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.

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.

Recently, wideband beamforming using extremely large-scale antenna array (ELAA) systems have gained much interest as a means to boost throughput in next generation (xG) networks. However, conventional phase shifter (PS)-based beamforming methods face challenges in wideband ELAA systems due to the beam squint effect, where beams at different frequencies become misaligned. Although the use of true time delay (TTD) can address this by creating frequency-dependent beamforming vectors, traditional TTD-based methods still experience considerable sidelobe leakage due to the mismatch between intended and generated beams. In this paper, we introduce a novel wideband beamforming architecture that dynamically configures connections between TTDs and PS subarrays using a switching network. By jointly optimizing subarray connections, TTD time delays, and PS phase shifts, wideband dynamic array-of-subarrays (WDAoSA) minimizes sidelobe gain and maximizes array gain in wideband ELAA systems. Numerical results show significant improvements in both array gain and data rate compared to conventional TTD-based methods.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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).

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 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.

© 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.