Integrated sensing and communication (ISAC) and the Frequency Range 3 (FR3) (upper mid-band) spectrum are among the key enablers of future wireless systems. ISAC promises new sensing functionalities for networks historically designed for communications, while the FR3 spectrum, approximately from 7 to 24GHz, offers large bandwidths and diverse propagation characteristics that significantly extend deployment possibilities. Motivated by the potential synergy between these two paradigms, this work presents an experimental investigation of a multiband ISAC channel in the FR3 range under realistic conditions. Using the Pi-Radio software-defined radio (SDR) platform and superresolution parameter estimation methods, we design a multiband testbed that measures sensing metrics such as the probability of detection (PD), probability of false alarm (PFA), and localization root mean-squared error (RMSE) across sub-bands at 6.5, 8.75, 10, 15, and 21.7 GHz. To analyze how communication performance reacts to environmental dynamics, we introduce the channel update rate gain (CURG), a new metric that quantifies achievable data-rate gains induced by target-dependent channel variations.

Neural network-based medium access control (MAC) protocol models (NPMs) improve goodput through site-specific operations but are vulnerable to shifts from their training network environments, such as changes in the number of user equipments (UEs) severely degrading goodput. To enhance resilience against such environmental shifts, we propose three novel token-based MAC protocol frameworks empowered by large language models (LLMs). First, we introduce a token-based protocol model (TPM), where an LLM generates MAC signaling messages. By editing LLM instruction prompts, TPM enables instant adaptation, which can be further enhanced by TextGrad, an LLM-based automated prompt optimizer. TPM inference is fast but coarse due to the lack of real interactions with the changed environment, and computationally intensive due to the large size of the LLM. To improve goodput and computation efficiency, we develop T2NPM, which transfers and augments TPM knowledge into an NPM via knowledge distillation (KD). Integrating TPM and T2NPM, we propose T3NPM, which employs TPM in the early phase and switches to T2NPM at a later stage. To optimize this phase switching, we design a novel metric of meta-resilience, which quantifies resilience to unknown target goodput after environmental shifts. Simulations corroborate that T3NPM achieves 20.56% higher meta-resilience than NPM with 19.8× lower computation cost than TPM in FLOPs.

In this paper, we investigate channel estimation for reconfigurable intelligent surface (RIS) empowered millimeter-wave (mmWave) multi-user single-input multiple-output communication systems using low-resolution quantization. Due to the high cost and power consumption of analog-to-digital converters (ADCs) in large antenna arrays and for wide signal bandwidths, designing mmWave systems with low-resolution ADCs is beneficial. To tackle this issue, we propose a channel estimation design using task-based quantization that considers the underlying hybrid analog and digital architecture in order to improve the system performance under finite bit-resolution constraints. Our goal is to accomplish a channel estimation task that minimizes the mean squared error distortion between the true and estimated channel. We develop two types of channel estimators: a cascaded channel estimator for an RIS with purely passive elements, and an estimator for the separate RIS-related channels that leverages additional information from a few semi-passive elements at the RIS capable of processing the received signals with radio frequency chains. Numerical results demonstrate that the proposed channel estimation designs exploiting task-based quantization outperform purely digital methods and can effectively approach the performance of a system with unlimited resolution ADCs. Furthermore, the proposed channel estimators are shown to be superior to baselines with small training overhead.

This paper investigates a practical precoding scheme for rate-splitting multiple access (RSMA) in multi-user multiple-input single-output (MU-MISO) systems where the nonlinear characteristics of power amplifiers are explicitly taken into account. To enhance performance in the presence of distortion caused by nonlinear power amplifiers, we propose an iterative accelerated gradient-based precoding scheme. The proposed precoding scheme is further integrated with a novel power scaling function that dynamically adjusts transmit power to satisfy practical power consumption constraints while maximizing an achievable sum rate. The gradient of the achievable sum rate for the considered system is derived to support efficient gradient-based optimization. We also prove that the proposed iterative precoding scheme converges to a critical point. Simulation results demonstrate that the proposed precoding scheme consistently outperforms modified conventional schemes including distortion-aware and weighted minimum mean square error beamforming, across both overloaded and underloaded scenarios. In particular, the proposed scheme demonstrates consistent performance in high-power regimes, where conventional schemes exhibit severe degradation due to distortion. Furthermore, the proposed precoding scheme exhibits faster convergence compared to the conventional benchmark.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems require accurate channel estimation for effective beamforming. Conventional compressed sensing (CS) algorithms utilize the inherent sparsity information of channels but suffer from basis mismatch problems for on-grid methods and high computational complexity for off-grid methods. Recently, generative model-based CS frameworks have shown promising results by directly learning the distribution of channel realizations, eliminating the need for prior sparsity information. However, these approaches rely on non-convex optimization in latent space that may converge to suboptimal solutions and exhibit generalization problems across diverse channel environments. Moreover, existing generative approaches have not adequately addressed the quantization effects of practical finite-resolution analog-to-digital converters (ADCs). To address these limitations, we propose a novel generative CS (GCS) algorithm using a conditional variational autoencoder (CVAE) that learns fundamental path components of mmWave MIMO channels rather than complete channel realizations while considering finite-resolution quantization. The CVAE is trained on single-path measurements with spatial frequency conditioning variables, enabling generalization across diverse channel environments without requiring environment-specific training data. Our algorithm directly encodes quantized received pilot signals and angular conditioning variables into latent representations, which are then fed into a decoder to output reconstructions conditioned on the angular parameters, with the decoder trained to be robust to perturbations from additive and quantization noise. The algorithm sequentially identifies path components by searching through spatial frequency codebooks with different conditioning values and applying gradient-based refinement for off-grid accuracy. Numerical results demonstrate that the proposed GCS algorithm outperforms conventional CS algorithms across various scenarios.

As a key technology for 6G, integrated sensing and communication (ISAC) is receiving considerable attention, and deploying a reconfigurable intelligent surface (RIS) can enhance both communication performance and sensing capability of ISAC by providing additional degrees of freedom. In this paper, we investigate a beam training framework for RIS-aided ISAC systems where beam alignment for a communication user equipment (UE) is conducted while simultaneously detecting a single target through its echo signal. Using codebooks constructed according to the principles of the 5G standard, we propose a partial search procedure that achieves low training overhead and mathematically show that this strategy is sufficient to identify a suitable codeword combination to serve the UE. By applying the auxiliary beam pair method, the target's angle information from the perspectives of the base station and RIS is obtained. Then, a high-accuracy closed-form localization is proposed based on the angle estimates, and we further extend the proposed technique to multi-target localization scenarios. Numerical results highlight the advantages of the proposed technique in the ISAC context, showing that the training procedure can effectively find a codeword combination and that the target localization technique outperforms the benchmarks.

The following paper presents a multi-band sensing channel quality analysis in the upper mid-band, also known as frequency range 3 (FR3). Measurements were conducted at 6.5 GHz, 8.75 GHz, 10 GHz, and 15 GHz, using a setup designed for integrated sensing and communication (ISAC). The sensing channel quality is evaluated using the estimation reliability metric, based on the iterative Levenberg–Marquardt (LM) algorithm. Given the static environment, we also validate a method to handle time-invariant dense multipath components (DMCs). Results show that lower bands enable the detection of more specular components due to lower path loss, but stronger DMC leads to lower estimation SNR. Higher bands provide cleaner estimates despite detecting fewer components. The trade-offs inherent to upper and lower FR3 bands highlight the potential of multi-band ISAC in the FR3 spectrum.

In this paper, we propose a reconfigurable intelligent surface (RIS) element allocation based low complexity algorithm for a multi-user multiple-input single-output orthogonal frequency division multiplexing system. First, we propose a concept called a metric that depends solely on the channel and represents the minimum rate. Through the metric, the RIS reflection coefficients can be updated without using alternating optimization. Next, we formulate and solve a metric maximization problem to verify that the metric successfully represents the desired objective, i.e., minimum rate maximization. Finally, by utilizing the concept of RIS element allocation, we propose a low complexity algorithm that maximizes the metric. Simulation results show that the proposed metric successfully represents the minimum rate and outperforms existing benchmarks. Moreover, the RIS element allocation algorithm displays remarkable performance, achieving similar performance with the existing benchmarks even with extremely low complexity.

Channel prediction addresses outdated channel state information by forecasting future channels based on past channel estimates. We propose a machine learning (ML)-based approach using neural networks to learn complex temporal statistics. Unlike conventional offline-trained predictors that suffer from unfamiliar environments, our online re-training framework adapts to varying channel conditions by re-training the networks from scratch. To minimize the re-training time for practical implementation, we introduce an aggregated learning (AL) approach for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. AL splits and aggregates training data in array or frequency domains of MIMO-OFDM channels, significantly reducing data collection time. Numerical results show that AL not only decreases training time overhead but also improves prediction performance across various scenarios.

In this paper, we consider people counting and localization systems exploiting channel state information (CSI) measured from commodity WiFi network interface cards (NICs). CSI has useful information of amplitude and phase to describe signal propagation affected by the number of people or their locations in a designated space. However, due to hardware impairments of transceivers, CSI measurement suffers from offsets such as packet boundary detection uncertainty, sampling time difference, and carrier frequency difference. Moreover, an uncontrollable external environment where other WiFi devices communicate each other induces interfering signals, resulting in erroneous CSI captured at a receiver. In this paper, preprocessing of CSI is first proposed for offset removal, and it guarantees low-latency operation without any filtering process. The number of samples collected for each specific scenario is kept after packet-preserving preprocessing, which can be fully utilized to learn neural network models. Afterwards, we design people counting and localization models based on pre-training. To be adaptive to different measurement environments, meta-learning-based people counting and localization models are also proposed. We provide computational and space complexity analyses, confirming that the proposed meta-learning-based people counting and localization models require comparable resources to conventional adaptive models. Numerical results show that, compared with other learning-based benchmarks, the proposed scheme can achieve high sensing accuracy.

In this paper, we propose a novel sum rate maximization algorithm with rate-splitting (RS) for a hybrid precoding downlink system. By utilizing the benefits of RS, we define a group RS (GRS) framework that serves multiple user equipments without being limited by the number of radio frequency chains. We then formulate and solve a sum rate maximization problem with a quality-of-service constraint to derive analog and digital precoders as well as rate-split. Through alternating optimization and a meticulous use of convex optimization techniques, we first derive the analog precoder utilizing the Riemannian conjugate gradient approach by defining a penalty factor and approximating the common rate. Next, the digital precoder and rate-split are derived by utilizing the successive convex approximation approach. To compensate for the excessive complexity of the proposed algorithm, we also derive a low complexity algorithm for the GRS framework that optimizes only the power of the digital beamformers and derives the directions of the analog and digital beamformers using linear operations. Simulation results verify the superiority of the GRS framework by comparing the performance with existing benchmarks. Furthermore, we corroborate via simulations that the low complexity algorithm achieves greater performance than the existing benchmarks even with low complexity.

Reconfigurable intelligent surfaces (RISs) provide alternative routes for reflected signals to network users, offering numerous applications. This paper explores an innovative approach of strategically deploying RISs along road areas to leverage various propagation and blockage conditions present in cellular networks with roads. To address the local network geometries shown by such networks, we use a stochastic geometry framework, specifically the Cox point processes, to model the locations of RISs and vehicle users. Then, we define the coverage probability as the chance that either a base station or an RIS is in line of sight (LOS) of the typical user and that the LOS signal has a signal-to-noise ratio (SNR) greater than a threshold. We derive the coverage probability as a function of key parameters such as RIS density and path loss exponent. We observe that the network geometry highly affects the coverage and that the proposed RIS deployment effectively leverages the underlying difference of attenuation and blockage, significantly increasing the coverage of vehicle users in the network. With experimental results addressing the impact of key variables to network performance, this work serves as a versatile tool for designing, analyzing, and optimizing RIS-assisted cellular networks with many vehicles.

Visible spectrum is an emerging frontier in wireless communications for enhancing connectivity and safety in vehicular environments. The vehicular visible light communication (VVLC) system is a key feature in leveraging existing infrastructures, but it still has several critical challenges. Especially, VVLC channels are highly correlated due to the small gap between light emitting diodes (LEDs) in each headlight, making it difficult to increase data rates by spatial multiplexing. In this paper, we exploit recently synthesized gold nanoparticles (GNPs) to reduce the correlation between LEDs, i.e., the chiroptical properties of GNPs for differential absorption depending on the azimuth angle of incident light are used to mitigate the LED correlation. In addition, we adopt a signal-to-leakage-plus-noise ratio (SLNR)-based precoder to support multiple users. The ratio of RGB light sources in each LED also needs to be optimized to maximize the sum SLNR satisfying a white light constraint for illumination since the GNPs can vary the color of transmitted light by the differential absorption across wavelength. The nonconvex optimization problems for precoders and RGB ratios can be solved by the generalized Rayleigh quotient with the approximated shot noise and successive convex approximation (SCA). The simulation results show that the SLNR-based precoder with the optimized RGB ratios significantly improves the sum rate in a multi-user vehicular environment and the secrecy rate in a wiretapping scenario. The proposed SLNR-based precoding verifies that the decorrelation between LEDs and the RGB ratio optimization are essential to enhance the VVLC performance.

Satellite networks are emerging as vital solutions for global connectivity beyond 5G. As companies such as SpaceX, OneWeb, and Amazon are poised to launch a large number of satellites in low Earth orbit, the heightened inter-satellite interference caused by mega-constellations has become a significant concern. To address this challenge, recent works have introduced the concept of satellite cluster networks where multiple satellites in a cluster collaborate to enhance the network performance. In order to investigate the performance of these networks, we propose mathematical analyses by modeling the locations of satellites and users using Poisson point processes, building on the success of stochastic geometry-based analyses for satellite networks. In particular, we suggest the lower and upper bounds of the coverage probability as functions of the system parameters, including satellite density, satellite altitude, satellite cluster area, path loss exponent, and the Nakagami parameterm. We validate the analytical expressions by comparing them with simulation results. Our analyses can be used to design reliable satellite cluster networks by effectively estimating the impact of system parameters on the coverage performance.

Precoding is a critical and long-standing technique in multi-user communication systems. However, the majority of existing precoding methods do not consider channel coding in their designs. In this paper, we consider the precoding problem in multi-user multiple-input single-output (MISO) systems, incorporating channel coding into the design. By leveraging the error-correcting capability of channel codes we increase the degrees of freedom in the transmit signal design, thereby enhancing the overall system performance. We first propose a novel data-dependent precoding framework for coded MISO systems, referred to aschannel-coded precoding(CCP), which maximizes the probability that information bits can be correctly recovered by the channel decoder. This proposed CCP framework allows the transmit signals to produce data symbol errors at the users’ receivers, as long as the overall information BER performance can be improved. We develop the CCP framework for both one-bit and multi-bit error-correcting capacity and devise a projected gradient-based approach to solve the design problem. We also develop a robust CCP framework for the case where knowledge of perfect channel state information (CSI) is unavailable at the transmitter, taking into account the effect of both noise and channel estimation errors. Finally, we conduct numerous simulations to verify the effectiveness of the proposed CCP and its superiority compared to existing precoding methods, and we identify situations where the proposed CCP yields the most significant gains.

Due to the high altitudes and large beam sizes of satellites, the curvature of the Earth’s surface can impact system-level performance. To consider this, 3GPP introduces the UV-plane beam mapping for system-level simulations of non-terrestrial networks (NTNs). This paper aims to provide a comprehensive understanding of how beams and user equipments (UEs) are placed on the UV-plane and subsequently mapped to the Earth’s surface. We present a general process of projecting UEs on the UV-plane onto the Earth’s surface. This process could offer a useful guideline for beam and UE deployment when evaluating the system-level performance of NTNs.

In this work, we compare emergent communication (EC) built upon multi-agent deep reinforcement learning (MADRL) and language-oriented semantic communication (LSC) empowered by a pre-trained large language model (LLM) using human language. In a multi-agent remote navigation task, with multimodal input data comprising location and channel maps, it is shown that EC incurs high training cost and struggles when using multimodal data, whereas LSC yields high inference computing cost due to the LLM's large size. To address their respective bottlenecks, we propose a novel framework of language-guided EC (LEC) by guiding the EC training using LSC via knowledge distillation (KD). Simulations corroborate that LEC achieves faster travel time while avoiding areas with poor channel conditions, as well as speeding up the MADRL training convergence by up to 61.8% compared to EC.

In the pursuit of ubiquitous connectivity, there is a growing interest in satellite systems owing to their large coverage. The deployment of a substantial number of satellites in low Earth orbits necessitates performance analyses and techniques to mitigate inter-satellite interference. Leveraging achievements in stochastic geometry, this paper proposes mathematical analyses for coverage performance by incorporating the concept of a satellite cluster, enabling joint transmission to address the challenges posed by mega-constellations. Furthermore, we investigate the influence of parameters such as cluster region on coverage performance. Validations of derived mathematical results are conducted through comparative analysis with simulation results. Our analyses can facilitate the efficient design of dependable satellite cluster systems by assessing the influence of design parameters on coverage performance.

Due to the simultaneous downlink and uplink transmissions in reconfigurable intelligent surface (RIS)-empowered frequency division duplexing (FDD) communication systems, it is necessary to design the RIS phase shifts to balance the performance of both directions at the same time. Focusing on a single-user multiple-input multiple-output system, we aim to maximize a weighted sum-rate for the downlink and uplink. To address the resulting non-convex optimization problem, we employ an alternating optimization (AO) algorithm, which includes two techniques for optimizing the phase shifts at the RIS. A manifold optimization-based algorithm is applied for the first technique, and a lower-complexity AO approach is developed for the second. Our numerical results demonstrate that the proposed algorithms lead to substantial enhancement of the entire system compared to existing baseline schemes.

Reconfigurable intelligent surface (RIS) technology, given its ability to favorably modify wireless communication environments, will play a pivotal role in the evolution of future communication systems. This paper proposes rate maximization techniques for both single-user and multiuser MIMO systems, based on the well-known weighted minimum mean square error (WMMSE) criterion. Using a suitable weight matrix, the WMMSE algorithm tackles an equivalent weighted mean square error (WMSE) minimization problem to achieve the sum-rate maximization. By considering a more practical RIS system model that employs a tensor-based representation enforced by the electromagnetic behavior exhibited by the RIS panel, we detail both the sum-rate maximizing and WMSE minimizing strategies for RIS phase shift optimization by deriving the closed-form gradient of the WMSE and the sum-rate with respect to the RIS phase shift vector. Our simulations reveal that the proposed rate maximization technique, rooted in the WMMSE algorithm, exhibits superior performance when compared to other benchmarks.

In this paper, we propose algorithms to minimize the energy consumption in millimeter wave/terahertz multi-user downlink communication systems. To ensure coverage in blockage-vulnerable high frequency systems, we consider cooperative rate-splitting (CRS) and transmission over multiple time blocks, where via CRS, multiple users cooperate to assist a blocked user. Moreover, we show that transmission over multiple time blocks provides benefits through smart resource allocation. We first propose a communication framework named improved distinct extraction-based CRS (iDeCRS) that utilizes the benefits of rate-splitting. With our transmission framework, we derive a performance benchmark assuming genie channel state information (CSI), i.e., the channels of the present and future time blocks are known, denoted as GENIE. Using the results from GENIE, we derive a novel efficiency constrained optimization (ECO) algorithm assuming instantaneous CSI. In addition, a simple but effective even data transmission (EDT) algorithm that promotes steady transmission along the time blocks is proposed. Simulation results show that ECO and EDT have satisfactory performances compared to GENIE. The results also show that ECO outperforms EDT when many users are cooperating, and vise versa.

In this paper, we propose reconfigurable intelligent surface (RIS) element allocation algorithms that maximize the minimum rate or sum rate in a multi-user (MU)-multiple-input single-output (MISO)-orthogonal frequency division multiplexing (OFDM) system aided with an RIS. Specifically, we first propose multiple metrics, which represent the minimum rate and sum rate, depending solely on the channel. We then propose multiple convex optimization algorithms to maximize the proposed metrics by updating the RIS matrix, validating that the metrics indeed represent the desired objectives successfully. Through the metrics, only a single RIS matrix update is required, contrast to existing works using alternating optimization. Finally, since the aforementioned optimization algorithms still suffer from high complexity, we propose RIS element allocation algorithms that update the RIS matrix to maximize the metrics with minimal complexities. Simulation results show that the RIS element allocation algorithms achieve remarkable performances close to existing benchmarks and exhaustive search with low complexities.

The extensive coverage offered by satellites makes them effective in enhancing service continuity for users on dynamic airborne and maritime platforms, such as airplanes and ships. In particular, geosynchronous Earth orbit (GEO) satellites ensure stable connectivity for terrestrial users due to their stationary characteristics when observed from Earth. This paper introduces a novel approach to model and analyze GEO satellite networks using stochastic geometry. We model the distribution of GEO satellites in the geostationary orbit according to a binomial point process (BPP) and examine satellite visibility depending on the terminal’s latitude. Then, we identify potential distribution cases for GEO satellites and derive case probabilities based on the properties of the BPP. We also obtain the distance distributions between the terminal and GEO satellites and derive the coverage probability of the network. We further approximate the derived expressions using the Poisson limit theorem. Monte Carlo simulations are performed to validate the analytical findings, demonstrating a strong alignment between the analyses and simulations. The simplified analytical results can be used to estimate the coverage performance of GEO satellite networks by effectively modeling the positions of GEO satellites.

Limited capacity of fronthaul links in a cell-free massive multiple-input multiple-output (MIMO) system can cause quantization errors at a central processing unit (CPU) during data transmission, complicating the centralized rate optimization problem. Addressing this challenge, we propose a harmony search (HS)-based algorithm that renders the combinatorial non-convex problem tractable. One of the distinctive features of our algorithm is its hierarchical structure: it first allocates resources at the access point (AP) level and subsequently optimizes for user equipment (UE), ensuring a more efficient and structured approach to resource allocation. Our proposed algorithm deals with rigorous conditions, such as asymmetric fronthaul bit allocation and distinct quantization error levels at each AP, which were not considered in previous works. We derive a closed-form expression of signal-to-interference-plus-noise ratio (SINR), in which additive quantization noise model (AQNM) based distortion error is taken into account, to define the mathematical expression of spectral efficiency (SE) for each UE. Also, we provide analyses on computational complexity and convergence to investigate the practicality of proposed algorithm. By leveraging various performance metrics such as total SE and max-min fairness, we demonstrate that the proposed algorithm can adaptively optimize the fronthaul bit allocation depending on system requirements. Finally, simulation results show that the proposed algorithm can achieve satisfactory performance while maintaining low computational complexity, as compared to the exhaustive search method.

This paper investigates reconfigurable intelligent surface (RIS)-aided frequency division duplexing (FDD) communication systems. Since the downlink and uplink signals are simultaneously transmitted in FDD, the phase shifts at the RIS should be designed to support both transmissions. Considering a single-user multiple-input multiple-output system, we formulate a weighted sum-rate maximization problem to jointly maximize the downlink and uplink system performance. To tackle the non-convex optimization problem, we adopt an alternating optimization (AO) algorithm, in which two phase shift optimization techniques are developed to handle the unit-modulus constraints induced by the reflection coefficients at the RIS. The first technique exploits the manifold optimization-based algorithm, while the second uses a lower-complexity AO approach. Numerical results verify that the proposed techniques rapidly converge to local optima and significantly improve the overall system performance compared to existing benchmark schemes.

In this paper, practical utilization of multiple distributed reconfigurable intelligent surfaces (RISs), which are able to conduct group-specific operations, for multi-group multicasting systems is investigated. To tackle the inter-group interference issue in the multi-group multicasting systems, the block diagonalization (BD)-based beamforming is considered first. Without any inter-group interference after the BD operation, the multiple distributed RISs are operated to maximize the minimum rate for each group. Since the computational complexity of the BD-based beamforming can be too high, a multicasting tailored zero-forcing (MTZF) beamforming technique is proposed to efficiently suppress the inter-group interference, and the novel design for the multiple RISs that makes up for the inevitable loss of MTZF beamforming is also described. Effective closed-form solutions for the loss minimizing RIS operations are obtained with basic linear operations, making the proposed MTZF beamforming-based RIS design highly practical. Numerical results show that the BD-based approach has ability to achieve high sum-rate, but it is useful only when the base station deploys large antenna arrays. Even with the small number of antennas, the MTZF beamforming-based approach outperforms the other schemes in terms of the sum-rate while the technique requires low computational complexity. The results also prove that the proposed techniques can work with the minimum rate requirement for each group.

Modulation classification (MC) is the first step performed at the receiver side unless the modulation type is explicitly indicated by the transmitter. Machine learning techniques have been widely used for MC recently. In this paper, we propose a novel MC technique dubbed as Joint Equalization and Modulation Classification based on Constellation Network (EMC2-Net). Unlike prior works that considered the constellation points as an image, the proposed EMC2-Net directly uses a set of 2D constellation points to perform MC. In order to obtain clear and concrete constellation despite multipath fading channels, the proposed EMC2-Net consists of equalizer and classifier having separate and explainable roles via novel three-phase training and noise-curriculum pretraining. Numerical results with linear modulation types under different channel models show that the proposed EMC2-Net achieves the performance of state-of-the-art MC techniques with significantly less complexity.

In this paper, we consider a cooperative rate-splitting (CRS) scenario in a terahertz (THz) downlink system. Specifically, we introduce a new CRS framework called extraction-based CRS (eCRS) that exploits the benefits of rate-splitting, grouping, and data extraction. Furthermore, by considering characteristics of the THz communication systems, we construct a novel cooperative channel model. We examine an extreme case of eCRS, where an optimization problem is formulated and solved based on the cooperative channel model. In simulation results, we corroborate the effectiveness of our proposed framework and the impact of the cooperative channel model.

This paper proposes a beamforming method under a per-antenna power constraint (PAPC). Although many beamformer designs with the PAPC need to solve complex optimization problems, the proposed complete power reallocation (CPR) method can generate beamformers with excellent performance only with linear operations. CPR is designed to have a simple structure, making it highly flexible and practical. In this paper, three CPR variations considering algorithm convergence speed, sum-rate maximization, and robustness to the channel uncertainty are developed. Simulation results verify that CPR and its variations satisfy their design criteria, and, hence, CPR can be readily utilized for various purposes.

Numerous studies claim that terahertz (THz) communication will be an essential piece of sixth-generation wireless communication systems. Its promising potential also comes with major challenges, in particular the reduced coverage due to harsh propagation loss, hardware constraints, and blockage vulnerability. To increase the coverage of THz communication, we revisit cooperative communication. We propose a new type of cooperative rate-splitting (CRS) called extraction-based CRS (eCRS). Furthermore, we explore two extreme cases of eCRS, namely, identical eCRS and distinct eCRS. To enable the proposed eCRS framework, we design a novel THz cooperative channel model by considering unique characteristics of THz communication. Through mathematical derivations and convex optimization techniques considering the THz cooperative channel model, we derive local optimal solutions for the two cases of eCRS and a global optimal closed form solution for a specific scenario. Finally, we propose a novel channel estimation technique that not only specifies the channel value, but also the time delay of the channel from each cooperating user equipment to fully utilize the THz cooperative channel. In simulation results, we verify the validity of the two cases of our proposed framework and channel estimation technique.

In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with semi-passive elements that can receive the signal in the active sensing mode. Ultimately, our goal is to minimize the channel estimation error using the received signal at the base station and additional information acquired from a small number of active sensors at the IRS. Unlike recent works on channel estimation with semi-passive elements that require both uplink and downlink training signals to estimate the UE-IRS and IRS-BS links, we only use uplink training signals to estimate all the links. To compute the minimum mean squared error (MMSE) estimates of all the links, we propose a novel variational inference-sparse Bayesian learning (VI-SBL) channel estimator that performs approximate posterior inference on the channel using VI with the mean-field approximation under the SBL framework. The simulation results show that VI-SBL outperforms the state-of-the-art baselines for IRS with passive reflecting elements in terms of the channel estimation accuracy and training overhead. Furthermore, VI-SBL with semi-passive elements is shown to be more spectral- and energy-efficient than the baselines with passive reflecting elements.

Accurate channel knowledge is critical in massive multiple-input multiple-output (MIMO), which motivates the use of channel prediction. Machine learning techniques for channel prediction hold much promise, but current schemes are limited in their ability to adapt to changes in the environment because they require large training overheads. To accurately predict wireless channels for new environments with reduced training overhead, we propose a fast adaptive channel prediction technique based on a meta-learning algorithm for massive MIMO communications. We exploit the model-agnostic meta-learning (MAML) algorithm to achieve quick adaptation with a small amount of labeled data. Also, to improve the prediction accuracy, we adopt the denoising process for the training data by using deep image prior (DIP). Numerical results show that the proposed MAML-based channel predictor can improve the prediction accuracy with only a few fine-tuning samples in various scenarios. The DIP-based denoising process gives an additional gain in channel prediction, especially in low signal-to-noise ratio regimes.

Due to its unique advantages, unmanned aerial vehicle (UAV) communication systems are promising candidates for next generation communication. Although, even with its potential, UAVs suffer from practical limitations such as en-ergy consumption. To mitigate this aspect, we propose a UAV energy consumption minimization technique through the assist of multiple terrestrial intelligent reflecting surfaces (IRSs). By generalizing the probabilistic line-of-sight model, we introduce a channel model capturing the characteristics of the IRSs and user equipments (UEs). Then, by deriving the closed form expression of the UE achievable rate, we formulate an optimization problem to minimize the UAV energy consumption for specific UE data requirements. With problem transformations for tractability and through the successive convex approximation technique, we achieve a local optimum solution. Through the results, we confirm that exploiting the IRSs is indeed extremely beneficial to minimize the UAV energy consumption.

In this paper, a novel channel estimation technique for intelligent reflecting surface (IRS)-aided single-user multiple-input multiple-output (SU-MIMO) systems is proposed. Based on dominant single-path channel approximation for IRS-related channels, the proposed technique conducts parameter estimation instead of straightforward full channel estimation. This makes the proposed technique practical with low training overhead, compared to the typical channel estimations that require a large number of training signals. Through the simulations, we verify that, because of its low training overhead, the proposed estimation technique can provide a higher effective spectral efficiency than that of existing channel estimation methods requiring high training overhead.

In this paper, we consider downlink low Earth orbit (LEO) satellite communication systems where multiple LEO satellites are uniformly distributed over a sphere at a certain altitude according to a homogeneous binomial point process (BPP). Based on the characteristics of the BPP, we analyze the distance distributions and the distribution cases for the serving satellite. We analytically derive the exact outage probability, and its approximated expression is obtained using the Poisson limit theorem. With these derived expressions, the system throughput maximization problem is formulated under the satellite-visibility and outage constraints. To solve this problem, we reformulate it with bounded feasible sets and propose an iterative algorithm to obtain near-optimal solutions. Simulation results perfectly match the derived exact expressions for the outage probability and system throughput. The analytical results of the approximated expressions are fairly close to those of the exact ones. It is also shown that the proposed algorithm for the throughput maximization is very close to the optimal performance obtained by a two-dimensional exhaustive search.

This paper considerssatellite eavesdroppersin uplink satellite communication systems where the eavesdroppers are randomly distributed at arbitrary altitudes according to homogeneous binomial point processes and attempt to overhear signals that a ground terminal transmits to a serving satellite. Non-colluding eavesdropping satellites are assumed, i.e., they do not cooperate with each other, so that their received signals are not combined but are decoded individually. Directional beamforming with two types of antennas: fixed- and steerable-beam antennas, is adopted at the eavesdropping satellites. The possible distribution cases for the eavesdropping satellites and the distributions of the distances between the terminal and the satellites are analyzed. The distributions of the signal-to-noise ratios (SNRs) at both the serving satellite and the most detrimental eavesdropping satellite are derived as closed-form expressions. The ergodic and outage secrecy capacities of the systems are derived with the secrecy outage probability using the SNR distributions. Simpler approximate expressions for the secrecy performance are obtained based on the Poisson limit theorem, and asymptotic analyses are also carried out in the high-SNR regime. Monte-Carlo simulations verify the analytical results for the secrecy performance. The analytical results are expected to be used to evaluate the secrecy performance and design secure satellite constellations by considering the impact of potential threats from malicious satellite eavesdroppers.

Medical applications have driven many areas of engineering to optimize diagnostic capabilities and convenience. In the near future, wireless body area networks (WBANs) are expected to have widespread impact in medicine. To achieve this impact, however, significant advances in research are needed to cope with the changes of the human body’s state, which make coherent communications difficult or even impossible. In this paper, we consider a realistic noncoherent WBAN system model where transmissions and receptions are conducted without any channel state information due to the fast-varying channels of the human body. Using distributed reception, we propose several symbol detection approaches where on-off keying (OOK) modulation is exploited, among which a supervised-learning-based approach is developed to overcome the noncoherent system issue. Through simulation results, we compare and verify the performance of the proposed techniques for noncoherent WBANs with OOK transmissions. We show that the well-defined detection techniques with a supervised-learning-based approach enable robust communications for noncoherent WBAN systems.

As communication systems that employ millimeter wave (mmWave) frequency bands must use large antenna arrays to overcome the severe propagation loss of mmWave signals, hybrid beamforming has been considered as an integral component of mmWave communications. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative technology that can significantly improve the performance of mmWave communication systems through the use of low-cost passive reflecting elements. In this paper, we study IRS-aided mmWave multiple-input multiple-output (MIMO) systems with hybrid beamforming architectures. We first exploit the sparse-scattering structure and large dimension of mmWave channels to develop the joint design of IRS reflection matrix and hybrid beamformer for narrowband MIMO systems. Then, we generalize the proposed joint design to broadband MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation by leveraging the angular sparsity of frequency-selective mmWave channels. Simulation results demonstrate that the proposed joint designs can significantly enhance the spectral efficiency of the systems of interest and achieve superior performance over the existing designs.

In this paper, channel estimation techniques and phase shift design for intelligent reflecting surface (IRS)-empowered single-user multiple-input multiple-output (SU-MIMO) systems are proposed. The two novel channel estimation techniques proposed in the paper, single-path approximated channel (SPAC) and selective emphasis on rank-one matrices (SEROM), have low training overhead to enable practical IRS-empowered SU-MIMO systems. SPAC is mainly based on parameter estimation by approximating IRS-related channels as dominant single-path channels. SEROM exploits IRS phase shifts as well as training signals for channel estimation and easily adjusts its training overhead. A closed-form solution for IRS phase shift design is also developed to maximize spectral efficiency where the solution only requires basic linear operations. Numerical results show that SPAC and SEROM combined with the proposed IRS phase shift design achieve high spectral efficiency even with low training overhead compared to existing methods.

Wireless communication systems are to use millimeter-wave (mmWave) spectra, which can enable extra radar functionalities. In this paper, we propose a multi-target velocity estimation technique using IEEE 802.11ad waveform in a vehicle-to-vehicle (V2V) scenario. We form a wide beam to consider multiple target vehicles. The Doppler shift of each vehicle is estimated from least square estimation (LSE) using the round-trip delay obtained from the auto-correlation property of Golay complementary sequences in IEEE 802.11ad waveform, and the phase wrapping is compensated by the Doppler shift estimates of proper two frames. Finally, the velocities of target vehicles are obtained from the estimated Doppler shifts. Simulation results show the proposed velocity estimation technique can achieve significantly high accuracy even for short coherent processing interval (CPI).

Time division duplexing (TDD) is adopted to exploit the uplink and downlink channel reciprocity in most of studies on massive multiple-input multiple-output (MIMO) systems. However, even in TDD, a base station (BS) still requires to transmit downlink training signals, which are named in the 3GPP standard as channel state information reference signals (CSI-RSs), to fully support spatial multiplexing in practice. This is because user equipments (UEs) may deploy less number of transmit antennas than receive antennas due to practical issues. Since uplink sounding reference signals (SRSs) are transmitted from only the transmit antennas of the UE, the BS is not able to obtain full downlink MIMO CSI by using channel reciprocity for spatial multiplexing. Hence, after reception of the downlink CSI-RSs, the UE still needs to feed back quantized CSI using a codebook to support spatial multiplexing. Taking practical antenna structures into account for reducing downlink CSI-RS overhead, this paper proposes possible approaches for downlink MIMO CSI reconstruction at the BS using the SRS with quantized downlink CSI to support spatial multiplexing. Numerical results show that the proposed techniques outperform the conventional one, i.e., solely based on the quantized CSI, in terms of the spectral efficiencies of spatial multiplexing.

This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor.

In this article, a channel estimator for wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid architectures and low-resolution analog-to-digital converters (ADCs) is proposed. To account for the propagation delay across the antenna array, which cannot be neglected in wideband mmWave massive MIMO systems, the discrete time channel that models the spatial wideband effect is developed. Also, the training signal design that addresses inter-frame, inter-user, and inter-symbol interferences is investigated when the spatial wideband effect is not negligible. To estimate the channel parameters over the continuum based on the maximum a posteriori (MAP) criterion, the Newtonized fully corrective forward greedy selection-cross validation-based (NFCFGS-CV-based) channel estimator is proposed. NFCFGS-CV is a gridless compressed sensing (CS) algorithm, whose termination condition is determined by the CV technique. The CV-based termination condition is proved to achieve the minimum squared error (SE). The simulation results show that NFCFGS-CV outperforms state-of-the-art on-grid CS-based channel estimators.

To get channel state information (CSI) at a base station (BS), most of researches on massive multiple-input multiple-output (MIMO) systems consider time division duplexing (TDD) to get benefit from the uplink and downlink channel reciprocity. Even in TDD, however, the BS still needs to transmit downlink training signals, which are referred to as channel state information reference signals (CSI-RSs) in the 3GPP standard, to support spatial multiplexing in practice. This is because there are many cases that the number of transmit antennas is less than the number of receive antennas at a user equipment (UE) due to power consumption and circuit complexity issues. Because of this mismatch, uplink sounding reference signals (SRSs) from the UE are not enough for the BS to obtain full downlink MIMO CSI. Therefore, after receiving the downlink CSI-RSs, the UE needs to feedback quantized CSI to the BS using a pre-defined codebook to support spatial multiplexing. In this paper, possible approaches to reconstruct full downlink MIMO CSI at the BS are proposed by exploiting both the SRS and quantized downlink CSI considering practical antenna structures with reduced downlink CSI-RS overhead. Numerical results show that the spectral efficiencies by spatial multiplexing based on the proposed downlink MIMO CSI reconstruction techniques outperform the conventional methods solely based on the quantized CSI.

The extension to millimeter-wave (mmWave) spectrum of communication frequency band makes it easy to implement a joint radar and communication system using single hardware. In this paper, we propose radar imaging based on the IEEE 802.11ad waveform for a vehicular setting. The necessary parameters to be estimated for inverse synthetic aperture radar (ISAR) imaging are sampled version of round-trip delay, Doppler shift, and vehicular velocity. The delay is estimated using the correlation property of Golay complementary sequences embedded on the IEEE 802.11ad preamble. The Doppler shift is first obtained from least square estimation using radar return signals and refined by correcting the phase uncertainty of Doppler shift by phase rotation. The vehicular velocity is determined from the estimated Doppler shifts and an equation of motion. Finally, an ISAR image is formed with the acquired parameters. Simulation results show that it is possible to obtain recognizable ISAR image from a point scatterer model of a realistic vehicular setting.

This paper proposes a beamforming design for millimeter-wave (mmWave) backhaul systems with dual-polarization antennas in uniform planar arrays (UPAs). The proposed design method optimizes a beamformer to mimic an ideal beam pattern, which has flat gain across its coverage, under the dominance of the line-of-sight (LOS) component in mmWave systems. The dual-polarization antenna structure is considered as constraints of the optimization. Simulation results verify that the resulting beamformer has uniform beam pattern and high minimum gain in the covering region.

There has been a continuing demand for improving the accuracy and ease of use of medical devices used on or around the human body. Communication is critical to medical applications, and wireless body area networks (WBANs) have the potential to revolutionize diagnosis. Despite its importance, WBAN technology is still in its infancy and requires much research. We consider body channel communication (BCC), which uses the whole body as well as the skin as a medium for communication. BCC is sensitive to the body's natural circulation and movement, which requires a noncoherent model for wireless communication. To accurately handle practical applications for electronic devices working on or inside a human body, we configure a realistic system model for BCC with on-off keying (OOK) modulation. We propose novel detection techniques for OOK symbols and improve the performance by exploiting distributed reception and supervised-learning approaches. Numerical results show that the proposed techniques are valid for noncoherent OOK transmissions for BCC.

This paper proposes hybrid beamforming designs for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) backhaul systems equipped with uniform planar arrays (UPAs) of dual-polarization antennas at both the transmit and receive base stations. The proposed beamforming designs are to near-optimally solve optimization problems taking the dual-polarization UPA structure into account. Based on the solutions of optimization problems, this paper shows it is possible to generate the optimal dual-polarization beamformer from the optimal single-polarization beamformer sharing the same optimality. As specific examples, squared error and magnitude of inner product are considered respectively for optimization criteria. To optimize proposed beamformers, partial channel information is needed, and the use of low overhead pilot sequences is also proposed to figure out the required information. Simulation results verify that the resulting beamformers have the most uniform gain (with the squared error criterion) or the highest average gain (with the magnitude of inner product criterion) in the covering region with the UPA of dual-polarization antennas.

In this paper, channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs) is considered. In the mmWave band, the number of propagation paths is small, which results in sparse virtual channels. To estimate sparse virtual channels based on the maximum a posteriori (MAP) criterion, sparsity-constrained optimization comes into play. In general, optimizing objective functions with sparsity constraints is NP-hard because of their combinatorial complexity. Furthermore, the coarse quantization of one-bit ADCs makes channel estimation a challenging task. In the field of compressed sensing (CS), the gradient support pursuit (GraSP) and gradient hard thresholding pursuit (GraHTP) algorithms were proposed to approximately solve sparsity-constrained optimization problems iteratively by pursuing the gradient of the objective function via hard thresholding. The accuracy guarantee of these algorithms, however, breaks down when the objective function is ill-conditioned, which frequently occurs in the mmWave band. To prevent the breakdown of gradient pursuit-based algorithms, the band maximum selecting (BMS) technique, which is a hard thresholder selecting only the "band maxima," is applied to GraSP and GraHTP to propose the BMSGraSP and BMSGraHTP algorithms in this paper.

Massive multiple-input multiple-output (MIMO) can improve the overall system performance significantly. Massive MIMO systems, however, may require a large number of radio frequency (RF) chains that could cause high cost and power consumption issues. One of promising approaches to resolve these issues is using low-resolution analog-to-digital converters (ADCs) at base stations. Channel estimation becomes a difficult task by using low-resolution ADCs though. This paper addresses the channel estimation problem for massive MIMO systems using one-bit ADCs when the channels are spatially and temporally correlated. Based on the Bussgang decomposition, which reformulates a non-linear one-bit quantization to a statistically equivalent linear operator, the Kalman filter is used to estimate the spatially and temporally correlated channel by assuming the quantized noise follows a Gaussian distribution. Numerical results show that the proposed technique can improve the channel estimation quality significantly by properly exploiting the spatial and temporal correlations of channels.

In large-scale antenna systems, using one-bit analog-to-digital converters (ADCs) has recently become important since they offer significant reductions in both power and cost. However, in contrast to high-resolution ADCs, the coarse quantization of one-bit ADCs results in an irreversible loss of information. In the context of channel estimation, studies have been developed extensively to combat the performance loss incurred by one-bit ADCs. Furthermore, in the field of array signal processing, direction-of-arrival (DOA) estimation combined with one-bit ADCs has gained growing interests recently to minimize the estimation error. In this paper, a channel estimator is proposed for one-bit ADCs where the channels are characterized by their angular geometries, e.g., uniform linear arrays (ULAs). The goal is to estimate the dominant channel among multiple paths. The proposed channel estimator first finds the DOA estimate using the maximum inner product search (MIPS). Then, the channel fading coefficient is estimated using the concavity of the log-likelihood function. The limit inherent in one-bit ADCs is also investigated, which results from the loss of magnitude information.

In this letter, a novel minimum mean square error (MSE) based channel estimation framework is proposed to reduce the downlink channel training overhead in frequency division duplexing massive multiple-input multiple-output systems, where the overhead reduction is achieved through training only a subset of antennas and by exploiting the spatial correlation between the antennas at the base station. Closed-form expressions of the analytical MSE and the asymptotic MSE of the system are obtained. Furthermore, a perfect match between theoretical and simulation results is observed, where the channel training overhead can be reduced by half with an acceptable performance.

Millimeter-wave (mmWave) systems use directional beams to support high-rate data communications. Small misalignment between the transmit and receive beams (e.g., due to the mobility) can result in a significant drop of the received signal quality, especially in line-of-sight communication channels. In this paper, we propose and evaluate high-resolution angle tracking strategies for wideband mmWave systems with mobility. We custom design pairs of auxiliary beams as the tracking beams, and use them to capture the angle variations, toward which the steering directions of the data beams are adjusted. Different from conventional beam tracking designs, the proposed framework neither depends on the angle variation model nor requires an on-grid assumption. For practical implementation of the proposed methods, we examine the impact of the array calibration errors on the auxiliary beam pair design. Numerical results reveal that by employing the proposed methods, good angle tracking performance can be achieved under various antenna array configurations, channel models, and mobility conditions.

Wireless relay networking has been proposed as a solution for extending coverage in the past few decades. The relay network facilitates communication between users who are unable to reliably share information due to severe pathloss or blockage. In this paper, we utilize spatial diversity of distributed multiple-input multiple-output systems for the relay network. Hence, the relay network consists of many separate relay nodes. Due to limited computational power, we assume each relay node receives the sum of the transmissions from all users and then performs one-bit quantization. The quantization bits from the relay network are broadcast back to the users through a downlink channel that is modeled as a low-rate binary symmetric channel. Based on the noisy quantization bits from the relay network and its own prior transmission, each user detects the transmitted symbols from other users. We first derive the maximum likelihood detector for the described system. Then we develop a sub-optimal detector called the orthogonal subset maximum likelihood (OSML) detector, which exploits only a subset of relay nodes for detection, to reduce the computational complexity. By using combinatorial geometry, we derive the minimum number of required relay nodes for the OSML detector to operate. The derived results are verified through numerical simulations.

The high hardware complexity of a massive MIMO base station, which requires hundreds of radio chains, makes it challenging to build commercially. One way to reduce the hardware complexity and power consumption of the receiver is to lower the resolution of the analog-to-digital converters (ADCs). We derive an achievable rate for a massive MIMO system with arbitrary quantization and use this rate to show that ADCs with as low as 3 bits can be used without significant performance loss at spectral efficiencies around 3.5 bpcu per user, also under interference from stronger transmitters and with some imperfections in the automatic gain control.

This paper studies the broadcast channel with M antennas at the base-station and M single antenna users. Most of the works in the literature assume perfect channel state information at both ends of each link. In this work, we assume that these links are spatially correlated and only the long-term statistical information corresponding to the covariance matrices of the links are available at both ends. We are interested in the design of beamforming vectors for data transmission to the M users to maximize the ergodic sum-rate obtained by treating interference as noise. In the simpler M = 2 setting, our prior work has shown the optimality of a generalized eigenvector beamformer structure for this problem. However, these results are not easily extendable to the general M user setting. We overcome these difficulties by first establishing a tractable approximation for the ergodic sum-rate in terms of the beamforming vectors and covariance matrices that is asymptotically tight in M. We then cast the sum-rate approximation maximization problem as a manifold optimization and illustrate the optimality of the generalized eigenvector structure in the general M user setting.

Fifth generation wireless networks are expected to utilize wide bandwidths available at millimeter wave (mmWave) frequencies for enhancing system throughput. However, the unfavorable channel conditions of mmWave links, such as, higher path loss and attenuation due to atmospheric gases or water vapor, hinder reliable communications. To compensate for these severe losses, it is essential to have a multitude of antennas to generate sharp and strong beams for directional transmission. In this paper, we consider mmWave systems using uniform planar array (UPA) antennas, which effectively place more antennas on a 2-D grid. A hybrid beamforming setup is also considered to generate beams by combining a multitude of antennas using only a few radio frequency chains. We focus on designing a set of transmit beamformers generating beams adapted to the directional characteristics of mmWave links assuming a UPA and hybrid beamforming. We first define ideal beam patterns for UPA structures. Each beamformer is constructed to minimize the mean squared error from the corresponding ideal beam pattern. Simulation results verify that the proposed codebooks enhance beamforming reliability and data rate in mmWave systems.

Analog-to-digital converters (ADCs) stand for a significant part of the total power consumption in a massive multiple-input multiple-output (MIMO) base station. One-bit ADCs are one way to reduce power consumption. This paper presents an analysis of the spectral efficiency of single-carrier and orthogonal-frequency-division-multiplexing (OFDM) transmission in massive MIMO systems that use one-bit ADCs. A closed-form achievable rate, i.e., a lower bound on capacity, is derived for a wideband system with a large number of channel taps that employ low-complexity linear channel estimation and symbol detection. Quantization results in two types of error in the symbol detection. The circularly symmetric error becomes Gaussian in massive MIMO and vanishes as the number of antennas grows. The amplitude distortion, which severely degrades the performance of OFDM, is caused by variations between symbol durations in received interference energy. As the number of channel taps grows, the amplitude distortion vanishes and OFDM has the same performance as single-carrier transmission. A main conclusion of this paper is that wideband massive MIMO systems work well with one-bit ADCs.

Channel estimation is of critical importance in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Due to the use of large antenna arrays, low-complexity mmWave specific channel estimation algorithms are required. In this paper, an auxiliary beam pair design is proposed to provide high-resolution estimates of the channel's angle-of-departure (AoD) and angle-of-arrival (AoA) for mmWave MIMO systems. By performing an amplitude comparison with respect to each auxiliary beam pair, a set of ratio measures that characterize the channel's AoD and AoA are obtained by the receiver. Either the best ratio measure or the estimated AoD is quantized and fed back to the transmitter via a feedback channel. The proposed technique can be incorporated into control channel design to minimize initial access delay. Though the design principles are derived assuming a high-power regime, evaluation under more realistic assumption shows that by employing the proposed method, good angle estimation performance is achieved under various signal-to-noise ratio levels and channel conditions.

In this paper, a novel two-dimensional super-resolution angle-of-departure (AoD) and angle-of-arrival (AoA) estimation technique is proposed for wideband millimeter-wave multiple-input multiple-output systems with dual-polarized antenna elements. The key ingredient of the proposed method is the custom designed beam pairs, from which there exists an invertible function of the AoD/AoA. A new multi-layer reference signal structure is developed for the proposed method to facilitate angle estimation for wideband channels with dual-polarized antenna elements. To reduce feedback in closed-loop frequency division duplexing systems, a novel differential feedback strategy is proposed to feedback the estimated angle pairs. Numerical results demonstrate that good azimuth/elevation AoD and AoA estimation performance can be achieved under different levels of signal-to-noise ratio, channel conditions, and antenna array configurations.

Uniform planar antenna (UPA) structures are expected to become popular for massive multiple-input multiple-output (MIMO) systems because they enable deployment of a large number of antennas in limited space. In frequency division duplexing (FDD) systems, quantized channel state information (CSI) should be fed back from users to base stations to make adaptive transmission possible. However, it is difficult to accurately quantize massive MIMO channels due to their large dimensions. In this paper, we propose practical CSI quantizers for full-dimension (FD) MIMO systems employing UPAs. We focus on quantizing and combining dominant beams in channels by keeping realistic channel properties and antenna structures into account. To scan a channel space, we also develop a multi-round beam search approach. Numerical results verify that the proposed quantizers show better quantization performance than the previous channel quantization techniques.

In this paper, auxiliary beam pair design is developed to provide high-resolution estimates of channel's elevation/azimuth angle-of-departure (AoD) and angle-of-arrival (AoA) for millimeter- wave full-dimension MIMO (FD-MIMO) systems. Pairs of custom designed analog beams are formed at both the transmitter and receiver to help acquire channel information. It is shown via simulation results that by employing the proposed method, promising elevation/azimuth AoD and AoA estimation performance can be achieved under various signal- to-noise ratio levels and channel conditions.

We investigate the performance of wideband massive MIMO base stations that use one-bit ADCs for quantizing the uplink signal. Our main result is to show that the many taps of the frequency-selective channel make linear combiners asymptotically consistent and the quantization noise additive and Gaussian, which simplifies signal processing and enables the straightforward use of OFDM. We also find that single-carrier systems and OFDM systems are affected in the same way by one-bit quantizers in wideband systems because the distribution of the quantization noise becomes the same in both systems as the number of channel taps grows.

Auxiliary beam pairs are proposed in millimeter-wave cellular systems for closed-loop hybrid precoding. Pairs of custom designed analog beams are formed to help acquire channel information. It is shown via simulations that auxiliary beam pairs have lower complexity and better achievable rates with a moderate amount of feedback, compared to conventional beam training methods.

Millimeter wave (mmWave) vehicular communication systems have the potential to improve traffic efficiency and safety. Lack of secure communication links, however, may lead to a formidable set of abuses and attacks. To secure communication links, a physical layer precoding technique for mmWave vehicular communication systems is proposed in this paper. The proposed technique exploits the large dimensional antenna arrays available at mmWave systems to produce direction dependent transmission. This results in coherent transmission to the legitimate receiver and artificial noise that jams eavesdroppers with sensitive receivers. Theoretical and numerical results demonstrate the validity and effectiveness of the proposed technique and show that the proposed technique provides high secrecy throughput when compared to conventional array and switched array transmission techniques.

In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a pair of high-resolution analog-to-digital converters (ADCs) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system.

Massive multiple-input multiple-output (MIMO) with uniform planar arrays (UPAs), which is often referred to as full-dimension (FD) MIMO, is being strongly considered for future wireless communication standards. FD-MIMO can control transmit and receive beams in both the horizontal and vertical domains and fully exploit the large number of antennas of massive MIMO. It is widely accepted that Kronecker-product codebooks using a discrete Fourier transform (DFT) structure are suitable to quantize the downlink channel at the user for FD-MIMO systems relying on frequency division duplexing (FDD). In this paper, we numerically study the characteristics of FD-MIMO channels using a three-dimensional (3D) channel model that captures realistic channel properties. Based on the study, we identify a limitation of conventional Kronecker-product codebooks and propose advanced channel quantization techniques that can further improve channel quantization quality.

Massive multiple-input multiple-output (MIMO) systems hold the potential to be an enabling technology for 5G cellular. Uniform planar array (UPA) antenna structures are a focus of much commercial discussion because of their ability to enable a large number of antennas in a relatively small area. With UPA antenna structures, the base station can control the beam direction in both the horizontal and vertical domains simultaneously. However, channel conditions may dictate that one dimension requires higher channel state information (CSI) accuracy than the other. We propose the use of an additional one bit of feedback information sent from the user to the base station to indicate the preferred domain on top of the feedback overhead of CSI quantization in frequency division duplexing (FDD) massive MIMO systems. Combined with variable-rate CSI quantization schemes, the numerical studies show that the additional one bit of feedback can increase the quality of CSI significantly for UPA antenna structures.

Small cell networks utilizing millimeter wave (mmWave) links are expected to enhance system throughput, since the wide bandwidths at mmWave frequencies can afford high data rates. Due to mmWave's unfavorable channel conditions, it is necessary for mmWave communication systems to use beamforming with a large number of antennas to generate sharp and strong beams. In this paper, we propose a codebook design algorithm for the beamforming by considering the directional characteristic of mmWave links. The proposed codebook design algorithm can be easily adapted to different kinds of antenna arrays. Simulation results show that the proposed codebook outperforms previously reported codebooks for mmWave systems.

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.

A linear beamformer design for the broadcast channel where the base station is equipped with Ntantennas and signals to M = 2 users (each with a single antenna) and where the vector channels are spatially correlated is considered. This problem is of relevance in wireless standardization efforts where two users are simultaneously scheduled (instead of the theoretically feasible Ntuser scheduling) to minimize signaling overhead. The users are assumed to have perfect channel state information (CSI), whereas the base station has statistical information of the channels. In the first part of this work, the role of the relevance of feedback is studied via the quantification of the gap in ergodic sum-rate between the perfect CSI and statistics-only extremes. In this direction, the importance of orthogonality of the dominant eigenmodes of the two users in maximizing the gap is established. In the second part of this work, in scenarios with a large gap, limited feedback beamforming codebooks and a codeword selection metric are designed for the low-rate feedback setting. The main contribution here is the proposal of a generalized eigenvector codebook and feedback of the codeword index that maximizes an estimate of the signal-to-interference-and-noise ratio (SINR) of each user. Extensions of this scheme are also proposed for the general Ntantenna case with M users, where M ≤ Nt. It is shown via numerical studies that this scheme leads to significant performance improvement across a large family of channels over schemes such as Grassmannian/Random Vector Quantization/single-user codebooks with the channel projection metric for codeword selection.

It is of great interest to develop efficient ways to acquire accurate channel state information (CSI) for massive multiple-input-multiple-output (MIMO) systems using frequency division duplexing (FDD). It is theoretically well known that the codebook size (in bits) for CSI quantization should be increased as the number of transmit antennas becomes larger, and 3GPP Long Term Evolution (LTE) and LTE-Advanced codebooks have sizes that scale according to this rule. It is hard to apply the conventional approach of using unstructured and predefined vector quantization codebooks for CSI quantization in massive MIMO because of the codeword search complexity. In this paper, we propose a trellis-extended codebook (TEC) that can be easily harmonized with current wireless standards, such as LTE or LTE-Advanced, because it can allow standardized codebooks designed for two, four, or eight antennas to be extended to larger arrays by using a trellis structure. TEC exploits a Viterbi decoder for CSI quantization and a convolutional encoder for CSI reconstruction. By quantizing multiple channel entries simultaneously using standardized codebooks in a state transition of a trellis search, TEC can achieve a fractional number of bits per channel entry quantization and a practical feedback overhead. Thus, TEC can solve both the complexity and the feedback overhead issues of CSI quantization in massive MIMO systems. We also develop trellis-extended successive phase adjustment (TE-SPA), which works as a differential codebook for TEC. This is similar to the dual codebook concept of LTE-Advanced. TE-SPA can reduce CSI quantization error with lower feedback overhead in temporally and spatially correlated channels. Numerical results verify the effectiveness of the proposed schemes in FDD massive MIMO systems.

Fifth-generation wireless systems are expected to employ multiple-antenna communication at millimeter wave (mmWave) frequencies using small cells within heterogeneous cellular networks. The high path loss of mmWave and the physical obstructions make communication challenging. To compensate for the severe path loss, mmWave systems may employ a beam alignment algorithm that facilitates highly directional transmission by aligning the beam direction of multiple antenna arrays. This paper discusses a mmWave system employing dual-polarized antennas. First, we propose a practical soft-decision beam alignment (soft-alignment) algorithm that exploits orthogonal polarizations. By sounding the orthogonal polarizations in parallel, the equality criterion of the Welch bound for training sequences is relaxed. Second, the analog beamforming system is adapted to the directional characteristics of the mmWave link, assuming a high Ricean K-factor and poor scattering environment. A soft-alignment algorithm enables the mmWave system to align a large number of narrow beams to the channel subspace in an attempt to effectively scan the mmWave channel. Third, we propose a method to efficiently adapt the number of channel sounding observations to the specific channel environment based on an approximate probability of beam misalignment. Simulation results show that the proposed soft-alignment algorithm with adaptive sounding time effectively scans the channel subspace of a mobile user by exploiting polarization diversity.

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.

Cellular wireless network architectures employing a tiered structure, consisting of traditional macro-cells and small-cells, have attracted much attention recently because of their potential to dramatically increase network capacity. These architectures can reduce the distances of transmit-receiver pairs, thus enhancing radio link qualities. However, a tiered cell deployment could incur severe cochannel interference. Prior work for single-tier networks has proposed coordinated multi-point (CoMP) transmission, which includes joint transmission, as a possible solution for overcoming the rate limitations induced by cochannel interference. Because users in each cell experience various interference conditions, each cell needs to support single transmission (in which only one cell transmits to a single user) and joint transmission. In a conventional implementation, a particular combination of transmissions over the entire network is fixed in advance for each time slot and opportunistic scheduling chooses one of the users supported by each transmission. However, when the number of users to be scheduled is small, the throughput improvement from opportunistic scheduling becomes limited. To overcome this problem, we propose a cumulative distribution function-based scheduling scheme which jointly chooses the transmitter set and the corresponding scheduled users in a two-tier network. It is shown that the throughput performance can be improved compared to those of the fixed slot resource allocation scheme because more users "compete" for slot resources.

The broadcast problem with multiple antennas at the transmitter end and a single antenna at each user is studied in this work. A low-complexity transmitter architecture where linear beamforming is used to convey information to the users, and multi-user interference is treated as noise at the receiver unit of each user is considered. The vector channels that connect the transmitter to the users are assumed to be spatially correlated. The goal of this work is the design of a low-rate limited feedback framework to improve the achievable sum-rate of the system relative to a baseline scenario where only correlation information is known at the transmitter. While prior work considers either a codebook structure that is designed for spatially uncorrelated channels or for spatially correlated point-to-point systems, it is argued here that a “good” codebook design should incorporate a statistical version of the interference structure that is seen at each user. Further, instead of feeding back the codeword index that is best aligned to the user's channel vector, the index that maximizes an estimate of the signal-to-interference-and-noise ratio as perceived by the user is fed back. It is shown via numerical results that the proposed framework results in a substantial performance improvement over existing schemes across a large family of spatial correlation and across a practically relevant operating regime.

Accurate channel state information (CSI) is essential for attaining beamforming gains in single-user (SU) multiple-input multiple-output (MIMO) and multiplexing gains in multi-user (MU) MIMO wireless communication systems. State-of-the-art limited feedback schemes, which rely on pre-defined codebooks for channel quantization, are only appropriate for a small number of transmit antennas and low feedback overhead. In order to scale informed transmitter schemes to emerging massive MIMO systems with a large number of transmit antennas at the base station, one common approach is to employ time division duplexing (TDD) and to exploit the implicit feedback obtained from channel reciprocity. However, most existing cellular deployments are based on frequency division duplexing (FDD), hence it is of great interest to explore backwards compatible massive MIMO upgrades of such systems. For a fixed feedback rate per antenna, the number of codewords for quantizing the channel grows exponentially with the number of antennas, hence generating feedback based on look-up from a standard vector quantized codebook does not scale. In this paper, we propose noncoherent trellis-coded quantization (NTCQ), whose encoding complexity scales linearly with the number of antennas. The approach exploits the duality between source encoding in a Grassmannian manifold (for finding a vector in the codebook which maximizes beamforming gain) and noncoherent sequence detection (for maximum likelihood decoding subject to uncertainty in the channel gain). Furthermore, since noncoherent detection can be realized near-optimally using a bank of coherent detectors, we obtain a low-complexity implementation of NTCQ encoding using an off-the-shelf Viterbi algorithm applied to standard trellis coded quantization. We also develop advanced NTCQ schemes which utilize various channel properties such as temporal/spatial correlations. Monte Carlo simulation results show the proposed NTCQ and its extensions can achieve near-optimal performance with moderate complexity and feedback overhead.

It is crucial to have accurate channel state information at the transmit side to achieve the maximum performance in multiple-input multiple-output (MIMO) systems, especially for multi-user systems. Frequency division duplexing systems based on limited feedback facilitate this, but these techniques are reliant on carefully design codebooks that are optimized for various antenna deployment scenarios. In this paper we discuss the channel model for rotated-dual-polarized (RDP) antenna systems and propose an efficient way of designing practical differential codebooks for RDP scenarios. We assess the performance of the proposed differential codebook design technique by simulations, and it is shown that codebooks designed by the proposed technique outperform conventional fixed and differential codebooks with small number of codewords.

Accurate channel direction information is essential to achieve considerable capacity gains in multiple-input multiple-output (MIMO) wireless communication systems. Limited feedback using a polar-cap differential codebook which utilizes the temporal correlation in multiple-input single-output (MISO) channels is presented in this paper. We first describe the general properties of the polar-cap differential codebook and then explain the design methodology of the size of the polar-cap given the temporal correlation coefficient. We also propose an enhancement of the polar-cap differential codebook which is suitable for a spatially correlated channel. We compare the polar-cap differential codebook with a rotation-based differential codebook in terms of the chordal distance to demonstrate the superiority of the polar-cap differential codebook. Monte Carlo simulation results show that the polar-cap differential codebook facilitates a significant performance gain in both temporally and spatially correlated channels.

We consider a system with precoding in the half duplex Decode-Forward (DF) relay networks, where multiple antennas are adopted at the source, the destination and the relay. Based on the channel state information (CSI), a few feedback bits are fed back from the destination to the source and the relay to select the source and the relay precoders. A suboptimal precoder selection criterion for the symbol vector error minimization and an optimal precoder selection criterion for the capacity maximization are proposed for the practical situation where relays are placed between the source and the destination. We analyze the diversity performance of the criteria and show that the full diversity is achieved when the rank one precoder sets span the vector channel space formed by the transmit antennas of the source and the those of the relay.

SU and MU-MIMO performance relies on accurate link adaptation in order to benefit from multi-user scheduling, beamforming, adaptive coding and modulation. Such accuracy highly depends on the type of the channel state information feedback. LTE-Advanced has defined two major types of feedback, i.e. implicit and explicit feedback. Implicit feedback makes some assumptions on the transmit precoding and receiver processing at the time of CSI and CQI feedback. The CSI is expressed in terms of a recommended precoder, commonly denoted as PMI. Explicit feedback refers to the feedback of channel information without making any assumption on the transmit and receiver processing. In this paper, we discuss pros and cons of such feedback mechanisms for both SU and MU-MIMO and compare performance of both approaches using system level simulations compliant with LTE-A system. It is shown that implicit feedback is the preferred feedback framework for both SU and MU-MIMO.

Given a contraint on the total amount of feedback overhead, we investigate the allocation of feedback bits (i.e. codebook sizes) among users in a limited feedback Zero Forcing Beamforming-based MU-MIMO scheme where users have the opportunity to adapt their codebooks as a function of their own channel statistics. We consider both single-polarized and dual- polarized scenarios. Using upper bounds on the total rate loss incurred by quantization (previously derived by the authors), the optimal bit allocation strategy among users is derived as a function of the users channel statistics and SNR. Closed form solutions of the optimal bit allocation at low and high SNR are also derived. It is shown that in single-polarized scenarios (resp. dual-polarized scenarios) at high SNR it is beneficial to allocate more bits to users experiencing rank deficient transmit correlation matrices (resp. low cross-polarization discrimination XPD) than to users experiencing well conditioned correlation matrices (resp. large XPD), while an opposite allocation should be done at low SNR. Simulation results are shown to confirm analytical derivations.

Zero-forcing beamforming (ZFBF) is a promising technique for multiuser MIMO systems. However, such method requires all the users to feed their channel information back to the base station (BS), so the BS can select the users and receive antennas as well as computing the transmit beamforming matrix. The quality of feedback information is crucial to the resultant system throughput. In order to reduce the power expenditure of the mobile stations (MS) while preserving the feedback quality, we propose a new feedback scheme based on the use of infrastructure relay stations (RS). The simulation results show that the scheme generally gives a better average throughput with lower SNR value requirements.

An efficient uplink user selection algorithm for distributed antenna systems (DAS) is proposed in this paper. Previous user selection algorithms in multiple-input-multiple-output (MIMO) systems are designed for conventional MIMO systems where antennas are located together. Since DAS can be thought as a variation of MIMO systems, the user selection algorithms which are originally designed for conventional MIMO systems can also be applied to DAS. However, the channel characteristics of DAS are different from those of conventional MIMO systems. Thus there should be specific user selection algorithm for DAS environments which utilizes the channel characteristics of DAS. This paper proposes an efficient uplink user selection algorithm specified for DAS. Simulation results show that the proposed algorithm gives near optimal performance which reaches up to 98.5% of the optimal spectral efficiency. Furthermore, the complexity of the proposed algorithm is also examined and the results show that it is easy to implement in practical systems.