Recently, agentic AI systems based on large language models (LLMs) have emerged as a promising paradigm for autonomous decision-making across various domains. In wireless communications, agentic AI can serve as an intelligent network orchestrator, which dynamically manages communication resources and adapts to changing conditions. Effective operation of agentic AI requires seamless integration of multimodal sensing data from distributed sensors, which poses significant challenges in communication overhead. In this paper, we propose multimodal agentic wireless network (MAWN), a cooperative agentic AI framework that intelligently selects sensing modalities and autonomously plans computational workflows to address various wireless tasks (e.g., beam management). MAWN consists of two specialized LLM agents: the sensor manager agent (SMA) and the function manager agent (FMA). The SMA minimizes data transmission overhead by selecting only necessary sensors while considering data availability, while the FMA dynamically composes optimal tool execution sequences based on task requirements. By combining two LLM agents, MAWN adaptively gathers information and handles requests with reduced communication overhead and no manual configuration. Simulation results demonstrate that MAWN achieves substantial performance improvements across diverse communication tasks.

For two-dimensional direction-of-arrival (2-D DOA) estimation, the uniform planar arrays (UPAs) can offer satisfactory estimation performance among various sensor array configurations, but is prone to be affected by the unknown mutual coupling effects. Existing 2-D DOA estimation algorithms accounting for the mutual coupling effects calibration either suffer from low estimation resolution or high computational complexity. To deal with this problem, we propose a tensor-based 2-D DOA estimation algorithm for UPAs in the presence of unknown mutual coupling. Specifically, by exploiting the block banded symmetric Toeplitz structure of the mutual coupling matrix, we construct a calibration matrix to relieve the mutual coupling effect. Then, the received signals are reformulated into a tensor format admitting the canonical polyadic decomposition, where the factor matrices incorporate the azimuth and elevation angles. By exploiting the inherent Vandermonde structure of the equivalent steering matrix, we develop an algebraic-based factor matrix estimation method without the necessity of iteration, followed by the azimuth and elevation angles extraction from the estimated factor matrices. In addition, the closed-form solutions of the mutual coupling coefficients are obtained based on the estimated angles. On this basis, we mathematically investigate the uniqueness condition of the tensor decomposition and the maximum number of resolvable targets. Moreover, we derive the Cramér-Rao bound to evaluate the performance limit for the considered 2-D DOA estimation problem with mutual coupling effects, and the computational complexity. Simulation results corroborate the superiority of the proposed tensor-based 2-D DOA estimation algorithm over competing methods in terms of complexity and resolution.

Beam management is an essential operation of next-generation (xG) wireless networks to compensate severe signal attenuation and ensure reliable communications over millimeter wave (mmWave) and terahertz (THz) bands. The primary objective of beam management is to determine beam directions that are properly aligned with the signal propagation paths. Conventional beam management techniques typically rely on geometric channel parameters such as angles, delays, and path gains. However, due to their lack of contextual awareness of the surrounding environment, these techniques fall short in handling the piecewise continuous changes in the geometric channel parameters caused by sudden obstructions or variations in scatterers. In this paper, we propose a novel beam management framework that utilizes environmental information extracted from sensor data (e.g., images) and pilot measurements to optimize beam directions. The main idea of the proposed scheme is to utilize visual channel parameters, including the positions of user equipment (UE), reflection points, and scatterers, which provide a direct visualization of the propagation environment. By tracking the visual channel parameters, dynamic changes in scatterers and propagation paths can be monitored over time. To analyze multimodal data and track these parameters, we exploit large multimodal model (LMM), a generative artificial intelligence (AI) model specialized in extracting correlated features across multimodal data and generating subsequent data. Simulation results show that the proposed scheme can accurately track the visual channel parameters and enhance data rate.

By harnessing compressed-sensing principles, sparse vector coding (SVC) compresses and delivers data with ultra-low latency and near-instantaneous reliability, making it a vital enabler of next-generation ultra-reliable and low-latency communications (xURLLC) services in sixth generation (6G) wireless communication systems. A fundamental challenge in SVC systems is developing a generalized sparse mapping mechanism that does not rely on either an index table or constellation labels. To meet such a requirement, this work proposes a base conversion index modulation (IM) that uses a novel mapping strategy for sparse vector construction. The proposed design achieves higher resource efficiency, requiring fewer positional resources for bit representation than conventional combination-based IM. Building on this foundation, a generalized SVC (GSVC) scheme is developed to enable fully pipelined bit-stream mapping and demapping. A further extension, termed enhanced GSVC (EGSVC), adopts a pairwise-grouped constellation assignment to improve the transmission performance of GSVC. Simulation results confirm that GSVC achieves block error rate (BLER) performance comparable to conventional SVC in single-block coding modes, yet significantly improves BLER under high-coding-rate multi-block coding modes. By balancing the constellation label count and the sparse vector length, EGSVC delivers superior BLER compared to conventional SVC schemes while maintaining lower latency.

Recently, large language models (LLMs) and large multimodal models (LMMs) have been successfully adopted in various fields due to their outstanding adaptability. One application of LMM in wireless communication is mobility management in ultra-dense network (UDN) systems. By exploiting its powerful contextual understanding capability, LMM can obtain abundant environmental awareness and effectively manage user equipment (UE) mobility. In this paper, we propose an LMM-aided geometry-aware mobility management (LMM-GMM) that estimates the long-term future data rates of the UE and makes a handover decision to maximize the data rates. LMM-GMM utilizes a geographic information system (GIS) map image in LMM, thereby identifying the UE position and corresponding propagation paths. Using this information, LMM-GMM can accurately predict the future data rate in dynamic scenarios and enhance the handover performance. Simulation results demonstrate that the proposed LMM-GMM can effectively perform mobility management in the UDN environments.

Compositional Zero-Shot Learning (CZSL) aims to identify unseen state-object compositions by leveraging knowledge learned from seen compositions. Existing approaches often independently predict states and objects, overlooking their relationships. In this paper, we propose a novel framework, learning primitive relations (LPR), designed to probabilistically capture the relationships between states and objects. By employing the cross-attention mechanism, LPR considers the dependencies between states and objects, enabling the model to infer the likelihood of unseen compositions. Experimental results demonstrate that LPR outperforms state-of-the-art methods on all three CZSL benchmark datasets in both closed-world and open-world settings. Through qualitative analysis, we show that LPR leverages state-object relationships for unseen composition prediction.

Millimeter wave (mmWave) communications have been recognized as an important technology to address the growing data demand of next-generation wireless communications. Since mmWave communications have the inherent disadvantage of severe signal attenuation, the beamforming technique using a large antenna array is widely adopted at the base station (BS). For achieving optimal beamforming performance, a beam management process for angular channel information must be carried out. However, conventional beam management techniques suffer from long latency and inaccuracies in determining the beam direction due to the quantized codebook design. To overcome this issue, we propose an active receiver-based beam management (AR-BM) in which the user equipment (UE) determines and reports the grid-free channel direction without the need for additional refinement stages, while also achieving more accuracy. In our scheme, BS transmits wide training beams generated by partially activated arrays. Then, UE determines and reports gridfree direction by updating the estimated direction comparing the measured reference signal received power (RSRP) and the function-derived RSRP. Numerical results show that the proposed scheme achieves a$\mathbf{1 4 \%}$improvement in the data rate and a$\mathbf{9 0 \%}$reduction in the latency.

Text-to-image generative models like DALL-E and Stable Diffusion have revolutionized visual content creation across various applications, including advertising, personalized media, and design prototyping. However, crafting effective textual prompts to guide these models remains challenging, often requiring extensive trial and error. Existing prompt inversion approaches, such as soft and hard prompt techniques, are not so effective due to the limited interpretability and incoherent prompt generation. To address these issues, we propose Visually Guided Decoding (VGD), a gradient-free approach that leverages large language models (LLMs) and CLIP-based guidance to generate coherent and semantically aligned prompts. In essence, VGD utilizes the robust text generation capabilities of LLMs to produce human-readable prompts. Further, by employing CLIP scores to ensure alignment with user-specified visual concepts, VGD enhances the interpretability, generalization, and flexibility of prompt generation without the need for additional training. Our experiments demonstrate that VGD outperforms existing prompt inversion techniques in generating understandable and contextually relevant prompts, facilitating more intuitive and controllable interactions with text-to-image models.

Large vision-language models (LVLMs) are increasingly deployed in interactive applications such as virtual and augmented reality, where a first-person (egocentric) view captured by head-mounted cameras serves as key input. While this view offers fine-grained cues about user attention and hand-object interactions, its narrow field of view and lack of global context often lead to failures on spatially or contextually demanding queries. To address this, we introduce a framework that augments egocentric inputs with third-person (exocentric) views, providing complementary information such as global scene layout and object visibility to LVLMs. We present E3VQA, the first benchmark for multi-view question answering with 4K high-quality question-answer pairs grounded in synchronized ego-exo image pairs. Additionally, we propose M3CoT, a training-free prompting technique that constructs a unified scene representation by integrating scene graphs from three complementary perspectives. M3CoT enables LVLMs to reason more effectively across views, yielding consistent performance gains (4.84\% for GPT-4o and 5.94\% for Gemini 2.0 Flash) over a recent CoT baseline. Our extensive evaluation reveals key strengths and limitations of LVLMs in multi-view reasoning and highlights the value of leveraging both egocentric and exocentric inputs. The dataset and source code are available at [https://github.com/Leeinsu1/Towards-Comprehensive-Scene-Understanding](https://github.com/Leeinsu1/Towards-Comprehensive-Scene-Understanding).

Recently, large multimodal models (LMMs) have garned significant attention for interpreting multimodal inputs to generate desired outputs. With the exponential growth of the range of tasks performed by autonomous devices, the central unit (CU) needs to handle LMMs to control these devices. Ensuring seamless command delivery to these devices requires scheduling, a task to allocate resource blocks (RBs) and choose modulation and coding scheme (MCS) index. However, in 6G environments, sudden channel changes make this task difficult. In this paper, we propose a novel LMM-based scheduling technique to address this issue. The core idea is to use LMM to predict future channel parameters (e.g., distance and angles) by analyzing both the visual sensing information and pilot signals. By predicting the presence of reliable path and geometric information of users from the sensing information and then combining these with past channel estimates, we can predict future channel parameters accurately, using which we can proactively make channel-aware scheduling decisions. Numerical results demonstrate that the proposed technique outperforms the conventional scheduling techniques in terms of total system throughput.

In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (NOMA) systems. Our approach employs an iterative and parallel interference removal strategy inspired by parallel interference cancellation (PIC), enhanced with deep learning to jointly tackle the AD, CE, and DD problems. Based on this approach, we develop three PIC frameworks, each of which is designed for either coherent or non-coherence schemes. The first framework performs joint AD and CE using received pilot signals in the coherent scheme. Building upon this framework, the second framework utilizes both the received pilot and data signals for CE, further enhancing the performances of AD, CE, and DD in the coherent scheme. The third framework is designed to accommodate the non-coherent scheme involving a small number of data bits, which simultaneously performs AD and DD. Through joint loss functions and interference cancellation modules, our approach supports end-to-end training, contributing to enhanced performances of AD, CE, and DD for both coherent and non-coherent schemes. Simulation results demonstrate the superiority of our approach over traditional techniques, exhibiting enhanced performances of AD, CE, and DD while maintaining lower computational complexity.

Recently, large multimodal models (LMMs) have been successfully adopted in various fields due to their outstanding adaptability and reasoning abilities. Despite their potential to automate diverse tasks in communications systems, application to the physical layer remains underexplored. The primary reason is that the traditional physical layer relies on analytic channel measurements (e.g., pilot measurements), which capture only quantitative changes in transmitted signals and fail to characterize qualitative physical interactions (e.g., reflections and blockages) with the environment. In this paper, we propose an LMM-based environment-aware channel estimation framework that captures the contextual channel information by leveraging both perceptual sensor data and numerical pilot measurements. The main idea of the proposed scheme is to utilize visual channel parameters (VCPs), i.e., positions of user equipment (UE), reflection points, and scatterers. Since VCPs provide a direct visualization of the propagation environment, we can identify how signals propagate and physically interacts with the surrounding objects. To extract the VCPs and learn their probability distributions, we develop a reflection learning technique based on LMM. By incorporating these parameters, we establish a fundamental channel knowledge map (CKM) between the UE position and the channel. Simulation results demonstrate that the proposed scheme can effectively predict the channel throughout the wireless environments.

As a means to provide ubiquitous connectivity across the ground-air-space 3D network, low Earth orbit (LEO) satellite mega-constellation systems comprising thousands of LEO satellites have attracted significant interest from both academia and industry recently. One major issue of LEO mega-constellation systems is the frequent handovers between satellites and beams, causing an increase in communication latency and deterioration of quality of service (QoS). In this paper, we propose a user-centric cooperative communication framework for next generation (xG) LEO satellite mega-constellation systems. In the proposed framework, a group of LEO satellites simultaneously serve all the user equipments (UEs) using the same timefrequency resources. By dynamically organizing the clusters of serving satellites and coordinating their joint transmission based on statistical channel state information (CSI), the handover frequency and inter-satellite interference can be reduced effectively, thereby achieving significant enhancements in the spectral efficiency and coverage probability. From the achievable rate analysis and extensive simulations on realistic xG LEO satellite communication environments, we show that the proposed scheme substantially improves the spectral efficiency and coverage over the conventional beam-centric systems.

This paper investigates beamforming design with partial channel state information (CSI) feedback aimed at maximizing the energy efficiency (EE) of a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user system. By leveraging the spatial reciprocity, which means that the path angle information (PAI) and power angular spectrum (PAS) of sparse paths are similar in the uplink and downlink channels, we can acquire the downlink PAI and PAS at the base station (BS) via uplink estimation. Subsequently, we select several paths that contribute to EE maximization from all the cascaded paths and define them as dominant paths. Consequently, only the small-scale fading coefficients (i.e., the normalized path gain information, NPGI) of these selected dominant paths need to be fed back from the user equipments (UEs), thereby significantly reducing the feedback overhead. Moreover, we update the active BS beamformer and passive RIS beamformer by using the feedback of partial NPGI to further improve the EE. Numerical results demonstrate the superiority of our proposed algorithms over conventional schemes.

Reconfigurable intelligent surface (RIS)-assisted wideband terahertz (THz) communications are essential for achieving ultra-high data rates in sixth-generation (6G) networks. By adjusting the phase shifts of reflecting elements, RIS can effectively reshape wireless channels to enhance overall performance. However, two major challenges arise in RIS-assisted THz systems: 1) the dual beam split effect, where the large bandwidth causes subcarrier beam directions to diverge at both base station (BS) and RIS; and 2) the near-field effect, where the channel becomes a nonlinear function of both angle and distance. In this paper, we propose a novel beamforming technique, termed RIS-assisted wideband beamforming (RWB), to address these challenges and maximize data rates in RIS-assisted wideband THz systems. The RWB scheme leverages partially-connected true time delays (TTDs) and phase shifters (PSs) to generate frequency-dependent BS transmit beamforming vectors, while utilizing passive reflecting elements to control the frequency-invariant RIS reflect beamforming vector. By jointly optimizing the transmit and reflect beamforming vectors on the Riemannian manifold of unit-modulus phase shifts, RWB effectively mitigates both beam split and near-field effects. Numerical evaluations demonstrate that RWB achieves substantial data rate improvements over conventional wideband beamforming schemes.

Unsupervised semantic segmentation (USS) aims to discover and recognize meaningful categories without any labels. For a successful USS, two key abilities are required: 1) information compression and 2) clustering capability. Previous methods have relied on feature dimension reduction for information compression, however, this approach may hinder the process of clustering. In this paper, we propose a novel USS framework called Expand-and-Quantize Unsupervised Semantic Segmentation (EQUSS), which combines the benefits of high-dimensional spaces for better clustering and product quantization for effective information compression. Our extensive experiments demonstrate that EQUSS achieves state-of-the-art results on three standard benchmarks. In addition, we analyze the entropy of USS features, which is the first step towards understanding USS from the perspective of information theory.

In this paper, we introduce a novel deep learning-assisted parallel interference cancellation (PIC) framework for joint activity detection (AD) and channel estimation (CE) in up-link grant-free non-orthogonal multiple access (NOMA) systems. Our framework employs an iterative and parallel interference removal strategy inspired by PIC, enhanced with deep learning to jointly tackle the AD and CE problems. The proposed framework consists of multiple uniform stages, each containing trainable CE modules and non-parameterized IC modules. Additionally, it integrates AD modules that estimate each device activity in a parallel manner by leveraging the received signals after interference removal processes. Through joint loss functions and IC modules, the proposed framework supports end-to-end training, contributing to enhanced performances of AD and CE. Simulation results demonstrate the superiority of the proposed framework over traditional techniques, exhibiting enhanced performances of AD and CE while maintaining lower computational complexity.

Recent progress of deep learning (DL) and computer vision (CV) have paved the way for the application of DL-based CV technologies in 6G wireless communications. DL-based CV is data-hungry, and thus it is important to collect a massive vision dataset designed for wireless applications. An aim of this paper is to propose a vision dataset called Vision Objects for Millimeter and Terahertz Communications (VOMTC) consisting of 20,232 pairs of RGB and depth images, each of which is manually annotated with three object classes (person, mobile, and laptop) and their corresponding boxes. To demonstrate the efficacy of VOMTC, we develop a CV-aided beamforming technique called VOMTC-based beam management (VBM). In VBM, the location of the mobile is extracted via the VOMTC-trained object detector and then a beam heading toward the extracted location is generated. Due to the use of this special object detector tailored for identifying mobiles, VBM enhances the chance of transmitting directional beams to the mobile location. Using the VOMTC test dataset, we show that VBM achieves 15% improvement in the data rate over the conventional CV-aided beam management.

Terahertz (THz) communications have become an important technology for achieving 6G networks that require higher data rates. To overcome severe signal attenuation in THz communications, highly directional beamforming technique using extremely large-scale antenna array (XL-array) is widely used at the base station (BS). Since the channel exhibits near-field characteristics at higher frequencies and wider antenna apertures, the BS must perform near-field beam training to obtain distance information as well as angle information of the channel. A major issue of conventional near-field beam training is the considerable beam training latency due to the large overhead incurred by angle and distance domain search. To address this issue, we propose a subarray-based near-field beam training that estimates the user location by performing frequency-dependent beam training on partitioned array antennas. In the first step, BS simultaneously searches multiple directions by generating multiple frequency-dependent beams using phase shifters (PSs) and true time delays (TTDs). In the second step, BS estimates the user angle and distance information by exploiting the extracted directions from subarrays. Numerical results show that the proposed scheme achieves 28% improvement in the data rate and 99% reduction in the latency.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered a promising solution to provide high-quality data services. To achieve full beamforming gain, the acquisition of accurate channel information is crucial for the success of massive MIMO systems. In recent years, numerous approaches have been suggested to acquire the downlink channel state information (CSI). However, a significant mismatch between the estimated channel and the actual channel for data transmission causes outdated CSI, leading to a severe degradation in spectral efficiency. In this paper, we propose a channel estimation technique for mmWave massive MIMO systems that obtains multipath components of the downlink channel from the previous channel sequence. To be specific, the proposed technique learns the spatio-temporal correlation be-tween multipath components by exploiting a Transformer-based framework. From the numerical results, we demonstrate that the proposed technique outperforms the conventional channel acquisition techniques in terms of normalized mean square error (NMSE).

In the 5G NR and 6G, vehicle-to-everything (V2X) communication has emerged as a crucial technology. In this paper, we present a novel task-oriented communications (ToC) approach that employs large multi-modal models (LMMs) for V2X tasks such as traffic management. The key idea of the proposed multi-modal task-oriented communications (MMToC) is to extract essential information relevant to vehicular tasks from the various sensing data, integrate the information, and infer the output of vehicular tasks using LMMs. Unlike traditional communication methods focusing on bit-level metrics such as bits per second and error rate, MMToC enhances task efficiency and performance by optimizing information transmission methods for specific vehicular tasks. MMToC exchanges only the essential feature vectors needed for vehicular tasks, thereby reducing communication overhead and improving task performance. Numerical results demonstrate that the proposed MMToC technique significantly increases average vehicle speed by more than 40% compared to conventional methods.

The ever-increasing data rate demand of 5G and 6G communication systems can be met by employing key technologies such as upper mid-band and millimeter (mmWave)-band communications. In upper mid and mmWave communications, beamforming techniques realized by multiple-input multiple-output (MIMO) systems are used to compensate for severe attenuation due to high diffraction and penetration loss. Unfortunately, the widely used codebook-based beam management techniques require complicated handshaking operations to determine the beam direction which incurs considerable processing latency and energy consumption. To address these issues, we propose the multi-modal beam management (MMBM) technique. The proposed MMBM exploits multi-modal sensing (i.e., LiDAR and RGB camera) along with computer vision (CV) techniques to identify the 3D location of mobile devices. With the 3D position, the base station (BS) can accurately steer directional beams toward the mobile’s position. Since the beam direction is determined without feedback delay or codebook quantization, MMBM achieves performance improvements in terms of data rate and latency. From the simulation results, we show that MMBM improves the sum rate by more than 15% compared to the existing codebook-based beamforming scheme in 5G NR.

To integrate a variety of data-demanding and delay-sensitive applications, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system has emerged as a key enabler for 6G wireless communications. To fully exploit mmWave massive MIMO system, acquisition of accurate channel information is of great importance while such is challenging due to short coherence time of mmWave channel. Therefore, to obtain accurate channel information in a real-time manner, we propose sensing-aided multi-modal channel prediction technique (SMCPT). By analyzing sensing images, we can accurately and quickly extract the positions of mobile devices as well as the positions, orientations, and materials of entities affecting signal propagation, thus enhancing channel prediction accuracy. From practical experiments, we show that SMCPT achieves more than 69% channel prediction accuracy gain over conventional schemes.

Ultra-dense networks (UDN) are anticipated to fulfill the high-performance demands for future applications of 5G and beyond communication networks. By exploiting recent advances in computer vision techniques, we propose a novel cell association technique, referred to as computer vision-based cell association (CV-CA), that circumvents the cumbersome CSI acquisition. The computer vision technique provides accurate 3D position and determines the line-of-sight communication link of all users within the UDN using RGB images. We demonstrate from the simulations that the proposed CV-CA outperforms 5G-NR and conventional cell association techniques in terms of total data rate.

In the era of 6G communication, the demand for accurate localization is ever-increasing to support a wide range of applications and devices. Due to the densely distributed obstacles in urban areas, it is difficult to locate the target accurately without knowing the reflection point in non-line-of-sight (NLoS) propagation. In such cases, the layout of the urban area can provide additional information in localization. In this work, we introduce a Transformer-based localization technique, referred as Map Embedded Localization Transformer (MELT), using the environment-awareness. Specifically, MELT learns the geometric correlations between wireless geometry presented in layout image and channel parameters by employing attention mechanism of Transformer. By utilizing the correlations, MELT estimates the target location accurately and robustly. Our simulation results demonstrate the effectiveness of the proposed scheme for localization in NLoS environments in terms of root mean square error (RMSE) and the coverage.

To meet the ever-increasing data rate demand expected in 6G networks, terahertz (THz) ultra-massive (UM) multiple-input multiple-output (MIMO) systems have gained much attention recently. One notable aspect of these systems is that the deployment of an extremely large-scale antenna array and high transmission frequency result in an expansion of the near-field region where the electromagnetic (EM) radiation is modeled as a spherical wave. In the near-field region, the channel becomes a function of a position of a user equipment (UE) rather than the direction, giving rise to a beam focusing operation that focuses the signal power onto the specific position. However, the traditional approaches relying on the sweeping of discretized beam codewords cannot support this ultra-sharp beam focusing operation in THz UM-MIMO systems. This paper proposes a novel beam focusing technique based on sensing and computer vision (CV) technologies. The essence of the proposed scheme is to estimate the UE’s position from the vision information using the CV technique and then generates the beam heading towards the estimated position. By replacing the discretized and time-consuming beam sweeping operation with a highly precise CV-based positioning, the positioning accuracy as well as the beam focusing gain can be improved significantly. Numerical results show that the proposed scheme achieves significant positioning accuracy and data rate gains over the conventional codebook-based beam focusing schemes.

Millimeter wave (mmWave) and terahertz (THz) communications have been considered as the key techniques to support extremely high data rates in the 6G system. One main limitation of the mmWave/THz communications is the severe path loss and low penetration power. For these reasons, it is expected that mmWave/THz communication will be mainly employed in the ultra-dense network (UDN) environment. In order to get the most out of the mmWave/THz UDN, a mobile should be associated to the base stations (BSs) providing a high quality-of-service (QoS). This task is challenging since the reliable path can be disappeared even with a small movement of a mobile. An aim of this paper is to propose a novel mobility management technique based on sensing and computer vision (CV). Our key idea is to predict the cell association from the visual sensing information and CV-based inference and decision. By extracting the geometric information of a mobile from the image and then using it for the downlink rate prediction, we preemptively switch the cell association in UDN. From the numerical evaluations on the realistic mmWave/THz UDN environments, we show that the proposed scheme achieves more than 30% throughput gain over the conventional mobility management techniques.

Beamforming design with partial channel estimation and feedback for frequency-division duplexing (FDD) reconfigurable intelligent surface (RIS) assisted systems is considered in this paper. We leverage the observation that path angle information (PAI) varies more slowly than path gain information (PGI). Then, several dominant paths are selected among all the cascaded paths according to the known PAI for maximizing the spectral efficiency of downlink data transmission. To acquire the dominating path gain information (DPGI, also regarded as the path gains of selected dominant paths) at the base station (BS), we propose a DPGI estimation and feedback scheme by jointly beamforming design at BS and RIS. Both the required number of downlink pilot signals and the length of uplink feedback vector are reduced to the number of dominant paths, and thus we achieve a great reduction of the pilot overhead and feedback overhead. Furthermore, we optimize the active BS beamformer and passive RIS beamformer by exploiting the feedback DPGI to further improve the spectral efficiency. From numerical results, we demonstrate the superiority of our proposed algorithms over the conventional schemes.

As a key technology to meet the ever-increasing data rate demand in beyond 5G and 6G communications, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have gained much attention recently. To make the most of mmWave massive MIMO systems, acquisition of accurate channel state information (CSI) at the base station (BS) is crucial. However, this task is by no means easy due to the CSI feedback overhead induced by the large number of antennas. In this paper, we propose a parametric CSI feedback technique for mmWave massive MIMO systems. Key idea of the proposed technique is to compress the mmWave MIMO channel matrix into a few geometric channel parameters (e.g., angles, delays, and path gains). Due to the limited scattering of mmWave signal, the number of channel parameters is much smaller than the number of antennas, thereby reducing the CSI feedback overhead significantly. Moreover, by exploiting the deep learning (DL) technique for the channel parameter extraction and the MIMO channel reconstruction, we can effectively suppress the channel quantization error. From the numerical results, we demonstrate that the proposed technique outperforms the conventional CSI feedback techniques in terms of normalized mean square error (NMSE) and bit error rate (BER).

Terahertz (THz) communication is envisaged as an attractive way to attain abundant spectrum resources for 6G wireless communications. One main difficulty of the THz communications is the severe attenuation of signal power caused by the high diffraction and penetration losses and atmospheric absorption. To compensate for the severe path loss, a beamforming technique realized by the massive multiple-input multiple-output (MIMO) has been widely used. Since the beamforming gain is maximized only when the beams are appropriately aligned with the signal propagation paths, acquisition of accurate beam directions is of paramount importance. A major issue of the conventional beam management schemes is the considerable latency being proportional to the number of training beams. In this paper, we propose a THz beam management technique that simultaneously generates multiple frequency-dependent beams using the true time delay (TTD)-based phase shifters. By closing the gap between the frequency-dependent beamforming vectors and the desired directional beamforming vectors using the TTD-based signal propagation network called intensifier, we generate very sharp training beams maximizing the beamforming gain. From the numerical results, we demonstrate that the proposed scheme achieves more than 70% reduction in the beam management latency and 60% increase in the data rate.

Recently, a reconfigurable intelligent surface (RIS) that controls the reflection characteristics of incident signals has received a great deal of attention. To make the most of the RIS-aided systems, an acquisition of RIS reflected channel information at the base station (BS) is crucial. However, this task is by no means easy due to the pilot overhead induced by the large number of reflecting elements. In this paper, we propose an efficient channel estimation and phase shift control technique reducing the pilot overhead of the RIS-aided mmWave systems. Key idea of the proposed scheme is to decompose the RIS reflected channel into three major components, i.e., static BS-RIS angles, quasi-static RIS-UE angles, and time-varying BS-RIS-UE path gains, and then estimate them in different time scales. By estimating the BS-RIS and RIS-UE angles occasionally and estimating only the path gains frequently, the proposed scheme achieves a significant reduction on the pilot overhead. Further, by optimizing the phase shifts using the channel components with relatively long coherence time, we can improve the channel estimation accuracy. From the performance analysis and numerical evaluations, we demonstrate that the proposed scheme achieves more than 60% pilot overhead reduction over the conventional techniques.

Weakly-supervised semantic segmentation aims to train a semantic segmentation network using weak labels. Among weak labels, image-level label has been the most popular choice due to its simplicity. However, since image-level labels lack accurate object region information, additional modules such as saliency detector have been exploited in weakly supervised semantic segmentation, which requires pixel-level label for training. In this paper, we explore a self-supervised vision transformer to mitigate the heavy efforts on generation of pixel-level annotations. By exploiting the features obtained from self-supervised vision transformer, our superpixel discovery method finds out the semantic-aware superpixels based on the feature similarity in an unsupervised manner. Once we obtain the superpixels, we train the semantic segmentation network using superpixel-guided seeded region growing method. Despite its simplicity, our approach achieves the competitive result with the state-of-the-arts on PASCAL VOC 2012 and MS-COCO 2014 semantic segmentation datasets for weakly supervised semantic segmentation. Our code is available at https://github.com/st17kim/semantic-aware-superpixel.

Recently, terahertz (THz) communication supported by the ultra-dense network (UDN) has received a great deal of attention as a means to satisfy stringent requirements in throughput, latency, and energy consumption in 6G. In the UDN supported by THz beamforming, handover, an action to change the base station (BS) serving the user, occurs frequently due to the small cell coverage and sudden line-of-sight (LoS) link blockage caused by the interruption of obstacles. To ensure the seamless connectivity in the THz UDN, mobility management, the process to identify the link deterioration and perform the handover, should be performed quickly and accurately. An aim of this paper is to propose a novel computer vision (CV)-aided framework to proactively control the mobility management in the THz communication regime. In our framework referred to as proactive computer vision-aided mobility management (P-CVMM), the position of the user (i.e., distance and angles) is extracted from the images via deep learning (DL)-based object detector and then exploited in predicting the optimal serving BS (S-BS). Since the sparse geometric channel parameters are immediately obtained without the complicated feedback process and the beam heading toward the user's future position can be generated without quantization, P-CVMM can predictively avoid radio link failure (RLF). Using the specially designed dataset called vision objects for mobility management (VOMM), we demonstrate that P-CVMM effectively avoids RLF and achieves more than 90% and 70% reduction in the localization error and handover interruption time (HIT) over the conventional schemes.

Terahertz (THz) multiple-input multiple-output (MIMO) beamforming is a key technology to support immersive mobile services in 6G communication systems. In the beamforming vector generation, the analog phase shifter which generates the phase invariant to the signal frequency have been widely used. However, since the spatial directions of the subcarrier channels are functions of the subcarrier frequency in the wideband THz systems, the beamforming techniques based on the analog phase shifters suffer from a severe beamforming gain loss. In this paper, we propose a novel beamforming technique that exploits the true time delay (TTD)-based phase shifters to generate multiple frequency-dependent beamforming vectors for the wideband THz systems. Intriguing feature of the proposed scheme is to exploit a deliberately designed TTD-based signal propagation network called calibrator to bridge the gap between the desired beamforming vectors and the frequency-dependent beamforming vectors. In doing so, the signal power is concentrated onto the mainlobe so that the generated beamforming vectors can achieve the maximum beamforming gain. From the numerical results, we demonstrate that the proposed scheme achieves more than 80% data rate gain over the conventional beamforming schemes.

To support extremely high data rates in 6G wireless networks, terahertz (THz) communication has attracted great interest in recent years. However, due to the strong directivity and severe signal attenuation of THz signal, the link quality is highly sensitive to obstacles, especially when there is only a line-of-sight (LoS) path. To enable proactive proactive handover to a transmitter with an alternative LoS link, accurate blockage prediction is essential. Unfortunately, existing methods focusing on outdoor environments often fail to predict the blockages in complicated indoor environments. In this paper, we propose a vision-aided blockage prediction framework that utilizes the sequences of historical RGB-depth (RGB-D) information and the beam index to detect and localize users and potential blockages, predict their trajectory, and foresee the blockages in dynamic indoor scenarios. Specifically, we first model the background and use a deep learning-based object detector to detect the users as well as potential blockages. We then predict the future locations of the users using an LSTM-based neural network. We demonstrate from numerical results that the proposed scheme outperforms conventional schemes in terms of blockage prediction accuracy and handover decision-making.

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the image attribute. In this paper, we put forth a new GZSL technique exploiting Vision Transformer (ViT) to maximize the attribute-related information contained in the image feature. In ViT, the entire image region is processed without the degradation of the image resolution and the local image information is preserved in patch features. To fully enjoy the benefits of ViT, we exploit patch features as well as the CLS feature in the extraction of the attribute-related image feature. In particular, we propose a novel attention-based module, called attribute attention module (AAM), to aggregate the attribute-related information in the patch features. From extensive experiments on benchmark datasets, we demonstrate that the proposed technique outperforms the state-of-the-art GZSL approaches by a large margin.

Image-text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa. One challenge of this task is the vulnerability to input image/text corruptions. Such corruptions are often unobserved during the training, and degrade the retrieval model’s decision quality substantially. In this paper, we propose a novel image-text retrieval technique, referred to as robust visual semantic embedding (RVSE), which consists of novel image-based and text-based augmentation techniques called semantic-preserving augmentation for image (SPAug-I) and text (SPAug-T). Since SPAug-I and SPAug-T change the original data in a way that its semantic information is preserved, we enforce the feature extractors to generate semantic-aware embedding vectors regardless of the corruption, improving the model’s robustness significantly. From extensive experiments using benchmark datasets, we show that RVSE outperforms conventional retrieval schemes in terms of image-text retrieval performance.

Transformer-based networks have achieved great success in image captioning because of the attention mechanism that finds relevant image locations for each word. However, the current cross-attention process, which aligns word-to-image, does not consider the spatial relationships existing in patch-to-patch. This lack of spatial information may cause incorrect descriptions that fail at generating words that correctly describe the positional relationships. In this paper, we introduce a novel cross-attention architecture that utilizes spatial information from coordinate differences between relevant image patches. In doing so, our new cross-attention process dynamically considers both the related contents and their spatial relationships in caption generation. In addition, we introduce an efficient implementation of relative spatial attention based on convolutional operations. Experimental results show that the proposed spatial cross-attention improves captions to correctly describe the spatial relationships of objects, leading to an increase of 0.7 CIDEr score on the MS-COCO dataset compared to the previous state-of-the-art.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered as a promising solution to provide high-quality data services. To get the most of beamforming gain, an acquisition of accurate channel information is crucial for the success of massive MIMO systems. In recent years, numerous approaches to reduce the excessive feedback overhead due to the large number of antennas have been suggested. However, they do not consider the long-range dependency of the channel state information (CSI) resulting from the correlation between the large-scale antennas and subcarriers. In this paper, we propose a deep learning-based parametric CSI feedback technique for mmWave massive MIMO systems to improve the performance of CSI compression and reconstruction with low feedback overhead. To be specific, the proposed scheme learns the correlation between channel parameters by exploiting Transformer architecture. From the numerical results, we demonstrate that the proposed scheme outperforms the conventional channel feedback schemes in terms of the normalized mean square error (NMSE) and the feedback overhead reduction.

Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited, the network can be biased on RGB information without considering the comprehensive view. We propose a novel depth estimation model named RElative Depth Transformer (RED-T) that uses relative depth as guidance in self-attention. Specifically, the model assigns high attention weights to pixels of close depth and low attention weights to pixels of distant depth. As a result, the features of similar depth can become more likely to each other and thus less prone to misused visual hints. We show that the proposed model achieves competitive results in monocular depth estimation benchmarks and is less biased to RGB information. In addition, we propose a novel monocular depth estimation benchmark that limits the observable depth range during training in order to evaluate the robustness of the model for unseen depths.

As a means to support extremely high data rates in 6G wireless networks, terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems have attracted great interest in recent years. Unfortunately, due to the strong directivity and severe attenuation of the THz signal, the number of propagation paths is at most a few in the THz band. In most cases, therefore, the THz channel matrix is a low-rank matrix, which dramatically limits the channel capacity of THz systems. To increase the channel capacity of THz systems, the array-of-subarray (AoSA) technique that exploits a group of widely-spaced antenna subarrays has been proposed. A major issue of the AoSA scheme is that the base station (BS) has to employ a large number of subarrays along with the radio frequency (RF) chains connected to the subarrays so that the power consumption is considerable. In this paper, we propose an efficient THz UM-MIMO subarray architecture maximizing the channel capacity while reducing the power consumption. Key idea of the proposed scheme referred to as distance-aware subarray selection (DSS), is to choose a small number of subarrays maximizing the channel capacity, and then activate only the RF chains connected to the chosen subarrays. From the simulation results, we demonstrate that the proposed DSS scheme achieves a significant channel capacity gain over the conventional schemes.

To support extremely high data rates in 6G wireless networks, reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communications have gained much attention. By controlling the phase shifts of reflecting elements, RIS can proactively modify the wireless THz channel, thereby enhancing the achievable data rate significantly. One major challenge of the wideband THz communication is the severe array gain loss caused by the beam split effect that the path components split into different spatial directions at different subcarrier frequencies. Therefore, the conventional phase shift control and beamforming techniques cannot be directly applied to wideband THz systems. In this paper, we propose a RIS-assisted frequency-dependent beamforming (R-FDB) technique maximizing the average data rate of the RIS-assisted wideband THz systems. Key idea of R-FDB is to alternately optimize the analog beamforming vector and the RIS phase shift vector by properly designing the parameters of the R-FDB network such that the average data rate of the wideband THz system is maximized. We demonstrate from the numerical evaluations that R-FDB achieves a significant data rate gain over the conventional schemes.

The main purpose of this paper is to propose an effective precoding technique for the frequency-division-duplexing (FDD)-based massive MIMO systems under the common scattering effect. Key idea of the proposed path-selective precoding (PSP) scheme is to choose a small paths maximizing the data rate and then exploit only the angle information of the chosen paths for the downlink data precoding. To efficiently select the paths for each mobile, we use the notion of leakage, a metric of how much signal power leaks into other mobiles. While the interference is a joint function of precoding vectors of different mobiles, the leakage is solely a function of the precoding vector of corresponding mobile so that the signal-to-leakage-and-noise-ratio (SLNR) maximization problem can be decoupled into the sub-problems for each mobile. To find out a near-optimal solution of the decoupled SLNR maximization problem, we propose a greedy algorithm that iteratively removes the index of shared paths from the candidate index set until the SLNR does not increase. We demonstrate from the simulation results that the proposed PSP scheme achieves the significant data rate gains over the conventional angular-domain precoding schemes.

To support extremely high data rates in 6G wireless networks, reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communications have gained much attention in recent years. By manipulating the phase shifts of reflecting elements, the RIS can proactively adjust the wireless propagation environment of THz systems, thereby enhancing the overall throughput significantly. To realize the full potential of RIS-assisted THz systems, an acquisition of accurate channel information is of great importance. However, since the wavefront of the THz electromagnetic signal is spherical, the conventional channel estimation techniques using the planar wavefront assumption suffer from severe performance degradation in the near-field RIS-assisted THz systems. An aim of this work is to propose an efficient channel estimation technique for near-field RIS-assisted wideband THz systems. Key idea of the proposed polar-domain frequency-dependent RIS-assisted channel estimation (PF-RCE) scheme is to estimate the sparse multipath components (i.e., angles, distances, and path gains) of the near-field THz channel by exploiting the polar-domain sparsity and common support properties. We demonstrate from the numerical evaluations that PF-RCE achieves a significant performance gain over the conventional THz channel estimation schemes in terms of the normalized mean square error (NMSE).

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the attribute. In this paper, we put forth a new GZSL technique that improves the GZSL classification performance greatly. Key idea of the proposed approach, henceforth referred to as semantic feature extraction-based GZSL (SE-GZSL), is to use the semantic feature containing only attribute-related information in learning the relationship between the image and the attribute. In doing so, we can remove the interference, if any, caused by the attribute-irrelevant information contained in the image feature. To train a network extracting the semantic feature, we present two novel loss functions, 1) mutual information-based loss to capture all the attribute-related information in the image feature and 2) similarity-based loss to remove unwanted attribute-irrelevant information. From extensive experiments using various datasets, we show that the proposed SE-GZSL technique outperforms conventional GZSL approaches by a large margin.

In this paper, a new version of time-frequency (T-F) ESPRIT algorithm with reduced computational complexity is proposed. The key idea of proposed covariance-based T-F ESPRIT (CB T-F ESPRIT) algorithm is to use the covariance-based DoA (CB-DoA) approach for the signal subspace construction. Specifically, the proposed CB T-F ESPRIT algorithm first constructs the time-frequency data model and then exploits the STFD matrix for the estimation of signal subspace. In particular, instead of directly performing EVD on the covariance matrix obtained from the averaged STFD matrix, the proposed scheme employs the CB-DoA approach which provides a lower computational complexity while maintaining the performance gain of T-F ESPRIT algorithm over the conventional ESPRIT algorithm. From the computational complexity analysis and the numerical evaluations, we demonstrate that CB T-F ESPRIT algorithm outperforms the conventional DoA estimation schemes with reduced computational complexity.

With the emergence of 5G era, network slicing has received much attention due to its ability to support various services. Network slicing is an approach to partition a single physical network into multiple slices supporting separate services and has been extended to the handover scenario (where the UE moves from one cell to another) recently. In this paper, we propose a deep reinforcement learning (DRL)-based handover-aware network slicing technique for the cell selection and network slicing. Key ingredient of the proposed technique is to use action elimination to reduce the size of slice allocation decision space. In our work, we first determine the target cell providing the maximum user-requested services to the handover UE, and then assign network slices to the handover UE by exploiting action elimination-assisted DRL. From the numerical results, we demonstrate that the proposed technique outperforms the conventional network slicing techniques in terms of throughput.

Reconfigurable intelligent surface (RIS) is a promising technology that can provide a virtual line-of-sight (LoS) link for the mmWave communications via intelligent signal reflection. In order to maximize the throughput of RIS-aided mmWave systems, acquisition of accurate downlink channel information at the base station (BS) is crucial. However, since the BS needs to acquire not only the conventional direct channel between the BS and user but also the channels reflected by RIS (i.e., BS to RIS and RIS to user channels), the pilot overhead is proportional to the number of RIS reflecting elements. In this paper, we propose an efficient channel estimation framework reducing the pilot overhead of RIS-aided mmWave systems. Key idea of proposed scheme is to decompose the RIS-aided channel into three major components, i.e., static BS- RIS angles, quasi-static RIS-user angles, and time-varying BS-RIS-user path gains, and then estimate them in different time scales. In doing so, the number of channel parameters to be estimated at each stage can be reduced significantly. From the simulation results, we demonstrate that the proposed scheme is very effective in reducing the pilot overhead of RIS-aided mmWave systems.

The terahertz (THz) communication systems as-sisted by ultra-massive (UM) number of antennas have been considered as a promising solution for future 6G wireless communications. As a means to overcome the severe propagation loss arising from THz band and thus achieve high beamforming gain, ultra-massive multiple-input-multiple-output (UM-MIMO) sys-tems have received much attention. To realize highly directional communications, acquisition of accurate channel state information is essential but the channel estimation techniques designed for the ideal far-field channel result in severe performance loss in the near-field region. In this paper, we propose an intelligent near-field channel estimation technique for THz UM-MIMO systems. To be specific, we extract the channel parameters, i.e., angles, distances, time delay, and complex gains, by exploiting the convolution neural network (CNN), a deep learning network specialized in capturing the spatially correlated features from the input data. From the simulation results, we demonstrate that the proposed scheme outperforms the conventional channel estimation schemes in terms of the bit error rate (BER) and the pilot overhead reduction.

This paper presents a joint modulation and coding technique called quantized sparse vector code (Q-SVC) for multiple-input multiple-output (MIMO) inter-symbol-interference (ISI) channel with low-resolution analog-to-digital converters (ADCs). The key idea of Q-SVC is to encode sparse information bits over the space-time domain by a superposition of selected dictionary vectors. To decode Q-SVC from coarsely-quantized measurements in a computationally efficient manner, we present a greedy sparse signal detection algorithm called Bayesian multipath matching pursuit (BMMP). The simulation results demonstrate that the proposed encoding and decoding pair can outperform the existing coded-modulation techniques in terms of both the frame error rate (FER) and the computational complexity.

Terahertz (THz) communication has been widely considered as a key enabler for future wireless systems. However, due to the strong directivity and severe attenuation of THz signals, the communication performance relies heavily on the existence of a line-of-sight (LoS) link. To deal with this problem, a reconfigurable intelligent surface (RIS) that modifies the wireless channel through intelligent signal reflection has gained much attention recently. In this paper, we propose an efficient near-field RIS-assisted wideband THz channel estimation scheme. Key idea of the proposed scheme, referred to as the polar-domain frequency-dependent RIS-assisted channel estimation (PF -RCE), is to exploit the polar-domain sparsity of the near-field channel and common support property of the wideband THz channel. To the best of our knowledge, this is the first work that investigates the characteristics of RIS-assisted wideband THz channel in the near- filed region and provides an efficient channel estimation scheme. From the numerical evaluations, we demonstrate that the proposed PF-RCE scheme achieves a significant performance gain over the conventional THz channel estimation schemes in terms of the normalized mean square error (NMSE).

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes. Conventionally, conditional generative models have been employed to generate training data for unseen classes from the attribute. In this paper, we propose a new conditional generative model that improves the GZSL performance greatly. In a nutshell, the proposed model, called conditional Wasserstein autoencoder (CWAE), minimizes the Wasserstein distance between the real and generated image feature distributions using an encoder-decoder architecture. From the extensive experiments on various benchmark datasets, we show that the proposed CWAE outperforms conventional generative models in terms of the GZSL classification performance.

Millimeter-wave (mmWave) communications will play an important role in 5G and 6G communication systems as a means to support extremely high data rates. One main bottleneck of the mmWave communication is the severe signal attenuation caused by the foliage loss, atmospheric absorption, body and hand losses in the mmWave band. To compensate for the severe path loss, multiple-input-multiple-output (MIMO) antenna array-based beamforming has been widely used. Since the beams should be aligned with the signal propagation paths to get the most of beamforming gain, acquisition of accurate channel knowledge, i.e., channel estimation, is the key to the success of mmWave MIMO systems. In this paper, we propose a new type of deep learning (DL)-based parametric channel estimation technique. In our work, DL figures out the direct mapping between the received pilot signal and the sparse channel parameters characterizing the angular domain channel. By exploiting the long short-term memory (LSTM) as a main deep neural network (DNN) engine, we extract the temporally correlated features of time-varying channel parameters and make a fast yet accurate estimation with relatively small pilot overhead. From the numerical experiments, we show that the proposed scheme is effective in estimating the mmWave MIMO channel in various mmWave downlink environments.

With the rapid development of intelligent transportation systems (ITS), a growing number of vehicular applications have emerged to provide an entirely new experience for our daily life. To provide low-latency and high reliable services for these applications, there has been growing interest in reconfigurable intelligent surface (RIS)-aided vehicle-to-everything (V2X) systems. In this paper, we propose an entirely different deep learning (DL)-based phase shift control scheme for fast time-varying V2X channel. The proposed scheme, henceforth referred to as LSTM-based phase shift control for V2X (L-PSCV), learns temporal variation of channels from past pilot sequence and then uses them to find out the optimal phase shift for instantaneous channel. From the numerical experiments on the V2X system, we demonstrate that the proposed L-PSCV scheme outperforms the conventional schemes in terms of sum-rate.

Recently, massive machine-type communications (mMTCs) have become one of key use cases for 5G. In order to support massive users transmitting small data packets at low rates, grant-free (GF) access and nonorthogonal multiple access (NOMA) have been suggested. Since each device transmits information without scheduling in the GF-NOMA systems, the device identification process, called active user detection (AUD), is required at the base station (BS). For the NOMA-based systems, the channel estimation (CE), an operation after the AUD, is a challenging task since multiple devices’ transmit signals and channels are superimposed in the same wireless resources. In this article, we propose a deep learning (DL)-based AUD and CE in the GF-NOMA systems. In our work, DL figures out the direct mapping between the received NOMA signal and the indices of active devices and associated channels using the long short-term memory (LSTM). From numerical experiments, we show that the proposed scheme is effective in handling the AUD and CE in the mMTC environments.

Sparse vector coding (SVC) is emerging as a key enabler for short-packet ultrareliable and low-latency communications (URLLCs), since it displays good block error rate (BLER) performance and can achieve low transmission latency. In this article, we propose an SVC-based sparse superimposed transmission (SVC-ST) coding scheme to further enhance the BLER performance of the SVC scheme. At the encoding side, a portion of transmission bits is represented by nonzero position indices of the sparse vector. The remaining bits are equally split into multiple streams and then mapped into the nonzero positions of sparse vector via quadrature amplitude modulation (QAM) with constellation rotation (CR). We afterward adopt the multipath matching pursuit-based soft decoding (MMP-SD) to recover the transmission packet. The BLER and bit error rate (BER) analyses of the SVC-ST scheme demonstrate the validity and rationality of our study. Moreover, we find from the simulation results that the proposed SVC-ST scheme outperforms SVC and its enhanced version (ESVC) schemes in terms of BLER and latency performance.

With the explosive growth in mobile data traffic, ultra-dense network (UDN) where a large number of small cells are densely deployed on top of macro cells has received a great deal of attention in recent years. While UDN offers a number of benefits, an upsurge of energy consumption in UDN due to the intensive deployment of small cells has now become a major bottleneck in achieving the primary goals viz., 100-fold increase in the throughput in 5G+ and 6G. In recent years, an approach to reduce the energy consumption of base stations (BSs) by selectively turning off the lightly-loaded BSs, referred to as the sleep mode technique, has been suggested. However, determining an appropriate active/sleep modes of BSs is a difficult task due to the huge computational overhead and inefficiency caused by the frequent BS mode conversion. An aim of this paper is to propose a deep reinforcement learning (DRL)-based approach to achieve a reduction of energy consumption in UDN. Key ingredient of the proposed scheme is to use decision selection network to reduce the size of action space. Numerical results show that the proposed scheme can significantly reduce the energy consumption of UDN while ensuring the rate requirement of network.

With the rapid development of intelligent transportation systems (ITS), mission-critical vehicular services such as vehicle platooning, safety alarming, and remote driving play a key role in the future ITS. In order to support these emerging services, low-latency and high reliability transmission should be ensured. In the 4G Long Term Evolution (LTE) and 5G New Radio (NR) vehicle-to-everything (V2X) systems, it is very difficult to meet the latency requirement since group of orthogonal frequency division multiplexing (OFDM) symbols are processed in a form of the resource block. In this paper, we propose a novel low-latency packet transmission scheme for V2X systems, referred to as partial sample transmission (PST). In PST, we map the transmit information into subcarrier positions and then decode it using a small fraction of received samples at the receiver. To perform the efficient decoding, we propose a deep learning (DL)-based PST decoding. From the numerical evaluations on the V2X system, we demonstrate that the proposed PST technique outperforms conventional transmission schemes in terms of the block error rate (BLER) and the signaling latency.

© 2002-2012 IEEE.Recently, reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiencies by reconfiguring the wireless propagation environment. In this paper, we propose an RIS phase shift and BS beamforming optimization technique that minimizes the uplink transmit power of the RIS-aided IoT network. Key idea of the proposed scheme, referred to as Riemannian conjugate gradient-based joint optimization (RCG-JO), is to jointly optimize the RIS phase shifts and the BS beamforming vectors using the Riemannian conjugate gradient technique. By exploiting the product Riemannian manifold structure of the sets of unit-modulus phase shifts and unit-norm beamforming vectors, we convert the nonconvex uplink power minimization problem into the unconstrained problem and then find out the optimal solution over the product Riemannian manifold. From the performance analysis and numerical evaluations, we demonstrate that the proposed RCG-JO technique achieves 94% reduction of the uplink transmit power over the conventional scheme without RIS.

Federated learning is a machine learning framework that enables AI models training over a network of multiple user devices without revealing user data stored in the devices. Popularly used federated learning technique to enhance the learning performance of user devices is to globally evaluate the learning model at the server by averaging the locally trained models of the devices. This global model is then sent back to the user devices so that every device applies it in the next training iteration. However, this average-based model is not always better than the local update model of a user device. In this work, we put forth a new update strategy based on the simulated annealing (SA) algorithm, in which the user devices choose their training parameters between the global evaluation model and their local models probabilistically. The proposed technique, dubbed simulated annealing-based federated learning (SAFL), is effective in solving a wide class of federated learning problems. From numerical experiments, we demonstrate that SAFL outperforms the conventional approach on different benchmark datasets, achieving an accuracy improvement of 50% in a few iterations.

With the rapid development of intelligent transportation systems (ITS), mission-critical vehicular services such as vehicle platooning, safety alarming, and remote driving play a vital role in the blueprint of the future ITS. In order to support these services, the high reliability and low latency are of great importance. In the 4G LTE/5G NR, it is difficult to satisfy these requirements since multiple OFDM symbols are processed as a bundle. In this paper, we propose a novel low latency packet transmission scheme, referred to as partial sample transmission (PST). Key idea of the proposed scheme is to transform the transmit information into subcarrier positions and then decode it using a small amount of time-domain received samples. In particular, in the PST decoding, we put forth an entirely different approach based on a deep neural network (DNN). From the numerical evaluations on realistic channel models, we demonstrate that the PST scheme outperforms the conventional transmission schemes in terms of the block error rate (BLER) and transmission latency.

Intelligent refiecting surface (IRS), a planar meta- surface consisting of a large number of reflecting elements, is a promising solution for improving the spectral efficiency of future wireless systems. In order to maximize the throughput gain of IRS, the base station (BS) needs to acquire not only the conventional direct channel between the BS and user equipment (UE) but also the IRS reflected channel. Since the dimension of the IRS reflected channel is proportional to the number of reflecting elements, the pilot overhead as well as the channel estimation error are tremendous, resulting in a significant data rate degradation. In this paper, we propose a deep learning (DL)-based approach to find out the IRS phase shift maximizing the data rate of IRS-aided communication systems. To achieve this goal, we express the relationship between the noisy estimated channel and the IRS phase shifts using the deep neural network. We then train the network parameters in the direction of maximizing the data rate formulated with the ideal channel. From the simulation results, we demonstrate that the proposed scheme outperforms the benchmark schemes by a large margin.

Employing intelligent reflecting surfaces (IRSs) is emerging as a green alternative to massive antenna systems for improving signal quality and suppressing interference. Specifically, IRS is a planar surface consisting of a large number of low-cost and passive elements each being able to reflect the incident signal independently with an adjustable phase shift, thus the three-dimension (3D) passive beamforming can be collaboratively achieved without the need of any transmit radio-frequency (RF) chains. In this paper, we study the uplink power control of an IRS-aided Internet of Things (IoT) network under the quality of service (QoS) constraints at each user. Our goal is to minimize the total user power by jointly optimizing the phase shifts of IRS reflecting elements and the receiving beamforming at the BS, subject to each user's individual signal-to-interference-plus-noise ratio (SINR) constraint which characterizes its QoS. To solve the formulated non-convex optimization problem, we develop an efficient scheme, called the Riemannian manifold-based alternating optimization (RM-AO). Simulation results demonstrate that the proposed RM-AO algorithm saves the uplink transmit power significantly.

MmWave cell-free systems where multiple base stations (BSs) cooperatively serve user using the mmWave band signal have gained much attention recently due to its capability to dramatically improve the system capacity. One potential drawback of mmWave cell-free systems is that an intensive deployment of BSs increases the energy consumption of network substantially. To improve the energy efficiency, acquisition of accurate downlink channel state information (CSI) at the BSs is essential. However, this task is not easy since the CSI feedback overhead scales linearly with the number of antennas as well as the number of BSs. In this paper, we propose an approach to maximize the energy efficiency of mmWave cell-free systems under the limited feedback. Key idea of the proposed energy-efficient dominating path selection (EE-DPS) algorithm is to choose a small number of paths in the angular domain channel and then exploit the channel information of chosen paths in the data precoding and power allocation. By choosing the dominating paths maximizing the energy efficiency and then feeding back the components of chosen paths, we achieve a considerable reduction in the feedback overhead. Numerical results demonstrate that EE-DPS achieves more than 80% energy efficiency improvement over the conventional CSI feedback-based schemes.

In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave) communications. Beam tracking is employed for transmitting the known symbols using thesounding beamsand tracking time-varying channels to maintain a reliable communication link. When the pose of a user equipment (UE) device varies rapidly, the mmWave channels also tend to vary fast, which hinders seamless communication. Thus, models that can capture temporal behavior of mmWave channels caused by the motion of the device are required, to cope with this problem. Accordingly, we employ a deep neural network to analyze the temporal structure and patterns underlying in the time-varying channels and the signals acquired by inertial sensors. We propose a model based on long short term memory (LSTM) that predicts the distribution of the future channel behavior based on a sequence of input signals available at the UE. This channel distribution is used to 1) control the sounding beams adaptively for the future channel state and 2) update the channel estimate through themeasurement update stepunder a sequential Bayesian estimation framework. Our experimental results demonstrate that the proposed method achieves a significant performance gain over the conventional beam tracking methods under various mobility scenarios.

As a means to achieve thousand-fold throughput improvements of future wireless communications, ultra-dense network (UDN) where a large number of small cells are densely deployed on top of the macro cells has received great deal of attention in recent years. While UDN offers number of benefits, intensive deployment of small cells may pose a serious concern in the energy consumption. Over the years, to reduce the energy consumption of UDN, an approach that turns off the lightly loaded base stations (BSs) has been proposed. However, determining the proper on/off modes of BSs is a challenging problem due to the huge computational overhead and inefficiency caused by the delayed decision. An aim of this paper is to propose a deep neural network (DNN)-based framework to achieve reduction of energy consumption in UDN. By exploiting the long short-term memory (LSTM) to extract the temporally correlated features from the channel information and the feedforward network to make BS on/off mode decision, we can control the on/off modes of BSs, thereby achieving a considerable reduction of the cumulative energy consumption. From the extensive simulations, we demonstrate that the proposed technique is effective in reducing the energy consumption of UDN.

In this paper, we propose a deep neural network based matrix completion approach for Internet of Things (IoT) localization. In the proposed method, we recast Euclidean distance matrix completion problem into the alternating minimization problem. By using a cascade of multiple deep neural networks to recover the location map of sensors (and the original distance matrix) from the noisy observed matrix, the proposed method can achieve an accurate reconstruction performance of the distance matrix. The numerical simulations demonstrate that the proposed method outperforms state-of-the-art matrix completion algorithms both in noisy and noiseless scenarios.

As a means to support the access of massive machine-type communication devices, grant-free access and nonorthogonal multiple access (NOMA) have received a lot of attention recently. In the grant-free environment, each device transmits information without scheduling. Hence, the device identification process called active user detection (AUD) is indispensable at the base station. After the AUD process, the channel estimation for active devices is performed in the base station before detecting the data. These processes are challenging problems in the NOMA-based systems since it is difficult to detect the active devices and estimate the channel of those devices from the superimposed received signal. In this paper, we propose a deep neural network (DNN)-based joint AUD and CE scheme for the practical mMTC systems. Specifically, the proposed scheme consists of long short term memory (LSTM)-based AUD (L-AUD) and DNN-based CE (D-CE). In L-AUD, by feeding the training data in the designed network, the proposed LSTM network is trained to exploit the extracted features when mapping the received NOMA signal to the indices of active devices. After the AUD process, by using the deeply stacked hidden layers, D-CE extracts the channel features and the codebook features of the active devices to map the received NOMA signal to the corresponding channel. As a result, the trained DNN can jointly handle the whole AUD and CE processes, achieving an accurate detection of the active devices and the small channel estimation error.

Orthogonal least squares (OLS) is a classic algorithm for sparse recovery, function approximation, and subset selection. In this paper, we analyze the performance guarantee of the OLS algorithm. Specifically, we show that OLS guarantees the exact reconstruction of any K-sparse vector in K iterations, provided that a sensing matrix has unit ℓ2-norm columns and satisfies the restricted isometry property (RIP) of order K + 1 with δK+1K= {1/√K, K = 1, 1/√(K + 1/4), K = 2, 1/√(K + 1/16), K = 3, 1/√K, K ≥ 4}. Furthermore, we show that the proposed guarantee is optimal in the sense that if δK+1≥ CK, then there exists a counterexample for which OLS fails the recovery.

Employing intelligent reflecting surfaces (IRSs) is emerging as a green alternative to improve the signal quality and suppress interference for massive antenna systems. Specifically, IRS is a planar surface consisting of a large number of low-cost and passive elements each being able to reflect the incident signal independently with an adjustable phase shift. In this paper, we study the power control problem at the user for an IRS-aided uplink system under the quality of service (QoS) constraints. Our goal is to minimize the total transmit power at the user by jointly optimizing the phase shifts of passive elements at the IRS and the receiving beamforming at the BS, subject to the signal-to-noise ratio (SNR) constraint at the user. To solve the resulting non-convex optimization problem, we develop an efficient algorithm, called the manifold-based alternating optimization (M-AO). Simulation results show that the proposed algorithm significantly saved the transmit power.

Cell-free system is a promising technology in which a group of base stations (BSs) cooperatively serves the user. One potential drawback of cell-free systems is that the intensive deployment of BSs might increase the energy consumption of the network. In order to improve the energy efficiency of cell- free systems, acquisition of accurate downlink channel state information (CSI) at the BSs is crucial. However, downlink CSI acquisition is not easy due to the CSI feedback overhead. This issue is even more pronounced in the cell-free systems since the user has to feed back the downlink CSIs of multiple BSs. In this paper, we propose energy-efficient dominating path selection (EE-DPS) algorithm to maximize the energy efficiency of cell- free systems with finite rate feedback. Key idea of the proposed algorithm is to choose a few dominating paths maximizing the energy efficiency and then only feed back the path gain information (PGI) of the chosen paths to the BSs. In doing so, the BSs can acquire relatively accurate PGI and utilize it to improve the energy efficiency. From the simulation results, we show that the proposed algorithm can improve the energy efficiency by more than 40% over the conventional power control schemes relying on the CSI feedback.

Ultra-reliable and low-latency communication (URLLC) is a new service category to accommodate emerging services requiring low end-to-end latency. In the 4G LTE/5G NR, it is very difficult to satisfy the URLLC requirements since multiple OFDM symbols are processed as a bundle. In this paper, we propose a novel low-latency packet transmission scheme, referred to as ultra-mini slot transmission (UMST), suitable for the short packet transmission in URLLC scenario. Key idea of the proposed scheme is to transform the transmit information into subcarrier positions and then decode it using a small amount of time-domain received samples. In particular, in the UMST decoding, we put forth an entirely different approach based on a deep neural network (DNN). From the numerical evaluations on realistic channel models, we demonstrate that the UMST scheme outperforms the conventional transmission schemes in terms of the block error rate (BLER) and transmission latency.

The energy consumption of cellular systems is becoming a matter of grave concern in both economic and environmental perspectives. Recently, in order to reduce the energy consumption of base stations (BSs), which takes the largest portion, turning off under-loaded BSs has been suggested. However, determining the on/off mode of BSs is a non-convex optimization problem. Also, the problem must be solved in accordance with the time-varying environment since the transition overhead in the future may outrun the power saving at the moment. In this paper, we propose Long Short-Term Memory (LSTM) based framework to make far-sighted control decisions maximizing energy efficiency from a long-term perspective. The LSTM-based network can intelligently determine the on/off modes, utilizing the time-correlated property of the channel and approximating complex mapping between channel state and desired power control coefficient. Lastly, through the convex optimization technique, the optimal power allocation for the active BSs can be found. Simulation results show that the proposed technique outperforms the conventional techniques by a large margin.

As a means to support the access of massive machine-type communication devices, grant-free access and non-orthogonal multiple access (NOMA) have received great deal of attention in recent years. In the grant-free transmission, each device transmits information without the granting process so that the base station needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to identify active devices from the superimposed received signal. An aim of this paper is to put forth a new type of AUD based on deep neural network (DNN). By feeding the training data in the properly designed DNN, the proposed AUD scheme learns the nonlinear mapping between the received NOMA signal and indices of active devices. As a result, the trained DNN can handle the whole AUD process, achieving an accurate detection of the active users. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches by a large margin in both AUD success probability and computational complexity.

Major goal of ultra reliable and low latency communication (URLLC) is to reduce the latency down to a millisecond (ms) level while ensuring reliability of the transmission. Since the current uplink transmission scheme requires a complicated handshaking procedure to initiate the transmission, to meet this stringent latency requirement is a challenge in wireless system design. In particular, in the time division duplexing (TDD) systems, supporting the URLLC is difficult since the mobile device has to wait until the transmit direction is switched to the uplink. In this paper, we propose a new approach to support a low latency access in TDD systems, called channel aware sparse transmission (CAST). Key idea of the proposed scheme is to encode a grant signal in a form of sparse vector. This together with the fact that the sensing mechanism preserves the energy of the sparse vector allows us to use the compressed sensing (CS) technique in CAST decoding. From the performance analysis and numerical evaluations, we demonstrate that the proposed CAST scheme achieves a significant reduction in access latency over the 4G LTE-TDD and 5G NR-TDD systems.

In this paper, we propose an efficient beam training technique for millimeter-wave (mmWave) communications. Beam training should be performed frequently when some mobile users are under high mobility to ensure the accurate acquisition of the channel state information. To reduce the resource overhead caused by frequent beam training, we introduce a dedicated beam training strategy which sends the training beams separately to a specific high mobility user (called a target user) without changing the periodicity of the conventional beam training. The dedicated beam training requires a small amount of resources because the training beams can be optimized for the target user. To satisfy the performance requirement with a low training overhead, we propose the optimal training beam selection strategy which finds the best beamforming vectors yielding the lowest channel estimation error based on the target user's probabilistic channel information. This dedicated beam training is combined with the greedy channel estimation algorithm that accounts for sparse characteristics and temporal dynamics of the target user's channel. Our numerical evaluation demonstrates that the proposed scheme can maintain good channel estimation performance with significantly less training overhead compared to the conventional beam training protocols.

Phase noise (PN) introduced by the oscillator at the base station and user side severely degrades the channel estimation performance. This paper investigates the impact of PN on downlink compressive channel estimation in massive multiple-input multiple-output (MIMO) systems. Particularly, the downlink compressive channel estimation with PN is modeled as a sparse signal recovery problem with additive correlated perturbation on the pilot matrix, which is a general formulation for both non-synchronous and synchronous PN. Based on this signal model, the performance of the equivalent sensing matrix is analyzed by invoking restricted isometry property (RIP) in compressive sensing. In addition, the upper bound for $l_{1}$ -minimization based channel estimation method and tight channel estimation bound are derived in the framework of RIP and Oracle least square methodology, respectively. Finally, we propose a PN-aware sparse Bayesian learning (PNA-SBL) algorithm to improve the channel estimation performance in the presence of synchronous PN. Simulation results demonstrate our analysis and superiority of the proposed PNA-SBL algorithm.

In this paper, we put forth a new joint sparse recovery algorithm called signal space matching pursuit (SSMP). The key idea of the proposed SSMP algorithm is to sequentially investigate the support of jointly sparse vectors to minimize the subspace distance to the residual space. Our performance guarantee analysis indicates that SSMP accurately reconstructs any row K-sparse matrix of rank r in the full row rank scenario if the sampling matrix A satisfies krank(A) ≥ K+1, which meets the fundamental minimum requirement on A to ensure exact recovery. We also show that SSMP guarantees exact reconstruction in at most K - r + [r/L] iterations, provided that A satisfies the restricted isometry property (RIP) of order L(K - r) + r + 1 %/L with δL(K-r)+r+1[7.8K]≤ 0.155. Furthermore, we show that under a suitable RIP condition, the reconstruction error of SSMP is upper bounded by a constant multiple of the noise power, which demonstrates the robustness of SSMP to measurement noise. Finally, from extensive numerical experiments, we show that SSMP outperforms conventional joint sparse recovery algorithms both in noiseless and noisy scenarios.

Cell-free system where a group of base stations (BSs) cooperatively serves users has received much attention as a promising technology for the future wireless systems. In order to maximize the cooperation gain in the cell-free systems, acquisition of downlink channel state information (CSI) at the BSs is crucial. While this task is relatively easy for the time division duplexing (TDD) systems due to the channel reciprocity, it is not easy for the frequency division duplexing (FDD) systems due to the CSI feedback overhead. This issue is even more pronounced in the cell-free systems since the user needs to feed back the CSIs of multiple BSs. In this paper, we propose a novel feedback reduction technique for the FDD-based cell-free systems. Key feature of the proposed technique is to choose a few dominating paths and then feed back the path gain information (PGI) of the chosen paths. By exploiting the property that the angles of departure (AoDs) are quite similar in the uplink and downlink channels (this property is referred to as angle reciprocity), the BSs obtain the AoDs directly from the uplink pilot signal. From the extensive simulations, we observe that the proposed technique can achieve more than 60% reduction in feedback overhead over the conventional CSI feedback scheme.

Hybrid analog-digital precoding is challenging for broadband millimeter-wave (mmWave) massive MIMO systems, since the analog precoder is frequency-flat but the mmWave channels are frequency-selective. In this paper, we propose a principal component analysis (PCA)-based broadband hybrid precoder/combiner design, where both the fully-connected array and partially-connected subarray (including the fixed and adaptive subarrays) are investigated. Specifically, we first design the hybrid precoder/combiner for fully-connected array and fixed subarray based on PCA, whereby a low-dimensional frequency-flat precoder/combiner is acquired based on the optimal high-dimensional frequency-selective precoder/combiner. Meanwhile, the near-optimality of our proposed PCA approach is theoretically proven. Moreover, for the adaptive subarray, a low-complexity shared agglomerative hierarchical clustering algorithm is proposed to group the antennas for the further improvement of spectral efficiency (SE) performance. Besides, we theoretically prove that the proposed antenna grouping algorithm is only determined by the slow time-varying channel parameters in the large antenna limit. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art schemes in SE, energy efficiency (EE), bit-error-rate performance, and the robustness to time-varying channels. Our work reveals that the EE advantage of adaptive subarray over fully-connected array is obvious for both active and passive antennas, but the EE advantage of fixed subarray only holds for passive antennas.

Cell-less system is a promising technology of the next generation wireless communications where a group of basestations intelligently recognizes user's communication environments and cooperatively serves the user. In order to maximize the gain obtained by the basestation cooperation, acquisition of accurate downlink channel state information (CSI) at the basestation is crucial. While this task is relatively easy for the time division duplexing (TDD) systems due to the channel reciprocity, it is not easy for the frequency division duplexing (FDD) systems due to the CSI feedback overhead. This issue is even more pronounced in the cell-less systems since the user needs to estimate and feed back the downlink CSIs of multiple basestations. In this paper, we propose a multi-path selection based feedback reduction technique for the FDD-based cell-less systems. Key ingredient of the proposed technique is that the spatial domain channel can be represented by a small number of multi-path components (angle of departure (AoD) and path gain). By exploiting the property that the AoDs are quite similar in the uplink and downlink channels, we only feed back the path gain information (PGI) to the basestations. Furthermore, by choosing a few paths maximizing the sum-rate and only feed back the PGI of the chosen paths, we can further reduce the feedback overhead substantially. From the simulations on realistic scenarios, we demonstrate that the proposed selective PGI feedback scheme is very effective in achieving the feedback overhead reduction compare to the conventional scheme relying on the CSI feedback.

Future mobile communication systems need to support wide variety of new services and applications. In order to support these, ITU-R introduced new types of use cases. One such use case, called ultra reliable and low latency communication (URLLC), concerns the reduction of latency down to a millisecond level while ensuring the reliability of the transmission. In case of uplink transmission, supporting the stringent latency requirement of URLLC is quite challenging due to a time-consuming and complicated handshaking process. In the time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is directed to the downlink. In this paper, we propose a new grant signaling scheme, referred to as channel-aware sparse signaling (CASS), to achieve a low latency access in the TDD-based URLLC systems. Key idea of CASS is to map the grant information into a small number of subcarriers and then decode it using a small number of early received samples. From the numerical evaluations, we demonstrate that the proposed CASS scheme achieves significant reduction in access latency over the conventional LTE-TDD systems.

Massive machine-type communication (mMTC), key component for internet of things (IoT), concerns the access of massive machine-type communication devices to the basestation. To support the massive connectivity, grant-free access and non-orthogonal multiple access (NOMA) have been recently introduced. In the grant-free transmission, each device transmits information without the granting process so that the basestation needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to find out the active devices from the superimposed received signal. An aim of this paper is to propose a new type of AUD scheme suitable for the highly overloaded mMTC, referred to as dimension spreading deep neural network-based AUD (DSDNN-AUD). The key feature of DSDNN-AUD is to set the dimension of hidden layers being larger than the size of a transmit vector to improve the representation quality of the support. In doing so, the proposed scheme can better discriminate the supports generated from correlated structured environment. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches in both AUD success probability and throughput performance.

Faster-Than-Nyquist (FTN) is a technique that can improve the spectral efficiency of communication systems by making better use of available spectrum resources at the cost of inter-symbol interference (ISI) and inter-carrier interference (ICI). In this paper, we propose a hybrid signaling scheme for a practical application of FTN for MIMO transmission. We propose a new slot structure optimized for the hybrid signaling supporting both FTN signaling and orthogonal frequency division multiplexing (OFDM) signaling. Specifically, in the proposed slot structure, data transmission is based on the FTN signaling and the pilot transmission is based on the OFDM signaling. Numerical results confirm that the proposed signaling scheme has clear benefit over the systems employing only OFDM or FTN signaling.

The fifth generation (5G) wireless systems aims to differentiate its services based on different application scenarios. Instead of constructing different physical networks to support each application, radio access network (RAN) slicing is deemed as a prospective solution to help operate multiple logical separated wireless networks in a single physical network. In this paper, we incorporate two typical 5G services, i.e., enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC), in a cloud RAN (C-RAN), which is suitable for RAN slicing due to its high flexibility. In particular, for eMBB, we make use of multicasting to improve the throughput, and for URLLC, we leverage finite blocklength capacity to capture the delay accurately. Our objective is to minimize the total power consumption, subject to the limited physical resource constraints. We formulate the problem as a nonconvex optimization problem and exploit efficient approaches to solve it, such as successive convex approximation and semidefinite relaxation. Simulation results show that our proposed algorithm saves system power consumption significantly.

In this paper, we analyze the performance guarantee of the rank aware order recursive matching pursuit (RA-ORMP) algorithm in recovering a group of jointly sparse vectors. Specifically, we show that RAORMP accurately reconstructs any group of l linearly independent jointly K-sparse vectors, provided that a sampling matrix satisfies the restricted isometry property (RIP) of order K + 1 with δK+1K+1≥ √l/K.

We introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized vehicle to everything (V2X) system model and focus on its capacity performance analysis. In order to solve the computation complicated problems of the involved exponential integral functions and infinite factorial expressions, we give approximate closed-form expressions with controllable arbitrary small errors. We find the accuracy of our approximate expressions is controlled by the division of $\frac{\pi}{2}$ in the urban and crowded (UC) scenario, and the truncation point $T$ in the suburban and remote (SR) scenario. Numerical results manifest 1) Increasing the number of V2X device, NOMA power and Rician factor value yields better capacity performance. 2) Effect of FD-NOMA is determined by the FD self-interference and the channel noise. 3) FD-NOMA has better latency performance compared to other schemes.

Massive multi-input multi-output systems with large-scale transmit antenna arrays can bring significant improvements in the spectral efficiency and energy efficiency. To fully enjoy the benefits of the massive MIMO systems, acquisition of accurate downlink channel state information (CSI) at the basestation is crucial. While the CSI acquisition is relatively easy for the time division duplexing (TDD) systems, it is quite burdensome for the frequency division duplexing (FDD) systems due to the CSI feedback overhead. In this paper, we propose a path selection based feedback reduction technique for the FDD-based massive MIMO systems. The key idea of the proposed scheme is to exploit the property that the spatial characteristics of the propagation channel can be represented by a small number of multi-path components (e.g., path angle and path gain). By quantizing the multi-path component information instead of the channel information, the number of bits required for the channel vector quantization scales linearly with the number of dominant paths, not the number of transmit antennas. From the simulation results, we demonstrate that the proposed scheme is very effective in achieving the feedback overhead reduction compare to the conventional scheme relying on the CSI feedback.

The fifth generation (5G) wireless system aims to differentiate its services based on different application scenarios. Instead of constructing different physical networks to support each application, radio access network (RAN) slicing is deemed as a prospective solution to help operate multiple logical separated wireless networks in a single physical network. In this paper, we incorporate two typical 5G services, i.e., enhanced Mobile BroadBand (eMBB) and ultra-reliable low-latency communications (URLLC), in a cloud RAN (C-RAN), which is suitable for RAN slicing due to its high flexibility. In particular, for eMBB, we make use of multicasting to improve the throughput, and for URLLC, we leverage the finite blocklength capacity to capture the delay accurately. We envision that there will be many slice requests for each of these two services. Accepting a slice request means a certain amount of revenue (consists of long-term revenue and shot-term revenue) is earned by the C-RAN operator. Our objective is to maximize the C-RAN operator's revenue by properly admitting the slice requests, subject to the limited physical resource constraints. We formulate the revenue maximization problem as a mixed-integer nonlinear programming and exploit efficient approaches to solve it, such as successive convex approximation and semidefinite relaxation. Simulation results show that our proposed algorithm significantly saves system power consumption and receives the near-optimal revenue with an acceptable time complexity.

Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In recovering sparsely represented multi-user vectors, compressed sensing-based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and channel estimation (CE) should be performed before data detection. In this paper, we propose the expectation propagation-based joint AUD and CE (EP-AUD/CE) technique for mMTC networks. The EP algorithm is a Bayesian framework that approximates a computationally intractable probability distribution to an easily tractable distribution. The proposed technique finds a close approximation of the posterior distribution of the sparse channel vector. Using the approximate distribution, AUD and CE are jointly performed. We show by numerical simulations that the proposed technique substantially enhances AUD and CE performances over competing algorithms.

Location awareness, providing the ability to identify the location of sensor, machine, vehicle, and wearable device, is a rapidly growing trend of hyper-connected society and one of the key ingredients for the Internet of Things (IoT) era. In order to make a proper reaction to the collected information from things, location information of things should be available at the data center. One challenge for the IoT networks is to identify the location map of whole nodes from partially observed distance information. The aim of this paper is to present an algorithm to recover the Euclidean distance matrix (and eventually the location map) from partially observed distance information. By casting the low-rank matrix completion problem into the unconstrained minimization problem in a Riemannian manifold in which a notion of differentiability can be defined, we solve the low-rank matrix completion problem using a modified conjugate gradient algorithm. From the convergence analysis, we show that localization in Riemannian manifold using conjugate gradient (LRM-CG) converges linearly to the original Euclidean distance matrix under the extended Wolfe's conditions. From the numerical experiments, we demonstrate that the proposed method, called LRM-CG, is effective in recovering the Euclidean distance matrix.

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CC&C) together. In this paper, we dissect the interactions of CC&C in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CC&C.

In order to meet the requirements of massively connected devices, different quality of services (QoS), various transmit rates, and ultra-reliable and low latency communications (URLLC) in vehicle-to-everything (V2X) communications, we introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized V2X system model. We, then, classify the V2X communications into two scenarios and give their exact capacity expressions. To solve the computation complicated problems of the involved exponential integral functions, we give the approximate closed-form expressions with arbitrary small errors. Numerical results indicate the validness of our derivations. Our analysis has that the accuracy of our approximate expressions is controlled by the division of π/2 in the urban and crowded scenarios, and the truncation point T in the suburban and remote scenarios. Numerical results manifest that: 1) increasing the number of V2X device, NOMA power, and Rician factor value yields a better capacity performance; 2) effect of FD-NOMA is determined by the FD self-interference and the channel noise; and 3) FD-NOMA has a better latency performance compared with other schemes.

Massive machine type communications (mMTC) is a service category in 5G to support Internet of Things (IoT). Typically, mMTC-based services require small volume of information. Since the current data transmission principle requires long codeblock to maximize the coding gain and hence is not adequate for short packet transmission, multiplexing mechanism to support short packet transmission in mMTC is required. In this paper, we propose a new type of uplink data transmission scheme suitable for the mMTC, called sparse vector coding (SVC). Key idea behind the proposed technique is to transmit the information after the sparse transformation. By mapping the information into the sparse vector and then transmitting it after the random non-orthogonal spreading, we cast the symbol detection problem into the sparse signal recovery problem in compressed sensing. We show from the simulations in the LTE uplink scenario and massive access scenario in 5G that the proposed SVC scheme outperforms conventional approaches and is very effective in short packet transmissions.

In orthogonal frequency division multiplexing (OFDM) systems, channel estimation is by far the most important operation in the receiver to ensure the accurate detection and decoding. Over the years, pilot-aided channel estimation has been widely used for this purpose. In open-loop systems, since there is no feedback link between the transmitter and receiver, an approach based on the equi-spaced pilot assignment has been widely employed. In this paper, we propose a closed-loop non-uniform pilot allocation strategy based on deep neural network (DNN) technique. From the numerical evaluations, we show that the proposed autoencoder-based pilot allocation technique outperforms conventional approaches by a large margin, demonstrating its ability to learn the statistical characteristics of the wireless channel.

In massive machine-type communication (mMTC) systems, a large number of machine-type devices sporadically transmit small packets with low rates. By exploiting the sporadic activity of machine-type devices, we can cast the detection problem as the compressive sensing-based multi-user detection (CS-MUD). In this paper, we propose a novel CS-MUD algorithm for the active user and symbol detection based on a maximum a posteriori probability (MAP) criterion. By exchanging extrinsic information between active user detector and symbol detector, the proposed algorithm improves the performance of active user detection and the reliability of symbol estimate. Numerical simulations demonstrate that the proposed algorithm achieves outstanding MUD performance.

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10^-5 block error rate within 1 ms transmission period. Since the current wireless standard designed to maximize the coding gain by transmitting capacity achieving long code-block is not relevant for this purpose, entirely new transmission strategy is required. In this paper, we propose a new approach to transmit short packet information, called sparse vector coding (SVC). Key idea behind the proposed method is to transmit the control channel information after the sparse vector transformation. By mapping the transmit information into the position of nonzero elements and then transmitting it after the random spreading, we obtain underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations on realistic channel setting and decoder performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced physical downlink control channel (PDCCH) scheme.

Joint sparse recovery aims to reconstruct multiple sparse signals having a common support using multiple measurement vectors (MMV). In this paper, we propose a robust joint sparse recovery algorithm, termed MMV multiple orthogonal least squares (MMV-MOLS). Owing to the novel identification rule that fully exploits the correlation between the measurement vectors, MMV-MOLS greatly improves the accuracy of the recovered signals over the conventional joint sparse recovery techniques. From the simulation results, we show that MMV-MOLS outperforms conventional joint sparse recovery algorithms, in both full row rank and rank deficient scenarios. In our analysis, we show that MMV-MOLS recovers any row K-sparse matrix accurately in the full row rank scenario with m = K + 1 measurements, which is, in fact, the minimum number of measurements to recover a row K-sparse matrix. In addition, we analyze the performance guarantee of the MMV-MOLS algorithm in the rank deficient scenario using the restricted isometry property (RIP).

Ultra reliable and low latency communications (URLLC) is a new use case in 5G wireless systems to accommodate emerging mission-critical services and applications. These include driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants. In order to support URLLC, there should be changes in the air interface (a.k.a. physical-layer). In this article, we provide physical layer challenges and solutions in 5G URLLC downlink. We discuss requirements of URLLC and then present the physical layer issues and new solutions including data structure, multiplexing schemes, and reliability assurance technologies, which have been adopted in the 5G new radio (NR).

Fifth generation (5G) wireless networks are currently being developed to handle wide variety of use cases. In order to support these cases, new types of requirements other than throughput enhancement have been introduced. One such requirement is to reduce the latency down to a millisecond (ms) level in ultra reliable and low latency communications (URLLC). In case of uplink transmission, supporting this stringent latency requirement is quite challenging since the scheduling procedure is a time-consuming and complicated handshaking process. In time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is assigned for the downlink. In this paper, we propose a low latency access scheme suitable for TDD-based URLLC. Key idea of the proposed scheme is to transmit the latency sensitive data immediately after the grant signaling. To support the fast uplink access, we introduce a fast grant signaling scheme based on the compressed sensing technique. Numerical results confirm that the proposed uplink access scheme is very effective in TDD-based URLLC systems.

In this paper, we propose a new greedy channel estimation technique which can reconstruct dynamic sparse signals for millimeter wave (mmWave) communication system. In presence of the mobile user under high mobility, the angle of arrival (AoA) and angle of departure (AoD) is changing with time. We assume that the AoA and AoD are varying by discrete-state Markov random process. We formulate the estimation problem for mmWave channel as joint estimation problem of two variables; 1)AoA and AoD indices, and 2)the amplitude vector in the angular domain. The proposed greedy algorithm effectively estimate the AoD and AoA indices accounting for sparse characteristics and dynamics of the channel. Our experimental evaluation demonstrates that the proposed algorithm can obtain good channel estimation performance with small amount of computational power.

Fifth generation (5G) wireless networks are currently being developed to handle wide variety of use cases. In order to support these cases, new types of requirements other than throughput enhancement have been introduced. One such requirement is to reduce the latency down to a millisecond (ms) level in ultra reliable and low latency communications (URLLC). In case of uplink transmission, supporting this stringent latency requirement is quite challenging and problematic since the scheduling procedure is a time-consuming and complicated handshaking process. In time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is assigned for the downlink. In this paper, we propose a new type of uplink transmission scheme for TDD-based URLLC. Key idea of the proposed scheme is to transmit the latency sensitive data immediately after performing the ultra-short one-way signaling from the basestation to the mobile device. To reduce the processing time of grant signal, we present a fast signaling mechanism, referred to as channel-aware sparse signaling (CASS). Numerical results confirm that the proposed uplink transmission scheme is very effective in TDD-based URLLC systems.

Cell-free massive MIMO system is one of a promising technology of 5G wireless communications that can provide high throughput from the basestation cooperation. To capitalize on the gain obtained by the basestation cooperation, the downlink channel state information (CSI) should be available at the basestations. In the popularly used frequency division duplexing (FDD) system, the downlink CSI must be fed back from the users. However, due to a large number of antennas and basestations, the feedback overhead is a serious concern in the cell-free systems. Recent studies have shown that the uplink and downlink channels have similar angle-of-departures (AoDs), so-called angle reciprocity. In this paper, we present an AoD-based statistical beamforming scheme for the cell-free massive MIMO systems that does not rely on the CSI feedback. Also, we provide an efficient solution for the power allocation problem that minimizes the total power consumption of the basestations. Simulation results demonstrate that the proposed scheme saves approximately 12% transmit power and has a 22% higher coverage probability compare to the conventional cellular systems.

Fifth generation wireless standards require much lower latency than what current wireless systems can guarantee. The main challenge in fulfilling these requirements is the development of short packet transmission, in contrast to most of the current standards, which use a long data packet structure. Since the available training resources are limited by the packet size, reliable channel and interference covariance estimation with reduced training overhead are crucial to any system using short data packets. In this paper, we propose an efficient receiver that exploits useful information available in the data transmission period to enhance the reliability of the short packet transmission. In the proposed method, the receive filter (i.e., the sample covariance matrix) is estimated using the received samples from the data transmission without using an interference training period. A channel estimation algorithm to use the most reliable data symbols as virtual pilots is employed to improve quality of the channel estimate. Simulation results verify that the proposed receiver algorithms enhance the reception quality of the short packet transmission.

Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.

We investigate the weighted-sum distortion minimization problem in transmitting two correlated Gaussian sources over Gaussian channels using two energy harvesting nodes. To this end, we develop off-line and online power control policies to optimize the transmit power of the two nodes. In the off-line case, we cast the problem as a convex optimization and investigate the structure of the optimal solution. We also develop a generalized waterfilling-based power allocation algorithm to obtain the optimal solution efficiently. For the online case, we quantify the distortion of the system using a cost function and show that the expected cost equals the expected weighted-sum distortion. Based on Banach's fixed point theorem, we further propose a geometrically converging algorithm to find the minimum cost via simple iterations. Simulation results show that our online power control outperforms the greedy power control where each node uses all the available energy in each slot and also performs close to that of the proposed off-line power control. Moreover, the performance of our off-line power control almost coincides with the performance limit of the system.

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, the 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10-5packet error rate within 1-ms transmission period. Since the current wireless transmission scheme, which is designed to maximize the coding gain by transmitting the capacity achieving long codeblock, is not relevant for this purpose, and a new transmission scheme to support URLLC is required. In this paper, we propose a new approach to support the short packet transmission, called sparse vector coding (SVC). The key idea behind the proposed SVC technique is to transmit the information after the sparse vector transformation. By mapping the information into the position of nonzero elements and then transmitting it after random spreading, we obtain an underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations and performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced scheme.

Cell-less communication is one of a promising technology of 5G wireless communications for providing a user-centric approach that goes beyond the regional cell of the conventional cellular system. In order to provide user-centric services, location information of the mobile station is required. In this paper, we propose a new localization technique to support the cell-less communication. The proposed scheme consists of two parts. First, estimating the signal parameters, angle of arrival (AOA) and time of arrival (TOA) through the maximum likelihood estimation. After the signal parameter estimation, the localization of the mobile station is performed using the estimated AOA-TOA information. Simulation results demonstrate that the proposed method achieves substantial performance in estimating the location of the mobile station.

Spectrum sensing is used to perceive the spectral environment over a wide frequency band. The multiple measurement vector (MMV) model can be applied to the spectrum sensing scenario since it enables jointly sparse signal recovery. In this paper, a novel spectrum sensing algorithm, referred to as multiple subspace matching pursuit (MSMP), is proposed to reduce the miss detection and false alarm events in the spectrum sensing. Numerical simulations demonstrate that the proposed algorithm shows the outstanding recovery performance with the reduction of the incorrect spectrum decisions.

In this paper, we propose a novel pilot beamforming and CSI acquisition strategy for IoT systems to achieve reduction in pilot overhead and enhancement in the channel estimation quality. Key idea of the proposed technique, called time-domain sparse beamforming (TDSB), is to sparsify the time-domain channel vector using the antenna-domain beamforming. As a result of TDSB, wideband channel information can be acquired with partial pilot symbols in time and frequency. From the numerical evaluations, we show that the proposed scheme outperforms conventional channel estimation schemes, and achieves substantial reduction in the pilot overhead.

In this paper, we propose a new recursive sparse channel recovery algorithm which can track time-varying support of angular domain channel response vector in mobility scenario for millimeter wave-band communications. We model the angle of departure (AoD) and the angle of arrival (AoA) using discrete state Markov random process and derive joint estimation of the time-varying support and amplitude of the angular domain channel vector. Using sequential Monte Carlo (SMC) method, the proposed channel estimation scheme tracks the support by drawing the samples from a posteriori distribution of the support indices while capturing the dynamics of time-varying amplitude using Kalman filter. Our simulation results show that the proposed algorithm yields significantly better tracking performance than the existing compressed sensing schemes.

Multiuser multiple-input multiple-output (MU-MIMO) transmission techniques have been popularly used to improve the spectral efficiency and user experience. However, due to the coarse knowledge of channel state information at the transmitter, the quality of transmit precoding to control multiuser interference is degraded, and hence, co-scheduled user equipment may suffer from large residual multiuser interference. In this paper, we propose a new channel estimation technique employing reliable soft symbols to improve the channel estimation and subsequent detection quality of MU-MIMO systems. To this end, we pick reliable data tones from both desired and interfering users and then use them as pilots to re-estimate the channel. In order to jointly estimate the channel and data symbols, we employ the expectation maximization algorithm, where the channel estimation and data decoding are performed iteratively. From numerical experiments in realistic MU-MIMO scenarios, we show that the proposed method achieves substantial performance gain in channel estimation and detection quality over conventional channel estimation approaches.

5G wireless standards require a much lower latency than what current wireless systems can guarantee. The main challenge to fulfill this requirement is the capability to support short packet transmission, in contrast to most of the current standards which use a long data packet structure. In this paper, we propose an efficient receiver technique that exploits information obtained during the data transmission period to improve the reception quality of the short packet transmission. Two key ingredients of the proposed method are 1) estimation of the receiver filter using the received samples in the data transmission period, not in the interference training period, and 2) soft decision- directed channel estimation that uses the data symbols for re-estimation of the channels. Numerical results confirm the effectiveness of the proposed receiver algorithms.

This paper presents a novel statistical error compensation (SEC) technique — algorithmic error cancellation (AEC)-for designing robust and energy-efficient signal processing and machine learning kernels on scaled process technologies. AEC exhibits a perfect error compensation (PEC) property, i.e., it is able to achieve a post-compensation error rate equal to zero. AEC generates a maximum likelihood (ML) estimate of the hardware error and employs it for error cancellation. AEC is applied to a voltage overscaled 45-tap, 45nm CMOS finite impulse response (FIR) filter employed in a EEG seizure detection system. AEC is shown to perfectly compensate for errors in the main FIR block and its reduced precision replica when they make errors at a rate of up to 73% and 98%, respectively. The AEC-based FIR is compared with an uncompensated architecture, and a fast architecture. AEC's error compensation capability enables it to achieve a 31.5% (at same supply voltage) and 19.7% (at same energy) speed-up over the uncompensated architecture, and a 8. 9% speed-up over a fast architecture at the same energy consumption. At fd, k = 452.3 MHz, AEC results in a 27.7% and 12.4% energy savings over the uncompensated and fast architectures, respectively.

Acquiring reliable channel state information (CSI) at the basestation is crucial to fully exploit the advantages of massive multiple-input multiple-output (MIMO) systems. However, giving the basestation knowledge of the downlink channel is an important and challenging problem in frequency division duplexing (FDD) systems when the number of basestation antennas is large. In this paper, we propose an eigenvalue decomposition based feedback compression (EFC) framework that transforms the channel information for pursuing further reduction in feedback overhead. The main idea of EFC is to use the compressed channels for generating short-term CSI by exploiting the dominant eigendirections of long-term channel-statistical information. By using the proposed codebook, each user terminal sends the compressed channel information as short-term feedback and channel-statistical information as long-term feedback. Numerical results demonstrate that the proposed method achieves significant feedback overhead reduction over the conventional beamforming techniques under the same target sum rate requirement.

Multiuser multiple-input multiple-output (MU-MIMO) transmission technique based on orthogonal frequency division multiplexing (OFDM) system has been received great deal of attention in recent years due to its potential higher spectral efficiency. However, because of the accuracy of channel state information, co-scheduled mobile users may suffer large residual multiuser interference in MU-MIMO system. In this paper, we propose a new channel estimation technique using expectation and maximization (EM) algorithm with reliable soft symbols for the MU-MIMO systems in LTE- Advanced. In the proposed scheme, we choose reliable data tones from both desired and interfering signals and improves the channel estimation quality using iterative process between channel estimation and data detection in frequency domain. We show that the proposed method achieves substantial performance gain over conventional channel estimation approaches.

We consider cluster-based routing protocols for energy harvesting wireless sensor networks. Since the energy harvesting process does not match the real energy demand, sensor nodes suffer from occasional energy shortages, especially when they serve as cluster head (CH) nodes. To address this problem, we propose a cluster-based routing protocol referred to as distance-and-energy-aware routing with energy reservation (DEARER). The DEARER protocol encourages nodes with high energy-arrival rate or being close to the sink to serve as CH nodes. Also, DEARER allows non-CH nodes to reserve a portion of the harvested energy for future use. In doing so, the DEARER selects “enabler” nodes as CH nodes and provides them with more energy, thereby mitigating the energy shortage events at CH nodes. By theoretical analysis and numerical experiments, we demonstrate that the DEARER protocol outperforms direct transmission and also approaches the genie-aided routing, where CH nodes are selected based on the real-time energy information of each node.

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.

In this paper, we consider a detection problem of the underdetermined system when the input vector is sparse and its elements are chosen from a set of finite alphabets. We propose a greedy sparse recovery algorithm dubbed as the sparse detection matching pursuit (SDMP) that is effective in recovering the sparse signals with integer constraint. In our performance guarantee analysis and empirical simulations, we show that SDMP is effective in recovering sparse signals in both noiseless and noisy scenarios.

In this paper, we propose a greedy tree search algorithm for Internet of Things (IoT) signal detection. The proposed method, referred to as matching pursuit with integer tree search (MP-ITS), recovers integer sparse vector (sparse vector whose nonzero elements are chosen from a set of finite alphabets) using the tree search. In order to control the computational burden yet maintains the effectiveness of the tree search, MP-ITS employs two strategies, viz., pre-screening to put a limitation on columns of the channel matrix and tree pruning to eliminate unpromising candidates from the tree. We show from the restricted isometry property (RIP) analysis and empirical simulations on realistic IoT scenarios that the proposed method is effective in recovering the sparse vector with integer constraint.

LTE-Advanced system has been deployed with 2 and 4 transmission antennas (Tx) while the specification supports up to 8Tx. Due to deployment space, antenna dimension and complexity, operators have not been interested in the deployment of 8Tx systems. Recently, three dimensional (3D) beamforming using 2D active antenna array has attracted significant attention in the wireless industry. By incorporating 2D active array into LTE-A systems, the system offers freedom in controlling radiation on elevation and horizontal dimension. In addition, 2D array antenna increases the number of antennas without exceeding form-factor where the conventional antennas are deployed. When the number of antennas increases in the form of 2D arrangement, spatial separation can be realized simultaneously in horizontal and elevation domain and vertical beam-steering can increase SINR of UEs in high floors. In this paper, we study the system operations and implementations for supporting 3D beamforming with 8Tx antennas. In our schemes, by reusing the conventional CSI feedback framework, the system can operate 2D active array without harming the backward compatibility. Evaluation results show that 3D beamforming provides capacity boosting over the conventional 2D beamforming systems while keeping same antenna structure.

Multiuser multiple-input multiple-output (MU-MIMO) transmission technique based on orthogonal frequency division multiplexing (OFDM) system has been received great deal of attention in recent years due to its potential higher spectral efficiency. However, because of the coarse knowledge of channel state information at the transmitter (CSIT), co-scheduled mobile users may suffer large residual multiuser interference in MU-MIMO system. In this paper, we propose a new channel estimation technique using virtual pilot signals for the MU-MIMO OFDM systems. In a nutshell, we choose reliable data tones from both desired and interfering signals as virtual pilot signal and improves the channel estimation quality using iterative detection and decoding (IDD) scheme. We show that the proposed method achieves substantial performance gain over conventional approaches employing single user detection or multiuser detection.

The papers in this special issue focus on the topic of location awareness for radio and networks. Localization-awareness using radio signals stands to revolutionize the fields of navigation and communication engineering. It can be utilized to great effect in the next generation of cellular networks, mining applications, health-care monitoring, transportation and intelligent highways, multi-robot applications, first responders operations, military applications, factory automation, building and environmental controls, cognitive wireless networks, commercial and social network applications, and smart spaces. A multitude of technologies can be used in location-aware radios and networks, including GNSS, RFID, cellular, UWB, WLAN, Bluetooth, cooperative localization, indoor GPS, device-free localization, IR, Radar, and UHF. The performances of these technologies are measured by their accuracy, precision, complexity, robustness, scalability, and cost. Given the many application scenarios across different disciplines, there is a clear need for a broad, up-to-date and cogent treatment of radio-based location awareness. This special issue aims to provide a comprehensive overview of the state-of-the-art in technology, regulation, and theory. It also presents a holistic view of research challenges and opportunities in the emerging areas of localization.

The papers in this special issue on location awareness will continue with the state-of-the-art in technology, regulation, and theory for the emerging field of localization. This second part issue continues from the July 2015, Part I, issue which discusses location awareness for radio and networks.

Recent works on massive multiple-input multiple-output (MIMO) have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the base station. In order to achieve the performance that massive MIMO systems promise, accurate transmit-side channel state information (CSI) should be available at the base station. While transmit-side CSI can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, explicit feedback of CSI from the user terminal to the base station is needed for frequency division duplexing (FDD) systems. In this paper, we propose an antenna grouping based feedback reduction technique for FDD-based massive MIMO systems. The proposed algorithm, dubbed antenna group beamforming (AGB), maps multiple correlated antenna elements to a single representative value using predesigned patterns. The proposed method modifies the feedback packet by introducing the concept of a header to select a suitable group pattern and a payload to quantize the reduced dimension channel vector. Simulation results show that the proposed method achieves significant feedback overhead reduction over conventional approach performing the vector quantization of whole channel vector under the same target sum rate requirement.

In this paper, we introduce a new symbol detection technique for large-scale multi-input multi-output (MIMO) systems. Based on the observation that detection errors produced by conventional linear detectors tend to be sparse in practical communication regime, we employ compressed sensing techniques to correct the symbol errors from the output of the linear detectors. The proposed symbol detector, referred to as post detection sparse error recovery (PDSR) technique is derived in two steps (1) sparse transform: transforming the original non-sparse system into a sparse error system and (2) sparse error recovery: applying the sparse signal recovery algorithm to estimate the error vector at the output of the transformed system. We show from the asymptotic mean square error (MSE) analysis that the proposed post detection technique based on compressed sensing can bring remarkable performance gains over the conventional detectors. The intensive simulations performed over large-scale MIMO systems also confirm the superiority of the PDSR algorithm.

Accurate transmit-side channel state information (CSI) is essential for achieving optimal performance of large-scale multiple-input multiple-output (MIMO) systems. However, due to high dimensionality of the massive MIMO systems, large overhead of the uplink CSI feedback is a big concern for frequency division duplexing (FDD) systems. In this paper, we propose antenna group scheduling (AGS) algorithm that combines antenna selection and user scheduling for pursuing further reduction in feedback overhead. The AGS method selects the antenna group with the maximum channel gain to reduce the dimension of channel vector and schedules users in the group maximizing the sum rate. Simulation results demonstrate that the proposed technique achieves significant feedback overhead reduction over the conventional beamforming techniques (33%~50%) under the same target sum rate requirement.

Recent works on massive multiple-input multiple-output (MIMO) have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the basestation. Although transmit-side channel state information (CSI) can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, explicit feedback of CSI from the user to the basestation is required for frequency division duplexing (FDD) systems. In this paper, we propose an antenna grouping based feedback reduction technique for FDD-based massive MIMO systems. The proposed algorithm, dubbed antenna group beamforming (AGB), groups antenna elements using pre-designed patterns. The proposed method introduces the concept of using a header of overall feedback resources to select a suitable group pattern and the payload to quantize the effective channel vector. Simulation results show that the proposed method achieves significant feedback overhead reduction over conventional approach.

In this paper, we propose a new sparse recovery algorithm referred to as the matching pursuit with a tree pruning (TMP) that performs efficient combinatoric search with the aid of greedy tree pruning. Two key ingredients of the TMP algorithm are pre-selection to put a restriction on the indices of columns in Φ being investigated and tree pruning to avoid the investigation of unpromising paths in the search. In the noisy setting, we show that TMP identifies the support (index set of nonzero elements) accurately when the signal power is larger than the constant multiple of noise power. In the empirical simulations, we confirm this results by showing that TMP performs close to an ideal estimator (often called Oracle estimate) for high signal-to-noise ratio (SNR) regime.

Large-scale multiple-input multiple-output(MIMO) systems with a large number of antennas at the basestation have drawn considerable interest because of potential ability to achieve high spectral efficiencies. In order to achieve optimal performance of large-scale MIMO systems, the basestation needs to know channel state information (CSI) perfectly. In terms of CSI acquisition, the basestation estimates the downlink channel through channel reciprocity in time division duplexing (TDD) or requires CSI feedback through the uplink in frequency division duplexing (FDD). Due to the large number of transmit antennas at the basestation, uplink CSI feedback would be a major hurdle in developing FDD large-scale MIMO systems. In this paper, we propose an efficient feedback compression technique for FDD large-scale MIMO systems. The proposed method reduces a dimension of vector quantization by grouping high correlated antenna elements. In fact, the proposed method invests a small portion of feedback resources to generate a grouped channel vector and the rest to quantize the grouped channel vector. Simulation results demonstrate that the proposed method achieves significant feedback overhead reduction over conventional methods.

In this paper, we propose an algorithm referred to as multipath matching pursuit (MMP) that investigates multiple promising candidates to recover sparse signals from compressed measurements. Our method is inspired by the fact that the problem to find the candidate that minimizes the residual is readily modeled as a combinatoric tree search problem and the greedy search strategy is a good fit for solving this problem. In the empirical results as well as the restricted isometry property-based performance guarantee, we show that the proposed MMP algorithm is effective in reconstructing original sparse signals for both noiseless and noisy scenarios.

Recent results show that non-parametric linear receiver equipped with multiple receive antennas is an effective solution for ad hoc network under the imperfect channel state information at receiver (CSIR). In this paper, we propose an interference aware node activation strategy that efficiently controls the data transmission and back off based on the measured interference power. Our numerical results show that if optimization over the channel estimation threshold is provided, then the effective data rate of the proposed scheme is higher than that of the conventional channel estimation even under the system delay occurred.

In this paper, we present a novel soft-input soft-output detector for large-scale MIMO systems that offers substantial complexity reduction over the existing detectors and performance close to the full dimensional symbol detector. The proposed algorithm, termed soft-input soft-output successive group (SSG) detector, detects a subset of symbols successively with an aid of the preprocessing designed to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of interfering symbol groups using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results performed on large-scale multi-input multi-output (MIMO) systems demonstrate that the proposed SSG detector achieves significant complexity reduction over the conventional approaches with negligible performance loss.

In this paper, we propose a sparse recovery algorithm, termed multiple path matching pursuit (MMP), that improves the recovery performance of sparse signals. By investigating the multiple paths and then choosing the most promising path in the final moment, the MMP algorithm improves the chance of finding the true support and therefore enhances the recovery performance. From the restricted isometry property (RIP) analysis, we show that the MMP algorithm can perfectly reconstruct any K-sparse (K >1) signals,√provided that the sensing matrix satisfies RIP with δK+L< √ L/√ K +3√ L. We demonstrate by empirical simulations that the MMP algorithm is very competitive in both noisy and noiseless scenarios.

Recently, a variety of low complexity soft-input soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-input soft-output detector detector at feasible complexity for massive MIMO systems due to the heavy burden in computational complexity. In this paper, we present a novel soft-input soft-output detector for massive MIMO systems that offers substantial complexity reduction over the existing detectors performance close to the full dimensional symbol detector. The proposed soft-input soft-output successive group (SSG) detector detects a subset of symbols successively with an aid of the deliberately designed preprocessing to suppress the inter-group interference. Simulation results performed on 8×8 MIMO flat fading channels demonstrate that the proposed SSG detector achieves significant complexity reduction over the conventional approaches with negligible performance loss.

In this work, we propose a simple yet effective beam-forming technique for multiple-input multiple-output (MIMO) multi-pair two-way relay channels. Two key ingredients in our technique are adoption of signal space alignment (SSA) for transmit and receive beamforming and amplify-and-forward (AF) relay beamforming based on advanced zero-forcing (ZF) criterion. From the sum-rate analysis on MIMO multi-pair two-way relay channels, we show that the proposed method achieves full multiplexing gain and substantial beamforming gain in realistic multi-pair two-way relay scenario.

Recent works on ad hoc network study have shown that achievable throughput can be made to scale linearly with the number of receive antennas even if the transmitter has only a single antenna. In this paper, we propose a non-parametric linear minimum mean square error (MMSE) receiver for achieving further gain in performance when the channel state information at receiver (CSIR) of interferers is imperfect. The key feature to make our approach effective is to exploit the autocorrelation of the received signal. In fact, by incorporating the desired channel information on top of the observations including interference and noise only, the proposed method achieves large fraction of the optimal MMSE transmission capacity without transmission rate loss. Simulation results on the realistic ad hoc network system show that the proposed non-parametric linear MMSE receiver brings substantial performance gain over existing multiple receive antenna algorithms.

Recent works on multiuser MIMO study have shown that the linear growth of capacity in single user MIMO system can be translated to the multiuser MIMO scenario as well. In this paper, we propose a method pursuing performance gain of vector perturbation in multiuser downlink systems. Instead of employing the maximum number of mobile users for communication, we use small part of them as virtual users for improving reliability of users participating communication. By controlling parameters of the virtual users including information and perturbation vector, we obtain considerable improvement in the effective SNR. Simulation results on the realistic multiuser MISO and MIMO downlink systems show that the proposed method brings substantial performance gain over the standard vector perturbation with marginal overhead in computations.

Block diagonalization (BD) algorithm is a generalization of the channel inversion that converts multiuser multi-input multi-output (MIMO) broadcast channel into single-user MIMO channel without inter-user interference. In this paper, we combine the BD technique with a minimum mean square error vector precoding (MMSE-VP) for achieving further gain in performance with minimal computational overhead. Two key ingredients to make our approach effective are the QR decomposition based block diagonalization and joint optimization of transmitter and receiver parameters in the MMSE sense. In fact, by optimizing precoded signal vector and perturbation vector in the transmitter and receiver jointly, we pursue an optimal balance between the residual interference mitigation and the noise enhancement suppression. From the sum rate analysis as well as the bit error rate simulations (both uncoded and coded cases) in realistic multiuser MIMO downlink, we show that the proposed BD-MVP brings substantial performance gain over existing multiuser MIMO algorithms.

Tree detection techniques are often used to reduce the complexity of a posteriori probability (APP) detection in multiantenna wireless communication systems. In this paper, we introduce an efficient soft-input soft-output tree detection algorithm that employs a new type of look-ahead path metric in the process of branch pruning (or sorting). While conventional path metrics depend only on symbols on a visited path, the new path metric accounts for unvisited parts of the tree in advance through an unconstrained linear estimator and adds a bias term that reflects the contribution of as-yet undecided symbols. By applying the linear estimate-based look-ahead path metric to an -algorithm that selects the best paths for each level of the tree, we develop a new soft-input soft-output tree detector, called an improved soft-input soft-output -algorithm (ISS-MA). Based on an analysis of the probability of correct path loss, we show that the improved path metric offers substantial performance gain over the conventional path metric. We also demonstrate through simulations that the proposed ISS-MA can be a promising candidate for soft-input soft-output detection in high-dimensional systems.

In this paper, we present a low-complexity list sphere detection algorithm for achieving near-optimal a posteriori probability (APP) detection in an iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of candidates is computationally inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and then derive a hypersphere radius satisfying this condition. Further, in order to exploit the original sphere constraint as it is instead of using necessary conditioned version, we combine a probabilistic tree pruning strategy and the proposed list sphere search. Two features, tightened hypersphere radius and probabilistic tree pruning, collaborate and improve the search efficiency in a complementary fashion. Through simulations on 4x4 MIMO system, we show that the proposed method provides substantial reduction in complexity while achieving negligible performance loss over the conventional list sphere detection.

In this paper, we consider a low-complexity multiuser detection for downlink of high speed packet access (HSPA) of universal mobile telecommunications system (UMTS). Instead of attempting to perform maximum likelihood (ML) detection of all users in multiple cells, which is impractical for battery powered mobile receiver, we utilize interference cancelled chips obtained from iterative linear minimum mean square error (LMMSE) estimation to perform a near ML detection in a reduced dimensional system. As a result, the worst case complexity of the detection process, achieved by the closest lattice point search (CLPS), is bounded to a controllable level irrespective of multipath spans. Furthermore, by exploiting the LMMSE estimate in tightening the hypersphere condition of the CLPS algorithm so called sphere decoding, we achieve significant improvement in search complexity. From simulations on realistic downlink communication scenario in HSPA systems, we show that the proposed method offers substantial performance gain over conventional receiver algorithms with reasonable complexity.

Block Diagonalization (BD) algorithm is a generalization of channel inversion scheme in multiuser multi-input multi-output (MIMO) broadcast channels. Although the BD algorithm is effective in removing interuser interference, it has a drawback that additional information should be delivered to the receiver. Recent work on BD with vector perturbation (VP) avoids the need for additional information and hence reduces receiver complexity. In this paper, we propose a method achieving further gain in the BD for multiuser MIMO downlink. By combining the BD and minimum mean square error vector precoding (MMSEVP), we pursue the balance between the interference suppression and the noise enhancement control, resulting in considerable improvement in the effective SINR. In fact, simulation results on the realistic multiuser downlink scenario show that the proposed method brings substantial performance gain over existing multiuser MIMO algorithms (BD, BD-VP and BD-WF).

In this paper, we consider a low-complexity multiuser detection for downlink of high speed packet access (HSPA) system. Instead of attempting to perform the maximum likelihood (ML) detection of all users in multiple cells, which is impractical for battery powered mobile receiver, we utilize interference cancelled chips obtained from iterative linear minimum mean square error (LMMSE) estimation to perform a near ML detection in a reduced dimensional system. As a result, the worst case complexity of the detection process, achieved by the closest lattice point search (CLPS), is bounded to a controllable level irrespective of multipath spans. Further, by exploiting the LMMSE estimate in tightening the hypersphere condition of the CLPS algorithm so called sphere decoding, we achieve significant improvement in search complexity. From the simulations on realistic downlink communication scenario in HSPA systems, we show that the proposed method offers substantial performance gain over conventional receiver algorithms with implementable complexity.

In this paper, we present a low-complexity list sphere search algorithm for achieving near-optimal a posteriori probability (APP) detection in iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of lattice points is inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and derive the optimal sphere radius satisfying this requirement. Further, in order to exploit the sphere constraint as it is instead of using necessary conditioned versions, we incorporate a probabilistic tree pruning strategy into the list sphere search. Through simulations on realistic IDD systems, we show that the proposed method provides considerable complexity savings while maintaining near-optimal performance.

In this paper, we put forth an approach pursuing performance gain of vector perturbation in multiuser MIMO downlink. Instead of achieving maximal sum capacity by employing maximum number of mobile users for communication, we sacrifice small part of them for improving the performance of ones participating communication. By controlling parameters of these sacrificing users, we achieve considerable improvement in the effective SNR, which can be translated to the large gain in bit error performance. From simulation results on 4×4 and 10×10 multiuser MIMO systems, we show that the proposed method brings substantial performance gain over the standard vector perturbation.

In this paper, we propose a new soft-input soft-output (SISO) multi-input multi-output (MIMO) detection technique, called an improved SISO M-algorithm (ISS-MA). We modify the conventional M-algorithm to improve the performance-complexity trade-off of the SISO symbol detector. Towards this end, an improved path metric is proposed, which accounts for the information on undecided symbols at a particular path visited. The inclusion of this information is enabled through a bias term which is added to the conventional path metric in order to reflect the contributions of the undecided symbols. We derive the bias term using soft unconstrained linear estimates of undecided symbols. As a result, the ISS-MA that picks up the best M candidates based on this modified path metric exhibits improved performance/complexity trade-off compared to the existing SISO detectors. According to extensive simulations performed over i.i.d. Rayleigh fading channels, the proposed SISO detector yields significantly lower complexity than other symbol detectors while maintaining strong performance especially in high dimensional systems.

In this paper, we present a low-complexity list sphere search algorithm for achieving near-optimal a posteriori (APP) detection in iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching fixed number of lattice points is inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and derive the optimal sphere radius satisfying this requirement. Through simulations on realistic IDD systems, we show that the proposed method provides considerable complexity savings while maintaining near-optimal performance.

In this letter, we propose an extension of the probabilistic tree pruning sphere decoding (PTP-SD) algorithm that provides further improvement of the computational complexity with minimal extra cost and negligible performance penalty. In contrast to the PTP-SD that considers the tightening of necessary conditions in the sphere search using per-layer radius adjustment, the proposed method focuses on the sphere radius control strategy when a candidate lattice point is found. For this purpose, the dynamic radius update strategy depending on the lattice point found as well as the lattice independent radius selection scheme are jointly exploited. As a result, while maintaining the effectiveness of the PTP-SD, further reduction of the computational complexity, in particular for high SNR regime, can be achieved. From simulations in multiple-input and multiple-output (MIMO) channels, it is shown that the proposed method provides a considerable improvement in complexity with near-ML performance.

Volume ray-casting with a higher order reconstruction filter and/or a higher sampling rate has been adopted in direct volume rendering frameworks to provide a smooth reconstruction of the volume scalar and/or to reduce artifacts when the combined frequency of the volume and transfer function is high. While it enables high-quality volume rendering, it cannot support interactive rendering due to its high computational cost. In this paper, we propose a fast high-quality volume ray-casting algorithm which effectively increases the sampling rate. While a ray traverses the volume, intensity values are uniformly reconstructed using a high-order convolution filter. Additional samplings, referred to as virtual samplings, are carried out within a ray segment from a cubic spline curve interpolating those uniformly reconstructed intensities. These virtual samplings are performed by evaluating the polynomial function of the cubic spline curve via simple arithmetic operations. The min max blocks are refined accordingly for accurate empty space skipping in the proposed method. Experimental results demonstrate that the proposed algorithm, also exploiting fast cubic texture filtering supported by programmable GPUs, offers renderings as good as a conventional ray-casting algorithm using high-order reconstruction filtering at the same sampling rate, while delivering 2.5x to 3.3x rendering speed-up.

In this paper, we propose a near-maximum likelihood (ML) detection method referred to as reduced dimension ML search (RD-MLS). The RD-MLS detector is based on a partitioned search method that divides the symbol space into two groups and searches over the vector space of one group instead of that comprising all of the symbols. First, a minimum mean square error (MMSE) dimension reduction operator suppressing the interference from the second group is applied, and then a list tree search (LTS) is performed over the symbols in the first group. For each lattice point of symbols for the first group found from the LTS, the rest of symbols are estimated by MMSE-decision feedback (MMSE-DF) estimation. Among these lattice point candidates, a final solution is chosen as a minimizer of the L2-norm criterion. From an asymptotic error probability analysis, we show that the dimension reduction loss is potentially compensated by the LTS gain proportional to the size of the list. Furthermore, we demonstrate through simulation on multi-input multi-output (MIMO) transmissions that the RD-MLS detector achieves substantial complexity reduction with relatively little performance loss over ML detection.

In this paper, we propose a novel radius control strategy for sphere decoding referred to as inter search radius control that provides further improvement of the computational complexity with minimal extra cost and negligible performance penalty. The proposed method focuses on the sphere radius control strategy when a candidate lattice point is found. For this purpose, the dynamic radius update strategy as well as the lattice independent radius selection scheme are jointly exploited. From simulations in multiple-input and multiple-output (MIMO) channels, it is shown that the proposed method provides a substantial improvement in complexity with near-ML performance.

In this paper, we propose a transmit diversity scheme based on the vector perturbation for a multiuser multiple-input multiple-output (MIMO) broadcasting system. Our method is inspired by the fact that precoding process of the vector perturbation designed to suppress the multiuser interference causes a reduction of the transmitted signal power. In order to boost up the transmit power, we employ a non-integer based vector perturbation together with temporal transmit diversity. We show from the simulation on 10×10 multiuser MIMO system with 16-QAM modulation that the proposed method outperforms the repetition scheme as well as the vector perturbation.

MOTIVATION: For high-throughput prediction of the helical arrangements of large RNA molecules, an innovative method termed multiplexed hydroxyl radical (*OH) cleavage analysis (MOHCA) has been proposed. A key step in this promising technique is to detect peaks accurately from noisy radioactivity profiles. Since manual peak finding is laborious and prone to error, an automated peak detection method to improve the accuracy and throughput of MOHCA is required. Existing methods were not applicable to MOHCA due to their high false positive rates. RESULTS: We developed a two-step computational method that can detect peaks from MOHCA profiles in a robust manner. The first step exploits an ensemble of linear and non-linear signal processing techniques to find true peak candidates. In the second step, a binary classifier trained with the characteristics of true and false peaks is used to eliminate false peaks out of the peak candidates. We tested the proposed approach with 2002 MOHCA cleavage profiles and obtained the median recall, precision and F-measure values of 0.917, 0.750 and 0.830, respectively. Compared with the alternatives considered, the proposed method was able to handle false peaks substantially better, thus resulting in 51.0-71.8% higher median values of precision and F-measure. AVAILABILITY: The software and supplementary data are available at http://dna.korea.ac.kr/pub/mohca.

BACKGROUND: The problem of locating valid peaks from data corrupted by noise frequently arises while analyzing experimental data. In various biological and chemical data analysis tasks, peak detection thus constitutes a critical preprocessing step that greatly affects downstream analysis and eventual quality of experiments. Many existing techniques require the users to adjust parameters by trial and error, which is error-prone, time-consuming and often leads to incorrect analysis results. Worse, conventional approaches tend to report an excessive number of false alarms by finding fictitious peaks generated by mere noise. RESULTS: We have designed a novel peak detection method that can significantly reduce parameter sensitivity, yet providing excellent peak detection performance and negligible false alarm rates from gas chromatographic data. The key feature of our new algorithm is the successive use of peak enhancement algorithms that are deliberately designed for a gradual improvement of peak detection quality. We tested our approach with real gas chromatograms as well as intentionally contaminated spectra that contain Gaussian or speckle-type noise. CONCLUSION: Our results demonstrate that the proposed method can achieve near perfect peak detection performance while maintaining very small false alarm probabilities in case of gas chromatograms. Given the fact that biological signals appear in the form of peaks in various experimental data and that the propose method can easily be extended to such data, our approach will be a useful and robust tool that can help researchers highlight valid signals in their noisy measurements.

This letter considers a low-complexity multiuser detection based on the closest lattice point search (CLPS) for high speed packet access (HSPA) system. Instead of attempting to solve the ML detection problem in the presence of intersymbol and inter-cell interference, we utilize interference cancelled chips obtained from a bidirectional decision feedback operation to detect symbols. As a result, the worst case complexity of the CLPS is bounded to a controllable level irrespective of multipath spans. From the simulation on single and multi cell downlink communications in HSPA systems, we show that the proposed method offers substantial performance gain over conventional RAKE and MMSE equalizer.

In this letter, we propose a simple yet effective modulation classification method for maximum likelihood multiuser detection. Our method is a modification of generalized likelihood ratio test (GLRT) that approximates the optimal classifier in the Bayesian sense. We show that the proposed method can be implemented by modifying the sphere decoding algorithm to support multimodulation. Simulation results in multiuser detection in high-speed downlink packet access (HSDPA) system show that the proposed method offers considerable performance gain over conventional RAKE and MMSE algorithms.

In this paper, we present a near ML-achieving sphere search technique that reduces the number of search operations significantly over existing sphere decoding (SD) algorithms. While the SD algorithm relies only on causal symbols in evaluating path metric, proposed method accounts for the contribution of non-causal symbols with the aid of per-path minimum mean square error (MMSE) symbol estimation. The ML and MMSE combined cost metric results in the tight necessary condition for sphere decision and hence expedites the pruning of subtrees unlikely to be survived. From the simulations performed over multi-input multi-output (MIMO) wireless channels, it is shown that the computational complexity of the proposed approach is substantially smaller than the existing SD algorithms while providing negligible performance loss.

In this paper, we present energy-efficient soft error-tolerant techniques for digital signal processing (DSP) systems. The proposed technique, referred to as algorithmic soft error-tolerance (ASET), employs low-complexity estimators of a main DSP block to achieve reliable operation in the presence of soft errors. Three distinct ASET techniques - spatial, temporal and spatio-temporal- are presented. For frequency selective finite-impulse response (FIR) filtering, it is shown that the proposed techniques provide robustness in the presence of soft error rates of up to P/sub er/=10/sup -2/ and P/sub er/=10/sup -3/ in a single-event upset scenario. The power dissipation of the proposed techniques ranges from 1.1 X to 1.7 X (spatial ASET) and 1.05 X to 1.17 X (spatio-temporal and temporal ASET) when the desired signal-to-noise ratio SNR/sub des/=25 dB. In comparison, the power dissipation of the commonly employed triple modular redundancy technique is 2.9 X.

We present a maximum likelihood-based error correction (ML-EC) technique which achieves significant power savings in digital filtering. Although voltage over-scaling (VOS) can achieve high energy efficiency, it can introduce "soft errors" which severely degrade the performance of the filter. The proposed scheme detects, estimates and corrects these soft errors via an ML-based algorithm that achieves up to 47% power savings without any SNR loss and up to 60% power savings with a 1.5 dB SNR loss for an example case study of a frequency-selective low-pass filter.

In this paper, we present a novel algorithmic noise-tolerance (ANT) technique referred to as reduced precision redundancy (RPR). RPR requires a reduced precision replica whose output can be employed as the corrected output in case the original system computes erroneously. When combined with voltage overscaling (VOS), the resulting soft digital signal processing system achieves up to 60% and 44% energy savings with no loss in the signal-to-noise ratio (SNR) for receive filtering in a QPSK system and the butterfly of fast Fourier transform (FFT) in a WLAN OFDM system, respectively. These energy savings are with respect to optimally scaled (i.e., the supply voltage equals the critical voltage V/sub dd-crit/) present day systems. Further, we show that the RPR technique is able to maintain the output SNR for error rates of up to 0.09/sample and 0.06/sample in an finite impulse response filter and a FFT block, respectively.

An adaptive error diffusion algorithm which exploits the statistical characteristics of the modified input is developed to match the grey-levels of the input and the halftoned images. We adjust the threshold by using the quantization error to reflect the local characteristics and reduce the memory requirements. The proposed algorithm is combined with a color inkjet printer model to compensate for the distortion caused by the dot size differences in each color.