This paper proposes a lightweight, pretraining-free framework for compressed channel state information (CSI) feedback under time-varying channel conditions. While existing deep learning-based methods suffer from performance degradation under environmental changes and require costly on-device retraining, our method enables a user equipment (UE) to locally rebuild a sparse linear codebook from fresh CSI samples gathered over a short post-shift interval. The updated codebook is then conveyed to the base station (BS) in a one-shot update with low signaling overhead. Extensive evaluations show that the proposed method substantially reduces post-shift CSI mismatch and recovers spectral efficiency under representative domain shifts. In several representative operating points, the adapted scheme operates close to the corresponding perfect-CSI benchmark, while the absolute throughput recovery becomes much larger in mismatch-sensitive transmission modes. The results further show that the practical throughput impact of CSI mismatch is strongly operating-mode dependent, which motivates system-level evaluation beyond reconstruction error alone.

Integrated sensing and communication (ISAC) is a headline feature for the forthcoming IMT-2030 and 6G releases, yet a concrete solution that fits within the established orthogonal frequency division multiplexing (OFDM) family remains an open problem. Specifically, Doppler-induced inter-carrier interference (ICI) destroys subcarrier orthogonality of OFDM sensing signals, blurring range–velocity maps and severely degrading sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) OFDM systems, this paper develops a model-driven ISAC framework that jointly optimizes transmit and receive beamforming to maximize multi-user communication sum-rate under sensing-performance constraints, while mitigating Doppler ICI. To this end, we propose a Doppler-correction filter network (DCFNet), an AI-native ISAC model that achieves fine-grained range-velocity estimation precision with minimal complexity and without altering the legacy frame structure. A bank of DCFs derived from the Doppler physics first shifts and suppresses dominant ICI components, and a subsequent deep neural network cancels the residual interference to yield a clean radar sensing image. To further enhance the range and velocity estimation precision, we propose DCFNet with local refinement (DCFNet-LR), which applies a generalized likelihood ratio test (GLRT) to refine target estimates of DCFNet to sub-cell precision. Simulation results show that DCFNet-LR runs 143 times faster than a maximumlike-lihood search and achieves significantly superior performance, reducing the range and velocity RMSE by factors of 2.7 × 10−4and 6.7 × 10−4compared to conventional detection methods.

This article investigates the security vulnerabilities of adversarial example-based image encryption by executing data reconstruction (DR) attacks on encrypted images. A representative image encryption method is the adversarial visual information hiding (AVIH), which uses type-I adversarial example training to protect gallery datasets used in image recognition tasks. In the AVIH method, the type-I adversarial example approach creates images that appear completely different but are still recognized by machines as the original ones. Additionally, the AVIH method can restore encrypted images to their original forms using a predefined private key generative model. For the best security, assigning a unique key to each image is recommended; however, storage limitations may necessitate some images sharing the same key model. This raises a crucial security question for AVIH: How many images can safely share the same key model without being compromised by a DR attack? To address this question, we introduce a dual-strategy DR attack against the AVIH encryption method by incorporating 1) generative-adversarial loss and 2) augmented identity loss, which prevent DR from overfitting-an issue akin to that in machine learning. Our numerical results validate this approach through image recognition and re-identification benchmarks, demonstrating that our strategy can significantly enhance the quality of reconstructed images, thereby requiring fewer key-sharing encrypted images. The source code to reproduce the results will be available in https://github.com/jonggyujang0123/Hiding_person.

We address a joint trajectory planning, user association, resource allocation, and power control problem to maximize proportional fairness in the aerial IoT network, considering practical end-to-end quality-of-service (QoS) and communication schedules. Though the problem is rather ancient, apart from the fact that the previous approaches have never considered user- and time-specific QoS, we point out a prevalent mistake in coordinate optimization approaches adopted by the majority of the literature. Coordinate optimization approaches, which repetitively optimize radio resources for a fixed trajectory and vice versa, generally converge to local optima when all variables are differentiable. However, these methods often stagnate at a non-stationary point, significantly degrading the network utility in mixed-integer problems such as joint trajectory and radio resource optimization. We detour this problem by converting the formulated problem into the Markov decision process (MDP). Exploiting the beneficial characteristics of the MDP, we design a non-iterative framework that cooperatively optimizes trajectory and radio resources without initial trajectory choice. The proposed framework can incorporate various trajectory-planning algorithms such as the genetic algorithm, tree search, and reinforcement learning. Extensive comparisons with diverse baselines verify that the proposed framework significantly outperforms the state-of-the-art method, nearly achieving the global optimum. Our implementation code is available athttps://github.com/hslyu/dbspf.

Recent research has shown a growing interest in per-instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss. However, conventional additive noise mechanisms apply identical noise to all query outputs, thereby deteriorating data statistics. In this study, we propose an instance-wise Laplace mechanism, which adds non-identical Laplace noises to the query output for each data instance. A challenge arises from the complex interaction of additive noise, where the noise introduced to individual instances impacts the pDP of other instances, adding complexity and resilience to straightforward solutions. To tackle this problem, we introduce an instance-wise Laplace mechanism algorithm via deep reinforcement learning and validate its ability to better preserve data statistics on a real dataset, compared to the original Laplace mechanism.

Deep neural networks (DNNs) have a risk of remembering sensitive data from their training datasets, inadvertently leading to substantial information leakage through privacy attacks like membership inference attacks. DP-SGD is a simple but effective defense method, incorporating Gaussian noise into gradient updates to safeguard sensitive information. With the prevalence of large neural networks, DP-signSGD, a variant of DP-SGD, has emerged, aiming to curtail memory usage while maintaining security. However, it is noteworthy that most DP-signSGD algorithms default to Gaussian noise, suitable only for DP-SGD, without scant discussion of its appropriateness for signSGD. Our study delves into an intriguing question: "Can we find a more efficient substitute for Gaussian noise to secure privacy in DP-signSGD?" We propose an answer with a Logistic mechanism, which conforms to signSGD principles and is interestingly evolved from an exponential mechanism. In this paper, we provide both theoretical and experimental evidence showing that our method surpasses DP-signSGD.

The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)—namely, minimizing service latency. Additionally, the use of MEC systems poses an additional problem arising from limited battery resources of MDs. This paper tackles the pressing challenge of latency-aware distributed task offloading optimization, where user association (UA), resource allocation (RA), full-task offloading, and battery of mobile devices (MDs) are jointly considered. In existing studies, joint optimization of overall task offloading and UA is seldom considered due to the complexity of combinatorial optimization problems, and in cases where it is considered, linear objective functions such as power consumption are adopted. Revolutionizing the realm of MEC, our objective includes all major components contributing to users’ quality of experience, including latency and energy consumption. To achieve this, we first formulate an NP-hard combinatorial problem, where the objective function comprises three elements: communication latency, computation latency, and battery usage. We derive a closed-form RA solution of the problem; next, we provide a distributed pricing-based UA solution. We simulate the proposed algorithm for various resource-intensive tasks. Our numerical results show that the proposed method Pareto-dominates baseline methods. More specifically, the results demonstrate that the proposed method can outperform baseline methods by1.62 times shorter latencywith41.2% less energy consumption.

Object detection in aerial images is a growing area of research, with maritime object detection being a particularly important task for reliable surveillance, monitoring, and active rescuing. Notwithstanding astonishing advances of computer visiontechnologies, detecting ships and floating matters in these images are challenging due to factors such as object distance. What makes it worse is pervasive sea surface effects such as sunlight reflection, wind, and waves. Hyperspectral image (HSI) sensors, providing more than 100 channels in wavelengths of visible and near-infrared, can extract intrinsic information of materials from a few pixels of HSIs.The advent of HSI sensors motivates us to leverage HSIs to circumvent false positives due to the sea surface effects.Unfortunately, there are few public HSI datasets due to the high cost and labor involved in collecting them, hindering object detection research based on HSIs. We have collected and annotated a new dataset called ``Multi-Modal Ship and flOating matter Detection in Aerial Images (M$^{2}$SODAI),'', which includes synchronized image pairs of RGB and HSI data, along with bounding box labels for nearly 6,000 instances per category. We also propose a new multi-modal extension of the feature pyramid network called DoubleFPN.Extensive experiments on our benchmark demonstrate that fusion of RGB and HSI data can enhance mAP, especially in the presence of the sea surface effects.

The renewable energy source (RES)-powered small cell base station (SBS) is a promising technology for the next-generation networks because RESs provide sustainable energy without being depleted. In RES-assisted networks, joint optimization of user association (UA) and power control (PC) is required to enhance the sum-rate performance by reducing inter-cell interference between SBSs. This paper tackles the UA and PC optimization for the sum-rate maximization under quality-of-service (QoS) and backhaul constraints. We formulate the problem as mixed-integer non-linear programming, in which UA and PC variables of different time slots are tightly coupled due to the RES energy dynamics model. Hence, designing a dynamic policy-based UA and PC with consideration of the future environments such as the quantity of channel gain and energy harvesting remains a challenge. First, we propose a deep unsupervised learning (DUL)-based UA scheme for a fixed PC variable. To lower the computational complexity and accelerate the convergence of the proposed learning-based optimization, we relax the UA variable, which originally has an extremely high dimension, into a low-dimensional continuous variable inspired by the Lagrangian method. Next, we propose a deep reinforcement learning (DRL)-based PC scheme, in which the stringent QoS and backhaul constraints are considered penalty terms on the reward design. The proposed DRL-based PC scheme facilitates dynamic PC inferring the relationship between the future environment and current PC. Simulation results demonstrate that the proposed scheme enhances the sum-rate by 10%, accommodates 3.3 percent point (%p) more QoS-qualified users, and reduces the computation time by 20 times compared to the conventional optimization-based method.

To maximize the spectral efficiency while satisfying the increasing mobile traffic demand, LTE can offload downlink traffics to unlicensed UNII bands at 5GHz via the standardized technology LTE-LAA (Licensed Assisted Access). In the scenario where LTE-LAA inevitably coexists with already pervasive WiFi devices which were designed without the consideration of LTE-LAA, recent studies have reported that the asymmetric hidden terminal (AHT) problem, caused by the difference in the listen-before-talk clear-channel-assessment (CCA) thresholds of WiFi and LTE-LAA, severely degrades the throughput and delay performance of both LTE-LAA and WiFi. As a remedy to the AHT problem, we propose a multi-antenna multi-user transmit precoding and power control techniques for LTE-LAA base stations to make neighboring Wi-Fi APs defer their new transmissions, thereby peacefully coexisting with them. The proposed scheme does not require any technical amendment or extra work on the conventional Wi-Fi. Based on our realistic simulator with essential MAC and PHY functions implemented, it is confirmed that the proposed scheme improves the throughput and delay performance of LTE-LAA while maintaining or even improving that of Wi-Fi.

Renewable energy source (RES)-powered base stations have received tremendous research interest in recent years because they can expand network coverage without building a power grid. This paper proposes a novel user association (UA), resource allocation (RA), and dynamic power control (PC) scheme to maximize the$\alpha $-fairness in RES-assisted small cell networks. The$\alpha $-fairness is a general notion that flexibly adjusts the balance between the throughput, proportional fairness, and max-min fairness according to$\alpha $. Nevertheless, none of the existing studies has proposed UA, RA, and PC to maximize the$\alpha $-fairness due to its NP-hardness. Furthermore, fixed-policy-based PC designs cannot consider time-varying environments (e.g., energy harvesting models and wireless channels) of the RES-assisted networks. We first provide a Lagrangian duality-based algorithm to solve the UA and RA problem for a fixed PC. Next, we propose a dynamic PC scheme based on deep reinforcement learning (DRL) that chooses the best PC considering the time-varying environments. However, because the UA and RA algorithm executed in each step of the dynamic PC requires a long computation time, we aim to accelerate the computation of the UA and RA with DRL. Inspired by the Lagrangian duality, we design a DRL-based UA and RA with a low-dimensional continuous variable by relaxing the UA variable, the cardinality of which increases exponentially with the number of base stations and users. The simulation results show that the proposed scheme achieves a 100 times shorter computation time than the optimization-based schemes by computing only two neural networks. In particular, although there have been numerous studies on the proportional fairness maximization, the proposed scheme outperforms the optimization-based schemes in the throughput, proportional fairness, and max-min fairness metrics.

By virtue of their ultra high resolution, scanning electron microscopes (SEMs) are essential to study topography, morphology, composition, and crystallography of materials, and thus are widely used for advanced researches in physics, chemistry, pharmacy, geology, etc. The major hindrance of using SEMs is that obtaining high quality images from SEMs requires a professional control of many control parameters. Therefore, it is not an easy task even for an experienced researcher to get high quality sample images without any help from SEM experts. In this paper, we propose and implement a deep learning-based autonomous SEM machine, which assesses image quality and controls parameters autonomously to get high quality sample images just as if human experts do. This world's first autonomous SEM machine may be the first step to bring SEMs, previously used only for advanced researches due to its difficulty in use, into much broader applications such as education, manufacture, and mechanical diagnosis, which are previously meant for optical microscopes.

As smart cameras are becoming ubiquitous in mobile robot systems, there is an increasing concern in camera devices invading people's privacy by recording unwanted images. We want to fundamentally protect privacy by blurring unwanted blocks in images, such as faces, yet ensure that the robots can understand the video for their perception. In this paper, we propose a novel mobile robot framework with a deep learning-based privacy-preserving camera system. The proposed camera system detects privacy-sensitive blocks, i.e., human face, from extreme low resolution (LR) images, and then dynamically enhances the resolution of only privacy-insensitive blocks, e.g., backgrounds. Keeping all the face blocks to be extreme LR of 15x15 pixels, we can guarantee that human faces are never at high resolution (HR) in any of processing or memory, thus yielding strong privacy protection even from cracking or backdoors. Our camera system produces an image on a real-time basis, the human faces of which are in extreme LR while the backgrounds are in HR. We experimentally confirm that our proposed face detection camera system outperforms the state-of-the-art small face detection algorithm, while the robot performs ORB-SLAM2 well even with videos of extreme LR faces. Therefore, with the proposed system, we do not too much sacrifice robot perception performance to protect privacy.

This paper presents an approach for recognizing human activities from extreme low resolution (e.g., 16x12) videos. Extreme low resolution recognition is not only necessary for analyzing actions at a distance but also is crucial for enabling privacy-preserving recognition of human activities. We design a new two-stream multi-Siamese convolutional neural network. The idea is to explicitly capture the inherent property of low resolution (LR) videos that two images originated from the exact same scene often have totally different pixel values depending on their LR transformations. Our approach learns the shared embedding space that maps LR videos with the same content to the same location regardless of their transformations. We experimentally confirm that our approach of jointly learning such transform robust LR video representation and the classifier outperforms the previous state-of-the-art low resolution recognition approaches on two public standard datasets by a meaningful margin.

Since License Assisted Access LTE (LAA-LTE) employs a conservative energy detection (ED) threshold for transmission to coexist with Wi-Fi in unlicensed bands, the spatial spectrum reuse of LAA-LTE can be significantly impaired. Such non-flexible thresholding has been introduced mainly due to ED's incapability of differentiating Wi-Fi frames from LTE frames. As a remedy, we design and develop an LAA-LTE transmitter based on a software-defined radio system proposing lightweight but effective Wi-Fi frame detection with which an LAA-LTE device can capture a Wi-Fi preamble by only using LTE's own time-domain samples. Experiments confirm the efficacy of the developed system.

License Assisted Access (LAA) LTE (LAA-LTE) is a new type of LTE that aggregates the licensed LTE bands with the unlicensed bands via carrier aggregation. To operate in unlicensed bands, LAA-LTE adopts the listen-before-talk policy and designs its channel access mechanism similar to WLAN's DCF. This paper considers an LAA-LTE eNB coexisting withasymmetric hiddenWi-Fi APs where the eNB can detect the APs while the APs cannot, which is caused by the asymmetric CCA thresholds. The behavior of such a network is modeled by a joint Markov chain (MC), using which steady-state probabilities, throughput, and channel access delay are derived analytically. An extensive evaluation confirms that the proposed analysis correctly models the dynamics of LAA-WLAN coexistence, and identifies important design guidelines for fair coexistence as follows. First, LAA-LTE should enable channel access priority class 4 to exploit its large contention window (CW). Second, LAA-LTE should re-design its CW doubling policy to restore the balance between LAA-LTE and WLAN in throughput and channel access delay. Third, to protect Wi-Fi, the maximum CW stage should be used more times by increasing the retry count.

Privacy protection from surreptitious video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.

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.

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.

The fundamental problem in multi-cell two-way relaying is low uplink achievable rate due to the fact that the uplink signal transmitted by a user is significantly contaminated by the downlink signal transmitted simultaneously by the base station (BS) in the neighboring cell, which has much greater power than the power of the user uplink signal. In this paper, a novel relay precoding optimization scheme is proposed in the two-cell two-way relay channel with only local channel state information in pursuit of reducing the inter-cell interference. The optimization problems for finding the relay precoding coefficients are decoupled at each relay, and can be solved using the parameters which are calculated offline according to the users' locations. Simulation results show that the proposed scheme outperforms the conventional two-way relaying and direct communication without relaying for cell-edge users, owing to reduced inter-cell interference.

In the fifth-generation mobile communications, futuristic wireless services such as remote surgery, streaming gaming, tactile internet, and augmented reality are envisioned, which require real- time end-to-end communication with near-zero latency. One of the essential enablers of near-zero latency may be asynchronous multiple-access for uplink, in which each user transmits instantaneously without waiting for the next frame to start. Generalized frequency division multiple-access (GFDMA) has been studied as a promising candidate for asynchronous multiple-access, however, conventional single-carrier FDMA (SC-FDMA) can also be adapted in an asynchronous manner for low latency. In this paper, via extensive numerical simulations, the spectral efficiency of GFDMA and conventional SC-FDMA is investigated in the asynchronous scenario with the presence of inter-user interference due to non-zero out-of-band emission of the spectrum. Simulation results show that the superiority between the two techniques is determined depending on the system parameters such as user density, number of guard band subcarriers, signal-to-noise ratio, and etc.

An adaptive overhearing protocol is proposed for the two-way multi-antenna-relay network composed of a base station (BS), relay, and two user equipments (UEs), where one UE is in the uplink (UL) transmission mode (UE-Tx) while the other is in the downlink (DL) reception mode (UE-Rx). Specifically, UE-Rx not only receives the DL signal transmitted by BS but also overhears the signal transmitted by UE-Tx, and exploits the overheard signal to improve the detection performance. The transmit adaptive weights of UE-Tx over the two times slots and the precoding matrix at the relay in the second time slot are jointly optimized via the proposed iterative algorithm in the sense of maximizing the minimum signal-to-interference-plus-noise-ratio. Numerical results show that the proposed joint design provides significant sum-rate gain over the existing overhearing scheme.

Cellular networks are becoming dense due to deployment of small cells and a number of user devices. Such networks are called ultra-dense networks (UDNs). In this paper, we propose a novel distributed scheduling with interference-aware power control for an uplink of the UDN operating with time-division duplex (TDD). In the proposed technique, each user adjusts transmit power according to a pre-determined threshold of generating interference to other cell base stations (BSs) and each BS selects the users having the highest effective channel gains adjusted according to the transmit power of users. We assume that each user has a single transmit antenna and each BSs have M receive antennas. It is shown that the proposed technique with a carefully chosen threshold significantly outperforms the existing distributed user scheduling schemes through extensive simulations. In addition, we prove that the optimal multiuser diversity gain, i.e., log log N is achieved by the proposed technique in each cell even in the presence of intercell interference when S = 1, if the number of users in a cell, K-1 N, scales faster than SNRK-1/1-ϵfor a constant ϵ ∈ (0, 1), where S denotes the number of scheduled users.

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.

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.

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.

Distributed sum-rate-maximizing covariance matrices design for full-duplex multi-input multi-output communication is considered, where the information of the loopback interference channels cannot be exchanged reliably due to their large dynamic ranges. We propose a structured covariance matrices design which finds the optimal balance between the two solutions in the extremes of the weak and strong self-interference through a single-parameter optimization. We further propose a low-complexity null projection matrix design algorithm, in which the solution in the strong self-interference regime is designed in the sense to maximize the received channel gain. Exploiting the well-posed structure, the proposed scheme nearly achieves the sum-rate of the previous scheme based on the gradient projection with significantly less amount of inter-node iterations, yielding an increased effective sum-rate and reduced overall complexity for the practial channel block lengths.

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 derive a new sum-rate lower bound of the multiuser multi-input multi-output (MU-MIMO) cellular two-way relay channel (cTWRC) which is composed of a base station (BS) and a relay station (RS), both with multiple antennas, and non-cooperative mobile stations (MSs), each with a single antenna. In the first phase, we show that network coding based on decode-and-forward relaying can be generalized to arbitrary input cardinality through proposed lattice code-aided linear precoding, despite the fact that precoding is permitted only at the BS due to non-cooperation among the MSs. In addition, a new sum-rate lower bound for the second phase is derived by showing that the two spatial decoding orders at the BS and MSs for one-sided zero-forcing dirty-paper-coding must be identical. From the fundamental gain of network coding, our sum-rate lower bound achieves the full multiplexing gain regardless of the number of antennas at the BS or RS, and strictly exceeds the previous lower bound which is based on traditional multiuser decoding in the first phase. Furthermore, it is shown that our lower bound asymptotically achieves the sum-rate upper bound in the presence of signal-to-noise ratio (SNR) asymmetry in high SNR regime, and sufficient conditions for this SNR asymmetry are drawn.

Lattice-reduction-aided precoding (LRP) provides near-capacity rates with the use of low-complexity linear receivers for coded multiple-input multiple-output (MIMO) systems. However, a large amount of feedback in the feedback for an integer or binary precoding matrix has been a bottleneck in its implementation. In this paper, we propose a codebook-based LRP scheme for limited-feedback coded MIMO systems. The proposed LRP scheme follows the fundamentals of the previous LRP scheme that employed multilevel binary coset coding so that the precoding matrix is binary. In the proposed LRP scheme, the conventional precoding matrix obtained from the Lenstra-Lenstra-Lovasz algorithm is modified to specific forms that are predefined in a codebook set. The new precoding matrix is selected such that the lower bound on the capacity is maximized for a given codebook set, while the codebook set is designed offline such that the upper bound of the average capacity loss induced by the limitation on the codebook size is minimized. The simulation results show that the proposed LRP scheme nearly achieves the achievable rate of the conventional LRP scheme with a greatly reduced amount of feedback.

A multiple-input multiple-output two-way relay channel consisting of two communication nodes and a full-duplex relay node in which no direct link exists between the two communication nodes is considered. We propose an achievable scheme that employs horizontally encoded lattice codes combined with generalized singular value decomposition-based precoding for the first phase. The second phase of the proposed scheme follows the fundamentals of the previous scheme, which uses vertically encoded structural bining, with the only difference that the added codeword of the two codewords from the communication nodes, instead of those two individual codewords, is decoded and retransmitted in the proposed scheme. We show that the proposed scheme achieves the cut-set bound asymptotically as the signal-to-noise ratios of the channels tend to infinity.

Deploying relay stations improves the signal-to- interference and noise ratio (SINR) by making a Pico cell environment. Nonetheless, a critical problem associated with deploying RS is additional resource consumption, which reduces the actual transmission rate. Two-way relaying schemes that use physical layer network coding (PNC) reduce the additional resource consumption and thereby improve the transmission rate. In this paper we propose a novel two-way relaying scheme for coded multiple-input multiple-output (MIMO) systems. The proposed scheme involves generalized Schur decomposition-based preceding and successive interference cancellation (SIC). By using the generalized Schur decomposition, orthogonal preceding matrices that minimize the transmit power can be applied, resulting in an improved transmission rate. We have evaluated the proposed system by comparing its transmission rate with that of several conventional schemes.

In cellular mobile communication systems, the link performance can be remarkably improved by deploying relays between the base station and the mobile station. In this paper we propose an efficient duplexing scheme so that both spatial and temporal gain by adding relays can be increased. Using the proposed relaying scheme, the conventional system of four-phase relaying can be simplified to a two-phase relaying system and the additional resource consumption due to relays can be minimized. We show the capacity gain for cell edge users with numerical results.

The performance of V-BLAST symbol detection can be seriously degraded if channel information is not perfect. We derive a new nulling matrix at the receiver using the modified TLS (Total Least Squares) solution under channel uncertainties. Using the new nulling matrix, we propose a modified vertical Bell laboratories layered space-time(V-BLAST) detection algorithm to reduce unexpected effects due to channel uncertainties. The proposed algorithm requires only one more scalar value, an upper bound on the 2-induced norm of a channel error matrix, which is relatively robust to the statistical changes of channel environment, than the conventional algorithm does, but the performance is better especially at high SNR. We simulate the proposed algorithm in the presence of receiver's mobility.