Multi-dimensional Channel Research Articles

5G SLAM Using the Clustering and Assignment Approach with Diffuse Multipath.

5G communication systems operating above 24 GHz have promising properties for user localization and environment mapping. Existing studies have either relied on simplified abstract models of the signal propagation and the measurements, or are based on direct positioning approaches, which directly map the received waveform to a position. In this study, we consider an intermediate approach, which consists of four phases—downlink data transmission, multi-dimensional channel estimation, channel parameter clustering, and simultaneous localization and mapping (SLAM) based on a novel likelihood function. This approach can decompose the problem into simpler steps, thus leading to lower complexity. At the same time, by considering an end-to-end processing chain, we are accounting for a wide variety of practical impairments. Simulation results demonstrate the efficacy of the proposed approach.

Outage Analysis for MISO Zero-Forcing Precoding With Outdated CSI

Zero-forcing precoding is a commonly-used multiple-user, multiple-input multiple-output beamforming technique. Applying such precoding, the signal-to-interference-plus-noise ratio (SINR) statistics with outdated channel state information, which involve various projections related to the multi-dimensional channel vectors and precoding vectors, have never been explicitly derived. In this paper, for the multiple-input single-output scenario with uniform power allocation, the distributions of these projections are derived, and then the SINR distribution and the conditional outage probability can be computed using those distributions.

Architecture and experimental evaluation of context-aware adaptation in vehicular networks

In vehicular networks, the propagation environment changes rapidly for mobile nodes. To achieve high throughput, wireless devices need to be highly adaptive to these environmental changes by altering their transmission parameters across different layers of the network stack. Sensors in mobile and vehicular nodes can be used to form an understanding of the surrounding context. Such contextual awareness is particularly important in vehicular networks as the frequent context switching and increased channel fluctuations can cause existing adaptation protocols to fail to converge to the optimal transmission parameters. In this paper, we leverage information about the environmental context to enable improved rate adaptation performance in vehicular networks. In particular, we propose a classification-based link-level adaptation framework, which can effectively learn the relationship between context information (such as velocity, SNR, and channel type) and the throughput of various transmission modes. We then quantify the throughput improvement using the proposed scheme and show that our proposed framework can significantly enhance the performance of rate adaptation. With experiments on emulated and in-field channels, we observe that the throughput increases by up to 245% over protocols which use SNR alone to make rate decisions. Based on an analysis of attribute importance, we identify channel type as a key parameter that affects classification performance. Since channel type often cannot be directly obtained, we propose a multi-dimensional channel inference method for use when knowledge about the channel type is not available. We demonstrate that the proposed channel inference achieves an accuracy of up to 94% in previously encountered channels and can quickly signal that a channel has not yet been encountered. The robustness of the proposed methods are demonstrated using experimental data from two different hardware platforms and three different carrier frequency bands. Lastly, we evaluate the most predominant Linux-based rate selection algorithm (Minstrel), study the relative rate selection accuracy of our approach, and analyze the key role that the retry mechanism in Minstrel plays on its performance.

People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms.

Subject counting systems are extensively used in ambient intelligence applications, such as smart home, smart building and smart retail scenarios. In this paper, we investigate the problem of transforming an unmodified WiFi radio infrastructure into a flexible sensing system for passive subject counting. We first introduce the multi-dimensional channel features that capture the subject presence. Then, we compare Bayesian and neural network based machine learning tools specialized for subject discrimination and counting. Ensemble classification is used to leverage space-frequency diversity and combine learning tools trained with different channel features. A combination of multiple models is shown to improve the counting accuracy. System design is based on a dense network of WiFi devices equipped with multiple antennas. Experimental validation is conducted in an indoor space featuring up to five moving people. Real-time computing and practical solutions for cloud migration are also considered. The proposed approach for passive counting gives detection results with 99% average accuracy.

π–π Interaction between self-assembled perylene diimide and 3D graphene for excellent visible-light photocatalytic activity

A three-dimensional (3D) graphene/self-assembled perylene imide (rGO/PDI) aerogel composite has been successfully constructed via a simple low-temperature hydrothermal method in this work. The 3D rGO/PDI composite exhibited superior visible-light photocatalytic performance. The removal rate was respectively 2.46 times and 3.33 times higher than that of PDI in static and dynamic catalytic systems. The satisfactory activity is mainly attributed to the more effective separation of electron–hole pairs. To be specific, the self-assembled PDI nanofibers and graphene are effectively combined via non-covalent π-π interaction. The π-π interaction enhances the long-range π-electrons delocalization and electron coupling effects, which is beneficial for the carrier mobility and the separation of electron–hole pairs. Meanwhile, the three-dimensional structure of graphene provides a fast multidimensional channel for electron transfer and effectively improves the adsorption capacity of PDI due to the large specific surface area. What’s more, the rGO/PDI composite exhibits great stability than the pure PDI. Overall, this work provides a new insight into improving the photocatalytic activity of self-assembled organic materials.

Amplitude SNR limitation in control over channels with bounded noise

This paper furnishes a new system-theoretic interpretation to the product of unstable poles of discrete-time linear systems. This index has been known to represent limitations of feedback control in various situations. Our result shows that the index gives the limitation of the signal-to-noise ratio (SNR) with respect to amplitude when the feedback loop is stabilized through a multidimensional channel with bounded noise.

HF Communication Quality Level Increase when Using the Pre-session Diagnostics of a Multidimensional Ionospheric Radio Channel

HF Communication Quality Level Increase when Using the Pre-session Diagnostics of a Multidimensional Ionospheric Radio Channel

Automatic seizure detection by modified line length and Mahalanobis distance function

Abstract Automatic seizure detection with high accuracy and in linear time has profound implications on therapeutic intervention mechanisms. In this work taking into account 12 popular seizure detection algorithms we have shown that line length is one feature that is extractable in linear time from EEG signals and capable of automatic seizure onset detection with highest accuracy among linear time extractable features. Also line length is less prone to give false positives. The detection accuracy has been ascertained by ROC curve analysis on Freiburg Seizure Prediction Project data containing intracranial EEG recordings of 87 seizures from 21 patients with sufficient interictal signals. Next, we have modified the classical line length feature extraction algorithm to improve its accuracy without any additional computational burden. Finally, we have applied both classical line length (LL) and modified line length (MLL) on all focal channels and detected seizures on multidimensional focal channel signals by Mahalanobis distance function (MDF). Both detected 73 out of 87 seizures. Area under the ROC curve (AUC), detection delay and false positive for LL and MLL are 0.951, 11.903 s, 0.201/h and 0.954, 11.698 s, 0.198/h respectively. Since LL has already been incorporated into an FDA approved commercially available closed loop intervention system, even this minute improvement may have significant healthcare implications.

High-Resolution Parameter Estimation for Time-Varying Double Directional V2V Channel

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.

On Random and Multidimensional Channel Effects in Cluttered Environments

Machine-to-machine devices have the potential to be placed in environments that are less than ideal for wireless propagation, such as factory floors or within metallic enclosures. In this letter, we illustrate that such environments not only produce the expected multipath, but can also depolarize the signal across all three spatial dimensions. Furthermore, we show that these effects are multidimensional in which they are highly sensitive to extremely small changes in user placement and frequency of operation. That is, these channels exhibit randomness based on a user's action. These effects motivate new channel characterization approaches and metrics along with the development of tripolar antenna designs. This letter concludes by presenting the efficacy of such a prototype antenna.

Optimal scheduling and power allocation in wireless networks with heavy traffic: the infinite time horizon case

ABSTRACTThis paper addresses the problem of joint transmit power allocation and time-slot scheduling in a wireless communication system with time-varying traffic. The system is handled by a single base station transmitting over time-varying channels. The operating time horizon is divided into time slots; a fixed amount of power is available at each time slot. The mobile users share each time slot and the power available at this time slot with the objective of minimising their expected total delay. This is reformulated, via a heavy traffic approximation, as the optimal control of a reflected diffusion in the positive orthant. We address this optimisation problem using the dynamic programming principle and establish Bellman's equation; here the main feature consists in how to deal with the reflection of the diffusion process on the boundary of its state space. A closed-form solution for the optimal control problem is provided. The proposed solution relies on, among other issues, the knowledge of the intensity of the driving process in the diffusion. Accordingly, we compute this intensity given a model for the multidimensional channel: a purely discontinuous process with known characteristics.

Adaptive Filtering of Concentrated Interference During Multidimensional Short-Wave Channel Sensing Using the Hardware-Software Complex, Implemented According to the Software-Defined Radio Technology

Adaptive Filtering of Concentrated Interference During Multidimensional Short-Wave Channel Sensing Using the Hardware-Software Complex, Implemented According to the Software-Defined Radio Technology

A Frequency Support System Based on the Passive Sensing of a Multidimensional Short-Wave Radio Channel

A Frequency Support System Based on the Passive Sensing of a Multidimensional Short-Wave Radio Channel

Ray tracing propagation modeling for future small-cell and indoor applications: A review of current techniques

Applied for the first time to mobile radio propagation modeling at the beginning of the nineties, ray tracing is now living a second youth. It is probably the best model to assist in the design and planning of future short-range, millimeter-wave wireless systems, where the more limited propagation environment with respect to UHF frequencies allows to overcome traditional high-CPU time limitations while the higher operating frequency makes ray-optics approximations less drastic and allows to achieve an unprecedented level of accuracy. An overview of ray tracing propagation modeling is given in this paper, with a special attention to future prospects and applications. In particular, frontiers of ray-based propagation modeling such as extension to diffuse scattering, multidimensional channel characterization, multiple-input multiple-output (MIMO) capacity assessments, and future applications such as real-time ray tracing are addressed in the paper with reference to the work recently carried out at the University of Bologna.

Optimal scheduling and power allocation in wireless networks with heavy traffic

This paper addresses the problem of joint transmit power allocation and time slot scheduling in a wireless communication system with time varying traffic. The system is handled by a single base station transmitting over time varying channels. This may be the case in practice of a hybrid TDMA-CDMA (Time Division Multiple Access-Code Division Multiple Access) system. The operating time horizon is divided into time slots; a fixed amount of power is available at each time slot. The users share each time slot and the power available at this time slot with the objective of minimizing the expected total queue length. The problem is reformulated, via a heavy traffic approximation, as the optimal control of a reflected diffusion in the positive orthant. We establish a closed form solution for the obtained control problem. The main feature that makes it possible is an astute choice of some auxiliary weighting matrices in the cost rate. The proposed solution relies also on the knowledge of the covariance matrix of the non-standard multi-dimensional Wiener process which is the driving process in the reflected diffusion. We then compute this covariance matrix given the stationary distribution of the multi-dimensional channel process. Further stochastic analysis is provided: the cost variance, and the Fokker–Planck equation for the distribution density of the queue length.

The importance of channel intersections in the catalytic performance of high silica stilbite

Researchers at Chevron recently discovered a route for making high silica stilbite (STI), a zeolite with intersecting 10- and 8-ring channels. This zeolite, named SSZ-75, provides an interesting comparison to two other two-dimensional 10/8-ring zeolites, namely, ferrierite (FER) and ZSM-57 (MFS). The catalytic properties of these zeolites are characterized here using the Constraint Index test, adsorption measurements, and toluene disproportionation and its alkylation with isopropyl alcohol. Results are then compared to ZSM-5 (a well-characterized 3-dimensional 10-ring zeolite) and TON (a 1-dimensional 10-ring zeolite). While the micropore volumes for several of these five materials are similar, the Constraint Index, which considers the competitive cracking of n-hexane and 3-methylpentane, differs greatly. On the other hand, toluene conversions along with selectivity to p-xylene for MFI and STI are remarkably similar. This indicates that the reaction volume available at the intersections of the 10- and 8-ring channels in STI is comparable with that of the 10-ring channel intersections in MFI. However, in toluene alkylation with isopropyl alcohol, differences in intersection volume between MFI and STI are revealed. These results nicely demonstrate how the size of the pores and geometry of the channel intersections (rather than just the size of the individual pores) impact the catalytic properties of zeolites with multidimensional channel systems.

Зондирование ионосферы и многомерного КВ радиоканала сигналами слинейной частотной модуляцией

Зондирование ионосферы и многомерного КВ радиоканала сигналами слинейной частотной модуляцией

Graph-Based Soft Channel Estimation for Fast Fading Channels

In this paper, a graph-based soft iterative receiver (GSIR) for fast fading channels is investigated. Soft channel estimation as well as soft-output data detection are jointly accomplished via Bayesian inference over a general factor graph. The key feature is a transfer node which enables information flow from one channel node to adjacent channel nodes. The performance characteristics of this receiver is investigated via an EXIT chart analysis and simulation results. Particular emphasis is on a proper channel code design. The algorithm is universally applicable to arbitrary (bijective and non-bijective) modulation formats and can easily be extended to multi-dimensional channel estimation.

Impact of Incomplete and Inaccurate Data Models on High Resolution Parameter Estimation in Multidimensional Channel Sounding

Multidimensional channel sounding aims to estimate the geometrical structure of multi-path wave propagation in terms of directions of arrival/departure, Doppler shift, time delay, and complex polarimetric path weights. Maximum likelihood parameter estimation based upon an underlying data model is used to achieve high-resolution of the path parameters and, thus, renders possible an antenna independent channel characterization. However, any mismatch of the underlying data model to physical reality imposes limits to accuracy and reliability of the estimation. To cope with the limited resolution capability of the setup we are using a propagation data model that does not only contain discrete deterministic components but also a non-resolvable stochastic part. Joint estimation of both components considerably enhances the estimation quality and finally allows the interpretation as specular and diffuse contribution of multi-path propagation respectively. However, besides of noise influence, the achievable resolution is further limited by the accuracy of the data model that describes the measurement setup. Since the antenna characteristics are very susceptible to calibration and modeling errors, the directional estimates are most error-prone. We refer to the antenna array calibration procedure and discuss common pitfalls in high-resolution multi-path direction estimation that are related to inaccurate and/or incomplete device data model. Depending on the type of the antenna array (linear, circular) this will inherently produce biased and artificially spread angular estimates. Only with precise knowledge of the model errors the stochastic part can be identified as diffuse propagation component vs. modeling error.

Spatial-domain-based multidimensional modulation for multi-Tb/s serial optical transmission

The multidimensional channel capacity studies indicate that the employment of multiple photon degrees of freedom-such as subcarrier, amplitude, phase, polarization, and space-can improve the spectral efficiency by several orders of magnitude higher than that claimed in any fiber-optic experiment reported to date. This dramatic increase in spectral efficiency through multiple photon degrees of freedom can provide revolutionary capabilities for future optical networks. Moreover, photons can carry both spin angular momentum (SAM) associated with polarization, and orbital angular momentum (OAM) associated with the azimuthal phase of the complex electric field. Because OAM eigenstates are orthogonal, an arbitrary number of bits per photon can be transmitted in principle. The ability to generate the OAM modes, such as Bessel modes, in multimode fibers (MMFs) will allow realization of fiber-optic communication networks with ultra-high bits-per-photon efficiencies. To this end, we propose here a spatial-domain-based multidimensional coded-modulation scheme as an enabling technology for multi-Tb/s serial optical transport. To demonstrate the capabilities of the proposed scheme, we show that an eight-dimensional (8D) spatial-domain-based coded modulation scheme outperforms a prior-art 128-point 4D scheme by 3.88 dB at BER of 10(-8) while providing 120 Gb/s higher aggregate information bit rate. The proposed 8D scheme also outperforms its conventional polarization-multiplexed QAM counterpart by even a larger, and indeed striking, margin of 8.39 dB (also at the BER of 10(-8)).