This paper presents a blockage prediction system based on artificial intelligence/machine learning (AI/ML) for millimeter wave (mmWave) communications, with a particular focus on vehicular scenarios. Although numerous related works on ML-based blockage prediction, these works focus on simple scenarios, where the trajectory of the moving object is either fixed or deterministic. In order to make the blockage prediction system more practical, this paper assumes a nondeterministic situation such as both communication terminals and blockers are moving. Furthermore, we have formulated a practical AI/ML-based blockage prediction procedure that is operable in 3GPP compliant systems with taking its beam measurement reports and frame structure. The proposed prediction method uses the measurement report for beam management as input of a neural network. The computer simulation is conducted to show prediction performance of proposed method.

Accurate path loss (PL) modeling is essential for evaluating and optimizing cell-free massive MIMO systems, especially in dense urban environments where traditional models fail to capture the complexity of real-world propagation. This paper introduces CUNEC (Cell-free massive MIMO for Urban Non-stationary Environments with Correlations), a novel PL model that accounts for spatial non-stationarity, inter-access point (AP)/user equipment (UE) correlations, and urban-specific propagation phenomena such as corner diffraction and street canyon waveguiding. CUNEC segments AP-UE paths by street order, models PL as a stochastic function of urban geometry, and integrates spatially correlated shadowing. The parameters are derived from large-scale ray tracing and validated against both additional ray tracing in New York, NY and real-world channel measurements in Los Angeles, CA. Compared to the conventional α–β model, CUNEC significantly improves accuracy in the considered urban propagation scenarios. An open-source dataset comprising over 30,000 AP locations and 128 UE positions is also released to support reproducible research and future system development.

Cell-free massive MIMO (CF-mMIMO), where each user equipment (UE) is connected to multiple access points (APs), is emerging as an important component for fifth-generation (5G) and sixth-generation (6G) cellular systems. Accurate channel models based on measurements are required to optimize the design and deployment of such systems. This paper presents an extensive measurement campaign for CF-mMIMO in an urban environment. A new “virtual AP” technique measures channels between 80 UE locations and more than 20, 000 possible micro-cellular AP locations. Measurements are done at 3.5 GHz carrier frequency with 350 MHz bandwidth (BW). The paper describes the measurement setup and data processing, shows sample results and their physical interpretation, and provides statistics for key quantities such as pathloss, shadowing, delay spread (DS), and delay window. We find pathloss coefficients of 2.9 and 10.4 for line-of-sight (LOS) and non line-of-sight (NLOS), respectively, where the high LOS coefficient is mainly because larger distance leads to more grazing angle of incidence and thus lower antenna gain in our setup. Shadowing standard deviations are 5.1/16.6 dB, and root mean squared (RMS) DSs of -80.6/-72.6 dBs, where dBs is defined as$t_{\mathrm { dBs}}=10\log _{10}(t_{\mathrm { sec}})$. The measurements can also be used for parameterizing a certain type of channel model, namely Cell-free massive MIMO for Urban Non-stationary Environment with Correlations (CUNEC), which will be reported in future work.

In this paper, we present a novel approach to analog beam selection in cell-free massive multi-input multi-output (CF-mMIMO) systems employing hybrid beamforming (BF) over millimeter-wave (mmWave) channels. In conventional CF-mMIMO systems with hybrid BF, an analog beam is selected by a central processing unit (CPU) from a predefined codebook. This selection is based on the received power at each access point (AP) and the fairness among user equipment (UE). However, this conventional method disregards the potential impact of interference and heavily relies on digital BF to mitigate it. Consequently, when utilizing low-complexity digital BF techniques such as maximum ratio transmission (MRT), the system's performance experiences degradation. Our proposed beam selection method takes into account both the received power and the interference power, effectively mitigating the influence of interference while maintaining an adequate level of received power. The numerical simulations validate the efficacy of our proposed approach.

This paper investigates multi-user grouping (MUG) to improve energy efficiency (EE) in cell-free massive multiple-input multiple-output systems with access point (AP) switch on/off (ASO). ASO puts those APs into sleep mode which have little contribution with any spatially-multiplexed user equipments (UEs). However, in general, ASO can be less effective especially when spatially-multiplexed UEs are widely distributed in the area. Therefore, it is crucial to systematically integrate ASO and MUG, where UEs are divided into groups for spatial multiplexing. As a concrete procedure, we propose a practical MUG-ASO algorithm utilizing the criteria based on correlations of large-scale fading among UEs for effective EE improvement. The proposed algorithm can effectively select closely locating UEs into the same group for spatial multiplexing, and then it can put comparatively large number of APs into sleep mode at each time slot. Numerical example simulations show the effectiveness of the integration for EE improvement; in a typical case, the proposed algorithm can improve EE by 29.4% at 50-percentile compared to the conventional ASO algorithms with random MUG without severe degradation of spectral efficiency.

The design of cell-free massive MIMO (CF-mMIMO) systems requires accurate, measurement-based channel models. This paper provides the first results from the by far most extensive outdoor measurement campaign for CF-mMIMO channels in an urban environment. We measured impulse responses between over 20, 000 potential access point (AP) locations and 80 user equipments (sUEs) at 3.5 GHz with 350 MHz bandwidth (BW). Measurements use a “virtual array” approach at the AP and a hybrid switched/virtual approach at the UE. This paper describes the sounder design, measurement environment, data processing, and sample results, particularly the evolution of the power-delay profiles (sPDPs) as a function of the AP locations, and its relation to the propagation environment.

Distributed antenna systems (DAS) have attracted significant attention for next-generation wireless systems. This largely motivated by the inherent macrodiversity, i.e., the fact that shadowing of the links to the different access points (APs) is different, which plays a major role in the reliability and capacity of such systems. However, shadowing correlation might reduce the benefits. In this paper, we provide the first stochastic geometry-based analysis of the impact of correlated shadowing on the uplink performance of DAS. Since the statistics of the SNR are determined by the second moment, we use Fenton-Wilkinson (F-W) moment matching and the second moment measure, to formulate it as triple integral. From this, we further construct two closed-form approximations with different degrees of accuracy and simplicity. Extensive Monte Carlo simulations validate the theoretical inferences and approximation accuracy. The results show that a large decorrelation distance will increase the variance of uplink SNR, and a small path-loss exponent leads to a stronger dependence of the second moment on the decorrelation distance. The results can also serve as the basis for future investigations of cell-free massive MIMO systems.

This paper suggests and analyzes adaptive bit allocation for the quantization of uplink signals of a cell-free massive MIMO (CF-mMIMO) system under limited fronthaul capacity. Specifically, we consider a CF-mMIMO system with hybrid processing, where at each access point (AP) a singular-value decomposition (SVD) reduces the number of streams that need to hauled, each stream is quantized with an adaptive number of bits, and a central processing unit (CPU) decodes the uplink signals. The hybrid processing, which the authors previously proposed, had been shown its potential to reduce fronthaul load without severe degradation of the spectral efficiency. However, as the bandwidths of the wireless system increases, the fronthaul capacity becomes comparatively tight, and the quantization noise would degrade the spectral efficiency severely. In order to improve the performance under such a scenario, this paper proposes algorithms for adaptive bit allocation of the output streams, based on the optimization of the average SNR, or the sum capacity. In addition, appropriate selection of the number of streams of the hybrid processing in each AP is also discussed. Computer simulations verify the effectiveness of these proposed methods.

Cell-free massive multi-input multi-output (CF-mMIMO) systems are one of the key technologies for 6G. Currently, performance assessment of such systems is hampered by the fact that there are no specific path loss (PL) models for CF-mMIMO. Conventional PL models based on Euclidean distance and log-normal shadowing assuming spatial stationarity across coverage area are usually employed for simplicity but show significant deviations from reality particularly in urban environments, which are the main deployment scenario for CF-mMIMO. In this work, we provide the first realistic channel model for CF-mMIMO systems in urban environments, introducing non-isotropic, non-stationary behavior in different parts of street canyon locations and incorporating both correlations between access points, and between user equipments. Simulation results demonstrate the superior reproduction of typical PL values in urban street canyons.

Cell-free massive multiple-input multiple-output systems are expected to provide faster and more robust connections to user equipments (UEs) by cooperation of a massive number of distributed access points (APs). Energy efficiency (EE) is becoming an important indicator to design and operate networks; to improve EE, use of sleep-mode of APs (SMA), also called AP switch on/off, for selected APs has been investigated. Although previous works analyze the performance of SMA in the presence of user-centric clustering (UCC), these two techniques are assumed to not affect each other. In this paper, we propose a new greedy combining algorithm (GCA), where SMA and UCC work alternately to obtain better performance, and show its superiority over a conventional algorithm. Example simulations show that GCA can achieve 44% higher total EE for 8 UEs and 59% for 16 UEs with 64 APs. Additionally, GCA also provides higher minimum spectral efficiency thanks to its structure of the algorithm.

This paper proposes hybrid signal processing schemes for the uplink cell-free massive MIMO; these schemes serve to reduce fronthaul loads to obtain a scalable centralized processing architecture. In this architecture, received signals of multiple receive antennas at the access points (APs) are compressed into fewer streams by local spatial signal processing and then the streams are forwarded to a central processing unit (CPU) via fronthaul, and the CPU performs scalable processing for channel estimation and signal detection based on partial minimum mean squared error (PMMSE). We propose two kinds of concrete local signal processing methods for this hybrid processing architecture: one is based on MMSE, and the other is based on principal component analysis (PCA) with eigenvalue decomposition (EVD). For the EVD, a local vector selection based EVD (LVS-EVD) that selects uniform number of eigenvectors for each AP in a standalone way, and a global vector selection based EVD (GVS-EVD) that determines the dimensions of the weight vector of each AP in the CPU, are further considered. Computer simulations verify the approaches and compare their effectiveness. In addition, we show that the GVS-EVD scheme can be operated with significantly reduced fronthaul loads without severe performance degradation.

Measurements of the propagation channels in real-world environments form the basis of all realistic system performance evaluations, as foundation of statistical channel models or to verify ray tracing. This is also true for the analysis of cell-free massive multi-input multi-output (CF-mMIMO) systems. However, such experimental data are difficult to obtain, due to the complexity and expense of deploying tens or hundreds of channel sounder nodes across the wide area a CF-mMIMO system is expected to cover, especially when different configurations and number of antennas are to be explored. In this paper, we provide a novel method to obtain channel data for CF-mMIMO systems using a channel sounder based on a drone, also known as a small unmanned aerial vehicle (UAV). Such a method is efficient, flexible, simple, and low-cost, capturing channel data from thousands of different access point (AP) locations within minutes. In addition, we provide sample 3.5 GHz measurement results analyzing deployment strategies for APs and make the data open source, so they may be used for various other studies. To our knowledge, our data are the first large-scale, real-world CF-mMIMO channel data.

Cell-free massive multiple-input multiple-output systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G. Recently, a measurement-based evaluation revealed that the performance of a semi-distributed deployment, where each AP has multiple antennas, is comparable to that of a fully-distributed deployment in terms of coverage in an indoor environment while reducing the number of APs. In this paper, we analyze the performance of various antenna distribution configurations, and show that semi-distributed deployments outperform fully-distributed deployment remarkably from both spectral and energy efficiency points of view. These characteristics of semi-distributed deployments enable us to construct more cost-effective networks, which is an important indicator to deploy the systems in real environment.

Abstract In this paper, we propose a novel contour‐based volume image segmentation technique. Our technique is based on an implicit surface reconstruction strategy, whereby a signed scalar field is generated from user‐specified contours. The key idea is to compute the scalar field in a joint spatial‐range domain (i.e., bilateral domain) and resample its values on an image manifold. We introduce a new formulation of Hermite radial basis function (HRBF) interpolation to obtain the scalar field in the bilateral domain. In contrast to previous implicit methods, bilateral HRBF (B‐HRBF) generates a segmentation boundary that passes through all contours, fits high‐contrast image edges if they exist, and has a smooth shape in blurred areas of images. We also propose an acceleration scheme for computing B‐HRBF to support a real‐time and intuitive segmentation interface. In our experiments, we achieved high‐quality segmentation results for regions of interest with high‐contrast edges and blurred boundaries.

MOTIVATION: The development of gene expression microarray technology has allowed the identification of differentially expressed genes between different clinical phenotypic classes of cancer from a large pool of candidate genes. Although many class comparisons concerned only a single phenotype, simultaneous assessment of the relationship between gene expression and multiple phenotypes would be warranted to better understand the underlying biological structure. RESULTS: We develop a method to select genes related to multiple clinical phenotypes based on a set of multivariate linear regression models. For each gene, we perform model selection based on the doubly-adjusted R-square statistic and use the maximum of this statistic for gene selection. The method can substantially improve the power in gene selection, compared with a conventional method that uses a single model exclusively for gene selection. Application to a bladder cancer study to correlate pre-treatment gene expressions with pathological stage and grade is given. The methods would be useful for screening for genes related to multiple clinical phenotypes. AVAILABILITY: SAS and MATLAB codes are available from author upon request.