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.

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.

Estimating downlink (DL) channel state information (CSI) in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems generally requires downlink pilots and feedback overheads. Accordingly, this paper investigates the feasibility of zero-feedback FDD massive MIMO systems based on channel extrapolation. We use the high-resolution parameter estimation (HRPE), specifically the space-alternating generalized expectation-maximization (SAGE) algorithm, to extrapolate the DL CSI based on the extracted parameters of multipath components in the uplink channel. We apply the HRPE to two different channel models: the vector spatial signature (VSS) model and the direction of arrival (DOA) model. We verify these methods through real-world channel data acquired from channel measurement campaigns with two different types of channel sounders: a) a switched array-based, real-time, time-domain, outdoors setup at 3.5 GHz, and b) a virtual array-based, high-accuracy, frequency-domain, indoors setup at 2.4 and 5-7 GHz. The performance metrics of the extrapolated channels that we evaluate include the mean squared error, beamforming efficiency, and spectral efficiency in multiuser MIMO scenarios. The results show that the HRPE-based channel extrapolation performs best under the simple VSS model, which does not require array calibration, and if the BS is in an open outdoor environment having line-of-sight (LOS) paths to well-separated users.

Designing beamforming techniques for the downlink (DL) of frequency division duplex (FDD) massive MIMO is known to be a challenging problem due to the difficulty of obtaining channel state information (CSI). Indeed, since the uplink-downlink bands are disjoint, the system cannot rely on channel reciprocity to estimate the channel from uplink (UL) pilots as in time division duplexing (TDD) system. Still, in this paper, we propose original designs for robust beamformers that do not require any feedback from the users and only rely on the transmission of UL pilots. The price to pay is that the beamformer is non-coherent in the sense that it does not leverage full knowledge of the phase of each multipath component. A large variety of novel designs are proposed under different criterion and partial phase knowledge.

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.

Channel estimation for the downlink of frequency division duplex (FDD) massive multiple-input-multiple output (MIMO) systems is well known to generate a large overhead as the amount of training generally scales with the number of transmit antennas in a MIMO system. In this paper, we consider the solution of extrapolating the channel frequency response from uplink pilot estimates to the downlink frequency band. This drastically reduces the downlink pilot overhead and completely removes the need for a feedback from the users. The price to pay is a degradation in the quality of the channel estimates, which reduces the downlink spectral efficiency. We first show that conventional estimators fail to achieve reasonable accuracy. We propose instead to use high-resolution channel estimation. We derive the Cramer-Rao lower bound (CRLB) of the mean squared error (MSE) of the extrapolated channel. Furthermore, a relationship between the imperfect channel state information (CSI) and the downlink user performance is derived. The extrapolation-based FDD massive MIMO performance is validated through numerical simulations and compared to a corresponding time division duplex (TDD) system. Considered figures of merit for extrapolation performance include channel MSE, beamforming efficiency, extrapolation range, spectral efficiency and uncoded symbol error rate. Our main conclusion is that channel extrapolation is a viable solution for FDD massive MIMO systems.

This paper investigates the capability of millimeter-wave (mmWave) channel sounders with phased arrays to perform super-resolution parameter estimation, i.e., determine the parameters of multipath components (MPC), such as direction of arrival and delay, with resolution better than the Fourier resolution of the setup. We analyze the question both generally, and with respect to a particular novel multi-beam mmWave channel sounder that is capable of performing multiple-input-multiple-output (MIMO) measurements in dynamic environments. We firstly propose a novel two-step calibration procedure that provides higher-accuracy calibration data that are required for Rimax or SAGE. Secondly, we investigate the impact of center misalignment and residual phase noise on the performance of the parameter estimator. Finally we experimentally verify the calibration results and demonstrate the capability of our sounder to perform super-resolution parameter estimation.

Application of massive multiple-input multipleoutput (MIMO) systems to frequency division duplex (FDD) is challenging mainly due to the considerable overhead required for downlink training and feedback. Channel extrapolation, i.e., estimating the channel response at the downlink frequency band based on measurements in the disjoint uplink band, is a promising solution to overcome this bottleneck. This paper presents measurement campaigns obtained by using a wideband (350 MHz) channel sounder at 3.5 GHz composed of a calibrated 64 element antenna array, in both an anechoic chamber and outdoor environment. The Space Alternating Generalized Expectation- Maximization (SAGE) algorithm was used to extract the parameters (amplitude, delay, and angular information) of the multipath components from the attained channel data within the â€training†(uplink) band. The channel in the downlink band is then reconstructed based on these path parameters. The performance of the extrapolated channel is evaluated in terms of mean squared error (MSE) and reduction of beamforming gain (RBG) in comparison to the â€ground truthâ€, i.e., the measured channel at the downlink frequency. We find strong sensitivity to calibration errors and model mismatch, and also find that performance depends on propagation conditions: LOS performs significantly better than NLOS.

Outdoor to indoor penetration loss is one of the crucial challenges faced at millimeter-wave frequencies. This paper presents the results from 28-GHz channel sounding campaigns performed to investigate the impact of this phenomenon on the wireless propagation channel characteristics in small cell and fixed wireless access scenarios. The measurements are performed with a real-time channel sounder equipped with phased array antennas that allow beam-forming and electronic beam steering for directionally resolved measurements. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We compare the measured path loss, delay spread, and angular spread for indoor and outdoor receiver locations for two different types of buildings. We find that the penetration loss strongly depends on the angle of incidence, and the scatterers on the outside of the building strongly impact how much power is coupled into the building. Based on the results, we provide statistical models for path loss, delay spread, and angular spread.

This paper investigates the effects of a human body shadowing for a device-to-device (D2D) communications scenario at 28 GHz. The measurements are performed with a real-time channel sounder equipped with fast-switching phased antenna arrays, which enables the directionally resolved wideband measurement of dynamic effects. By exploiting the phase coherence of the setup, the multi-path components can be tracked over time, observing the temporal variations of the channel characteristics. This paper presents results of the human body shadowing in an outdoor (plaza) environment at two different link distances: 5 m and 10 m. We then analyze results corresponding to the assumption of fixed beams with different beamwidth, a 12° directional single beam and a 102° sectoral combined beam. More importantly, for the first time, the time-varying angular power spectrum and the temporal evolutions of the mean angle and the angular spread statistics in a dynamic channel of walking pedestrians are presented, which cannot be measured with traditional horn antenna channel sounders.

This paper presents the results from a 28 GHz channel sounding campaign performed to investigate the effects of outdoor to indoor penetration on the wireless propagation channel characteristics for an urban microcell in a fixed wireless access scenario. The measurements are performed with a real-time channel sounder, which can measure path loss up to 169 dB, and equipped with phased array antennas that allow electrical beam steering for directionally resolved measurements in dynamic environments. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. For outdoor and indoor receiver locations, we compare path loss, delay spreads and angular spreads obtained for two different types of buildings.