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Won-Yong Shin

Won-Yong Shin appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Information Theorymath.ITNetworking and Internet ArchitectureArtificial IntelligenceMachine LearningSocial and Information NetworksDistributed, Parallel, and Cluster ComputingInformation RetrievalNeural and Evolutionary Computingphysics.comp-phComputation and LanguageComputational Engineering, Finance, and Scienceeess.SYMultiagent SystemsMultimediaphysics.app-phphysics.atom-phphysics.chem-phphysics.data-anSystems and Control

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Published work

29 published item(s)

preprint2026arXiv

Hyperbolic Heterogeneous Graph Transformer

In heterogeneous graphs, we can observe complex structures such as tree-like or hierarchical structures. Recently, the hyperbolic space has been widely adopted in many studies to effectively learn these complex structures. Although these methods have demonstrated the advantages of the hyperbolic space in learning heterogeneous graphs, most existing methods still have several challenges. They rely heavily on tangent-space operations, which often lead to mapping distortions during frequent transitions. Moreover, their message-passing architectures mainly focus on local neighborhood information, making it difficult to capture global hierarchical structures and long-range dependencies between different types of nodes. To address these limitations, we propose Hyperbolic Heterogeneous Graph Transformer (HypHGT), which effectively and efficiently learns heterogeneous graph representations entirely within the hyperbolic space. Unlike previous message-passing based hyperbolic heterogeneous GNNs, HypHGT naturally captures both local and global dependencies through transformer-based architecture. Furthermore, the proposed relation-specific hyperbolic attention mechanism in HypHGT, which operates with linear time complexity, enables efficient computation while preserving the heterogeneous information across different relation types. This design allows HypHGT to effectively capture the complex structural properties and semantic information inherent in heterogeneous graphs. We conduct comprehensive experiments to evaluate the effectiveness and efficiency of HypHGT, and the results demonstrate that it consistently outperforms state-of-the-art methods in node classification task, with significantly reduced training time and memory usage.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations

Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine meshes, leading to substantial computational overhead. Super-resolution techniques aim to mitigate this cost by reconstructing high-resolution (HR), high-fidelity solutions from low-cost, low-resolution (LR) counterparts. However, training neural networks for super-resolution often demands large amounts of expensive HR supervision data. To address this challenge, we propose SuperMeshNet, an HR data-efficient super-resolution framework for mesh-based simulations aided by message passing neural networks (MPNNs). At its core, SuperMeshNet introduces complementary learning, a semi-supervised approach that effectively leverages both 1) a small amount of paired LR-HR data and 2) abundant unpaired LR data via two jointly trained, complementary MPNN-based models. Additionally, our model is enriched by inductive biases, which are empirically shown to further improve super-resolution performance. Extensive experiments demonstrate that SuperMeshNet requires 90% less HR data to achieve even lower root mean square error (RMSE) than that of the fully supervised benchmark without the inductive biases. The source code and datasets are available at https://github.com/jykim-git/SuperMeshNet.git.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor

Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate prediction from static atomic structures challenging. The current standard approach, molecular dynamics (MD) simulations, suffers from prohibitively high computational cost. Recent autoregressive learning-based MD acceleration methods requiring sequential inference remain slow and prone to error accumulation; in contrast, existing non-autoregressive material property prediction models are less accurate because they fail to exploit dynamics. Moreover, existing methods typically benefit from datasets either with or without atomic trajectories, but not both. To overcome these limitations, we propose a non-autoregressive learning framework based on auxiliary modality learning, which treats atomic trajectories as an auxiliary modality during training but does not require them at inference. This enables the predictor to learn dynamics without sequential inference while benefiting from both types of datasets. As a result, our framework achieves over 200 times speedup compared to autoregressive models on the dataset with atomic trajectories while substantially reducing prediction error relative to non-autoregressive benchmarks across both types of datasets. Our code is available at https://github.com/jykim-git/MD.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TRACE: Tourism Recommendation with Accountable Citation Evidence

Tourism is a high-stakes setting for conversational recommender systems (CRS): a plausible-sounding suggestion can waste real money and trip time once a traveler acts on it. Existing CRS benchmarks primarily evaluate systems with a single Recall@k score over entity mentions, and tourism-specific resources add spatial or knowledge-graph context, yet none of them couple multi-turn recommendation with verbatim review-span evidence and rejection recovery. This leaves an evaluation gap for tourism recommendation that is simultaneously trustworthy, verifiable, and adaptive: recommend the right point of interest (POI) for multi-aspect preferences (such as cuisine, price, atmosphere, walking distance), justify each suggestion with verifiable evidence from prior visitors so the traveler can act without trial and error, and recover when the first recommendation is rejected mid-dialogue. We introduce TRACE, where each item is a multi-turn tourism recommendation dialogue with review-span citations and explicit rejection turns: 10,000 dialogues over 2,400 Yelp POIs and 34,208 reviews across eight U.S. cities, paired with 14 retrieval, planning, and LLM baselines, along with 25 metrics organized under Accuracy, Grounding, and Recovery. Across these baselines, TRACE reveals the Three-Competency Gap: LLM Zero-Shot leads in closed-set Recall@1 and rejection recovery but cites less densely than retrievers; non-LLM retrievers achieve surface-verbatim grounding but with low accuracy; Multi-Review Synthesis fails at recovery. The Grounding Score agrees with human citation precision (Spearman rho=+0.80, p<10^-20), and paired t-tests reproduce the per-baseline ranking (p<0.01 on the dominant contrasts). TRACE reframes accountable tourism recommendation as a joint target (right POI, verifiable evidence, adaptive repair) rather than a single-axis leaderboard.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SiReN: Sign-Aware Recommendation Using Graph Neural Networks

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 sign-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 multi-layer 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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Transport Capacity Optimization for Resource Allocation in Tera-IoT Networks

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multi-device terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporate both rate and transmission distance into 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 the rate-distance products over 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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data

We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line. Given heterogeneous time series data consisting of operation cycle signals and sensor signals, we aim at discovering abnormal events. Motivated by our empirical findings that conventional single-stage benchmark approaches may not exhibit satisfactory performance under our challenging circumstances, we propose a two-stage deep anomaly detection (TDAD) framework in which two different unsupervised learning models are adopted depending on types of signals. In Stage I, we select anomaly candidates by using a model trained by operation cycle signals; in Stage II, we finally detect abnormal events out of the candidates by using another model, which is suitable for taking advantage of temporal continuity, trained by sensor signals. A distinguishable feature of our framework is that operation cycle signals are exploited first to find likely anomalous points, whereas sensor signals are leveraged to filter out unlikely anomalous points afterward. Our experiments comprehensively demonstrate the superiority over single-stage benchmark approaches, the model-agnostic property, and the robustness to difficult situations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Personalized Preference Learning Framework for Caching in Mobile Networks

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Interference-Aware Opportunistic Random Access in Dense IoT Networks

It is a challenging task to design a random access protocol that achieves the optimal throughput in multi-cell random access with decentralized transmission due to the difficulty of coordination. In this paper, we present a decentralized interference-aware opportunistic random access (IA-ORA) protocol that enables us to obtain the optimal throughput scaling in an ultra-dense multi-cell random access network with one access point (AP) and a number of users. In sharp contrast to opportunistic scheduling for cellular multiple access where users are selected by base stations, under the IA-ORA protocol, each user opportunistically transmits with a predefined physical layer (PHY) data rate in a decentralized manner if not only the desired signal power to the serving AP is sufficiently large but also the generating interference leakage power to the other APs is sufficiently small (i.e., two threshold conditions are fulfilled). As a main result, it is shown that the optimal aggregate throughput scaling (i.e., the MAC throughput of $\frac{1}{e}$ in a cell and the power gain) is achieved in a high signal-to-noise ratio regime if the number of per-cell users exceeds some level. Additionally, it is numerically demonstrated via computer simulations that under practical settings, the proposed IA-ORA protocol outperforms conventional opportunistic random access protocols in terms of aggregate throughput.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Capacity of Three-Dimensional Erasure Networks

In this paper, we introduce a large-scale three-dimensional (3D) erasure network, where $n$ wireless nodes are randomly distributed in a cuboid of $n^λ\times n^μ\times n^ν$ with $λ+μ+ν=1$ 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 $n^{\min\{1-λ,1-μ,1-ν\}}$ can be achieved in the 3D erasure network. This implies that the aggregate throughput scaling $n^{2/3}$ can be achieved in 3D cubic erasure networks while $\sqrt{n}$ can be achieved in two-dimensional (2D) square erasure networks. The gain comes from the fact that, compared to 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.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Group Communication Over LTE : A Radio Access Perspective

Long Term Evolution (LTE), which has its root on commercial mobile communications, recently becomes an influential solution to future public safety communications. To verify the feasibility of LTE for public safety, it is essential to investigate whether an LTE system optimized for one-to-one communications is capable of providing group communication, which is one of the most important service concepts in public safety. In general, a number of first responders for public safety need to form a group for communicating with each other or sharing the common data for collaboration on their mission. In this article, we analyze how the current LTE system can support group communication in a radio access aspect. Based on the requirements for group communication, we validate whether each LTE-enabled radio access method can efficiently support group communication. In addition, we propose a new multicast transmission scheme, termed index-coded Hybrid Automatic Retransmission reQuest (HARQ). By applying the index coding concept to HARQ operations, we show that the LTE system can provide group communication more sophisticatedly in terms of radio resource efficiency and scalability. We finally evaluate the performance of LTE-enabled group communication using several radio access methods and show how the proposed transmission scheme brings the performance enhancement via system level simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On the Degrees-of-Freedom of the Large-Scale Interfering Two-Way Relay Network

Achievable degrees-of-freedom (DoF) of the large-scale interfering two-way relay network is investigated. The network consists of $K$ pairs of communication nodes (CNs) and $N$ relay nodes (RNs). It is assumed that $K\ll N$ and each pair of CNs communicates with each other through one of the $N$ relay nodes without a direct link between them. Interference among RNs is also considered. Assuming local channel state information (CSI) at each RN, a distributed and opportunistic RN selection technique is proposed for the following three promising relaying protocols: amplify--forward, decode--forward, and compute--forward. As a main result, the asymptotically achievable DoF is characterized as $N$ increases for the three relaying protocols. In particular, a sufficient condition on $N$ required to achieve the certain DoF of the network is analyzed. Through extensive simulations, it is shown that the proposed RN selection techniques outperform conventional schemes in terms of achievable rate even in practical communication scenarios. Note that the proposed technique operates with a distributed manner and requires only local CSI, leading to easy implementation for practical wireless systems.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Opportunistic Network Decoupling With Virtual Full-Duplex Operation in Multi-Source Interfering Relay Networks

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 $K\times N\times K$ 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 a relatively easy implementation.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

A New Understanding of Friendships in Space: Complex Networks Meet Twitter

Studies on friendships in online social networks involving geographic distance have so far relied on the city location provided in users' profiles. Consequently, most of the research on friendships have provided accuracy at the city level, at best, to designate a user's location. This study analyzes a Twitter dataset because it provides the exact geographic distance between corresponding users. We start by introducing a strong definition of "friend" on Twitter (i.e., a definition of bidirectional friendship), requiring bidirectional communication. Next, we utilize geo-tagged mentions delivered by users to determine their locations, where "@username" is contained anywhere in the body of tweets. To provide analysis results, we first introduce a friend counting algorithm. From the fact that Twitter users are likely to post consecutive tweets in the static mode, we also introduce a two-stage distance estimation algorithm. As the first of our main contributions, we verify that the number of friends of a particular Twitter user follows a well-known power-law distribution (i.e., a Zipf's distribution or a Pareto distribution). Our study also 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 concrete evidence that Twitter can be a useful platform for assigning a more accurate scalar value to the degree of friendship between two users.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Clarifying the Role of Distance in Friendships on Twitter: Discovery of a Double Power-Law Relationship

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.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Elastic Routing in Wireless Ad Hoc Networks With Directional Antennas

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.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

GreenInfra: Capacity of Large-Scale Hybrid Networks With Cost-Effective Infrastructure

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 multi-antenna 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, $C_{\textrm{BS}}$, 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 $C_{\textrm{BS}}$ scales slower than $n^ε$ for an arbitrarily small $ε>0$ (i.e., the regimes where $C_{\textrm{BS}}$ 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 $C_{\textrm{BS}}$. To validate the throughput scaling law for finite values of system parameters, numerical evaluation is also shown via computer simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Ad Hoc Networking With Cost-Effective Infrastructure: Generalized Capacity Scaling

Capacity scaling of a large hybrid network with unit node density, consisting of $n$ 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 deal with a general scenario where the number of BSs, the number of antennas at each BS, and the backhaul link rate can scale at arbitrary rates relative to $n$ (i.e., we introduce three scaling parameters). 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, three-dimensional information-theoretic operating regimes are explicitly identified according to the three scaling parameters. In particular, we show the case where our network having a power limitation is also fundamentally in the degrees-of-freedom- or infrastructure-limited regime, or both. In addition, a generalized cut-set upper bound under the network model is derived by cutting not only the wireless connections but also the wired connections. It is shown that our upper bound matches the achievable throughput scaling even under realistic network conditions such that each backhaul link rate scales slower than the aforementioned minimum-required backhaul link rate.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Opportunistic Downlink Interference Alignment

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 (DoF) 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 required number of feedback bits leading to the same performance as that of the ODIA assuming perfect feedback. We also modify the original ODIA in order to further improve 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 distributed and decoupled manner, while requiring no information exchange among BSs and no iterative beamformer optimization between BSs and users, thus leading to an easier implementation.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Codebook-Based Opportunistic Interference Alignment

Opportunistic interference alignment (OIA) asymptotically achieves the optimal degrees-of-freedom (DoF) in interfering multiple-access channels (IMACs) in a distributed fashion, as a certain user scaling condition is satisfied. For the multiple-input multiple-output IMAC, it was shown that the singular value decomposition (SVD)-based beamforming at the users fundamentally reduces the user scaling condition required to achieve any target DoF compared to that for the single-inputmultiple-output IMAC. In this paper, we tackle two practical challenges of the existing SVD-based OIA: 1) the need of full feedforward of the selected users' beamforming weight vectors and 2) a low rate achieved based on the exiting zero-forcing (ZF) receiver. We first propose a codebook-based OIA, in which the weight vectors are chosen from a pre-defined codebook with a finite size so that information of the weight vectors can be sent to the belonging BS with limited feedforward. We derive the codebook size required to achieve the same user scaling condition as the SVD-based OIA case for both Grassmannian and random codebooks. Surprisingly, it is shown that the derived codebook size is the same for the two considered codebook approaches. Second, we take into account an enhanced receiver at the base stations (BSs) in pursuit of improving the achievable rate based on the ZF receiver. Assuming no collaboration between the BSs, the interfering links between a BS and the selected users in neighboring cells are difficult to be acquired at the belonging BS. We propose the use of a simple minimum Euclidean distance receiver operating with no information of the interfering links. With the help of the OIA, we show that this new receiver asymptotically achieves the channel capacity as the number of users increases.

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preprint2013arXiv

Opportunistic Interference Alignment for MIMO Interfering Multiple-Access Channels

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 \textit{user scaling law} required to achieve $KS$ degrees-of-freedom (DoF), where $S(\le M)$ 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 $\text{SNR}^{(K 1)S}$, 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 $\text{SNR}^{(K-1)S-L+1}$ through 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.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Can One Achieve Multiuser Diversity in Uplink Multi-Cell Networks?

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 $K\log(\text{SNR}\log N)$ in a high signal-to-noise ratio (SNR) regime, if the total number of users in a cell, $N$, scales faster than $\text{SNR}^{\frac{K-1}{1-ε}}$ for a constant $ε\in(0,1)$. 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.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

On the Order Optimality of Large-scale Underwater Networks

Capacity scaling laws are analyzed in an underwater acoustic network with $n$ regularly located nodes on a square, in which both bandwidth and received signal power can be limited significantly. 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. Cut-set upper bounds on the throughput scaling are then derived in both extended and dense networks having unit node density and unit area, respectively. It is first analyzed that under extended networks, the upper bound is inversely proportional to the attenuation parameter, thus resulting in a highly power-limited network. Interestingly, it is seen that the upper bound for extended networks is intrinsically related to the attenuation parameter but not the spreading factor. On the other hand, in dense networks, we show that there exists either a bandwidth or power limitation, or both, according to the path-loss attenuation regimes, thus yielding the upper bound that has three fundamentally different operating regimes. Furthermore, we describe an achievable scheme based on the simple nearest-neighbor multi-hop (MH) transmission. We show that under extended networks, the MH scheme is order-optimal for all the operating regimes. An achievability result is also presented in dense networks, where the operating regimes that guarantee the order optimality are identified. It thus turns out that frequency scaling is instrumental towards achieving the order optimality in the regimes. Finally, these scaling results are extended to a random network realization. As a result, vital information for fundamental limits of a variety of underwater network scenarios is provided by showing capacity scaling laws.

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preprint2011arXiv

Opportunistic Interference Mitigation Achieves Optimal Degrees-of-Freedom in Wireless Multi-cell Uplink Networks

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 $S$ of simultaneously transmitting users per cell: 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 $N$ of users in a cell scales at least as $\text{SNR}^{(K-1)M}$. Similarly, it turns out that the OIA scheme with $S$($<M$) selected users achieves $KS$ DoFs, if $N$ scales faster than $\text{SNR}^{(K-1)S}$. 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.

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preprint2011arXiv

Parallel Opportunistic Routing in Wireless Networks

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 inter-user interference. The scaling behavior of conventional multi-hop transmission that does not employ opportunistic routing is also examined for comparison. Our results indicate that our opportunistic routing can exhibit a net improvement in overall power--delay trade-off 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.

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preprint2010arXiv

Improved Capacity Scaling in Wireless Networks With Infrastructure

This paper analyzes the impact and 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 arbitrary rates relative to $n$. 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 multi-hop (IMH) routing protocol. Then, their achievable throughput scalings are analyzed. These schemes are compared against two conventional schemes without BSs: the multi-hop (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.

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preprint2010arXiv

On Capacity Scaling of Underwater Networks: An Information-Theoretic Perspective

Capacity scaling laws are analyzed in an underwater acoustic network with $n$ regularly located nodes on a square. 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. Interestingly, it is seen that unlike the case of wireless radio networks, our upper bound is intrinsically related to the attenuation parameter but not the spreading factor. 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 $\sqrt{n}$ (or faster). Finally, these scaling results are extended to a random network realization.

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