Wireless channel modelling for indoor populated environments : statistical characterization of narrowband channels at 2.45 Ghz and 5.2 Ghz

The recent development of indoor wireless local area network (WLAN) standards at 2.45 GHz and 5 GHz has led to increased interest in propagation studies at these frequency bands. Within the indoor environment, human body effects can strongly reduce the quality of wireless communication systems. Human body effects can cause temporal variations and shadowing due to pedestrian movement and antenna- body interaction with portable terminals. This book presents a statistical characterisation, based on measurements, of human body effects on indoor narrowband channels at 2.45 GHz and at 5.2 GHz. A novel cumulative distribution function (CDF) that models the 5 GHz narrowband channel in populated indoor environments is proposed. This novel CDF describes the received envelope in terms of pedestrian traffic. In addition, a novel channel model for the populated indoor environment is proposed for the Multiple-Input Multiple-Output (MIMO) narrowband channel in presence of pedestrians at 2.45 GHz. Results suggest that practical MIMO systems must be sufficiently adaptive if they are to benefit from the capacity enhancement caused by pedestrian movement.

Efficient wireless broadband communications to rural area

This paper discusses how internet services can be brought one step closer to the rural dispersed communities by improving wireless broadband communications in those areas. To accomplish this objective we describe the use of an innovative Multi-User-Single-Antenna for MIMO (MUSA-MIMO) technology using the spectrum currently allocated to analogue TV. MUSA-MIMO technology can be considered as a special case of MIMO technology, which is beneficial when provisioning reliable and high-speed communication channels. This paper describes channel modelling techniques to characterise the MUSA-MIMO system allowing an effective deployment of this technology. Particularly, it describes the development of a novel MUSA MIMO channel model that takes into account temporal variations in the rural wireless environment. This can be considered as a novel approach tailor-maid to rural Australia for provisioning efficient wireless broadband communications.

Tracking analysis of an LMS decision feedback equalizer for a wireless channel

We consider a time varying wireless fading channel, equalized by an LMS Decision Feedback equalizer (DFE). We study how well this equalizer tracks the optimal MMSEDFE (Wiener) equalizer. We model the channel by an Autoregressive (AR) process. Then the LMS equalizer and the AR process are jointly approximated by the solution of a system of ODEs (ordinary differential equations). Using these ODEs, we show via some examples that the LMS equalizer moves close to the instantaneous Wiener filter after initial transience. We also compare the LMS equalizer with the instantaneous optimal DFE (the commonly used Wiener filter) designed assuming perfect previous decisions and computed using perfect channel estimate (we will call it as IDFE). We show that the LMS equalizer outperforms the IDFE almost all the time after initial transience.

CM-based blind equalization of time-varying SIMO-FIR channel with single pulsation estimation

It is known that the constant modulus (CM) property of the source signal can be exploited to blindly equalize time-invariant single-inputmultiple-output (SIMO) and finite-impulse-response (FIR) channels. However, the time-invariance assumption about the channel cannot be satisfied in several practical applications, e.g., mobile communication. In this paper, we show that, under some mild conditions, the CM criterion can be extended to the blind equalization of a time-varying channel that is described by the complex exponential basis expansion model (CE-BEM). Although several existing blind equalization methods that are based on the CE-BEM have to employ higher order statistics to estimate all nonzero channel pulsations, the CM-based method only needs to estimate one pulsation using second-order statistics, which yields better estimation results. It also relaxes the restriction on the source signal and is applicable to some classes of signals with which the existing methods cannot deal.

Estimation of basis frequencies for time-varying SIMO channels : a second-order method

The complex exponential basis expansion model (CE-BEM) provides an accurate description for the time-varying (TV) channels encountered in mobile communications. Many blind channel identification and equalization approaches based on the CE-BEM require precise knowledge of the basis frequencies of TV channels. Existing methods for basis frequency estimation usually resort to the higher-order statistics of channel outputs and impose strict constraints on the source signal. In this paper, we propose a novel method to estimate the basis frequencies for blind identification and equalization of time-varying single-input multiple-output (SIMO) finite-impulse-response (FIR) channels. The proposed method exploits only the second-order statistics of channel outputs and does not require strong conditions on the source signal. As a result, it exhibits superior performance to the existing basis frequency estimation methods. The validity of our method is demonstrated by numerical simulations.

Modelling the broadband wireless channel in rural Australia

The internet infrastructure which supports high data rates has a major impact on the Australian economy and the world. However, in rural Australia, the provision of broadband services to an internet dispersed population over a large geographical area with low population densities remains both an economic and technical challenge [1]. Furthermore, the implementation of currently available technologies such as fibre-to-the-premise (FTTP), 3G, 4G and WiMAX seems to be impractical, considering the low population density that is distributed in a large area. Therefore, new paradigms and innovative telecommunication technologies need to be explored to overcome the challenges of providing faster and more reliable broadband internet services to internet dispersed rural areas. The research project implements an innovative Multi-User- Single-Antenna for MIMO (MUSA-MIMO) technology using the spectrum currently allocated to analogue TV. MUSAMIMO technology can be considered as a special case of MIMO technology, which is beneficial when provisioning reliable and high-speed communication channels. Particularly, the abstract describes the development of a novel MUSA-MIMO channel model that takes into account temporal variations in the rural wireless environment. This can be considered as a novel approach tailor-made to rural Australia for provisioning efficient wireless broadband communications.

Time-varying bispectral analysis of auditory evoked multi-channel scalp EEG

Time-varying bispectra, computed using a classical sliding window short-time Fourier approach, are analyzed for scalp EEG potentials evoked by an auditory stimulus and new observations are presented. A single, short duration tone is presented from the left or the right, direction unknown to the test subject. The subject responds by moving the eyes to the direction of the sound. EEG epochs sampled at 200 Hz for repeated trials are processed between -70 ms and +1200 ms with reference to the stimulus. It is observed that for an ensemble of correctly recognized cases, the best matching timevarying bispectra at (8 Hz, 8Hz) are for PZ-FZ channels and this is also largely the case for grand averages but not for power spectra at 8 Hz. Out of 11 subjects, the only exception for time-varying bispectral match was a subject with family history of Alzheimer’s disease and the difference was in bicoherence, not biphase.

Time-varying bispectral analysis of visually evoked multi-channel EEG

Theoretical foundations of higher order spectral analysis are revisited to examine the use of time-varying bicoherence on non-stationary signals using a classical short-time Fourier approach. A methodology is developed to apply this to evoked EEG responses where a stimulus-locked time reference is available. Short-time windowed ensembles of the response at the same offset from the reference are considered as ergodic cyclostationary processes within a non-stationary random process. Bicoherence can be estimated reliably with known levels at which it is significantly different from zero and can be tracked as a function of offset from the stimulus. When this methodology is applied to multi-channel EEG, it is possible to obtain information about phase synchronization at different regions of the brain as the neural response develops. The methodology is applied to analyze evoked EEG response to flash visual stimulii to the left and right eye separately. The EEG electrode array is segmented based on bicoherence evolution with time using the mean absolute difference as a measure of dissimilarity. Segment maps confirm the importance of the occipital region in visual processing and demonstrate a link between the frontal and occipital regions during the response. Maps are constructed using bicoherence at bifrequencies that include the alpha band frequency of 8Hz as well as 4 and 20Hz. Differences are observed between responses from the left eye and the right eye, and also between subjects. The methodology shows potential as a neurological functional imaging technique that can be further developed for diagnosis and monitoring using scalp EEG which is less invasive and less expensive than magnetic resonance imaging.

Training and Voids in Receive Antenna Subset Selection in Time-Varying Channels

Receive antenna selection (AS) provides many benefits of multiple-antenna systems at drastically reduced hardware costs. In it, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. Due to the nature of AS, the channel estimates at different antennas, which are required to determine the best subset for data reception, are obtained from different transmissions of the pilot sequence. Consequently, they are outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is necessary and optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N , we highlight a new issue of ``training voids'', in which the last pilot transmission is not fully exploited by the receiver. We then present new ``void-filling'' methods that exploit these voids and greatly improve the performance of AS. The optimal subset selection rules with void-filling, in which different antennas turn out to have different numbers of estimates, are also explicitly characterized. Closed-form equations for the symbol error probability with and without void-filling are also developed.

Receive Antenna Selection for Time-Varying Channels Using Discrete Prolate Spheroidal Sequences

Receive antenna selection (AS) has been shown to maintain the diversity benefits of multiple antennas while potentially reducing hardware costs. However, the promised diversity gains of receive AS depend on the assumptions of perfect channel knowledge at the receiver and slowly time-varying fading. By explicitly accounting for practical constraints imposed by the next-generation wireless standards such as training, packetization and antenna switching time, we propose a single receive AS method for time-varying fading channels. The method exploits the low training overhead and accuracy possible from the use of discrete prolate spheroidal (DPS) sequences based reduced rank subspace projection techniques. It only requires knowledge of the Doppler bandwidth, and does not require detailed correlation knowledge. Closed-form expressions for the channel prediction and estimation error as well as symbol error probability (SEP) of M-ary phase-shift keying (MPSK) for symbol-by-symbol receive AS are also derived. It is shown that the proposed AS scheme, after accounting for the practical limitations mentioned above, outperforms the ideal conventional single-input single-output (SISO) system with perfect CSI and no AS at the receiver and AS with conventional estimation based on complex exponential basis functions.

Modelling the high-latitude ionosphere for time-varying plasma convection

The paper discusses how variations in the pattern of convective plasma flows should beincluded in self-consistent time-dependent models of the coupled ionosphere-thermosphere system. The author shows how these variations depend upon the mechanism by which the solar wind flow excites the convection. The modelling of these effects is not just of relevance to the polar ionosphere. This is because the influence of convection is not confined to high latitudes: the resultant heating and composition changes in the thermosphere are communicated to lower latitudes by the winds which are also greatly modified by the plasma convection. These thermospheric changes alter the global distribution of plasma by modulatingthe rates of the chemical reactions which areresponsible for the loss of plasma. Hence the modelling of these high-latitude processes is of relevanceto the design and operation of HF communication, radar and navigation systems worldwide.

Channel estimation for time-varying MIMO relay systems

In this paper, we investigate the channel estimation problem for multiple-input multiple-output (MIMO) relay communication systems with time-varying channels. The time-varying characteristic of the channels is described by the complex-exponential basis expansion model (CE-BEM). We propose a superimposed channel training algorithm to estimate the individual first-hop and second-hop time-varying channel matrices for MIMO relay systems. In particular, the estimation of the second-hop time-varying channel matrix is performed by exploiting the superimposed training sequence at the relay node, while the first-hop time-varying channel matrix is estimated through the source node training sequence and the estimated second-hop channel. To improve the performance of channel estimation, we derive the optimal structure of the source and relay training sequences that minimize the mean-squared error (MSE) of channel estimation. We also optimize the relay amplification factor that governs the power allocation between the source and relay training sequences. Numerical simulations demonstrate that the proposed superimposed channel training algorithm for MIMO relay systems with time-varying channels outperforms the conventional two-stage channel estimation scheme.

Modelling stock volatilities during financial crises:a time varying coefficient approach

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.