1,093 research outputs found
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Faster Information Propagation on Highways: a Virtual MIMO Approach
In vehicular communications, traffic-related information should be spread
over the network as quickly as possible to maintain a safe and reliable
transportation system. This motivates us to develop more efficient information
propagation schemes. In this paper, we propose a novel cluster-based
cooperative information forwarding scheme, in which the vehicles
opportunistically form virtual antenna arrays to boost one-hop transmission
range and therefore accelerate information propagation along the highway. Both
closed-form results of the transmission range gain and the improved Information
Propagation Speed (IPS) are derived and verified by simulations. It is observed
that the proposed scheme demonstrates the most significant IPS gain in moderate
traffic scenarios, whereas too dense or too sparse vehicle density results in
less gain. Moreover, it is also shown that increased mobility offers more
contact opportunities and thus facilitates information propagation.Comment: IEEE 2014 Global Telecommunications Conference (GLOBECOM 2014) -
Communication Theory Symposiu
Mobile Cell-Free Massive MIMO: Challenges, Solutions, and Future Directions
Cell-free (CF) massive multiple-input multiple-output (MIMO) systems, which
exploit many geographically distributed access points to coherently serve user
equipments via spatial multiplexing on the same time-frequency resource, has
become a vital component of the next-generation mobile communication networks.
Theoretically, CF massive MIMO systems have many advantages, such as large
capacity, great coverage, and high reliability, but several obstacles must be
overcome. In this article, we study the paradigm of CF massive MIMO-aided
mobile communications, including the main application scenarios and associated
deployment architectures. Furthermore, we thoroughly investigate the challenges
of CF massive MIMO-aided mobile communications. We then exploit a novel
predictor antenna, hierarchical cancellation, rate-splitting and dynamic
clustering system for CF massive MIMO. Finally, several important research
directions regarding CF massive MIMO for mobile communications are presented to
facilitate further investigation.Comment: 9 pages, 4 figures, 2 tables, accepted by IEEE Wireless
Communications Magazin
MASSIVE MIMO FOR HIGH-SPEED TRAIN COMMUNICATION SYSTEMS
With the current development in wireless communications in high-mobility systems such as high-speed train (HST), the HST scenario is accepted as among the different scenarios for the fifth-generation (5G). Massive Multiple-Input-Multiple-Output (MIMO) systems, which are equipped with tens or hundreds of antennas has become an improved MIMO system which can assist in achieving the ever-growing demand of data for 5G wireless communication systems. In this study, the associated 5G technologies, as well as the equivalent channel modeling in HST settings and the challenges of deploying massive MIMO on HST, was investigated The channel model was modeled using the WINNER II channel model. With regrads, the proposed non-stationary IMT-A massive MIMO channel models, the essential statistical properties such as the spatial cross-correlation function (CCF), local temporal autocorrelation function (ACF) of the massive MIMO channel model using different propagation scenarios such as open space, viaduct and cutting was analyzed and investigated. The results from the simulations were compared with the analytical results in other to show that the statistical properties vary with time as a result of the non-stationarity of the proposed channel model. The agreement between the stationary interval of the non-stationary IMT-A channel model and the HST under different propagation scenarios shows the efficiency of the proposed channel model. Based on findings; the impact of the deployment of a large antenna on the channel capacity should be thoroughly investigated under different HST propagation scenario. Also, more HST train propagation scenarios such as the tunnel, hilly terrain, and the station should be considered in the non-stationary IMT-A massive MIMO channel models
Channel Prediction Using Ordinary Differential Equations for MIMO systems
Channel state information (CSI) estimation is part of the most fundamental problems in 5G wireless communication systems. In mobile scenarios, outdated CSI will have a serious negative impact on various adaptive transmission systems, resulting in system performance degradation. To obtain accurate CSI, it is crucial to predict CSI at future moments. In this paper, we propose an efficient channel prediction method in multiple-input multiple-output (MIMO) systems, which combines genetic programming (GP) with higher-order differential equation (HODE) modeling for prediction, named GPODE. In the first place, the variation of one-dimensional data is depicted by using higher-order differential, and the higher-order differential data is modeled by GP to obtain an explicit model. Then, a definite order condition is given for the modeling of HODE, and an effective prediction interval is given. In order to accommodate to the rapidly changing channel, the proposed method is improved by taking the rough prediction results of Autoregression (AR) model as a priori, i.e., Im-GPODE channel prediction method. Given the effective interval, an online framework is proposed for the prediction. To verify the validity of the proposed methods, We use the data generated by the Cluster Delay Line (CDL) channel model for validation. The results show that the proposed methods has higher accuracy than other traditional prediction methods
Propagation channel characterisation and modelling for high-speed train communication systems
High-mobility scenarios, e.g., High-Speed Train (HST) scenarios, are expected to be
typical scenarios for the Fifth Generation (5G) communication systems. With the
rapid development of HSTs, an increasing volume of wireless communication data
is required to be transferred to train passengers. HST users demand high network
capacity and reliable communication services regardless of their locations or speeds,
which are beyond the capability of current HST communication systems. The features
of HST channels are significantly different from those of low-mobility cellular
communication systems. For a proper design and evaluation of future HST wireless
communication systems, we need accurate channel models that can mimic the
underlying channel characteristics, especially the non-stationarity for different HST
scenarios. Inspired by the lack of such accurate HST channel models in the literature,
this PhD project is devoted to the modelling and simulation of non-stationary
Multiple-Input Multiple-Output (MIMO) channels for HST communication systems.
In this thesis, we first give a comprehensive review of the measurement campaigns
conducted in different HST scenarios and address the recent advances in HST channel
models. We also highlight the key challenges of HST channel measurements and
models. Then, we study the characterisation of non-stationary channels and propose
a theoretical framework for deriving the statistical properties of these channels.
HST wireless communication systems encounter different channel conditions due to the
difference of surrounding geographical environments or scenarios. HST channel models
in the literature have either considered large-scale parameters only and/or neglected
the non-stationarity of HST channels and/or only consider one of the HST scenarios.
Therefore, we propose a novel generic non-stationary Geometry-Based Stochastic
Model (GBSM) for wideband MIMO HST channels in different HST scenarios, i.e.,
open space, viaduct, and cutting. The corresponding simulation model is then developed
with angular parameters calculated by the Modified Method of Equal Area
(MMEA). The system functions and statistical properties of the proposed channel
models are thoroughly studied. The proposed generic non-stationary HST channel
models are verified by measurements in terms of stationary time for the open space
scenario and the Autocorrelation Function (ACF), Level Crossing Rate (LCR), and
stationary distance for the viaduct and cutting scenarios. Transmission techniques which are capable of utilising Three-Dimensional (3D) spatial
dimensions are significant for the development of future communication systems.
Consequently, 3D MIMO channel models are critical for the development and evaluation
of these techniques. Therefore, we propose a novel 3D generic non-stationary
GBSM for wideband MIMO HST channels in the most common HST scenarios. The
corresponding simulation model is then developed with angular parameters calculated
by the Method of Equal Volume (MEV). The proposed models considers several timevarying
channel parameters, such as the angular parameters, the number of taps, the
Ricean K-factor, and the actual distance between the Transmitter (Tx) and Receiver
(Rx). Based on the proposed generic models, we investigate the impact of the elevation
angle on some of the channel statistical properties. The proposed 3D generic
models are verified using relevant measurement data.
Most standard channel models in the literature, like Universal Mobile Telecommunications
System (UMTS), COST 2100, and IMT-2000 failed to introduce any of the HST
scenarios. Even for the standard channel models which introduced a HST scenario,
like IMT-Advanced (IMT-A) and WINNER II channel models, they offer stationary
intervals that are noticeably longer than those in measured HST channels. This has
inspired us to propose a non-stationary IMT-A channel model with time-varying parameters
including the number of clusters, powers, delays of the clusters, and angular
parameters. Based on the proposed non-stationary IMT-A channel model, important
statistical properties, i.e., the time-variant spatial Cross-correlation Function (CCF)
and time-variant ACF, are derived and analysed. Simulation results demonstrate
that the stationary interval of the developed non-stationary IMT-A channel model
can match that of relevant HST measurement data.
In summary, the proposed theoretical and simulation models are indispensable for the
design, testing, and performance evaluation of 5G high-mobility wireless communication
systems in general and HST ones in specific
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