Stochastic Channel Research Articles

Geometry-Cluster-Based Stochastic MIMO Model for Vehicle-to-Vehicle Communications in Street Canyon Scenarios

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.

Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) system with stochastic wireless channels and task arrivals is considered. In order to minimize long-term average computation cost in terms of power consumption and buffering delay at each user, a deep reinforcement learning (DRL)-based dynamic computation offloading strategy is investigated to build a scalable system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn decentralized computation offloading policies at all users respectively, where local execution and task offloading powers will be adaptively allocated according to each user’s local observation. Numerical results demonstrate that the proposed DDPG-based strategy can help each user learn an efficient dynamic offloading policy and also verify the superiority of its continuous power allocation capability to policies learned by conventional discrete action space-based reinforcement learning approaches like deep Q-network (DQN) as well as some other greedy strategies with reduced computation cost. Besides, power-delay tradeoff for computation offloading is also analyzed for both the DDPG-based and DQN-based strategies.

Measurement‐Based Experimental Statistical Modeling of Propagation Channel in Industrial IoT Scenario

AbstractThis paper presents 3–4 and 38–40 GHz wideband channel characteristics for industrial internet‐of‐things (IIoT) scenario based on measurements. The channel measurement equipment applied in the measurements consists of a programmable vector network analyzer and necessary auxiliaries. An extensive amount of data in line‐of‐sight (LoS) and non‐line‐of‐sight (NLoS) scenarios is collected with omnidirectional antennas in both transmitter and receiver ends. With the Space‐Alternating Generalized Expectation‐maximization (SAGE) algorithm, the multipath components (MPCs) of the received signals are extracted to calculate the channel model parameters, for example, channel gain coefficient, delay spread, and Ricean K factor. By studying the statistical characteristics of these parameters, a stochastic channel model in IIoT scenario is initially established and compared with the existing indoor channel models. Moreover, the distinctive characteristics of channel parameters in the millimeter wave band and sub‐6 GHz band in the IIoT scenario are demonstrated.

Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

AbstractIn this article, stochastic channel models for massive multiple‐input multiple‐output (M‐MIMO) and extreme large MIMO (XL‐MIMO) system applications are described, evaluated, and systematically compared. This work aims to cover new aspects of M‐MIMO stochastic channel models in a comprehensive and systematic way. For that, we compare different models, presenting graphically and intuitively the behavior of each model. Each M‐MIMO channel model emulates the environment using different methodologies and properties. Using metrics such as capacity, SINR, singular values decomposition, and condition number, one can understand the influence of each characteristic on the modeling and how it differentiates from other models. Moreover, in new XL‐MIMO scenarios, where the near‐field and visibility region effects arise, our finding demonstrate that for the two assumed schemes of clusters distribution, the clusters location influences the performance of the conjugate beamforming and zero‐forcing precoding due to the correlation effect, which have been analyzed from the geometric M‐MIMO channel models.

Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models

We propose Monte Carlo maximum likelihood estimation as a novel approach in the context of calibration and selection of stochastic channel models. First, considering a Turin channel model with inhomogeneous arrival rate as a prototypical example, we explain how the general statistical methodology is adapted and refined for the specific requirements and challenges of stochastic multipath channel models. Then, we illustrate the advantages and pitfalls of the method based on simulated data. Finally, we apply our calibration method to wideband signal data from indoor channels.

5G tool for evaluation and comparision of energy efficiency of mobile radio channel using second-order statistics

In order to increase energy efficiency over transmissions channels, common approach for optimization tasks is by means of channel’s second-order statistics. Actual channel modeling tools for 5G networks end with channel’s first-order statistics, although these metrics are not sufficient when channel conditions are rapidly changing, either in time, frequency or space. In this paper, we establish a tool for evaluation and comparision of energy efficiency of mobile radio channel using its second-order statistics, especially level crossing rate (LCR) and average fade durations (AFD), as they can implicitly pinpoint to transmission configurations that are energy efficient or, as oposit, become a waste of energy. Using both deterministic and stochastic channel modeling, we present results after simulations of Rayleigh channel for narrowband case and further extend it to passband cases, suitable for 5G scenario. We conclude about the energy efficiency of different transmission schemes used by the 5G physical layer observing LCR and AFD values.

Computationally Efficient Millimeter-Wave Backscattering Models: A Single-Scattering Model

The use of millimeter-wave (mm-wave) frequency bands for fifth-generation (5G) cellular mobile communications has led to intense interest from academia and industry over these spectrum resources. Despite extensive measurement campaigns and channel modeling efforts, there is a lack of deterministic backscattering models addressing the impact of the size and orientation of static scatterers on the radio channel. In this article, two 3-D computationally efficient models for calculating backscattering based on the Fresnel integrals and the error function are proposed and validated both against simulations and measurements. In addition, applying the same methodology, state-of-the-art mm-wave blockage (forward-scattering) models are modified to capture backscattered fields. Furthermore, both the introduced and the modified models preserve the structure of geometry-based stochastic channel models (GSCMs) and thus their implementation in system-level simulators is substantially beneficial due to their good accuracy and short computation time.

Map-Based Millimeter-Wave Channel Models: An Overview, Data for B5G Evaluation and Machine Learning

Within the mm-Wave range of the B5G communication systems, there will appear many types of applications with different link types. In discussions on how to cover the various channel modeling requirements of such applications, some researchers have suggested map-based channel models that are based on a ray-tracing algorithm. This article thus begins with an overview of mapbased mm-Wave channel models. The overview includes available modeling requirements with map-based channel models and the categorization of map-based channel parameters. We then explain why map-based channel models are necessary for researchers trying to evaluate novel technologies in the mm-Wave range. They are particularly necessary when the technologies operate with a new link type or when channel behaviors of a user exhibit user-specific characteristics. Next, we share the measurement data and the map-based channel parameters, which can model new link types and have user-specific characteristics. Finally, as a use case of the proposed channel model, we evaluate a machine-learning-based beam-selection algorithm that exploits power delay profiles through the shared database and a geometry-based stochastic channel model (GSCM). The delay profiles of the shared database are user-specific so they are highly correlated with angular parameters. Numerical results show that the algorithm can be more accurately evaluated through the shared database than through a GSCM. We expect that our overview and sharing the database will enable researchers to readily design a map-based channel model and evaluate B5G and machine-learning technologies.

Human-Behavior and QoE-Aware Dynamic Channel Allocation for 5G Networks: A Latent Contextual Bandit Learning Approach

With the rapid advance of smart wireless technologies, a plethora of human behavioral data are generated in 5G networks, which is reported capable to improve network performance by leveraging intelligent channel resource allocation through big data analytics. However, what information can be extracted for the network mobility management, how to exploit the knowledge for resource allocation and to meet the user-centric quality of experience (QoE) are not well understood and fully explored. To address this problem, we propose an online learning algorithm for dynamic channel allocation based on contextual multi-armed bandit (CMAB) theory. Especially, we divide the stochastic human behavioral data into two categories: the user location and the QoE-driven context. Noticing that the distributions of CSI vary spatially, we define a set of user’s geographic locations that shares the same set of CSI distributions as a cluster , and the stochastic channel distributions vary across clusters. The problem is formulated as a novel latent SCB problem, where the proposed agnostic SCB algorithm could automatically find the underlying clusters and significantly improve the learning performance. We then extend our online learning algorithm into the practical multi-user random access scenario. We conduct experiments on a real dataset collected from China Mobile, which indicate that our algorithms outperform existing approaches tremendously and perform extremely well in large-scale and high-mobility networks.

Coherence properties of high-frequency wave field propagating through inhomogeneous ionosphere with anisotropic random irregularities of electron density: 2. Further analysis and numerical results

Analytic theory, developed in the companion paper in order to describe the coherence properties of the high frequency wave field propagating through the stochastic transionospheric channel with the inhomogeneous background layer and anisotropic fluctuations of the electron density, is further analyzed extended and specified to obtain the numerical results, which illustrate the fields coherence properties for realistic parameters of the channel and geometries of propagation.

Coherence properties of high-frequency wave field propagating through inhomogeneous ionosphere with anisotropic random irregularities of electron density: 1. Theoretical background

Rigorous analytic solution is constructed for the first of 2 s-order space position and frequency coherence function (which is alternatively also termed as symmetric) of the high-frequency wave field propagating through the stochastic transionospheric channel with the inhomogeneous layer of the background ionosphere and anisotropic fluctuations of the electron density in the approximation of the appropriate Markov equation. It is obtained employing the anisotropic parabolic model of the effective structure function of the fractional electron density fluctuations with two different scales along and across the Earth's magnetic field. The explicit analytic solution is presented, which is capable of describing the coherence properties of the field for any given inhomogeneous distribution of the electron density in the background ionosphere along a path of propagation. The procedure of calibrating the spatial scales of the anisotropic parabolic model of the electron density fluctuations along and across the Earth's magnetic field lines in order to provide the adequate solution to correspond to the realistic anisotropic inverse power law spectrum of the electron density fluctuations is presented in the companion paper.

Stochastic Channel Access in Underwater Networks With Statistical Interference Modeling

Designing efficient medium access control protocols for underwater acoustic sensor networks (UW-ASNs) is a major challenge because of the spatial and temporal interference uncertainty caused by asynchronous transmissions and by the low propagation speed of sound. To address these challenges, in this article we propose a new approach for distributed underwater medium access based on lightweight and asynchronous distributed algorithms that optimize the access probability profile over a series of time slots based on a new statistical physical interference model. The latter is based on measuring the level of interference at multiple instants of time in each time slot in order to capture the effects of temporal uncertainty and of unaligned interference. At each measurement instant, the statistical properties of time-varying interference are represented by a Gamma probability distribution. The model is validated through extensive channel measurement experiments conducted with an underwater acoustic testbed in Lake LaSalle. Based on this model, we formulate the problem of queue-aware stochastic channel access. The objective is to maximize the sum throughput of a set of concurrent and mutually interfering source-destination pairs by letting the transmitters adjust their own transmission probability profiles, without collaborating with each other, over a series of time slots based on a statistical characterization of interference obtained through past observations. We propose an iterative distributed solution algorithm for this problem based on a best-response strategy. At each iteration, each node individually solves a non-convex optimization problem of logarithmic complexity. The performance of the proposed distributed algorithm is evaluated by comparing it with two alternative distributed schemes and with the global optimum obtained through a newly-developed centralized globally optimal solution algorithm. Results indicate that by jointly taking the queueing and multi-slot optimization into consideration considerable improvement in terms of sum-throughput can be achieved by the proposed distributed algorithm.

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

In this paper, we discuss the effects on throughput and fairness of dynamic channel bonding (DCB) in spatially distributed high-density wireless local area networks (WLANs). First, we present an analytical framework based on continuous-time Markov networks (CTMNs) for depicting the behavior of different DCB policies in spatially distributed scenarios, where nodes are not required to be within the carrier sense range of each other. Then, we assess the performance of DCB in high-density IEEE 802.11ac/ax WLANs by means of simulations. We show that there may be critical interrelations among nodes in the spatial domain–even if they are located outside the carrier sense range of each other–in a chain reaction manner. Results also reveal that, while always selecting the widest available channel normally maximizes the individual long-term throughput, it often generates unfair situations where other WLANs starve. Moreover, we show that there are scenarios where DCB with stochastic channel width selection improves the latter approach both in terms of individual throughput and fairness. It follows that there is not a unique optimal DCB policy for every case. Instead, smarter bandwidth adaptation is required in the challenging scenarios of next-generation WLANs.

Impact of Interparticle Interaction on Thermodynamics of Nano-Channel Transport of Two Species.

Understanding the function and control of channel transport is of paramount importance for cell physiology and nanotechnology. In particular, if several species are involved, the mechanisms of selectivity, competition, cooperation, pumping, and its modulation need to be understood. What lacks is a rigorous mathematical approach within the framework of stochastic thermodynamics, which explains the impact of interparticle in-channel interactions on the transport properties of the respective species. To achieve this, stochastic channel transport of two species is considered in a model, which different from mean field approaches, explicitly conserves the spatial correlation of the species within the channel by analysis of the stochastic dynamics within a state space, the elements of which are the channel’s spatial occupation states. The interparticle interactions determine the stochastic transitions between these states. Local flow and entropy production in this state space reveal the respective particle flows through the channel and the intensity of the Brownian ratchet like rectifying forces, which these species exert mutually on each other, together with its thermodynamic effectiveness and costs. Perfect coupling of transport of the two species is realized by an attractive empty channel and strong repulsive forces between particles of the same species. This confines the state space to a subspace with circular topology, in which the concentration gradients as thermodynamic driving forces act in series, and channel flow of both species becomes equivalent. For opposing concentration gradients, this makes the species with the stronger gradient the driving, positive entropy producing one; the other is driven and produces negative entropy. Gradients equal in magnitude make all flows vanish, and thermodynamic equilibrium occurs. A differential interparticle interaction with less repulsive forces within particles of one species but maintenance of this interaction for the other species adds a bypass path to this circular subspace. On this path, which is not involved in coupling of the two species, a leak flow of the species with less repulsive interparticle interaction emerges, which is directed parallel to its concentration gradient and, hence, produces positive entropy here. Different from the situation with perfect coupling, appropriate strong opposing concentration gradients may simultaneously parallelize the flow of their respective species, which makes each species produce positive entropy. The rectifying potential of the species with the bypass option is diminished. This implies the existence of a gradient of the other species, above which its flow and gradient are parallel for any gradient of the less coupled species. The opposite holds for the less coupled species. Its flow may always be rectified and turned anti-parallel to its gradient by a sufficiently strong opposing gradient of the other one.

A 2-D Geometry-Based Stochastic Channel Model for 5G Massive MIMO Communications in Real Propagation Environments

Spherical wavefront and array nonstationarity due to physically large antenna array are two typical characteristics specific to massive multiple-input–multiple-output (M-MIMO) propagation. Unfortunately, traditional MIMO channel models need to be enhanced to better characterize these two new propagation features. In light of this limitation, this article proposes an enhanced two-dimensional geometry-based stochastic channel model for 5G M-MIMO communications in real propagation environments. Specifically, a coordinate-based channel model framework is first introduced. Based on this, the impact of spherical wavefront can be mathematically described with the aid of some spatial geometric manipulations. Moreover, a general Markov process is developed to model the array nonstationarity characteristic due to cluster appearance and disappearance along the array. Meanwhile, the associated approach to derive the state transition probabilities of the proposed Markov process is detailed. Finally, capabilities of the proposed channel model in characterizing spherical wavefront and array nonstationarity of M-MIMO channels are shown by the numerical simulations. Moreover, validity of our proposal is verified by the measurement data.

The COST IRACON Geometry-Based Stochastic Channel Model for Vehicle-to-Vehicle Communication in Intersections

Vehicle-to-vehicle (V2V) wireless communications can improve traffic safety at road intersections and enable congestion avoidance. However, detailed knowledge about the wireless propagation channel is needed for the development and realistic assessment of V2V communication systems. We present a novel geometry-based stochastic MIMO channel model with support for frequencies in the band of 5.2–6.2 GHz. The model is based on extensive high-resolution measurements at different road intersections in the city of Berlin, Germany. We extend existing models, by including the effects of various obstructions, higher order interactions, and by introducing an angular gain function for the scatterers. Scatterer locations have been identified and mapped to measured multi-path trajectories using a measurement-based ray tracing method and a subsequent RANSAC algorithm. The developed model is parameterized, and using the measured propagation paths that have been mapped to scatterer locations, model parameters are estimated. The time variant power fading of individual multi-path components is found to be best modeled by a Gamma process with an exponential autocorrelation. The path coherence distance is estimated to be in the range of 0–2 m. The model is also validated against measurement data, showing that the developed model accurately captures the behavior of the measured channel gain, Doppler spread, and delay spread. This is also the case for intersections that have not been used when estimating model parameters.

Thinning and multilevel Monte Carlo methods for piecewise deterministic (Markov) processes with an application to a stochastic Morris–Lecar model

AbstractIn the first part of this paper we study approximations of trajectories of piecewise deterministic processes (PDPs) when the flow is not given explicitly by the thinning method. We also establish a strong error estimate for PDPs as well as a weak error expansion for piecewise deterministic Markov processes (PDMPs). These estimates are the building blocks of the multilevel Monte Carlo (MLMC) method, which we study in the second part. The coupling required by the MLMC is based on the thinning procedure. In the third part we apply these results to a two-dimensional Morris–Lecar model with stochastic ion channels. In the range of our simulations the MLMC estimator outperforms classical Monte Carlo.

Energy Efficient Transmit-Receive Hybrid Spatial Modulation for Large-Scale MIMO Systems

We consider a point to point large-scale multiple-input multiple-output (MIMO) system operating in the millimeter wave (mmWave) band and an outdoor scenario. Novel transmit and receive spatial modulation (SM) schemes are proposed for uplink (UL) and downlink (DL) data transmission phases based on a novel energy efficient hybrid user terminal architecture. The analog circuitry of the proposed hybrid architecture is divided into two stages: phase shifters and analog switches. The phase shifting stage assures high gain and overcomes the severe path-loss caused by outdoor mmWave propagation. The analog switching stage smartly allocates the antennas to be used at the phase shifting stage and combats the spatial correlation. We provide the analysis of the spectral efficiency $({{SE}})$ of the UL and DL systems. Next, we propose a reduced complexity algorithm that jointly optimizes the analog beamformer and combiner design of the UL and DL circuitry to maximize the energy efficiency $({{EE}})$ . Finally, we compare and evaluate the performance of the proposed algorithm in terms of the ${{SE}}$ and ${{EE}}$ assuming both stochastic and realistic channel models.

Conditional probability on full Łukasiewicz tribes

We study notions of conditional probability and stochastic dependence/independence in an upgraded probability model in which the space of events is modeled by a full Łukasiewicz tribe of all measurable functions from some measurable space into [0, 1]. Our study is based on properties of joint experiments and the notion of stochastic channel, a construct equivalent to the notion of Markov kernel between two measurable spaces. Using the notion of a degenerated stochastic channel, a channel transmitting no stochastic information between two spaces, we define an asymmetrical independence of random experiments. Finally, we define the notion of conditional probability on full Łukasiewicz tribes.

Learning Parameters of Stochastic Radio Channel Models From Summaries

Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other on deep learning, for fitting stochastic channel models to data directly. The proposed methods make use of easy-to-compute summary statistics of measured data instead of relying on extracted multipath components. Moreover, the need for post-processing of the extracted multipath components is omitted. Taking the polarimetric propagation graph model as an example stochastic model, we present relevant summaries and evaluate the performance of the proposed methods on simulated and measured data. We find that the methods are able to learn the parameters of the model accurately in simulations. Applying the methods on 60 GHz indoor measurement data yields parameter estimates that generate averaged power delay profile from the model that fits the data.