Graph pooling has gained significant progress in recent years as an effective solution for graph-level property classification tasks. With the emergence of research on Heterogeneous Information Networks (HINs), this paper argues that graph-level datasets for graph classification should be treated as HINs rather than homogeneous graphs to enhance information aggregation. We propose HINPool, a novel and general graph pooling framework for graph-level property classification with HINs. First, we devise a systematic HIN construction procedure from the original data to capture complex interactions. Next, we introduce a type-aware heterogeneous graph pooling method featuring a Type-Aware Selector (TAS) to select essential nodes and a Readout Aggregator (RA) to fuse critical information into a graph-level representation. Finally, a cross-layer fusion function is applied to combine the output embeddings from each graph pooling layer, creating a final graph representation for downstream classification tasks. Our approach achieves near state-of-the-art performance on widely used graph classification benchmark datasets, demonstrating significant improvements in four out of five datasets. This work redefines the strategy for graph-level property classification with HGNNs and heterogeneous graph pooling to model intricate relationships, enhancing performance without requiring extensive domain-specific knowledge.

As online ecosystems grow increasingly complex, personalized recommendation systems must integrate user preferences across heterogeneous content sources and interaction scenarios. However, conventional methods typically model each source and scenario in isolation, hindering their ability to capture shared and complementary signals across contexts. In this work, we propose MM4Rec, a unified framework for multi-source and multi-scenario recommendation. MM4Rec introduces a Source-Aware Transformer Encoder to jointly model heterogeneous inputs, a Multi-Scenario Behavior Extraction Layer based on a multi-mixture-of-experts architecture to capture scenario-specific dynamics, and a Trend-Aware Learner to enhance temporal representation learning. Extensive experiments on three real-world datasets demonstrate that MM4Rec consistently outperforms strong baselines across standard recommendation metrics. To facilitate future research, we also release two large-scale datasets encompassing diverse sources and scenarios.

Sequential recommendation in e-commerce utilizes users' anonymous browsing histories to personalize product suggestions without relying on private information. Existing item ID-based methods and multimodal models often overlook the temporal alignment of modalities like textual descriptions, visual content, and prices in user browsing sequences. To address this limitation, this paper proposes the Multimodal Time-aligned Shared Token Recommender (MTSTRec), a transformer-based framework with a single time-aligned shared token per product for efficient cross-modality fusion. MTSTRec preserves the distinct contributions of each modality while aligning them temporally to better capture user preferences. Extensive experiments demonstrate that MTSTRec achieves state-of-the-art performance across multiple sequential recommendation benchmarks, significantly improving upon existing multimodal fusion. Our code is available at https://github.com/idssplab/MTSTRec.

Tags play a critical role in enhancing product discoverability, optimizing search results, and enriching recommendation systems on e-commerce platforms. Despite the recent advancements in large language models (LLMs), which have shown proficiency in processing and understanding textual information, their application in tag generation remains an under-explored yet complex challenge. To this end, we introduce a novel method for automatic product tagging using LLMs to create behavior-enhanced tags (BETags). Specifically, our approach begins by generating base tags using an LLM. These base tags are then refined into BETags by incorporating user behavior data. This method aligns the tags with users' actual browsing and purchasing behavior, enhancing the accuracy and relevance of tags to user preferences. By personalizing the base tags with user behavior data, BETags are able to capture deeper behavioral insights, which is essential for understanding nuanced user interests and preferences in e-commerce environments. Moreover, since BETags are generated offline, they do not impose real-time computational overhead and can be seamlessly integrated into downstream tasks commonly associated with recommendation systems and search optimization. Our evaluation of BETag across three datasets--- Amazon (Scientific), MovieLens-1M, and FreshFood---shows that our approach significantly outperforms both human-annotated tags and other automated methods. These results highlight BETag as a scalable and efficient solution for personalized automated tagging, advancing e-commerce platforms by creating more tailored and engaging user experiences.

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imbalanced data can lead to sub-optimal performance. Traditional oversampling methods, while effective, risk overfitting and face difficulties in reintegrating synthetic samples into the original graph. In this study, we introduce Framework of Imbalanced Node Classification on heterogeneous graph neural network with GAN (FincGAN), a new framework that utilizes oversampling techniques to address class imbalance in heterogeneous graphs. Instead of duplicating existing samples, FincGAN employs a Generative Adversarial Network (GAN) to create synthetic samples and uses deep learning-based edge generators to connect them back to the original graph. Our evaluations on spam user detection in the Amazon and Yelp Review datasets show that FincGAN outperforms baseline models in all essential metrics, including F-score and AUC-PRC score, showing its effectiveness in addressing class imbalance.

Push notifications efficiently deliver real-time messages, boosting user engagement and website traffic. However, users often passively receive notifications without active interaction in recommendation contexts. Consequently, for precise recommendations, Click-Through Rate (CTR) prediction for push notifications requires addressing challenges such as user temporal and contextual preferences, the dynamic nature of user click behavior, and limited interactions between users and items. We propose Push4Rec, a novel push notification recommendation model designed explicitly for news articles. Push4Rec integrates pivotal learners to extract information adeptly. It assesses click behavior, captures preferences, and comprehends trends’ influence. A fusion function and gating network ensure versatile extraction of user click preferences. We assessed Push4Rec using a real-world push notification dataset from our partnering company. Push4Rec outperformed benchmark models, delivering state-of-the-art results across all evaluation metrics. Thus, we believe that Push4Rec, with its novel approach, sets a new standard in push notification services, driving forward the field of personalized recommendation systems.

The early identification of an individual's dementia risk is crucial for disease prevention and the design of insurance products in an aging society. This study aims to accurately predict the future incidence risk of dementia in individuals by leveraging the advantages of neural networks. This is, however, complicated by the high dimensionality and sparsity of the International Classification of Diseases (ICD) codes when utilizing data from Taiwan's National Health Insurance, which includes individual profiles and medical records. Inspired by the click-through rate (CTR) problem in recommendation systems, where future user behavior is predicted based on their past consumption records, we address these challenges with a multimodal attention network for dementia (MAND), which incorporates an ICD code embedding layer and multihead self-attention to encode ICD codes and capture interactions among diseases. Additionally, we investigate the applicability of several CTR methods to the dementia prediction problem. MAND achieves an AUC of 0.9010, surpassing traditional CTR models and demonstrating its effectiveness. The highly flexible pipelined design allows for module replacement to meet specific requirements. Furthermore, the analysis of attention scores reveals diseases highly correlated with dementia, aligning with prior research and emphasizing the interpretability of the model. This research deepens our understanding of the diseases associated with dementia, and the accurate prediction provided can serve as an early warning for dementia occurrence, aiding in its prevention.

In this paper, we address the challenge of discovering financial signals in narrative financial reports.As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals.To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-andcontrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships.We also create and publicly release a humanannotated dataset for our task.Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.

Graph neural networks are a promising deep learning method that can apply graph structures to various tasks. In real-world scenarios, we often have heterogeneous graphs, wherein different node and edge types capture complex interactions between nodes. High-dimensional node features provide rich information about the target nodes. Conventional heterogeneous graph neural networks (HGNN) focus more on graph structures than node features and may have difficulties extracting knowledge from complex node features. In this study, we propose a novel framework, the tree-boosted heterogeneous graph neural network abbreviated as TreeXGNN, which could efficiently and automatically extract target node features via gradient-boosted decision trees (GBDT). It integrates community structure information with proper fusion modules and a shared feature space design on HGNN. We achieved state-of-the-art performance on the three well-known heterogeneous graph benchmark datasets, IMDB, DBLP, and ACM, and significantly improved performance compared to previous studies. Our work paves the foundation for integrating tree-based models to boost HGNNs for general community analysis.

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset.

We consider a resource allocation and offloading decision-making problem in a mobile edge computing (MEC) network. Since the locations of user equipments (UEs) vary over time in practice, we consider a dynamic network, where the UEs could leave or join the network coverage at any location. Since the joint offloading decision that minimizes the network cost also varies with the topology, the expected best offloading decision for the previous topology would not match the new topology. Consequently, the system suffers from recurring cost peaks due to the topology change. Thus, we propose a robust distributed hierarchical online learning approach to enhance the algorithm’s robustness and reduce the cost peaks. Specifically, the UEs learn the utility of each offloading decision via deep Q networks (DQNs) from their interaction with the MEC network. Meanwhile, the computational access points (CAPs) train their deep neural networks (DNNs) online with the real-time data collected from the UEs to predict their corresponding Q-value vectors. Therefore, the UEs and CAPs form a hierarchical collaborative-learning structure. When the topology changes, each UE downloads its Q-value vector as the Q-bias vector and learns its difference from the actual Q-value vector via its DQN. With different agents learning distributedly, both the peak and sum costs are reduced as the joint offloading decision could start from a near-local-optimal point. In simulations, our robust approach successfully reduces the peak cost and sum cost by up to 50% and 30%, respectively. This demonstrates the need for a robust learning algorithm design in a practical dynamic MEC network.

This paper introduces a novel high performing multimodal deep learning architecture(Trans-DiCE) for stock movement prediction utilizing financial indicators and news data. Our multimodal architecture uses dilated causal convolutions and Transformer blocks for feature extraction from both data sources. The masked multi-head self-attention layers inside Transformers preserve causality and improve features based on contextual information. To integrate the derived multimodal model representations, we use stacked Transformer blocks. We show empirically that our model performs best compared to state-of-the-art baseline methods for S&P 500 index and individual stock prediction and provides a significant 3.45% improvement from 74.29% to 77.74%. We also demonstrate our model’s utility for the Portfolio Management task. We propose a Deep Reinforcement Learning Framework utilizing Trans-DiCE for Portfolio Optimization, providing noticeable gain on Sharpe Ratio and 7.9% increase in Portfolio Value over the existing state of the art Models.

We consider a multi-user multi-server mobile edge computing (MEC) network with time-varying fading channels and formulate an offloading decision and resource allocation problem. To solve this mixed-integer non-convex problem, we propose two hybrid approaches that learn offloading strategy with DQN (opt-DQN) or Q-table (opt-QL) at each user equipment (UE). The communication resources are allocated with an optimization algorithm at each computational access point (CAP). We also propose a pure DQN method that learns both the offloading strategy and resource allocation via Q-learning (QL). We analyze the convergence behavior of the QL-based algorithms from a game-theoretical perspective and demonstrate the performance of the proposed hybrid approaches for different network sizes. The simulation results show that the hybrid approaches reach lower costs than other baseline algorithms and the pure-DQN approach. Moreover, the performance of the pure-DQN approach degrades severely as the network size increases, while opt-DQN still performs the best, followed by opt-QL. These observations demonstrate that the hybrid approach that combines the advantages of both QL and convex optimization is a promising design for a multi-user MEC network, wherein complicated offloading and resource allocation strategies need to be determined in a timely and accurate fashion.

We consider a dynamic mobile edge computing (MEC) network with multiple computational access points (CAPs) that serve user equipment (UEs). We assume that UEs could join or leave the network due to mobility, resulting in the dynamic change in the network topology. To fully exploit the computational resource in the MEC network, the offloading decision, transmission power, and the computational resource should be appropriately allocated, and a robust design that addresses the above issues is necessary. In this work, we propose a robust hierarchical learning approach that applies deep Q networks (DQNs) and deep neural networks (DNNs) at the UEs and CAPs, respectively. Each UE interacts with the network environment and learns the best offloading decision policy in a local scope. Via sharing the local best policy learned by the UEs to the CAPs, the CAPs learn the relation between the UE location and the locally best strategy. The proposed robust approach suppresses the peak in cost caused by the dynamic topology change by up to 160% compared with a non-robust algorithm in the simulation. This demonstrates the necessity and benefit of robust design in a more realistic and dynamic MEC network.

The purpose of this research is to develop a high performing model for stock movement prediction utilizing financial indicators and news data. Until recently, the majority of prediction models have employed only the financial indicators, but they possess the risk of missing unconventional agitators that can be derived from other heterogeneous sources. To address this, few research studies began to explore the use of news data and other social features along with financial indicators. In this work, we propose a novel integrative approach to effectively blend views from the news and financial time series. We generate event-knowledge representations from news data by capturing direct and inverse relationships among event tuples, and then apply attention mechanism to infer inter-day relationships among the representations. To capture temporal dynamics of financial indicators, we further integrate an attention augmented dilated causal convolutional network. We report empirically that our model achieves a substantial 5% improvement from 68.81% to 74.29% in stock movement prediction for the Standard & Poor's 500 (S&P500) index and companies over existing models.

Recently, Mobile-Edge Computing (MEC) has been considered as a powerful supplement to a wireless network by processing computationally intensive tasks for resource-limited mobile devices. However, despite saving computational energy at User Equipment (UE), there is additional transmission energy consumption. As a result, the joint offloading strategy should be carefully selected to save energy and computational time. In this work, we investigated a sum cost minimization problem in a multi-UE multi-computing access point (CAP) system with time-varying channels. Our approach combines the optimization-based resource allocation algorithm with a Q-learning-based strategy selection mechanism. Without the need for communication overhead for CSI and inter- neighborhood cost value exchange, our algorithm shows prominent performance over the benchmark schemes with moderate assumptions.

Deep learning models have been shown to be capable of extracting high-level representations from the increasing amount of customer-level data generated via fast-growing financial activities. In financial data, dynamic features that evolve with time are commonly observed. However, such time dependencies are often ignored in classical classification models. In this study, we propose to learn a Recurrent Neural Network (RNN) feature extractor with GRU on credit card payment history to leverage the time dependencies embedded in these dynamic features. Input sequences are first preprocessed by this feature extractor. The extracted dynamic features along with the static features are then utilized to train an enhanced RNN model (RNN-RF) to predict credit card client defaults. Numerical experiments confirmed that the enhanced RNN predictor indeed provides the best performance in both lift index (0.659) and AUC (0.782) compared to the other benchmark models. The proposed model allows us to effectively combine static and dynamic features to provide superior predictive performance for financial data.

In this work, we consider coordinated beamforming design in a multi-user multiple-input single output (MISO) downlink heterogeneous cellular network (HCN) that combines radio frequency (RF) and visible light communication (VLC). VLC can exploit the illumination efficiently but cannot transmit signal in the absence of a line-of-sight (LOS) since the performance will be degraded significantly. Our goal in this paper is to maximize energy efficiency (EE) of the entire communication system with quality-of-service (QoS) constraints of all users in the femtocell. We formulate the problem as a fractional programming problem, which is difficult to solve due to the non-convex objective function and constraints. To tackle this problem, we introduce slack variables and use several approximation techniques to reformulate the original problem into an approximated convex problem. To further improve EE of the system, we apply such convex approximation successively to devise an successive convex approximation (SCA) algorithm. Based on extensive numerical experiments, we observe that the EE performance of the proposed mixed RF/VLC network architecture is significantly better than that without VLC by as much as 67.6% and 43.5% when the RF and VLC circuit power are low (1 W for both cases). In summary, we demonstrate that the RF/VLC network architecture is much more energy efficient than those without VLC.

We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis. The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality (LMI) equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.

This paper investigates the optimal power allocation that maximizes the system utility for a full-duplex (FD) amplify-and-forward (AF) relaying two-way relay network (TWRN) with a rate outage and a power constraint. Both cases of an individual and a sum power constraint are considered. To formulate the corresponding optimization problem, closed-form expressions of outage probabilities are needed. With FD transceivers, the derivation of such closed-form expressions becomes too involved. Thus, approximate closed-form expressions were derived instead. The resulting optimization problem is still non-convex and difficult to solve. Via solving a series of approximate convex problems, a successive convex approximation (SCA) algorithm was proposed. Our simulation results demonstrate the proposed SCA algorithm achieves near-optimal performance and outperforms the full power allocation under the individual power constraint and uniform power allocation under the sum power constraint both in system utility and in power consumption. A tradeoff between the FD system and the half-duplex (HD) system with respect to the residual self-interference was also observed.

In this paper, we focus on maximizing the system utility (e.g., the weighted sum-rate, weighted geometric mean rate, and the harmonic mean rate) of a two-way relay network (TWRN) from the outage probability perspective; a TWRN has multiple relay nodes and two terminal nodes. We assume amplify-and-forward relaying with analog network coding protocol and half-duplex transmission with perfect channel state information at the receiver ends and channel distribution information at the transmitter ends. We derive the approximated closed-form for the outage probability of a TWRN; however, the approximated outage constraints lead to a non-convex structure for the considered problem. Based on the successive convex approximation technique, we obtain near optimal solution for the non-convex problem. Moreover, we derive closed-form solutions for the maximization problem for the weighted sum rate maximization problem for a TWRN with a single relay node and two relay nodes under individual power constraints. Our simulation results demonstrate the accuracy of our outage probability approximation model and the advantages of our algorithm over naive methods of full and uniform power allocation.

In this study, robust biological filters with an external control to match a desired input/output (I/O) filtering response are engineered based on the well-characterized promoter-RBS libraries and a cascade gene circuit topology. In the field of synthetic biology, the biological filter system serves as a powerful detector or sensor to sense different molecular signals and produces a specific output response only if the concentration of the input molecular signal is higher or lower than a specified threshold. The proposed systematic design method of robust biological filters is summarized into three steps. Firstly, several well-characterized promoter-RBS libraries are established for biological filter design by identifying and collecting the quantitative and qualitative characteristics of their promoter-RBS components via nonlinear parameter estimation method. Then, the topology of synthetic biological filter is decomposed into three cascade gene regulatory modules, and an appropriate promoter-RBS library is selected for each module to achieve the desired I/O specification of a biological filter. Finally, based on the proposed systematic method, a robust externally tunable biological filter is engineered by searching the promoter-RBS component libraries and a control inducer concentration library to achieve the optimal reference match for the specified I/O filtering response.

For wireless amplify-and-forward (AF) relay networks, this work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework. It is proved that under two sufficient conditions: 1) the objective function of the random search algorithm is continuous and all its local maxima are global maxima in the considered feasible set, and 2) the origin is an interior point within the support of the probability measure for the random perturbation, the corresponding adaptive distributed beamforming schemes converge almost surely. While the second sufficient condition can be controlled by system designer and satisfied with relative ease, the first sufficient condition initially seems strict. Surprisingly, further analysis on the signal-to-noise ratio (SNR) functions in AF relay networks with individual and total power constraints demonstrates that, in both scenarios, local maxima are global maxima and hence, the first sufficient condition is satisfied. Finally, the proposed framework was extended to analyze adaptive distributed beamforming schemes in an asynchronous setting, and simulation results were provided to further validate our analysis.

This paper considers beamforming designs for weighted sum rate maximization (WSRM) in a multiple-input single-output interference channel subject to probability constraints on the rate outage. We claim that the outage probability constrained WSRM problem is an NP-hard problem, and therefore focus on devising efficient approximation methods. In particular, inspired by an insightful problem reformulation, a pricing-based sequential optimization (PSO) algorithm is proposed for efficiently handling the considered outage constrained WSRM problem. We show that the proposed PSO algorithm has semi-analytical beamforming solutions in each iteration, and hence can be efficiently implemented. Moreover, the PSO algorithm upon convergence attains a point satisfying Karush-Kuhn-Tucker (KKT) conditions of the original outage constrained problem. Simulation results demonstrate that the proposed PSO algorithm not only yields competing weighted sum rate performance, but also is computationally more efficient than the existing method [1].

To track a desired signal-to-interference-plus-noise-ratio (SINR) that allows for higher system throughput and better link quality in an interference limited wireless communication system, a feedback power controller is designed to obtain the optimal SINR tracking control. In this paper, we propose the multi-objective (MO) H2/H∞optimal power control for interference limited wireless systems that provides quality solutions for both objectives. The considered multi-objective H2/H∞power tracking control is complicated in nature and an equivalent formulation that minimizes the upper bounds of both objectives is proposed. Furthermore, the resulting constraints for the MO H2/H∞power control problem is transformed as three linear matrix inequalities (LMIs), leading to a LMIs-constrained MO problem (MOP). By combining the LMI toolbox in MATLAB with an evolutionary searching algorithm, a set of H2/H∞solutions called Pareto optimal solutions can be obtained and a particular solution can be selected based on the tradeoff determined by the system designer. Finally, to illustrate our design procedure, we provide numerical simulations for a typical interference limited wireless system, i.e., a direct-sequence code division multiple access (DS-CDMA) cellular system. Simulation results demonstrate that the optimal tradeoff indeed can be achieved by the proposed MO H2/H∞power control.

This paper investigates the problem of adaptive distributed beamforming for wireless sensor/relay networks. Existing schemes seek to adaptively achieve distributed phase alignment for beamforming via the help of an error-free, one-bit feedback link from the receiver to all distributed transmitters. Under such setting, we proposed a Deterministic Bisection Search Algorithm (DBSA), designed to achieve distributed phase alignment efficiently in a greedy fashion. Extensive simulations demonstrated significant improvement in the convergence time of the proposed DBSA compared with existing schemes. For wireless sensor/relay networks, inherent issues of system uncertainty such as random node addition or removal often occur due to power depletion or requests from new nodes to join the network. To resolve these issues, DBSA was further extended and numerical experiments have shown that the extended version is indeed robust against such system uncertainty.

This work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework. Once reformulated as local random search algorithms, it is proved that under two sufficient conditions: a) the objective function of the algorithm is continuous and all its local maxima are global maxima, and b) the origin is an interior point within the range of the considered transformation of the random perturbation, the corresponding adaptive distributed beamforming schemes converge both in probability and in mean. This proof of convergence is general since it can be applied to analyze randomized adaptive distributed beamforming schemes with any type of objective functions and probability measures as long as both the sufficient conditions are satisfied. Further, this framework can be generalized to analyze an asynchronous scheme where distributed transmitters can only update their beamforming coefficients asynchronously. Simulation results are also provided to validate our analyses.

This paper considers weighted sum rate maximization of multiuser multiple-input single-output interference channel (MISO-IFC) under outage constraints. The outage-constrained weighted sum rate maximization problem is a nonconvex optimization problem and is difficult to solve. While it is possible to optimally deal with this problem in an exhaustive search manner by finding all the Pareto-optimal rate tuples in the (discretized) outage-constrained achievable rate region, this approach, however, suffers from a prohibitive computational complexity and is feasible only when the number of transmitter-receive pairs is small. In this paper, we propose a convex optimization based approximation method for efficiently handling the outage-constrained weighted sum rate maximization problem. The proposed approximation method consists of solving a sequence of convex optimization problems, and thus can be efficiently implemented by interior-point methods. Simulation results show that the proposed method can yield near-optimal solutions.

A bio-inspired robust adaptive random search algorithm (BioRARSA), designed for distributed beamforming for sensor and relay networks, is proposed in this work. It has been shown via a systematic framework that BioRARSA converges in probability and its convergence time scales linearly with the number of distributed transmitters. More importantly, extensive simulation results demonstrate that the proposed BioRARSA outperforms existing adaptive distributed beamforming schemes by as large as 29.8% on average. This increase in performance results from the fact that BioRARSA can adaptively adjust its sampling stepsize via the ``swim'' behavior inspired by the bacterial foraging mechanism. Hence, the convergence time of BioRARSA is insensitive to the initial sampling stepsize of the algorithm, which makes it robust against the dynamic nature of distributed wireless networks.

The focus of this work is on the analysis of transmit beamforming schemes with a low-rate feedback link in wireless sensor/relay networks, where nodes in the network need to implement beamforming in a distributed manner. Specifically, the problem of distributed phase alignment is considered, where neither the transmitters nor the receiver has perfect channel state information, but there is a low-rate feedback link from the receiver to the transmitters. In this setting, a framework is proposed for systematically analyzing the performance of distributed beamforming schemes. To illustrate the advantage of this framework, a simple adaptive distributed beamforming scheme that was recently proposed by Mudambai et al. is studied. Two important properties of the received signal magnitude function are derived. Using these properties and the systematic framework, it is shown that the adaptive distributed beamforming scheme converges both in probability and in mean. Furthermore, it is established that the time required for the adaptive scheme to converge in mean scales linearly with respect to the number of sensor/relay nodes.

Space-time codes leverage the availability of multiple antennas to enhance the reliability of communication over wireless channels. While space-time codes have initially been designed with a focus on open-loop systems, recent technological advances have enabled the possibility of low-rate feedback from the receiver to the transmitter. The focus of this paper is on the implications of this feedback in a single-user multi-antenna system with a general model for spatial correlation. We assume a limited feedback model, that is, a coherent receiver and statistical knowledge at both the ends, along with B bits of error-free quantized channel information at the transmitter. We study space-time coding with a family of linear dispersion (LD) codes that meet an additional orthogonality constraint so as to ensure low-complexity decoding. Our results show that, when the number of bits of feedback (B) is small, a space-time coding scheme that is equivalent to beamforming and does not code across time is optimal in a weak sense in that it maximizes the average received SNR. As B increases, this weak optimality transitions to optimality in a strong sense that is characterized by the maximization of average mutual information. Thus, from a system designer's perspective, our work suggests that beamforming may not only be attractive from a low-complexity viewpoint, but also from an information-theoretic viewpoint.

The design of space-time codes for frequency flat, spatially correlated MIMO fading channels is considered. The focus of the paper is on the class of space-time block codes known as linear dispersion (LD) codes, introduced by Hassibi and Hochwald. The LD codes are optimized with respect to the mutual information between the inputs to the space-time encoder and the output of the channel. The use of the mutual information as both a design criterion and a performance measure is justified by allowing soft decisions at the output of the space-time decoder. A spatial Fourier (virtual) representation of the channel is exploited to allow for the analysis of MIMO channels with quite general fading statistics. Conditions, known as generalized orthogonal conditions (GOC's), are derived for an LD code to achieve an upper bound on the mutual information, with the understanding that LD codes that achieve the upper bound, if they exist, are optimal. Explicit code constructions and properties of the optimal power allocation schemes are also derived. In particular, it is shown that optimal LD codes correspond to beamforming to a single virtual transmit angle at low SNR, and a necessary and sufficient condition for beamforming to be optimal is provided. Due to the nature of the code construction, it is further observed that the optimal LD codes can be designed to adapt to the statistics of different scattering environments. Finally, numerical results are provided to illustrate the optimal code design for three examples of sparse scattering environments. The performance of the optimal LD codes for these scattering environments is compared with that of LD codes designed assuming the i.i.d. Rayleigh fading (rich scattering) model, and it is shown that the optimal LD codes perform significantly better. The optimal LD codes are also compared to beamforming LD codes and it is shown that beamforming is nearly optimal over a range of SNR's of interest.

The design of spatio-temporal power allocation schemes is considered for space-time coding over spatially correlated multiple-input multiple-output (MIMO) channels. The focus is on linear dispersion (LD) space-time codes that are constructed to maximize the mutual information between the input of the space-time encoder and the output of the channel. While perfect channel state information (CSI) is assumed at the receiver, three cases are considered for the CSI at the transmitter: 1) perfect CSI is available, 2) only statistical CSI is available, and 3) partial CSI in the form of a B-bit quantized channel information along with the statistical information is available. In all the three cases, it is shown that the optimal temporal power allocation is uniform. The optimal spatial power allocation for the case where only statistical CSI is available was studied previously in [1] where it was shown to be a nontrivial function of the spatial correlation. Here, the cases of perfect and partial CSI are studied. For the perfect CSI case, it is shown that it is optimal to excite only one spatial mode. For the partial and statistical CSI cases, the optimal allocation excites multiple modes, in general. However, it is attractive to use a low-complexity scheme that excites only the dominant spatial mode. We show that this low-complexity scheme is near-optimal in two settings: 1) large receive antenna asymptotics, and 2) for fixed antenna dimensions, when the transmit and receive covariance matrices are ill- and well-conditioned, respectively. Based on the optimal schemes for the extreme cases of perfect and statistical CSI, low-complexity spatial power allocation for the case of partial CSI is considered. Simulation results indicate that even in this case, exciting one spatial mode leads to a minimal loss in performance over the optimal spatial power allocation scheme.

With partial channel state information (CSI) at the transmitter, the design of space-time codes for frequency flat, spatially correlated MIMO fading channels is considered. The focus of the paper is on the class of space-time block codes known as linear dispersion (LD) codes, introduced by Hassibi and Hochwald. For perfect CSI at the transmitter, the LD codes are optimized with respect to the instantaneous mutual information between the inputs to the space-time encoder and the output of the channel. An equivalent optimization problem is proposed and can that be solved by standard convex optimization algorithms. It is then conjectured that the LD codes obtained by maximizing the instantaneous mutual information converge to that obtained from maximizing the averaged mutual information in the large antenna asymptote. Based on the insights drawn from the conjecture, a limited feedback scheme for LD codes is proposed assuming a common codebook at the transmitter and receiver. The numerical result for a scattering environment suggests that the proposed feedback scheme achieves high performance at low complexity.