Doppler spread estimation in mobile fading channels

The Doppler spread, or equivalently, the mobile speed, is a measure of the spectral dispersion of a mobile fading channel. Accurate estimation of the mobile speed is important in wireless mobile applications which require such as knowledge of the rate of channel variations. In this dissertation, first the performance of classical crossing- and covariance-based speed estimators is studied. Next, the problem of mobile speed estimation using diversity combining is investigated. Then, a nonparametric estimation technique is proposed that is robust to different channel variations. Finally, cyclostationarity-based speed estimators which can be applied either blindly or with the aid of pilot data, are developed. A unified framework for the performance analysis of well-known crossing and covariance based speed estimation techniques is presented. This allows a fair analytical comparison among all the methods. Interestingly, it is proved that all these methods are asymptotically equivalent, i.e., for large observation intervals. The extensive performance analysis, supported by Monte Carlo simulations, has revealed that depending on the channel condition and the observation interval, one needs to use a crossing or a covariance based technique to achieve the desired estimation accuracy over a large range of mobile speeds. Two common diversity schemes, selection combining (SC) and maximal ratio combining (MRC), are considered for Doppler spread estimation. Four new estimators are derived which rely on the inphase zero crossing rate, inphase rate of maxima, phase zero crossing rate, and the instantaneous frequency zero crossing rate of the output of SC. Two estimators, which work based on the level crossing rates of the envelopes at the output of SC and MRC, are also proposed. The performances of all these estimators are investigated in realistic noisy environments with different kinds of scatterings and different numbers of diversity branches. Then a novel speed estimation technique is proposed that is applicable to both mobile and base stations, based on the characteristics in the power spectrum of mobile fading channels. The analytic performance analysis, verified by Monte Carlo simulations, shows that this low-complexity estimator is not only robust to both Gaussian and non-Gaussian noises, but also insensitive to nonisotropic scattering observed at the mobile. The estimator performs very well in both two- and three-dimensional propagation environments. By taking advantage of resolvable paths in wideband fading channels, the robustness against both nonisotropic scattering and line of sight can be further increased, due to the differences among the Doppler spectra observed at different paths. This technique is also extended to base stations with antenna arrays. By exploiting the spatial information, the proposed space-time estimator exhibits excellent performance over a wide range of noise power, nonisotropic scattering, and the line-of-sight component. This is all verified by simulation. The utility of the new method is further demonstrated by applying it to the measured data. Finally, to design robust blind and data-aided mobile speed estimators, a proposal is made to exploit the inherent cyclostationarity of linearly modulated signals transmitted through fading channels. Two categories of cyclic-correlation- and cyclic-spectrum-based methods are developed. Extension to space-time speed estimation at the base station in macrocells is also provided. In comparison with the existing methods, the new estimators can be used without any need for pilot tones and are robust to additive stationary noise or interference of any color or distribution. Unlike the conventional multi-antenna based method, the proposed space-time speed estimator does not assume the receiver noise to be spatially white. A suboptimal training sequence is also devised for pilot-symbol assisted methods, to reduce the estimation error. The performance of the proposed estimators are illustrated via extensive Monte Carlo simulations

Stochastic differential equations for performance analysis of wireless communication systems

This paper addresses the difficulty of characterizing the time-varying nature of fading channels. The current time-invariant models often fall short of capturing and tracking these dynamic characteristics. To overcome this limitation, we explore using of stochastic differential equations (SDEs) and Markovian projection to model signal envelope variations, considering scenarios involving Rayleigh, Rice, and Hoyt distributions. Furthermore, it is of practical interest to study the performance of channels modeled by SDEs. In this work, we investigate the fade duration metric, representing the time during which the signal remains below a specified threshold within a fixed time interval. We estimate the complementary cumulative distribution function (CCDF) of the fade duration using Monte Carlo simulations, and analyze the influence of system parameters on its behavior. Finally, we leverage importance sampling, a known variance-reduction technique, to estimate the tail of the CCDF efficiently

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

A Non-Stationary Mobile-to-Mobile Channel Model Allowing for Velocity and Trajectory Variations of the Mobile Stations

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Scalable Map Information Dissemination for Connected and Automated Vehicle Systems

Situational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle this challenge, the inter-vehicle communication system is envisioned that allows vehicles to exchange self-status updates aiming to extend their effective field of view and thus compensate for the limitations of the vehicle tracking subsystem that relies substantially on onboard sensing devices. Tracking capability in such systems can be further improved through the cooperative sharing of locally created map data instead of transmitting only self-update messages containing core basic safety message (BSM) data. In the cooperative sharing of safety messages, it is imperative to have a scalable communication protocol to ensure optimal use of the communication channel. This dissertation contributes to the analysis of the scalability issue in vehicle-to-everything (V2X) communication and then addresses the range issue of situational awareness in CAV systems by proposing a content-adaptive V2X communication architecture. To that end, we first analyze the BSM scheduling protocol standardized in the SAE J2945/1 and present large-scale scalability results obtained from a high-fidelity simulation platform to demonstrate the protocol\u27s efficacy to address the scalability issues in V2X communication. By employing a distributed opportunistic approach, the SAE J2945/1 congestion control algorithm keeps the overall offered channel load within an optimal operating range, while meeting the minimum tracking requirements set forth by upper-layer applications. This scheduling protocol allows event-triggered and vehicle-dynamics driven message transmits that further the situational awareness in a cooperative V2X context. Presented validation results of the congestion control algorithm include position tracking errors as the performance measure, with the age of communicated information as the evaluation measure. In addition, we examine the optimality of the default settings of the congestion control parameters. Comprehensive analysis and trade-off study of the control parameters reveal some areas of improvement to further the algorithm\u27s efficacy. Motivated by the effectiveness of channel congestion control mechanism, we further investigate message content and length adaptations, together with transmit rate control. Reasonably, the content of the exchanged information has a significant impact on the map accuracy in cooperative driving systems. We investigate different content control schemes for a communication architecture aimed at map sharing and evaluate their performance in terms of position tracking error. This dissertation determines that message content should be concentrated to mapped objects that are located farther away from the sender to the edge of the local sensor range. This dissertation also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system

Simultaneous wireless information and power transfer (SWIPT) in cooperative networks

2019 Spring.Includes bibliographical references.In recent years, the capacity and charging speed of batteries have become the bottleneck of mobile communications systems. Energy harvesting (EH) is regarded as a promising technology to significantly extend the lifetime of battery-powered devices. Among many EH technologies, simultaneous wireless information and power transfer (SWIPT) proposes to harvest part of the energy carried by the wireless communication signals. In particular, SWIPT has been successfully applied to energy-constrained relays that are mainly or exclusively powered by the energy harvested from the received signals. These relays are known as EH relays, which attract significant attention in both the academia and the industry. In this research, we investigate the performance of SWIPT-based EH cooperative networks and the optimization problems therein. Due to hardware limitations, the energy harvesting circuit cannot decode the signal directly. Power splitting (PS) is a popular and effective solution to this problem. Therefore, we focus on PS based SWIPT in this research. First, different from existing work that employs time-switching (TS) based SWIPT, we propose to employ PS based SWIPT for a truly full-duplex (FD) EH relay network, where the information reception and transmission take place simultaneously at the relay all the time. This more thorough exploitation of the FD feature consequently leads to a significant capacity improvement compared with existing alternatives in the literature. Secondly, when multiple relays are available in the network, we explore the relay selection (RS) and network beamforming techniques in EH relay networks. Assuming orthogonal bandwidth allocation, both single relay selection (SRS) and general relay selection (GRS) without the limit on the number of cooperating relays are investigated and the corresponding RS methods are proposed. We will show that our proposed heuristic GRS methods outperform the SRS methods and achieve very similar performance compared with the optimal RS method achieved by exhaustive search but with dramatically reduced complexity. Under the shared bandwidth assumption, network beamforming among EH relays is investigated. We propose a joint PS factor optimization method based on semidefinite relaxation. Simulations show that network beamforming achieves the best performance among all other cooperative techniques. Finally, we study the problem of power allocation and PS factor optimization for SWIPT over doubly-selective wireless channels. In contrast to existing work in the literature, we take the channel variation in both time and frequency domains into consideration and jointly optimize the power allocation and the PS factors. The objective is to maximize the achievable data rate with constraints on the delivered energy in a time window. Since the problem is difficult to solve directly due to its nonconvexity, we proposed a two-step approach, named joint power allocation and splitting (JoPAS), to solve the problem along the time and frequency dimensions sequentially. Simulations show significantly improved performance compared with the existing dynamic power splitting scheme. A suboptimal heuristic algorithm, named decoupled power allocation and splitting (DePAS), is also proposed with significantly reduced computational complexity and simulations demonstrate its near-optimum performance