is derived, which also enables optimal power allocation between the source and the relay. The time-sharing factor in TDM transmissions is optimized to improve achievable rates. The region where relayed transmission can provide a lower minimum energy per bit than direct transmission, as well as the optimal relay placement for these two transmission schemes, are also investigated. Numerical results delineate the advantages of relayed, relative to direct, transmissions.

Adaptive modulation requires channel state information (CSI), which can be acquired at the receiver by inserting pilot symbols in the transmitted signal. We first analyze the effect linear minimum mean square error (MMSE) channel estimation and prediction errors have on bit-error rate (BER). Based on this analysis, we develop adaptive pilot symbol assisted modulation (PSAM) schemes that account for both channel estimation and prediction errors to meet a target BER. While pilot symbols facilitate channel acquisition, they consume part of transmitted power and bandwidth, which in turn, reduces spectral efficiency. With imperfect (and thus, partial) CSI available at the transmitter and receiver, two questions arise naturally: how often should pilot symbols be transmitted, and how much power should be allocated to pilot symbols. We address these two questions by optimizing pilot parameters to maximize spectral efficiency.

Cooperative diversity (CD) networks have been receiving a lot of attention recently as a distributed means of improving error performance and capacity. For sufficiently large signal-to-noise ratio (SNR), this paper derives the average symbol error probability (SEP) for analog forwarding CD links. The resulting expressions are general as they hold for an arbitrary number of cooperating branches, arbitrary number of cooperating hops per branch, and various channel fading models. Their simplicity provides valuable insights to the performance of CD networks and suggests means of optimizing them. Besides revealing the diversity, they clearly show from where this advantage comes from and prove that presence of diversity does not depend on the specific (e.g., Rayleigh) fading distribution. Finally, they explain how diversity is improved in multihop CD networks.

Relay links are expected to play a critical role in the design of wireless networks. This paper investigates the energy efficiency of relay communications in the low-power regime under two different scenarios: when the relay has unlimited power supply and when it has limited power supply. A system with a source node, a destination node, and a single relay operating in the time division duplex (TDD) mode was considered. Analysis and simulations are used to compare the energy required for transmitting one information bit in three different relay schemes: amplify and forward (AnF), decode and forward (DnF), and block Markov coding (BMC). Relative merits of these relay schemes in comparison with direct transmissions (direct Tx) are discussed. The optimal allocation of power and transmission time between source and relay is also studied.

Within a new paradigm, where wireless user cooperation is viewed as a form of (opportunistic) multipath, we exploit the unique capabilities of direct-sequence spread spectrum transmissions in handling multipath to design a novel spectrally efficient protocol for wireless cooperative networks. We show how and why our proposed system achieves diversity without increasing bandwidth. After analyzing its performance, we deduce that user capacity can be significantly improved with respect to existing third generation cellular systems in the uplink. This is particularly interesting since our scheme can be readily integrated in such networks without major changes in the existing standards.

Cooperative diversity (CD) networks have been receiving a lot of attention recently as a distributed means of improving error performance and capacity. This paper derives the average symbol error probability (SEP) for amplify and forward CD links. The resulting expressions are general as they hold for an arbitrary number of cooperating branches, arbitrary number of cooperating hops per branch, and many channel fading models. Their simplicity provides valuable insights to the performance of CD networks and allows their optimization. Besides revealing the diversity advantage, they clearly show from where this advantage comes from and prove that the diversity advantage holds independently of the channel fading model. Finally, explain how diversity is improved in multihop CD networks.

Relay links are expected to play a critical role in the design of wireless networks. In this paper, we investigate the energy efficiency of relay communications under two different scenarios: when the relay has unlimited and when it has limited power supply. Relative merits of these relay schemes in comparison with direct transmissions are discussed.

We analyze the stability properties of slotted Aloha with capture for random access over fading channels when the number of users is very large. We consider the most commonly used power capture model, namely the one based on signal-to-interference-plus-noise ratio (SINK). Existing works have shown that for this model the capture capability alone can stabilize the slotted Aloha when the distribution of the received user power satisfies certain conditions. However, our analysis shows that as long as the received powers of all the users are independent and identically distributed with finite mean, the system is unstable. We further establish that power or probability control based on decentralized channel state information (CSI) can not render an infinite-user system stable.

In the presence of frequency-selective multipath fading channels, code-division multiple access (CDMA) suffers from multiuser interference (MUI) and intersymbol interference (ISI); but when properly designed, it enjoys multipath diversity. Orthogonal frequency-division multiple access (OFDMA) is MUI-free, but it does not enable the available channel diversity without employing error-control coding. On the other hand, coded OFDMA may achieve lower diversity than a CDMA system employing the same error-control codes. In this paper, we merge the advantages of OFDMA and CDMA to minimize MUI effects, and also enable the maximum available diversity for every user. In our group orthogonal multicarrier CDMA (GO-MC-CDMA) scheme, groups of users share a set of subcarriers. By judiciously choosing group subcarriers, we guarantee that every user transmits with maximum diversity. MUI is only present among users in the same group, and is suppressed via multiuser detection, which becomes practically feasible because we assign a small number of users per group. Performance is analyzed, and simulations are carried out to illustrate the merits of GO-MC-CDMA relative to existing alternatives.

We analyze the average symbol error rate (SER) of M-PSK and M-QAM modulations with transmit antenna selection diversity (SD) and receive generalized selection combining (GSC) in Rayleigh fading channels. SER formulas are derived in closed form, and numerical results show that transmit SD and receive GSC are flexible to tradeoff performance for complexity.

While the knowledge of each channel realization is not available in a system with differential space-time modulation, channel correlation can be easily estimated without training at the receiver, and exploited by the transmitter to enhance the error probability performance. We develop a transmission scheme that combines transmit-beamforming with differential space-time modulation based on orthogonal space-time block coding. Error probability is analyzed for both correlated and independent channels. Based on the error probability analysis, we derive power loading coefficients to improve performance.

While rapid variations of the fading channel cause intercarrier interference (ICI) in orthogonal frequency-division multiplexing (OFDM), thereby degrading its performance considerably, they also introduce temporal diversity, which can be exploited to improve performance. We first derive a matched-filter bound (MFB) for OFDM transmissions over doubly selective Rayleigh fading channels, which benchmarks the best possible performance if ICI is completely canceled without noise enhancement. We then derive universal performance bounds which show that the time-varying channel causes most of the symbol energy to be distributed over a few subcarriers, and that the ICI power on a subcarrier mainly comes from several neighboring subcarriers. Based on this fact, we develop low-complexity minimum mean-square error (MMSE) and decision-feedback equalizer (DFE) receivers for ICI suppression. Simulations show that the DFE receiver can collect significant gains of ICI-impaired OFDM with affordable complexity. In the relatively low Doppler frequency region, the bit-error rate of the DFE receiver is close to the MFB.

We analyze the system performance of code-division multiple-access (CDMA) random access systems with linear receivers and packet combing in multipath fading channels. Both slotted and unslotted CDMA systems with random spreading codes are considered. The analysis is based on large systems in which both the offered load and the processing gain tend to infinity but their ratio is fixed. It is relatively easy to characterize the traffic in such large systems, which enables us to derive the system throughput and average delay. From the analysis results, it is observed that multiuser detection and packet combining substantially improve the system performance.

The minimum variance receiver has been previously proposed to suppress the multiple-access interference (MAI) blindly in CDMA channels by minimizing the receiver's output variance based on constrained optimization. In this paper, we propose a low-complexity minimum variance receiver that achieves almost the same performance as the high-complexity one with the optimal constraint vector. The proposed receiver is a RAKE receiver followed by an equal gain combiner. The impulse response of the filter in each finger is obtained by the minimum variance criterion. The low-rank minimum variance receiver uses a reduced-rank approximation of the estimated data covariance matrix. Its performance is better than the full-rank version of the receiver. The receiver is implemented adaptively with the same structure as a generalized sidelobe canceler.

It has been shown that cyclostationarity in the received signal allows the receiver to blindly identify the channel impulse response using only second-order statistics. In orthogonal frequency-division multiplexing (OFDM) systems, cyclostationarity is embedded at the transmitter due to cyclic prefix. In this paper, a subspace approach based on second-order statistics is proposed for blind channel identification in OFDM systems. We derive a sufficient condition that guarantees all the channels to be identifiable no matter what their zero locations are. Computer simulations demonstrate the superior performance of the proposed algorithm over methods reported earlier in the literature.

The minimum variance receiver has been proposed to blindly suppress the multiple-access interference in CDMA channels by minimizing the receiver's output variance based on constrained optimization. We consider the reduced-rank minimum variance receiver in asynchronous CDMA channels. The reduced-rank detector uses a reduced-rank approximation of the covariance matrix of the received data. When the covariance is known, the reduced-rank receiver cannot perform better than the full-rank receiver. However, when the covariance matrix is estimated, we demonstrate that the optimal reduced-rank receiver outperforms the full-rank receiver. The reduced-rank receiver is implemented adaptively using a subspace tracking algorithm; its performance is better than the conventional full-rank recursive least-square adaptive receiver.

Conventional spread spectrum communication methods essentially map a discrete symbol from an alphabet to a length-N real valued sequence, which represents a point in the N-dimensional signal constellation. The real valued sequence is in turn transmitted over a communications channel. However, communication schemes for the purpose of multimedia steganography (or data hiding) have to transmit the real valued sequence corresponding to a point in the signal constellation superimposed on the original or cover content (without affecting the fidelity of the original content noticeably). The detector therefore, has to estimate the origin of the signal constellation, and thereafter proceed to decode the transmitted symbol. In this paper, we explore some efficient solutions for signaling methods employing floating signal constellations.