Rich Multipath Channels Research Articles

Blind Identification of Multichannel Systems Based on Sparse Bayesian Learning

Reliable wireless communication often requires accurate knowledge of the underlying multipath channels. Numerous measurement campaigns have shown that physical multipath channels tend to exhibit a sparse structure. Conventional blind channel identification (BCI) strategies such as the least squares, which are known to be optimal under the assumption of rich multipath channels, are ill-suited to exploiting the inherent sparse nature of multipath channels. Recently, l1-norm regularized least-squares-type approaches have been proposed to address this problem with a single parameter governing all coefficients, which is equivalent to maximum a posteriori probability estimation with a Laplacian prior for the channel coefficients. Since Laplace prior is not conjugate to the Gaussian likelihood, no closed form of Bayesian inference is possible. Following a different approach, this paper deals with blind channel identification of a single-input multiple-output (SIMO) system based on sparse Bayesian learning (SBL). The inherent sparse nature of wireless multipath channels is exploited by incorporating a transformative cross relation formulation into a general Bayesian framework, in which the filter coefficients are governed by independent scalar parameters. A fast iterative Bayesian inference method is then applied to the proposed model for obtaining sparse solutions, which completely eliminates the need for computationally costly parameter fine tuning, which is necessary in the l1-norm regularization method. Simulation results are provided to demonstrate the superior effectiveness of the proposed channel estimation algorithm over the conventional least squares (LS) scheme as well as the l1-norm regularization method. It is shown that the proposed algorithm exhibits superior estimation performance compared to both LS and l1-norm regularization methods.

Power Boosting and Compensation During OTA Testing of a Real 4G LTE Base Station in Reverberation Chamber

A fully operational 4G long-term evolution (LTE) base station is tested in a reverberation chamber to analyze its performance in the presence of a multipath environment, typical of wireless and vehicular communications. Transmission quality parameters are measured ranging from the empty chamber situation (very rich multipath channel) to a very high loading condition to mitigate multipath. In that way, both outdoor and indoor propagation are accounted for. A large attenuation is inserted between the transmitter and the antenna to reduce the signal received by the user to real-life values encountered in both outdoor and indoor environments. In these scenarios, operators may choose to transmit a constant power spectral density throughout all LTE spectrum, or to increase the energy of control channels at the expense of data channels in order to enforce transmission and provide better quality to the user, especially in poor radio conditions. This is called “power boosting,” and its effect is analyzed in this paper.

Asynchronous multiuser underwater acoustic communications (L)

An asynchronous multiuser system is proposed to support multiple-access underwater communications without the use of code-division multiple-access or a feedback channel. The rich multipath channels experienced by spatially separated users will be sufficient to ensure separation of collided packets at the base station. The iterative receiver will employ a combination of adaptive time-reversal processing, matching pursuit, and successive interference cancellation in a block-wise fashion to achieve multiuser separability. Data collected during the KAM11 experiment are used to illustrate the system's capability in a dynamic, time-varying environment.

RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms

In this paper, we discuss the emerging application of device-free localization (DFL) using wireless sensor networks, which find people and objects in the environment in which the network is deployed, even in buildings and through walls. These networks are termed “RF sensor networks” because the wireless network itself is the sensor, using radio-frequency (RF) signals to probe the deployment area. DFL in cluttered multipath environments has been shown to be feasible, and in fact benefits from rich multipath channels. We describe modalities of measurements made by RF sensors, the statistical models which relate a person's position to channel measurements, and describe research progress in this area.

Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> High-rate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Training-based methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional training-based channel estimation methods, typically comprising linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels encountered in practice tend to exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present a new approach to estimating sparse (or effectively sparse) multipath channels that is based on some of the recent advances in the theory of compressed sensing. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing (CCS), can potentially achieve a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional least-squares-based training methods. </para>

DIVERSITY TECHNIQUES WITH PARALLEL DIPOLE ANTENNAS: RADIATION PATTERN ANALYSIS

Two parallel dipoles are assessed for antenna diversity. The three-dimensional radiation pattern is considered for signals correlation coefficient. The pattern analysis reveals that, depending on dipole spacing, three types of diversity techniques are generated: space, amplitude-pattern and phase-pattern diversity. The weighting of each technique in signals correlation coefficient mitigation is investigated. The results show that for closely spaced dipoles, the generated phase- pattern diversity is the most dominant factor which greatly reduces the signals correlation coefficient. The diversity configuration is measured in a rich scattering environment. Results include signals correlation coefficient, diversity gain for selection combining and maximum ratio combining, effective diversity gain and antenna radiation efficiency can be demonstrated. We show that in rich multipath channel the minimum spatial distance, for effective diversity gain performance, is reduced from 0.5λ for uncoupled dipoles to 0.15λ for coupled dipoles.

Signal Reception for Space-Time Differentially Encoded Transmissions over FIR Rich Multipath Channels

With sophisticated signal and information processing algorithms, air interfaces with space-time (ST) coding and multiple reception antennas substantially improve the reliability of wireless links. This paper proposes a new receiver algorithm for differential ST coded transmissions over the finite-impulse-response (FIR) rich multipath fading channels. The symbol detection introduced in this paper is a deterministic subspace-based approach in a multiple-input and multiple-output (MIMO) system framework. The receiver (i) operates in a blind fashion without estimating the channel or its inverse and (ii) is able to work with a small number of signal samples and hence can be applied in the quasistatic channels. The proposed scheme employs multiple antennas at both sides of the transceiver and exploits both the antenna diversity and the multiple constant modulus (MCM) characteristics of the signaling. The receiver is able to blindly mitigate the intersymbol interference (ISI) in a rich multipath propagation environment, and this has been verified through the extensive Monte Carlo simulations.