Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend on instantaneous CSI. This work extends traditional prediction approaches by focusing on four-dimensional (4D) CSI, which includes predictions over time, bandwidth, and antenna (TX and RX) space. Such a comprehensive framework is essential for addressing the dynamic nature of mobility environments within intelligent transportation systems, necessitating the capture of both temporal and spatial dependencies across diverse domains. To address this complexity, we propose a novel context-conditioned spatiotemporal predictive learning method. This method leverages causal convolutional long short-term memory (CA-ConvLSTM) to effectively capture dependencies within 4D CSI data, and incorporates context-conditioned attention mechanisms to enhance the efficiency of spatiotemporal memory updates. Additionally, we introduce an adaptive meta-learning scheme tailored for recurrent networks to mitigate the issue of accumulative prediction errors. We validate the proposed method through empirical studies conducted across three different geometric configurations and mobility scenarios. Our results demonstrate that the proposed approach outperforms existing state-of-the-art predictive models, achieving superior performance across various geometries. Moreover, we show that the meta-learning framework significantly enhances the performance of recurrent-based predictive models in highly challenging cross-geometry settings, thus highlighting its robustness and adaptability.

Estimating vehicle locations is important for realizing Intelligent Transportation Systems (ITS). This paper considers utilizing vehicle-to-vehicle (V2V) communication for relative vehicular localization. In particular, we develop a machine learning (ML) solution that uses the Channel State Information (CSI) from multiple-antenna transceivers for vehicular localization. We develop suitable pre-processing to obtain a compact CSI representation as an input feature to the ML solution. The proposed solution is then based on feed-forward neural networks. Training and evaluation are done on measured real-world data in the 5.9 GHz band. The performance on two routes shows that the proposed feature may improve the performance while reducing the number of trainable parameters. Furthermore, the paper raises a number of interesting observations regarding the learnability in V2V ML-based localization solutions.

Channel information is essential to unleash the benefits of 5G New Radio (NR) by enabling network intelligence that adapts transmissions to users’ channels. In this paper, we propose model-driven feature design and use support vector machine (SVM) to classify users’ speed range. Our model-driven features are designed based on stochastic channel modeling. Multiple features are derived from time-domain cross-correlation and time-domain auto-correlation function of the sounding reference signals. The classifier is trained and verified with extensive standard compliant simulation channels at different SNR levels and speeds, and attains greater than 90% accuracy.

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then determine the cluster types based on their evolution patterns. We classify the determined clusters into line-of-sight, single-bounce reflections from static interaction objects (IOs), single-bounce reflections from mobile IOs, multiple-bounce reflections, and density multipath components (DMCs). Particularly, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This paper presents the complete parameterization of the channel model. We validate this model by comparing the delay spread and the angular spreads of arrival/departure obtained from the proposed model with the measurement data.

Many wireless networks, including 5G NR (New Radio) and future beyond 5G cellular systems, are expected to operate on multiple frequency bands. This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE). While the millimeter-wave band might offer higher data rate, there is a significant probability of outage during which the communication should be carried on the (more reliable) centimeter-wave band. With mobility, the BA can be perceived as a sequential problem, where the BS uses previously observed information to predict the best band for a future time step. We formulate the BA as a binary classification problem and propose supervised Machine Learning (ML) solutions. We study the problem when both the BS and the UE use (i) omnidirectional antennas and (ii) both use directional antennas. In the omnidirectional case, we derive analytical benchmark solutions based on the Gaussian Process (GP) assumption for the inter-band shadow fading. In the directional case, where the labeling is shown to be complex, we propose an efficient labeling approach based on the Viterbi Algorithm (VA). We compare the performances for two channel models: (i) a stochastic channel and (ii) a ray-tracing based channel.

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance.

Discriminating between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, orLOS identification, is important for a variety of purposes in wireless systems, including localization and channel modeling. LOS identification is especially challenging in vehicle-to-vehicle (V2V) networks since a variety of physical effects that occur at different spatial/temporal scales can affect the presence of LOS. This paper investigates machine learning techniques for LOS identification in V2V networks using an extensive set of measurement data and then develops robust and efficient identification solutions. Our approach exploits several static and time-varying features of the channel impulse response (CIR), which are shown to be effective. Specifically, we develop a fast identification solution that can be trained by using the power angular spectrum. Moreover, based on the measurement data, we also compare three different machine learning methods, i.e., support vector machine, random forest, and artificial neural network, in terms of their ability to train and generate the classifier. The results of our experiments conducted under various V2V environments, which were then validated using$K$-fold cross-validation, show that our techniques can distinguish the LOS/NLOS conditions with an error rate as low as 1%. In addition, we investigate the impact of different training and validating strategies on the identification accuracy.

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.

Downlink channel estimation in massive MIMO systems is well known to generate a large overhead in frequency division duplex (FDD) mode as the amount of training generally scales with the number of transmit antennas. Using instead an extrapolation of the channel from the measured uplink estimates to the downlink frequency band completely removes this overhead. In this paper, we investigate the theoretical limits of channel extrapolation in frequency. We highlight the advantage of basing the extrapolation on high-resolution channel estimation. A lower bound (LB) on the mean squared error (MSE) of the extrapolated channel is derived. A simplified LB is also proposed, giving physical intuition on the SNR gain and extrapolation range that can be expected in practice. The validity of the simplified LB relies on the assumption that the paths are well separated. The SNR gain then linearly improves with the number of receive antennas while the extrapolation performance penalty quadratically scales with the ratio of the frequency and the training bandwidth. The theoretical LB is numerically evaluated using a 3GPP channel model and we show that the LB can be reached by practical high-resolution parameter extraction algorithms. Our results show that there are strong limitations on the extrapolation range than can be expected in SISO systems while much more promising results can be obtained in the multiple-antenna setting as the paths can be more easily separated in the delay-angle domain.

This paper presents the system design, calibration, and example measurements of a novel ultra-wideband, low-cost, real-time channel sounder. The channel sounder operates in the frequency range from 3 to 18 GHz with a 15-GHz bandwidth thus providing a Fourier delay resolution of 66.7 ps. It sequentially measures overlapping sub-bands of 1-GHz bandwidth to cover the whole frequency range. By using a waveform generator and a digitizer with relatively lower sample rates, the design significantly lowers the cost compared with sampling the whole band simultaneously. With the transmitting power of 40 dBm, the maximum measurable path loss is 132 dB without accounting for possible antenna gains. In this paper, we also describe the calibration procedure along with the method to stitch sub-bands into a combined channel response across 15 GHz of bandwidth. The channel sounder performance is compared with a vector network analyzer (VNA) measurements acquired in the same static channels. In contrast to VNAs, however, our channel sounder can measure the whole band in 6 ms and can, thus, operate in dynamic environments with coherence time more than 6 ms. Additionally, we present sample results from a measurement campaign performed in an indoor corridor investigating the delay spreads statistics over the range of 3–18 GHz.

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.

Time-division multiplexed (TDM) channel sounders, in which a single RF chain is connected sequentially via an electronic switch to different elements of an array, are widely used for the measurement of double-directional/MIMO propagation channels. This paper investigates the impact of array switching patterns on the accuracy of parameter estimation of multipath components (MPC) for a time-division multiplexed (TDM) channel sounder. The commonly-used sequential (uniform) switching pattern poses a fundamental limit on the number of antennas that a TDM channel sounder can employ in fast time-varying channels. We thus aim to design improved patterns that relax these constraints. To characterize the performance, we introduce a novel spatio-temporal ambiguity function, which can handle the non-idealities of real-world arrays. We formulate the sequence design problem as an optimization problem and propose an algorithm based on simulated annealing to obtain the optimal sequence. As a result, we can extend the estimation range of Doppler shifts by eliminating ambiguities in parameter estimation. We show through Monte Carlo simulations that the root mean square errors of both direction of departure and Doppler are reduced significantly with the new switching sequence. The results are also verified with actual vehicle-to-vehicle (V2V) channel measurements.

This paper presents results from channel sounding campaigns that investigate the impact of foliage blockage on the wireless propagation channel characteristics at 28 GHz. The measurements are performed with a real-time channel sounder equipped with phased array antennas that allow beamforming 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 derive models for the distance-dependent foliage attenuation for different receiver heights. Additionally, we compare the angular spread and delay spread for links with and without foliage blockage to understand the effects of foliage on all channel characteristics.

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 investigates the impact of array switching patterns on the accuracy of parameter estimation of multipath components for a time division multiplexed (TDM) channel sounder. To measure fast time- varying channels, the conventional uniform array switching pattern poses a fundamental limit of the number of antennas that a TDM channel sounder can utilize. We propose a method, which is based on the simulated annealing algorithm, to find non-uniform array switching patterns for realistic antenna arrays, so that we can extend the Doppler estimation range of the channel sounder by suppressing the high sidelobes in the spatio-temporal ambiguity function. Monte Carlo simulations demonstrate that the optimal switching sequence leads to significantly smaller root mean square errors of both direction of departure and Doppler. Results can be applied in both vehicle-to-vehicle and mobile millimeter wave MIMO channel measurements.

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.

This paper presents results from the (to our knowledge) first dynamic double-directionally resolved measurement campaign at mm-wave frequencies for an outdoor microcellular scenario. The measurements are performed with USC's real- time channel sounder equipped with phased array antennas that can steer beams electrically in microseconds, allowing directional measurements in dynamic environments. Exploiting the phase coherency of the setup, the multi-path components can be tracked over time to investigate the temporal dependencies of the channel characteristics. We present results for time-varying path-loss, delay spread, mean angles and angular spreads observed at the transmitter (TX) and receiver (RX) in the presence of moving vehicles and pedestrians. Additionally, we investigate excess losses observed due to blockage by vehicles and compare the cases when TX and RX are using fixed beams or when they are capable of adjusting beam directions dynamically.

Fifth generation cellular systems will operate in a wide range of frequencies, covering both the traditional cellular bands, and mm-wave frequency bands, either selecting a specific band or operating in multiple bands simultaneously. For the design of such systems, a detailed understanding of the frequency dependence of the propagation channel is essential. While many channel measurements exist in either microwave bands (20 GHz), the results are not easily comparable, and furthermore the transition between these bands is not well studied. This paper aims to bridge the gap by providing the results from a channel measurement campaign conducted in urban macro and micro-cellular environments, over the continuous frequency band of 3–18 GHz. We characterize the pathloss, shadow fading, root-mean-square (RMS) delay spreads, Ricean factor and coherence bandwidth in urban macro-cellular and urban micro-cellular environments. Measurements were taken in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments. The pathloss exponents and the RMS delay spreads increase with the base station height in NLOS environment; but not much dependency is observed in the LOS environment. By dividing the wideband channel transfer function into subbands of 1 GHz each, we study the frequency dependence of pathloss, shadow fading, Ricean factor, delay spread, and coherence bandwidth in the 3–18 GHz band. The pathloss exponents vary significantly with frequency, but not-monotonically. The shadow fading and the Ricean factor increase with frequency. The RMS delay spreads decrease with frequency in the LOS environments, but they do not change significantly in the NLOS environments. The coherence bandwidth values do not change significantly with frequency in either environment.

Considering the design of two-stage beamformers for the downlink of multi-user massive multiple-input multiple-output systems in frequency division duplexing mode, this paper investigates the case where both the link ends are equipped with hybrid digital/analog beamforming structures. A virtual sectorization is realized by channel-statistics-based user grouping and analog beamforming, where the user equipment only needs to feedback its intra-group effective channel, and the overall cost of channel state information (CSI) acquisition is significantly reduced. Under the Kronecker channel model assumption, we first show that the strongest eigenbeams of the receive correlation matrix form the optimal analog combiner to maximize the intra-group signal to inter-group interference plus noise ratio. Then, with the partial knowledge of instantaneous CSI, we jointly optimize the digital precoder and combiner by maximizing a lower bound of the conditional average net sum rate. Simulations over the propagation channels obtained from geometric-based stochastic models, ray tracing results, and measured outdoor channels, demonstrate that our proposed beamforming strategy outperforms the state-of-the-art methods.

This paper presents results from the (to our knowledge) first double- directionally resolved measurement campaign at mm-wave frequencies in a suburban microcell. The measurements are performed with a real-time channel sounder equipped with phased antenna arrays that allows electrical beam steering in microseconds, and which can measure path-loss of up to 169 dB. Exploiting the phase coherency of the measurements in the different beams, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We present statistics of channel characteristics such as path-loss, shadowing and delay spread results for line-of-sight and non-line-of-sight cases, as well as sample results for power angular spectrum and extracted multi- path components.

This paper introduces a real-time multiple input and multiple output (MIMO) channel sounder to measure the vehicle-to-vehicle (V2V) propagation channel at 5.9 GHz. Compared to the existing V2V channel sounders, our design emphasizes on improving the stability of the setup and increasing the MIMO snapshot rate. The system stability allows us to develop a high resolution parameter extraction algorithm for the data analysis, which jointly estimates parameters of multipath components such as time-of-arrival, direction of departure, direction of arrival and Doppler shift. The second emphasis increases the maximal absolute Doppler shift to 806 Hz, which the system can estimate without ambiguity. The increased snapshot rate also provides a more fluent representation of the channel dynamics. We verify the design of the channel sounder with actual V2V channel measurements. Results suggest that 80 percent of the sample snapshots have a diffuse power ratio less than 20 percent and the extracted dominant specular paths match well with the environment and dynamics of the measurement.

This paper presents first measurement results on truck-to-car and truck-to-truck propagation channels at 5.9 GHz. We measured the channel with a self-built real-time channel sounder with multiple antennas at both transmitter and receiver. We present sample results for two safety-related scenarios in a highway and urban environment, respectively. We analyze the measured data with a high-resolution parameter extraction algorithm, which provides both spatial and temporal characterization of the channel. The results show that the channel exhibits a higher Doppler dispersion in the highway scenario than in the urban scenario. Besides, the metallic trailer, which is higher than the antenna, acts as a strong reflector when the trucks face each other and an effective shadowing object when the trucks face away.

In this paper, we present a novel real-time MIMO channel sounder for 28 GHz. Until now, the common practice to investigate the directional characteristics of millimeter-wave channels has been using a rotating horn antenna. The sounder presented here is capable of performing horizontal and vertical beam steering with the help of phased arrays. Thanks to the fast beam-switching capability, the proposed sounder can perform measurements that are directionally resolved both at the transmitter (TX) and receiver (RX) as fast as 1.44 milliseconds compared to the minutes or even hours required for rotating horn antenna sounders. This does not only enable us to measure more points for better statistical inference but also allows us to perform directional analysis in dynamic environments. Equally important, the short measurement time combined with the high phase stability of our setup limits the phase drift between TX and RX, enabling phase-coherent sounding of all beam pairs even when TX and RX are physically separated and have no cabled connection for synchronization. This ensures that the measurement data is suitable for high-resolution parameter extraction algorithms. Along with the system design and specifications, this paper also discusses the measurements performed for verification of the sounder. Furthermore, we present sample measurements from a channel sounding campaign performed on a residential street.

This paper reports the first truck-to-truck (T2T) wireless channel measurement conducted at 5.9GHz with multiple antennas at both transmitter and receiver. We present directional analysis of a sample route in downtown Los Angeles, CA USA, where two trucks drive towards each other. Measurements are performed with a self-built real-time MIMO channel sounder and evaluated with RiMAX, a high resolution parameter estimation algorithm, providing time-of-arrival, directional of departure, directional of arrival and Doppler shift for discrete multipath components as well as diffuse multipath scattering. Compared to vehicular channels for passenger cars, our measurements show that the metallic trailer, which is higher than the installed antennas, acts as a strong reflector in line-of-sight (LOS) condition and increases the average received power and angular spread. The extra power attenuation however can be as high as 35dB after two trucks pass each other and the trailers obstruct the LOS.

This paper describes a high-resolution parameter estimation algorithm applied to the vehicle-to-vehicle (V2V) multi-dimensional channel measurements. The algorithm, which is based on a maximum likelihood estimator, jointly estimates parameters of multipath components, such as time-of-arrival, direction of departure, direction of arrival, and Doppler shift. It serves to provide a comprehensive understanding of the V2V propagation channel. We propose computationally attractive methods to initialize parameters in the global search and evaluate key components in the local optimization. We also apply the algorithm to actual V2V channel measurement data. The results suggest an overall good performance of the estimator, where 80% of the snapshots have a diffuse power ratio less than 20% and the dominant specular paths match well with the environment and dynamics of the measurement.

The path loss model describing the power-law dependency on distance plus a log-normally distributed shadowing attenuation, is a staple of link budgets and system simulations. Determination of the parameters of this model is usually done from measurements and ray tracing. We show that the typical least-square fitting to those data points is inherently biased to give the best fitting to the link distances that happen to have more evaluation points; this bias might be highly undesirable in various types of simulations that use the resulting model. In this paper we present a weighted fitting method to address this issue. While it is unavoidable that fits are better for one distance range than another, we argue that such a decision should be made consciously, and adjusted to the type of simulation for which the path loss model should be used. We discuss the weighting functions for different purposes, and show their impact on prediction accuracy of signal level, interference level, and capacity in a hexagonal cellular grid simulation. As examples, weighted fitting models are presented for 28 GHz channels in urban macrocells, and it is shown that the fitting accuracy can be improved by our approach.

It has been widely recognized that relaying is an important method for increasing the reliability and spectral efficiency of communications systems, and it is thus helpful for improving the performance of vehicle-to-vehicle (V2V) communication systems. However, designing and evaluating V2V relay networks require understanding the effect of shadowing, as this critically impacts the performance of the relay system. Even though the theoretic performances of various relaying schemes have been well investigated, there is a lack of empirical test that incorporates realistic shadowing effects. In this paper, we analyze the performance of relaying transmission in V2V scenarios based on measurements in scenarios where shadowing occurs through large vehicles such as buses. We investigate several potential locations for the relay nodes, and the measurements are performed with two static transmitters (TX) and one dynamic receiver (RX). Outage probabilities of several relaying schemes such as multi-hop decode-and- forward, multi-hop amplify-and-forward, and diversity-amplify-and-forward are estimated and discussed based on the measured instantaneous end- to-end signal-to-noise ratio (SNR). It is found that: (i) shadowing effect caused by the bus between V2V line-of-sight (LOS) links increases the outage probability for the non-LOS (NLOS) direct transmission; (ii) using relay node on the bus roof can significantly improve transmission, however, a strong shadowing effect may reduces the acceptable communication distance of relaying scheme; and (iii) the diversity-amplify-and- forward relaying scheme generally has the best performance. Our results can be used to design a relay system for V2V communications.

This paper revisits estimation and tracking of channel parameters of multiple-input multiple-output (MIMO) systems. Since initialization of channel parameters has always been regarded as a computationally complex problem, we develop a new parameter initialization method that exploits the nature of the data structure of channel measurements. For medium or large problems it leads to a more than 500 times runtime acceleration for parameter initialization. We show that the runtime of the new initialization method increases much more slowly compared with that of the traditional initialization method when the dimensions of measurement data or search grids grow.

There has been an increasing interest in Full- Dimension (FD) MIMO, i.e., exploiting the elevation domain (in addition to the azimuth domain) for increasing the capacity of multi-antenna systems. A first prerequisite for designing FD MIMO is an understanding of the elevation characteristics of wireless propagation channels, in particular elevation at the base station. This paper utilizes a state-of-art ray-tracing simulation software to investigate and quantify these elevation characteristics in urban macro cellular (UMa) environments. We study in detail the height and distance dependency of these channel parameters such as mean elevation angle and elevation spread. More importantly we find that several of these parameters exhibit markedly different characteristics depending on whether the UEs are close to exterior walls or they are deeper inside the buildings. Thus, the traditional method of "factoring" the propagation into an outdoor part and an indoor part is not applicable, and we propose more accurate fitting equations.

Massive MIMO systems are well-suited for mm-Wave communications, as large arrays can be built with reasonable form factors, and the high array gains enable reasonable coverage even for outdoor communications. One of the main obstacles for using such systems in frequency-division duplex mode, namely, the high overhead for the feedback of channel state information (CSI) to the transmitter, can be mitigated by the recently proposed joint spatial division and multiplexing (JSDM) algorithm. In this paper, we analyze the performance of this algorithm in somerealisticpropagation channels that take into account the partial overlap of the angular spectra from different users, as well as the sparsity of mm-Wave channels. We formulate the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power in a graph-theoretic framework. As the resulting problems are numerically difficult, we proposed (sub optimum) greedy algorithms as efficient solution methods. Numerical examples show that the different algorithms may be superior in different settings. We furthermore develop a new, “degenerate” version of JSDM that only requires average CSI at the transmitter and thus greatly reduces the computational burden. Evaluations in propagation channels obtained from ray tracing results, as well as inmeasuredoutdoor channels, show that this low-complexity version performs surprisingly well in mm-Wave channels.