Large language model (LLM)-based recommender systems have achieved high-quality performance by bridging the discrepancy between the item space and the language space through item tokenization. However, existing item tokenization methods typically require training separate models for each item domain, limiting generalization. Moreover, the diverse distributions and semantics across item domains make it difficult to construct a unified tokenization that preserves domain-specific information. To address these challenges, we propose UniTok, a Unified item Tokenization framework that integrates our own mixture-of-experts (MoE) architecture with a series of codebooks to convert items into discrete tokens, enabling scalable tokenization while preserving semantic information across multiple item domains. Specifically, items from different domains are first projected into a unified latent space through a shared encoder. They are then routed to domain-specific experts to capture the unique semantics, while a shared expert, which is always active, encodes common knowledge transferable across domains. Additionally, to mitigate semantic imbalance across domains, we present a mutual information calibration mechanism, which guides the model towards retaining similar levels of semantic information for each domain. Comprehensive experiments on wide-ranging real-world datasets demonstrate that the proposed UniTok framework is (a) highly effective: achieving up to 51.89% improvements over strong benchmarks, (b) theoretically sound: showing the analytical validity of our architectural design and optimization; and (c) highly generalizable: demonstrating robust performance across diverse domains without requiring per-domain retraining, a capability not supported by existing baselines.

Conversational recommender systems (CRSs) aim to provide personalized item recommendations along with explanations based on the conversations with users. While advancements in language models (LMs) have facilitated CRSs, limitations remain when LMs lack sufficient knowledge about item features that are essential for accurate recommendations and appropriate explanations. To alleviate this issue, retrieval-augmented language models (RALMs) have been introduced; however, they introduce a new challenge: the inclusion of less-relevant knowledge in retrieved passages. To address this limitation, we propose a novel CRS framework, MOCHA, which enhances RALMs through a multi-stage item/feature selection with Chain-of-Thought (CoT) reasoning. Specifically, MOCHA systematically identifies relevant knowledge by first selecting the item to recommend and then selecting its features to explain; each selection is performed via CoT reasoning. Experimental results on two public CRS datasets demonstrate that MOCHA significantly improves the recommendation accuracy, and provides informative and factually-correct explanations for the recommended items.

The self-attention mechanism has been adopted in various popular message passing neural networks (MPNNs), enabling the model to adaptively control the amount of information that flows along the edges of the underlying graph. Such attention-based MPNNs (Att-GNNs) have also been used as a baseline for multiple studies on explainable AI (XAI) since attention has steadily been seen as natural model interpretations, while being a viewpoint that has already been popularized in other domains (e.g., natural language processing and computer vision). However, existing studies often use naive calculations to derive attribution scores from attention, undermining the potential of attention as interpretations for Att-GNNs. In our study, we aim to fill the gap between the widespread usage of Att-GNNs and their potential explainability via attention. To this end, we propose GAtt, edge attribution calculation method for self-attention MPNNs based on the computation tree, a rooted tree that reflects the computation process of the underlying model. Despite its simplicity, we empirically demonstrate the effectiveness of GAtt in three aspects of model explanation: faithfulness, explanation accuracy, and case studies by using both synthetic and real-world benchmark datasets. In all cases, the results demonstrate that GAtt greatly improves edge attribution scores, especially compared to the previous naive approach.

Recent face anti-spoofing (FAS) methods have shown remarkable cross-domain performance by employing vision-language models like CLIP. However, existing CLIP-based FAS models do not fully exploit CLIP's patch embedding tokens, failing to detect critical spoofing clues. Moreover, these models rely on a single text prompt per class (e.g., 'live' or 'fake'), which limits generalization. To address these issues, we propose MVP-FAS, a novel framework incorporating two key modules: Multi-View Slot attention (MVS) and Multi-Text Patch Alignment (MTPA). Both modules utilize multiple paraphrased texts to generate generalized features and reduce dependence on domain-specific text. MVS extracts local detailed spatial features and global context from patch embeddings by leveraging diverse texts with multiple perspectives. MTPA aligns patches with multiple text representations to improve semantic robustness. Extensive experiments demonstrate that MVP-FAS achieves superior generalization performance, outperforming previous state-of-the-art methods on cross-domain datasets. Code: https://github.com/Elune001/MVP-FAS.

In e-commerce platforms, profit-aware recommender systems aim to improve the platform's profits while maintaining high overall accuracy by recommending items with high profits as top-ranked items. We explore two issues faced by existing model-based profit-aware approaches (i.e., MBAs) when training recommendation models for profit enhancement. First, existing MBAs tend to inaccurately infer the item ranking without considering the user's current preference for each item through their profit-based weighting scheme. Second, through the point-wise learning-to-rank (LTR), the model is optimized solely for the preference score of each item independently rather than being directly optimized for the overall ranking of items. To tackle these issues, we propose a novel MBA that involves three key steps: (S1) defining the Current Preference incorporated with Profit (i.e., CPP) for items; (S2) classifying items through CPP; and (S3) training the model by list-wise LTR based on CPP. Extensive experimental results using real-world platform datasets demonstrate that our approach improves accuracy by approximately 4% and profits by about 24% compared to the best-competing method.

Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborative filtering, requiring consideration of multiple ratings compared to single-criterion counterparts, often poses practical challenges in achieving state-of-the-art performance along with scalable model training. To solve this problem, we propose CA-GF, a training-free MC recommendation method, which is built upon criteria-aware graph filtering for efficient yet accurate MC recommendations. Specifically, first, we construct an item-item similarity graph using an MC user-expansion graph. Next, we design CA-GF composed of the following key components, including 1) criterion-specific graph filtering where the optimal filter for each criterion is found using various types of polynomial low-pass filters and 2) criteria preference-infused aggregation where the smoothed signals from each criterion are aggregated. We demonstrate that CA-GF is (a) efficient: providing computational efficiency, offering an extremely fast runtime of less than 0.2 seconds even on the largest benchmark dataset, (b) accurate: outperforming benchmark MC recommendation methods, achieving substantial accuracy gains up to 24% compared to the best competitor, and (c) interpretable: providing interpretations for the contribution of each criterion to the model prediction based on visualizations.

With the prevalence of social media platforms, accurately identifying the same users across different networks through network alignment has become crucial. Existing methods often struggle due to sparse or absent user-identifiable information (node attributes), highlighting the need for augmenting node attributes. However, research on attribute augmentation remains largely under-explored. In this study, we aim to design augmented attributes that enhance network alignment by reflecting three key structural C haracteristics: (C1) global structural characteristic, reflects the global network structure; (C2) seed-based structural characteristic, leverages cross-network structural information associated with seed nodes; (C3) multi-aspect structural characteristic, employs diverse structural relationship measures. To this end, we propose a novel approach for designing trustworthy Augmented Seed-baSed and multI-aspect STructurAl iNformaTion (ASSISTANT) attributes. To enhance alignment performance, we also present a learning module that utilizes a gate mechanism to select the most effective measure dynamically. Extensive experiments across various datasets demonstrate the following: 1) Our network alignment framework, which includes a gate mechanism module, significantly outperforms state-of-the-art methods in alignment accuracy; 2) other state-of-the-art methods using ASSISTANT attributes as input substantially boosts their own alignment accuracy; and 3) using only ASSISTANT attributes without any training process also leads to effective alignment, showcasing their high trustworthiness.

Traffic speed prediction is a crucial task for optimizing navigation systems and reducing traffic congestion. Although there have been efforts to improve the accuracy of speed prediction by incorporating auxiliary features, such as traffic flow, weather, and time, types of auxiliary features are limited and their detailed relationships with speed have not been explored yet. In our study, we present the individual spatio-temporal (IST) dependencies on flow and speed, and characterize three types of IST-dependencies with the flow-to-flow, speed-to-speed, and flow-to-speed graphs. Then, we propose Auxiliary feature-aided Attention Network (ARIAN), a novel approach to judiciously learning the degrees of IST-dependencies with the three graphs and predicting the future speed by leveraging various auxiliary features. Through comprehensive experiments using 3 real-world datasets, we validate the superiority of ARIAN over 10 state-of-the-art methods and the effectiveness of each auxiliary feature and each dependency learner in ARIAN.

In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitations of existing GCN-based multimedia recommender systems: (L1) although multimodal features of interacted items by a user can reveal her preferences on items, existing methods utilize GCN designed to focus only on capturing collaborative signals, resulting in insufficient reflection of the multimodal features in the final user/item embeddings; (L2) although a user decides whether to prefer the target item by considering its multimodal features, existing methods represent her as only a single embedding regardless of the target item's multimodal features and then utilize her embedding to predict her preference for the target item. To address the above issues, we propose a novel multimedia recommender system, named MONET, composed of following two core ideas: modality-embracing GCN (MeGCN) and target-aware attention. Through extensive experiments using four real-world datasets, we demonstrate i) the significant superiority of MONET over seven state-of-the-art competitors (up to 30.32% higher accuracy in terms of recall@20, compared to the best competitor) and ii) the effectiveness of the two core ideas in MONET. All MONET codes are available at https://github.com/Kimyungi/MONET.

Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although various explanation methods for GNNs have been developed, most studies have focused on instance-level explanations, which produce explanations tailored to a given graph instance. In our study, we propose Prototype-bAsed GNN-Explainer ([Formula: see text]), a novel model-level GNN explanation method that explains what the underlying GNN model has learned for graph classification by discovering human-interpretable prototype graphs. Our method produces explanations for a given class, thus being capable of offering more concise and comprehensive explanations than those of instance-level explanations. First, [Formula: see text] selects embeddings of class-discriminative input graphs on the graph-level embedding space after clustering them. Then, [Formula: see text] discovers a common subgraph pattern by iteratively searching for high matching node tuples using node-level embeddings via a prototype scoring function, thereby yielding a prototype graph as our explanation. Using six graph classification datasets, we demonstrate that [Formula: see text] qualitatively and quantitatively outperforms the state-of-the-art model-level explanation method. We also carry out systematic experimental studies by demonstrating the relationship between [Formula: see text] and instance-level explanation methods, the robustness of [Formula: see text] to input data scarce environments, and the computational efficiency of the proposed prototype scoring function in [Formula: see text].

In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new Si gn-aware Recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users' preferences, which is split into two edge-disjoint graphs with positive and negative edges each; 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multilayer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings; and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.

The vision of pervasiveartificial intelligence(AI) services can be realized by training an AI model on time using real-time data collected byinternet of things(IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices poses a significant challenge. To address this limitation, we propose a novel offloading architecture, calledjoint data deepening-and-prefetching(JD2P), which is feature-by-feature offloading comprising two key techniques. The first one isdata deepening, where each data sample’s features are sequentially offloaded in the order of importance determined by the data embedding technique such asprinciple component analysis(PCA). Offloading is terminated once the already transmitted features are sufficient for accurate data classification, resulting in a reduction in the amount of transmitted data. The criteria to offload data are derived for binary and multi-class classifiers, which are designed based onsupport vector machine(SVM) anddeep neural network(DNN), respectively. The second one isdata prefetching, where some features potentially required in the future are offloaded in advance, thus achieving high efficiency via precise prediction and parameter optimization. We evaluate the effectiveness of JD2P through experiments using the MNIST dataset, and the results demonstrate its significant reduction in expected energy consumption compared to several benchmarks without degrading learning accuracy.

Considerable attention has been paid to discover new materials with a desired set of properties for consumer electronics. However, the databases of the properties of a wide range of materials are not readily available, making data-driven discovery of novel materials challenging. In this study, we present an AI-empowered one-stop alloy database platform as an example and showcase its performance.

The vision of pervasive machine learning (ML) services can be realized by training an ML model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. On the other hand, high dimensional data with a heavy volume causes a significant burden to an IoT device with a limited energy budget. To cope with the limitation, we propose a novel offloading architecture, called joint data deepening and prefetching (JD2P), which is feature-by-feature offloading comprising two key techniques. The first one is data deepening, where each data sample's features are sequentially offloaded in the order of importance determined by the data embedding technique such as principle component analysis (PCA). No more features are offloaded when the features offloaded so far are enough to classify the data, resulting in reducing the amount of offloaded data. The second one is data prefetching, where some features potentially required in the future are offloaded in advance, thus achieving high efficiency via precise prediction and parameter optimization. To verify the effectiveness of JD2P, we conduct experiments using the MNIST and fashion-MNIST dataset. Experimental results demonstrate that the JD2P can significantly reduce the expected energy consumption compared with several benchmarks without degrading learning accuracy.

For high-quality conversational recommender systems (CRS), it is important to recommend the suitable items by capturing the items' features mentioned in the dialog and to explain the appropriate ones among the various features of the recommended item. We argue that the CRS model should be a domain-expert who is (1) knowledgeable about the relationships between items and their various features and (2) able to explain the recommended item with its features relevant to dialog context. To this end, we propose a novel framework, named as LATTE, to pre-train each core module in CRS (i.e., the recommendation and the conversation module) through abundant external data. For the recommendation module, we pre-train the recommendation module to comprehensively understand the relationships between items and their various features by leveraging both multi-reviews and a knowledge graph. For pre-training the conversation module, we create the synthetic dialogs, which contain responses providing the explanation relevant to the dialog context by using all the items' features and dialog templates. Through extensive experiments on two public CRS datasets, we demonstrate that LATTE exhibits (1) the effectiveness of each module in LATTE, (2) the superiority over 7 state-of-the art methods, and (3) the interpretations based on visualization.

The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplored how to design MC recommender systems with GNNs. In light of this, we make the first attempt towards designing a GNN-aided MC recommender system. Specifically, rather than straightforwardly adopting existing GNN-based recommendation methods, we devise a novel criteria preference-aware light graph convolution (CPA-LGC ) method, which is capable of precisely capturing the criteria preference of users as well as the collaborative signal in complex high-order connectivities. To this end, we first construct an MC expansion graph that transforms user-item MC ratings into an expanded bipartite graph to potentially learn from the collaborative signal in MC ratings. Next, to strengthen the capability of criteria preference awareness, CPA-LGC incorporates newly characterized embeddings, including user-specific criteria-preference embeddings and item-specific criterion embeddings, into our graph convolution model. Through comprehensive evaluations using four real-world datasets, we demonstrate (a) the superiority over benchmark MC recommendation methods and benchmark recommendation methods using GNNs with tremendous gains, (b) the effectiveness of core components in CPA-LGC, and (c) the computational efficiency.

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges, such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. OurFiFomethod completes two tasks: 1) it clusters devices based on the unweighed pair group method with the arithmetic average and 2) it creates the main axis and subaxes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority ofFiFoby using a real-world data set. Through intensive and comprehensive simulations, we show that the proposedFiFomethod outperforms benchmark algorithms in terms of the forwarding efficiency.

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose [Formula: see text], a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, [Formula: see text] first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into [Formula: see text] to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that [Formula: see text] consistently outperforms state-of-the-art NA methods.

Network alignment (NA) is the task of finding the correspondence of nodes between two networks. Since most existing NA methods have attempted to discover every node pair at once, they may fail to utilize node pairs that have strong consistency across different networks in the NA task. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of either node pairs exhibiting strong consistency or prior matching information. Specifically, the proposed method gradually aligns nodes based on both the similarity of embeddings generated using graph neural networks (GNNs) and the Tversky similarity, which is an asymmetric set similarity using the Tversky index applicable to networks with different scales. Experimental evaluation demonstrates that Grad-Align consistently outperforms state-of-the-art NA methods in terms of the alignment accuracy. Our source code is available at https://github.com/jindeok/Grad-Align.

Aside the high performance of graph neural networks (GNNs), considerable attention has recently been paid to explanations of black-box deep learning models. Unlike most studies focusing on model explanations based on a specific graph instance, we propose Prototype-bAsed GNN-Explainer (PAGE), a novel model-level explanation method for graph-level classification that explains what the underlying model has learned by providing human-interpretable prototypes. Specifically, our method performs clustering on the embedding space of the underlying GNN model; extracts embeddings in each cluster; and discovers prototypes, which serve as model explanations, by estimating the maximum common subgraph (MCS) from the extracted embeddings. Experimental evaluation demonstrates that PAGE not only provides high-quality explanations but also outperforms the state-of-the-art model-level method in terms of consistency and faithfulness that are performance metrics for quantitative evaluations.

The discovery of community structures in social networks has gained considerable attention as a fundamental problem for various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffective without costly data acquisition. To tackle this challenge, we present META-CODE, a novel end-to-end solution for detecting overlapping communities in networks with unknown topology via exploratory learning aided by easy-to-collect node metadata. Specifically, META-CODE consists of three steps: 1) initial network inference, 2) node-level community-affiliation embedding based on graph neural networks (GNNs) trained by our new reconstruction loss, and 3) network exploration via community-affiliation-based node queries, where Steps 2 and 3 are performed iteratively. Experimental results demonstrate that META-CODE exhibits (a) superiority over benchmark methods for overlapping community detection, (b) the effectiveness of our training model, and (c) fast network exploration.

In a news recommender system, a user tends to click on a news article if she is interested in its topic understood by looking at its title. Such a behavior is possible since, when viewing the title, humans naturally think of the contextual meaning of each title word by leveraging their own background knowledge. Motivated by this, we propose a novel personalized news recommendation framework CAST (Context-aware Attention network with a Selection module for Title word representation), which is capable of enriching title words by leveraging body text that fully provides the whole content of a given article as the context. Through extensive experiments, we demonstrate (1) the effectiveness of core modules in CAST, (2) the superiority of CAST over 9 state-of-the-art news recommendation methods, and (3) the interpretability with CAST.

Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their satisfactory performance is not without prior anchor link information and/or node attributes, which may not always be available. In this paper, we propose Grad-Align+, a novel NA method using node attribute augmentation that is quite robust to the absence of such additional information. Grad-Align+ is built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers only a part of node pairs until all node pairs are found. Specifically, Grad-Align+ is composed of the following key components: 1) augmenting node attributes based on nodes' centrality measures, 2) calculating an embedding similarity matrix extracted from a graph neural network into which the augmented node attributes are fed, and 3) gradually discovering node pairs by calculating similarities between cross-network nodes with respect to the aligned cross-network neighbor-pair. Experimental results demonstrate that Grad-Align+ exhibits (a) superiority over benchmark NA methods, (b) empirical validation of our theoretical findings, and (c) the effectiveness of our attribute augmentation module.

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multidevice terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporateboth rate and transmission distanceinto the objective function of our problem formulation with key features of THz bands, including the spreading and molecular absorption losses. More specifically, as a performance metric of Tera-IoT networks, we adopt the transport capacity (TC), which is defined as the sum of therate–distance productsover all users. This metric has been widely adopted in large-scale ad hoc networks and would also be appropriate for evaluating the performance of various Tera-IoT applications. We then formulate an optimization problem that aims at maximizing the TC. Moreover, motivated by the importance of the transmission distance that is very limited due to the high path loss in THz bands, our optimization problem is extended to the case of allocating the subwindow, transmit power, and transmission distance. We show how to solve our problems via an effective two-stage resource allocation strategy. We demonstrate the superiority of our adaptive solution over benchmark methods via intensive numerical evaluations for various environmental setups of large-scale Tera-IoT networks.

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missing parts of the networks by performing network completion should precede downstream applications. However, despite this need, the recovery of missing nodes and edges in such incomplete networks is an insufficiently explored problem due to the modeling difficulty, which is much more challenging than link prediction that only infers missing edges. In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs. Specifically, our method first learns a likelihood over edges via an autoregressive generative model, and then identifies the graph that maximizes the learned likelihood conditioned on the observable graph topology. Moreover, we propose a computationally efficient [Formula: see text] algorithm that consecutively finds individual nodes that maximize the probability in each node generation step, as well as an enhanced version using the expectation-maximization algorithm. The runtime complexities of both algorithms are shown to be almost linear in the number of nodes in the network. We empirically demonstrate the superiority of DeepNC over state-of-the-art network completion approaches.

The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks’ topology and functions. However, most social network data are collected from partially observable networks with both missing nodes and edges . In this article, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong to multiple communities at once. To solve this problem, we introduce KroMFac , a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. Specifically, from an inferred Kronecker generative parameter matrix, we first estimate the missing part of the network. As our major contribution to the proposed framework, to improve community detection accuracy, we then characterize and select influential nodes (which tend to have high degrees) by ranking, and add them to the existing graph. Finally, we uncover the community structures by solving the regularized NMF-aided optimization problem in terms of maximizing the likelihood of the underlying graph. Furthermore, adopting normalized mutual information (NMI), we empirically show superiority of our KroMFac approach over two baseline schemes by using both synthetic and real-world networks.

Relation prediction is a fundamental task in network analysis which aims to predict the relationship between two nodes. Thus, this differes from the traditional link prediction problem predicting whether a link exists between a pair of nodes, which can be viewed as a binary classification task. However, in the heterogeneous information network (HIN) which contains multiple types of nodes and multiple relations between nodes, the relation prediction task is more challenging. In addition, the HIN might have missing relation types on some edges and missing node types on some nodes, which makes the problem even harder.

This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm. Moreover, we analyze the corresponding level of the approximation that our greedy algorithm can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters.

In this paper, we present CR-Graph (community reinforcement on graphs), a novel method that helps existing algorithms to perform more-accurate community detection (CD). Toward this end, CR-Graph strengthens the community structure of a given original graph by adding non-existent predicted intra-community edges and deleting existing predicted inter-community edges. To design CR-Graph, we propose the following two strategies: (1) predicting intra-community and inter-community edges (i.e., the type of edges) and (2) determining the amount of edges to be added/deleted. To show the effectiveness of CR-Graph, we conduct extensive experiments with various CD algorithms on 7 synthetic and 4 real-world graphs. The results demonstrate that CR-Graph improves the accuracy of all underlying CD algorithms universally and consistently.

We study multi-stream transmission in the K x N x K channel with interfering relay nodes, consisting of K multi-antenna source-destination (S-D) pairs and N single-antenna half-duplex relay nodes between the S-D pairs. We propose a new achievable scheme operating with partial effective channel gain, termed multi-stream opportunistic network decoupling (MS-OND), which achieves the optimal degrees of freedom (DoF) under a certain relay scaling law. Our protocol is built upon the conventional OND that leads to virtual full-duplex mode with one data stream transmission per S-D pair, generalizing the idea of OND to multi-stream scenarios by leveraging relay selection and interference management. Specifically, two subsets of relay nodes are opportunistically selected using alternate relaying in terms of producing or receiving the minimum total interference level. For interference management, each source node sends S (1 <; S <; M) data streams to selected relay nodes with random beamforming for the first hop, while each destination node receives its desired S streams from the selected relay nodes via opportunistic interference alignment for the second hop, where M is the number of antennas at each source or destination node. Our analytical results are validated by numerical evaluation.

A large-scale content-centric mobile ad hoc network employing subpacketization is studied in which each mobile node having finite-size cache moves according to the reshuffling mobility model and requests a content object from the library independently at random according to the Zipf popularity distribution. Instead of assuming that one content object is transferred in a single time slot, we consider a more challenging scenario where the size of each content object is considerably large and thus only a subpacket of a file can be delivered during one time slot, which is motivated by a fast mobility scenario. Under our mobility model, we consider a single-hop-based content delivery and characterize the fundamental tradeoffs between throughput and delay. The order-optimal throughput-delay tradeoff is analyzed by presenting the following two content reception strategies: the sequential reception for uncoded caching and the random reception for maximum distance separable (MDS)-coded caching. We also perform numerical evaluation to validate our analytical results. In particular, we conduct performance comparisons between the uncoded caching and the MDS-coded caching strategies by identifying the regimes in which the performance difference between the two caching strategies becomes prominent with respect to system parameters such as the Zipf exponent and the number of subpackets. In addition, we extend our study to the random walk mobility scenario and show that our main results are essentially the same as those in the reshuffling mobility model.

In this paper, we introduce a network-decomposed hierarchical cooperation (HC) protocol and completely characterize the corresponding throughput-delay tradeoff for a large wireless ad hoc network formed in the context of social relationships. Instead of randomly picking source-destination pairings, we first consider a distance-based social formation model characterized by the social group density γ and the number of social contacts per node q where the probability that any two nodes in distance d away from each other are socially connected is assumed to be proportional to d-γ, which is a feasible scenario. Then, using muiltihop and network-decomposed HC protocols under our social formation model, we analyze a generalized throughput-delay tradeoff according to the operating regimes with respect to parameters γ and q in both a dense network of unit area and an extended network of unit node density via a non-straightforward network transformation strategy. Our main results reveal that as γ increases, performance on the throughput-delay tradeoff can remarkably be improved, compared to the network case with no social relationships. It is also shown that in the dense network, the network-decomposed HC protocol always outperforms the multihop protocol, while the superiority of the network-decomposed HC depends on γ and the path-loss exponent in the extended network.

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, where it can discover multiple clusters with arbitrary shapes. DBSCAN works properly when the input data type is homogeneous, but the DBSCAN's approach may not be sufficient when the input dataset has textual heterogeneity (e.g., when we intend to find clusters from geo-tagged posts on social media relevant to a certain point-of-interest (POI)), thus leading to poor performance. In this paper, we present DBSTexC, a new density-based clustering algorithm using spatio--textual information on Twitter. We first define POI-relevant and POI-irrelevant tweets as the records that contain and do not contain a POI name or its coherent variations, respectively. By taking into account the fractions of POI-relevant and POI-irrelevant tweets, our DBSTexC algorithm shows a much higher clustering quality than the DBSCAN case in terms of the F1 score and its variants. DBSTexC can be thought of as a generalized version of DBSCAN due to the findings that it performs identically as DBSCAN when the inputs are homogeneous and far outperforms DBSCAN when the heterogeneous input data type is given.

Due to the difficulty of coordination in multi-cell random access, it is a practical challenge how to achieve the optimal throughput with decentralized transmission. In this paper, we propose a decentralized multi-cell aware opportunistic random access (MA-ORA) protocol that almost achieves the optimal throughput scaling in a multi-cell random access network with one access point (AP) and multiple users in each cell. Under our MA-ORA protocol, each user opportunistically transmits with a predefined physical layer data rate if the desired signal power to the serving AP is sufficiently large and the generating interference power is sufficiently small. As a main result, it is shown that the proposed MA-ORA protocol can achieve both the almost full multiplexing and multiuser diversity gains in a high signal-to-noise ratio regime if the number of users scales faster than a certain level. Our results are validated by computer simulations.

We propose a decentralized multi-cell aware opportunistic random access (MA-ORA) protocol that almost achieves the optimal throughput scaling in a K-cell random access network with one access point (AP) and N users in each cell. Under our MA-ORA protocol, users opportunistically transmit with a predefined physical layer data rate in a decentralized manner if the desired signal power to the serving AP is sufficiently large and the generating interference power to other APs is sufficiently small. It is proved that the aggregate throughput scales as k/e (1 - ϵ)log(SNR log N) in a high signal-to-noise ratio (SNR) regime if N scales faster than SNR (k-1)/(1-δ) for small constants ϵ,δ > 0. Our analytical result is validated by computer simulations.

We introduce a new achievability scheme, termed opportunistic network decoupling (OND), operating in virtual full-duplex mode. In the scheme, a novel relay scheduling strategy is utilized in the K × N × K channel with interfering relays, consisting of K source-destination pairs and N half-duplex relays in-between them. A subset of relays using alternate relaying is opportunistically selected in terms of producing the minimum total interference level, thereby resulting in network decoupling. As our main result, it is shown that under a certain relay scaling condition, the OND protocol achieves K degrees of freedom even in the presence of interfering links among relays. Numerical evaluation is also shown to validate the performance of the proposed OND. Our protocol basically operates in a fully distributed fashion along with local channel state information, thereby resulting in relatively easy implementation.

Throughput scaling laws of an ad hoc network equipping directional antennas at each node are analyzed. More specifically, this paper considers a general framework in which the beam width of each node can scale at an arbitrary rate relative to the number of nodes. We introduce an elastic routing protocol, which enables to increase per-hop distance elastically according to the beam width, while maintaining an average signal-to-interference-and-noise ratio at each receiver as a constant. We then identify fundamental operating regimes characterized according to the beam width scaling and analyze throughput scaling laws for each of the regimes. The elastic routing is shown to achieve a much better throughput scaling law than that of the conventional nearest-neighbor multihop for all operating regimes. The gain comes from the fact that more source-destination pairs can be simultaneously activated as the beam width becomes narrower, which eventually leads to a linear throughput scaling law. In addition, our framework is applied to a hybrid network consisting of both wireless ad hoc nodes and infrastructure nodes. As a result, in the hybrid network, we analyze a further improved throughput scaling law and identify the operating regime where the use of directional antennas is beneficial. In addition, we perform numerical evaluation in both ad hoc and hybrid networks, which completely validates our analytical results.

In this paper, we propose an opportunistic downlink interference alignment (ODIA) for interference-limited cellular downlink, which intelligently combines user scheduling and downlink IA techniques. The proposed ODIA not only efficiently reduces the effect of inter-cell interference from other-cell base stations (BSs) but also eliminates intra-cell interference among spatial streams in the same cell. We show that the minimum number of users required to achieve a target degrees-of-freedom can be fundamentally reduced, i.e., the fundamental user scaling law can be improved by using the ODIA, compared with the existing downlink IA schemes. In addition, we adopt a limited feedback strategy in the ODIA framework, and then analyze the number of feedback bits required for the system with limited feedback to achieve the same user scaling law of the ODIA as the system with perfect channel state information. We also modify the original ODIA in order to further improve the sum-rate, which achieves the optimal multiuser diversity gain, i.e., log log N, per spatial stream even in the presence of downlink inter-cell interference, where N denotes the number of users in a cell. Simulation results show that the ODIA significantly outperforms existing interference management techniques in terms of sum rate in realistic cellular environments. Note that the ODIA operates in a non-collaborative and decoupled manner, i.e., it requires no information exchange among BSs and no iterative beamformer optimization between BSs and users, thus leading to an easier implementation.

In this paper, we propose a novel user scheduling that achieves the optimal multi-user diversity gain in multi-cell uplink networks with multiple eavesdroppers. In the proposed scheduling, each base station (BS) selects a certain user based on two pre-determined thresholds (i.e., scheduling criteria). The first threshold is related to the amount of generating interference of the users to other BSs and the second threshold is related to the maximum amount of information overheard by the eavesdroppers in the network. Simulation results show that the proposed scheduling significantly outperforms the other scheduling algorithms in terms of secrecy throughput. Note that the proposed scheduling can operate with a distributed manner when time division duplex is adopted, i.e., no coordination among different BSs is needed.

This paper studies a decentralized power allocation for uplink random access of secondary users in cognitive network with successive interference cancellation. First, for the additive white Gaussian noise channel, we propose a novel algorithm, which enables to obtain discrete power levels and optimize the corresponding probabilities, iterating per-level from the lowest power level to the highest one. Under a fading environment, we further propose an opportunistic transmission protocol to reduce the interference temperature at the primary base station, by allocating powers only to the secondary users who will result in small interference. The proposed algorithms are verified via computer simulations.

We analyze the optimal throughput-delay trade-off in content-centric mobile heterogeneous networks (HetNets), where each node moves according to the random walk mobility model and requests a content object from the library independently at random, according to a Zipf popularity distribution. Instead of allowing access to all content objects at base stations (BSs) via costly backhaul, we consider a more practical scenario where mobile nodes and BSs, each having a finite-size cache space, are able to cache a subset of content objects so that each request is served by other mobile nodes or BSs via multihop transmissions. Under the protocol model, we characterize a fundamental throughput-delay trade-off in terms of scaling laws by introducing our content delivery routing protocol and the corresponding optimal caching allocation strategy.

The cost-effective impact and fundamental limits of infrastructure support with rate-limited wired backhaul links (i.e., GreenInfra support), directly connecting base stations (BSs), are analyzed in a large-scale hybrid network of unit node density, where multiantenna BSs are deployed. We consider a general scenario such that the rate of each BS-to-BS link scales at an arbitrary rate relative to the number of randomly located wireless nodes n. For the operating regimes with respect to the number of BSs and the number of antennas at each BS, we first analyze the minimum rate of each backhaul link CBS, required to guarantee the same throughput scaling as in the infinite-capacity backhaul link case. We then identify the operating regimes in which the required rate CBS scales slower than nϵfor an arbitrarily small ϵ>0 (i.e., the regimes where CBS does not need to be infinitely large). We also show the case where our network with GreenInfra is fundamentally in the infrastructure-limited regime, in which the performance is limited by the rate of backhaul links. In addition, we derive a generalized throughput scaling law including the case where the rate of each backhaul link scales slower than CBS. To validate the throughput scaling law for finite values of system parameters, numerical evaluation is also shown via computer simulations.

In this paper, we introduce a large-scale 3D erasure network, where n wireless nodes are randomly distributed in a cuboid of nλ× nμ× nνwith λ + μ + ν = 1 for λ, μ, ν > 0, and completely characterize its capacity scaling laws. Two fundamental path-loss attenuation models (i.e., exponential and polynomial power-law models) are used to suitably model an erasure probability for packet transmission. Then, under the two erasure models, we introduce a routing protocol using percolation highway in 3D space, and then analyze its achievable throughput scaling laws. It is shown that, under the two erasure models, the aggregate throughput scaling nmin{1-λ,1-μ,1-ν}can be achieved in the 3D erasure network. This implies that the aggregate throughput scaling n2/3can be achieved in 3D cubic erasure networks, while √n can be achieved in 2D square erasure networks. The gain comes from the fact that, compared with 2D space, more geographic diversity can be exploited via 3D space, which means that generating more simultaneous percolation highways is possible. In addition, cut-set upper bounds on the capacity scaling are derived to verify that the achievable scheme based on the 3D percolation highway is order-optimal within a polylogarithmic factor under certain practical operating regimes on the decay parameters.

This study analyzes friendships in online social networks involving geographic distance with a geo-referenced Twitter dataset, which provides the exact distance between corresponding users. We start by introducing a strong definition of "friend" on Twitter, requiring bidirectional communication. Next, by utilizing geo-tagged mentions delivered by users to determine their locations, we introduce a two-stage distance estimation algorithm. As our main contribution, our study provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides much more fine-grained social ties in space, compared to the conventional results showing a homogeneous power-law with distance.

Opportunistic interference alignment (OIA) is known to achieve the optimal degrees-of-freedom (DoF) in the interfering multiple-access channel (IMAC) with independent and identically distributed (i.i.d.) Rayleigh fading, provided that a certain user scaling condition is satisfied. We analyze the performance of OIA in a poor scattering K-cell single-input multiple-output IMAC, where there exist finite paths between the transmitter and receiver sides. Under the feasible model, we derive a new fundamental user scaling law, required to achieve a target DoF, which generalizes the existing achievability result shown for the i.i.d. Rayleigh fading case. Our main result indicates that KS DoF is achievable if the number of per-cell mobile stations (MSs) scales at least as SNR(K-1)min(L,S), where L denotes the number of paths and S denotes the number of simultaneously transmitting MSs per cell. To verify our achievability result for finite system parameters, computer simulations are performed along with comparison to the i.i.d. Rayleigh channel case. The amount of leakage of interference is numerically evaluated and is shown to be consistent with our theoretical result. The achievable sum-rates are also evaluated.

Opportunistic interference alignment (OIA) has been known to asymptotically achieve the optimal degrees-of-freedom (DoF) in multi-input multi-output (MIMO) interfering multiple-access channels (IMACs) as the number of users scales with signal-to-noise ratio, even though no collaboration between base stations (BSs) is assumed. In some previous studies on OIA, the zero-forcing (ZF) receiver has been used at the BSs since it is sufficient to achieve the optimal DoF. In this paper, we propose a simple minimum distance (MD) receiver in a MIMO IMAC model, enabling us to implement the OIA scheme with no information of other-cell interfering links. Surprisingly, we show that as the number of users increases, the MD receiver not only guarantees the optimal DoF but also asymptotically achieves the optimal capacity obtained along with full information of other-cell interfering links. Simulation results indicates that the MD receiver indeed outperforms the conventional ZF receiver even in practical cellular setups.

In this paper, we propose an opportunistic interference alignment (OIA) technique for cellular downlink networks, which efficiently reduces the effect of inter-cell interference from base stations (BSs) in other cells and eliminates intra-cell interference among spatial streams in the same cell. We show that the user scaling per cell required to achieve a target degrees-of-freedom can be fundamentally lowered, compared with the previous results. In addition, we relate the derived user scaling law to the interference decaying rate with respect to the number of users for given signal-to-noise ratio. Simulation results show that the proposed OIA significantly outperforms the previous schemes in terms of both sum-interference and achievable sum-rate even in practical environments.

We introduce a new achievability scheme, termed opportunistic network decoupling (OND), where a novel relay scheduling strategy is utilized in the K × N × K channel with interfering relays, consisting of K source - destination pairs and N half-duplex relays in-between them. A subset of relays using alternate relaying is opportunistically selected in terms of producing the minimum total interference level, thereby resulting in network decoupling. As our main result, it is shown that under a certain relay scaling condition, the OND protocol with alternate half-duplex relaying achieves K degrees-of-freedom even in the presence of interfering links among relays. Numerical evaluation is also shown to validate the performance of the proposed OND scheme.

Capacity scaling of a large hybrid network with unit node density, consisting of wireless ad hoc nodes, base stations (BSs) equipped with multiple antennas, and one remote central processor (RCP), is analyzed when wired backhaul links between the BSs and the RCP are rate-limited. We first derive the minimum backhaul link rate required to achieve the same capacity scaling law as in the infinite-capacity backhaul link case. Assuming an arbitrary rate scaling of each backhaul link, a generalized achievable throughput scaling law is then analyzed in the network based on using one of pure multihop, hierarchical cooperation, and two infrastructure-supported routing protocols, and moreover, information-theoretic operating regimes are identified. In addition, to verify the order optimality of our achievability result, a generalized cut-set upper bound under the network model is derived by cutting not only the wireless connections but also the wired connections.

We introduce an opportunistic downlink interference alignment (ODIA) for interference-limited cellular downlink, which intelligently combines user scheduling and downlink IA techniques. The proposed ODIA not only efficiently reduces the effect of inter-cell interference from other-cell base stations (BSs) but also eliminates intra-cell interference among spatial streams in the same cell. We show that compared to the existing downlink IA schemes, the minimum number of users required to achieve a target degrees-of-freedom (DoF) can be fundamentally reduced, i.e., the fundamental user scaling law can be improved, by using the ODIA. In addition, we introduce a limited feedback strategy in our ODIA framework, and then analyze the minimum number of feedback bits required to obtain the same performance as that of the ODIA assuming perfect feedback.

Throughput scaling law of a large wireless network equipping directional antennas at each node is analyzed based on the information-theoretic approach. More specifically, this paper considers a general framework in which the beamwidth of each node can scale at an arbitrary rate relative to the number of nodes in the network. We introduce an elastic routing protocol, which enables to increase per-hop distance elastically according to the beamwidth, while maintaining an average signal-to-interference-and-noise ratio at each receiver as a constant. This elastic routing is shown to achieve a much better throughput scaling law than that of the conventional nearest-neighbor multihop routing. The gain comes from the fact that more source-destination pairs can be activated simultaneously as the beamwidth becomes narrower, which eventually leads to a linear throughput scaling law.

Due to the difficulty of coordination in cellular uplink networks, it is a practical challenge to achieve better throuhgput with a distributed scheduling. Futhermore, multiple antennas at mobile stations (MSs) can be utilized for reducing interference or for improving the desired signal strength. In this paper, we investigate a joint design of beamforming and user scheduling for multi-cell multiple input multiple output (MIMO) uplink networks. In the proposed scheme, each BS with M antennas adopts M random beamforming techniques and each MS with L antennas utilizes a single beamforming vector which minimizes the sum power of generating interferences to home cell as well as other cells. In each cell, then, the BS selects M MSs such that both sufficiently large desired signal power and sufficiently small generating interference are guaranteed. Numerical results show that the proposed scheme outperforms the existing distributed schemes in terms of sum-rate in practical environments.

We consider the problem of energy-efficient base-station (BS) planning for green cellular network design. There exist a number of criteria for greenness in the literature, but we focus only on the energy-normalized throughput. We first model energy consumption for a heterogeneous network, consisting of macro and micro base-stations, and a network topology. Then, we propose an iterative algorithm for the green BS planning problem in the one-dimensional case: 1) We find the positions of base-stations that maximize the energy-normalized throughput for a fixed number of base-stations, and 2) find the optimal number of base-stations which maximizes the energy-normalized throughput. In this work, we evaluate the energy-normalized throughput accounting for adaptive modulation rates with power control, which is a common feature of modern communications systems. The convergence to a local maximum for the proposed algorithm is shown using computer simulations, and the corresponding energy-normalized throughputs are evaluated for a number of system model parameters.

In this paper, we focus on a X channel; a system with two transmitters and two receivers each equipped with a single antenna, where independent messages need to be conveyed over channels from each transmitter to each receiver. A key concept of that arises in the context of the X channel is interference alignment (IA) that refers to an overlap of signal spaces occupied by undesired interference at each receiver while keeping desired signal spaces distinct. However, all the channel responses and the precise time synchronization are required at every transmitter and receiver to realize the IA, which are seriously demanding in practice. In this paper, we propose semi-blind IA (S-BIA) scheme exploiting frequency selectivity of wireless channels using orthogonal frequency division multiplexing (OFDM), which only requires channel responses corresponding to each transmitter. Numerical results confirm that proposed S-BIA can achieve the same degrees of freedom as conventional IA even in the presence of time synchronization errors.

The impact and information-theoretic limits of infrastructure support with rate-limited wired links are analyzed in hybrid ad hoc networks, where multi-antenna base stations (BSs) are deployed and the rate of each BS-to-BS link scales at an arbitrary rate relative to the number of randomly located wireless nodes. For the operating regimes with respect to the number of BSs and the number of antennas per BS, we first analyze the minimum rate of each BS-to-BS link, Cbs, required to guarantee the capacity scaling for the network using infinite-capacity backhaul links. We then identify the operating regimes in which the required rate CBs scales much slower than 1. We also show the achievable throughput scaling for the case where the rate of each BS-to-BS link scales lower than CBs.

We study benefits of opportunistic routing in a large wireless ad hoc network by examining how the power, delay, and total throughput scale as the number of source-destination pairs increases up to the operating maximum. Our opportunistic routing is novel in a sense that it is massively parallel, i.e., it is performed by many nodes simultaneously to maximize the opportunistic gain while controlling the interuser interference. The scaling behavior of conventional multihop transmission that does not employ opportunistic routing is also examined for comparison. Our main results indicate that our opportunistic routing can exhibit a net improvement in overall power-delay tradeoff over the conventional routing by providing up to a logarithmic boost in the scaling law. Such a gain is possible since the receivers can tolerate more interference due to the increased received signal power provided by the multi user diversity gain, which means that having more simultaneous transmissions is possible.

In this paper, we propose a new simple relaying strategy based on bit-interleaved convolutionally coded star-quadrature amplitude modulation (QAM) along with coherent/noncoherent detection. Star-QAM is composed of multiple concentric circles of phase-shift keying (PSK). Exploiting this property, a hard limiter is used to enhance power amplifier (PA) efficiency at the relay. Moreover, we show that the proposed approach retains differential detectability, which results in a significant reduction of receiver complexity with robustness against phase ambiguity. By analyzing our proposed cooperation with coherent/noncoherent detection in terms of asymptotic pairwise error probability (PEP), we show that the full diversity order can be achieved on the condition that the minimum free distance of the convolutional codes is larger than the predetermined value specified by the number of available relays. Furthermore, the effectiveness of the proposed scheme in terms of PA efficiency is confirmed by comparing the statistical distributions of the corresponding instantaneous signal powers. All the theoretical results agree with those obtained by computer simulations.

We consider the K-cell multiple-input multiple-output (MIMO) interfering multiple-access channel (IMAC) with time-invariant channel coefficients, where each cell consists of a base station (BS) with M antennas and N users having L antennas each. In this paper, we propose two opportunistic interference alignment (OIA) techniques utilizing multiple transmit antennas at each user: antenna selection-based OIA and singular value decomposition (SVD)-based OIA. Their performance is analyzed in terms of user scaling law required to achieve KS degrees-of-freedom (DoF), where S(≤ M) denotes the number of simultaneously transmitting users per cell. We assume that each selected user transmits a single data stream at each time-slot. It is shown that the antenna selection-based OIA does not fundamentally change the user scaling condition if L is fixed, compared with the single-input multiple-output (SIMO) IMAC case, which is given by SNR(K-1)S, where SNR denotes the signal-to-noise ratio. In addition, we show that the SVD-based OIA can greatly reduce the user scaling condition to SNR(K-1)S-L+1through optimizing a weight vector at each user. Simulation results validate the derived scaling laws of the proposed OIA techniques. The sum-rate performance of the proposed OIA techniques is compared with the conventional techniques in MIMO IMAC channels and it is shown that the proposed OIA techniques outperform the conventional techniques.

We consider a new opportunistic interference alignment (OIA) for the K-cell multiple-input multiple-output (MIMO) interfering multiple-access channel (IMAC) with time-invariant channel coefficients, where each cell consists of a base station (BS) with M antennas and N mobile stations (MSs) having L antennas each. In this paper, we propose three OIA techniques: antenna selection-based OIA, singular value decomposition (SVD)-based OIA, and vector-quantized (codebook-based) OIA. Then, their performance is analyzed in terms of user scaling law required to achieve KS degrees-of-freedom (DoF), where S(≤ M) denotes the number of simultaneously transmitting MSs per cell. As our main result, it is shown that the antenna selection-based OIA does not fundamentally change the user scaling required to achieve KS DoF if L is fixed, compared with the single-input multiple-output (SIMO) IMAC case. In contrast, it is shown that the SVD-based OIA can greatly reduce the required user scaling to SNR(K-1) S-L+1through optimizing weight vectors at each MS. Furthermore, we show that the vector-quantized OIA can achieve the same user scaling as the SVD-based OIA case if the codebook size is beyond a certain value. For the vector-quantized OIA, we analyze a fundamental tradeoff between the quantization level (i.e., codebook size) and the required user scaling.

We introduce an opportunistic interference mitigation (OIM) protocol, where a user scheduling strategy is utilized in K-cell uplink networks with time-invariant channel coefficients and base stations (BSs) having M antennas. Each BS opportunistically selects a set of users who generate the minimum interference to the other BSs. Two OIM protocols are shown according to the number of simultaneously transmitting users per cell, S: opportunistic interference nulling (OIN) and opportunistic interference alignment (OIA). Then, their performance is analyzed in terms of degrees-of-freedom (DoFs). As our main result, it is shown that KM DoFs are achievable under the OIN protocol with M selected users per cell, if the total number of users in a cell, N, scales at least as SNR(K-1)M. Similarly, it turns out that the OIA scheme with S((K-1)S. These results indicate that there exists a trade-off between the achievable DoFs and the minimum required N. By deriving the corresponding upper bound on the DoFs, it is shown that the OIN scheme is DoF-optimal. Finally, numerical evaluation, a two-step scheduling method, and the extension to multi-carrier scenarios are shown.

We introduce a distributed opportunistic scheduling (DOS) strategy, based on two pre-determined thresholds, for uplink K-cell networks with time-invariant channel coefficients. Each base station (BS) opportunistically selects a mobile station (MS) who has a large signal strength of the desired channel link among a set of MSs generating a sufficiently small interference to other BSs. Then, performance on the achievable throughput scaling law is analyzed. As our main result, it is shown that the achievable sum-rate scales as K log(SNR log N) in a high signal-to-noise ratio (SNR) regime, if the total number of users in a cell, N, scales faster than SNRK-1/1-εfor a constant ε∈(0,1). This result indicates that the proposed scheme achieves the multiuser diversity gain as well as the degrees-of-freedom gain even under multi-cell environments. Simulation results show that the DOS provides a better sum-rate throughput over conventional schemes.

In this paper, a low-complexity routing strategy is introduced for a hybrid network in which n wireless nodes are randomly located and multiple base stations (BSs) are used. We allow different transmission rates according to the geographic location of each source in the network. Our achievable scheme consists of the following two routing schemes: a BS-based single-hop (SH) routing in the up/downlink and conventional multi-hop transmission. Specifically, in the proposed BS-based scheme, when a source-destination (S-D) pair is inside the center region with respect to its BS, the source is allowed to transmit data to its closest BS via SH. Our results indicate that a logarithmic boost (i.e., power gain) is provided in the throughput scaling law, compared to the conventional schemes, as the number of BSs is greater than a certain threshold. The gain comes from the fact that S-D pairs in their center regions can exploit per-node transmission rates log n due to the increased received signal power from/to their closest BS.

This paper analyzes the impact and benefits of infrastructure support in improving the throughput scaling in networks ofnrandomly located wireless nodes. The infrastructure uses multiantenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbitrary rates relative ton. Under the model, capacity scaling laws are analyzed for both dense and extended networks. Two BS-based routing schemes are first introduced in this study: an infrastructure-supported single-hop (ISH) routing protocol with multiple-access uplink and broadcast downlink and an infrastructure-supported multihop (IMH) routing protocol. Then, their achievable throughput scalings are analyzed. These schemes are compared against two conventional schemes without BSs: the multihop (MH) transmission and hierarchical cooperation (HC) schemes. It is shown that a linear throughput scaling is achieved in dense networks, as in the case without help of BSs. In contrast, the proposed BS-based routing schemes can, under realistic network conditions, improve the throughput scaling significantly in extended networks. The gain comes from the following advantages of these BS-based protocols. First, more nodes can transmit simultaneously in the proposed scheme than in the MH scheme if the number of BSs and the number of antennas are large enough. Second, by improving the long-distance signal-to-noise ratio (SNR), the received signal power can be larger than that of the HC, enabling a better throughput scaling under extended networks. Furthermore, by deriving the corresponding information-theoretic cut-set upper bounds, it is shown under extended networks that a combination of four schemes IMH, ISH, MH, and HC is order-optimal in all operating regimes.

Capacity scaling laws are analyzed in an underwater acoustic network with n regularly located nodes. A narrow-band model is assumed where the carrier frequency is allowed to scale as a function of n. In the network, we characterize an attenuation parameter that depends on the frequency scaling as well as the transmission distance. A cut-set upper bound on the throughput scaling is then derived in extended networks. Our result indicates that the upper bound is inversely proportional to the attenuation parameter, thus resulting in a highly power-limited network. Furthermore, we describe an achievable scheme based on the simple nearest-neighbor multi-hop (MH) transmission. It is shown under extended networks that the MH scheme is order-optimal as the attenuation parameter scales exponentially with √n (or faster). Finally, these scaling results are extended to a random network realization.

We introduce a multiuser (MU) scheduling method for amplify-and-forward (AF) relay systems, which opportunistically selects both the transmission mode, i.e., either one- or two-hop transmission, and the desired user. A closed-form expression for the average achievable rates is derived under two transmissions with MU scheduling, and its asymptotic solution is also analyzed in the limit of large number N of mobile stations (MSs). Based on the analysis, we perform our two-step scheduling algorithm: the transmission mode selection followed by the user selection that needs partial feedback for instantaneous signal-to- noise ratios (SNRs) to the base station (BS). We also analyze the average SNR condition where the MU diversity gain is fully exploited. In addition, it is examined how to further reduce a quantity of feedback under certain conditions. The proposed scheduling algorithm shows the comparable achievable rates to those of optimal one using full feedback information, while its required feedback information is reduced below half of the optimal one.

We analyze the benefits of infrastructure support in improving the throughput scaling in networks of n randomly located wireless nodes. The infrastructure uses multi-antenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbtrary rates relative to n. We introduce two multi-antenna BS-based routing protocols and analyze their throughput scaling laws. Two conventional schemes not using BSs are also shown for comparison. In dense networks, we show that the BS-based routing schemes do not improve the throughput scaling. In contrast, in extended networks, we show what our BS-based routing schemes can, under certain network conditions, improve the throughput scaling significantly.

In this paper, we analyze the diversity-multiplexing tradeoff (DMT), originally introduced by Zheng and Tse, and outage performance for Rician multiple-input-multiple-output (MIMO) channels. The DMT characteristics of Rayleigh and Rician channels are shown to be identical. In a high signal-to-noise ratio (SNR) regime, the log-log plot of outage probability versus SNR curve for a Rician channel is a shifted version of that for the corresponding Rayleigh channel. The SNR gap between the outage curves of the Rayleigh and Rician channels is derived. The DMT and outage performance are also analyzed for Rician multiple-input-single-output (MISO)/single-input-multiple-output (SIMO) channels over a finite SNR regime. A closed-form expression for the outage probability is derived and the finite SNR DMT characteristic is analyzed. It is observed that the maximum diversity gain can be achieved at some finite SNR-the maximum gain tends to increase linearly with the Rician factor. The finite SNR diversity gain is shown to be a linear function of the finite SNR multiplexing gain. The consistency between the DMTs for finite and infinite SNRs is also shown.

We study the benefits of opportunistic routing in wireless networks by examining how the power and delay scale as the number of source-destination (S-D) pairs increases, where S-D pairs are randomly located over the network. The scaling behavior of conventional multi-hop transmission that does not employ opportunistic routing is also examined. The results indicate that the opportunistic routing can exhibit better power- delay trade-off than the conventional routing while providing up to a logarithmic boost in the scaling law. The gain comes from the fact that the system with opportunistic routing can tolerate more interference due to increased received signal power from utilizing the multi-user diversity gain. Furthermore, we derive an upper bound on the total throughput using the cut-set theorem. It is shown that the achievable rates of the conventional and opportunistic routing schemes become close to the upper bound when the number of S-D pairs is large enough.

We analyze the outage performance and diversity-multiplexing tradeoff (DMT), originally introduced by Zheng and Tse, for multiple antenna Rician channels or channels with partial channel state information at the transmitter (CSIT). The asymptotic behaviors of the outage are analyzed in the limit of high signal-to-noise ratio (SNR). We also analyze the SNR where the maximum diversity order (MDO) is obtained for Rician channels, which serves as a desired operation point. In addition, by applying the outage analysis for Rician channels we present the differential DMT (DDMT) to develop a better understanding of the asymptotic relationship between the outage probability, transmission rate, SNR, and Rician factor

Presented in this paper is a study of the capacity evaluation of various multiuser MIMO schemes in cellular environments. The throughputs per user of the generalized zero-forcing with rank adaptation and vector perturbation schemes are compared with the capacity bound of the Gaussian MIMO broadcast channel, obtained by dirty paper coding under proportional fairness scheduling. The average cell throughputs of these schemes are also compared. From these comparisons, this study provides vital information for applying multiuser MIMO schemes in multicell environments