Cássio B. Ribeiro

Interference-Aware Resource Allocation for Device-to-Device Radio Underlaying Cellular Networks

Future cellular networks such as IMT-Advanced are expected to allow underlaying direct Device-to-Device (D2D) communication for spectrally efficient support of e.g. rich multimedia local services. Enabling D2D links in a cellular network presents a challenge in radio resource management due to the potentially severe interference it may cause to the cellular network. We propose a practical and efficient scheme for generating local awareness of the interference between the cellular and D2D terminals at the base station, which then exploits the multiuser diversity inherent in the cellular network to minimize the interference. System simulations demonstrate that substantial gains in cellular and D2D performance can be obtained using the proposed scheme.

Device-to-Device Communication Underlaying Cellular Communications Systems

In this article we propose to facilitate local peer-to-peer communication by a Device-to-Device (D2D) radio that operates as an underlay network to an IMT-Advanced cellular network. It is expected that local services may utilize mobile peer-to-peer communication instead of central server based communication for rich multimedia services. The main challenge of the underlay radio in a multi-cell environment is to limit the interference to the cellular network while achieving a reasonable link budget for the D2D radio. We propose a novel power control mechanism for D2D connections that share cellular uplink resources. The mechanism limits the maximum D2D transmit power utilizing cellular power control information of the devices in D2D communication. Thereby it enables underlaying D2D communication even in interference-limited networks with full load and without degrading the performance of the cellular network. Secondly, we study a single cell scenario consisting of a device communicating with the base station and two devices that communicate with each other. The results demonstrate that the D2D radio, sharing the same resources as the cellular network, can provide higher capacity (sum rate) compared to pure cellular communication where all the data is transmitted through the base station.

On the Performance of Device-to-Device Underlay Communication with Simple Power Control

We address device-to-device (D2D) communication as a potential resource reuse technique underlaying the cellular network. We consider the shared channel of the two systems as an interference channel and formulate the statistics of the signal to interference plus noise ratio (SINR) of all users. The potential performance of D2D communication is evaluated by considering a scenario where only limited interference coordination between the cellular and the D2D communication is possible. We apply a simple power control method to the D2D communication which constrains the SINR degradation of the cellular link to a certain level. Results show that the SINR statistics of the D2D users is comparable to that of the cellular user in most of the cell area. Scheduling gain is possible by properly assigning either of the downlink (DL) or the uplink (UL) resources to the D2D communication.

Stochastic maximum likelihood method for propagation parameter estimation

We will derive a stochastic maximum likelihood method for estimating spatio-temporal channel parameters. Such estimators are needed in propagation studies where extensive channel measurements and sounding are required. These are seminal tasks in the process of developing advanced channel models. The proposed method employs angular Von Mises distribution model which is appropriate for directional data typically observed in channel measurement campaigns. The signal model is stochastic. The performance of the proposed method is compared to SAGE algorithm where the signal model is deterministic. The computational complexity of the proposed method is lower and channel parameters are estimated with higher fidelity because the underlying distribution model is well-suited for directional data.

Joint Angular- and Delay-Domain MIMO Propagation Parameter Estimation Using Approximate ML Method

In this paper, we derive an estimation method that jointly estimates the parameters of the concentrated propagation paths and the distributed scattering component that are frequently observed in multiple-input multiple-output (MIMO) channel sounding measurements. The joint angular-delay domain model leads to a correlation matrix with high dimensionality, which makes direct implementation of a maximum-likelihood (ML) estimator unfeasible. We derive low-complexity methods for computing approximate ML estimates that exploit the structure of the covariance matrices. We propose an iterative two-step procedure that alternates between the estimation of the parameters of the concentrated propagation paths and the parameters of the distributed scattering. For the distributed scattering, the estimator first optimizes the parameters describing their time-delay structure. Then, using the estimated time-delay parameters, the parameters of the angular distributions are optimized. We present simulation results and compare the estimated time-delay and angular distributions to the actual distributions, demonstrating that high-quality estimates are obtained. The large sample performance of the estimator is studied by establishing the Cramer-Rao lower bound (CRLB) and comparing it to the variances of the estimates. The simulations show that the variance of the proposed estimation technique reaches the CRLB for relatively small sample size for most parameters, and no bias is observed.

Stochastic Maximum Likelihood Estimation of Angle- and Delay-Domain Propagation Parameters

In this paper we derive an estimator for both time-delay and angular channel propagation parameters of the diffuse scattering component that is frequently observed in channel sounding measurements. The joint angular-delay model leads to correlation matrix with high dimensionality, which prevents direct implementation of a maximum-likelihood (ML) estimator using finite precision arithmetics and finite memory resources. We derive low complexity methods for computing the ML estimates that exploit the structure of the covariance matrices. The estimator is based on a two step procedure: first, the parameters of the power delay profile are estimated, as well as measurement noise power. Then, using the estimated time-delay parameters, the parameters of the angular distributions are estimated. We present simulation results and compare the estimated time-delay and angular distributions to the actual distributions, showing that high precision estimates are obtained

Stochastic Maximum-Likelihood Method for MIMO Propagation Parameter Estimation

In this paper, we derive a stochastic maximum-likelihood (ML) method for estimating spatio-temporal parameters for multiple-input multiple-output (MIMO) channels. Such estimators are needed in propagation studies where extensive channel measurements and sounding are required. These are seminal tasks in the process of developing advanced channel models. The proposed method employs an angular von Mises distribution model which is appropriate for angular data observed in channel measurement campaigns. The signal model is stochastic, and consequentially the method is particularly useful for estimation of the diffuse scattering component. This approach leads to lower complexity and faster convergence in comparison to deterministic models. These benefits are due to lower dimensionality of the model, leading to a simpler optimization problem. The statistical performance of the estimator is studied by establishing the Crameacuter-Rao lower bound (CRLB) and comparing the variances. The simulations show that the variance of the proposed estimation technique reaches the CRLB for relatively small sample size. The estimator is robust in the sense that meaningful results are obtained when applied to data generated by channel models other than the one used in its derivation

Propagation parameter estimation in MIMO systems using mixture of angular distributions model

For the development of future wireless systems, it is crucial to create accurate channel models. Channel sounding using antenna arrays and consequently propagation parameter estimation are key tasks in creating such models. In this paper we present an estimator for the angular distribution of the diffuse scattering component that is observed in channel sounding measurements. The angular distribution is modeled as a mixture of Von Mises distributions, which correspond to scatterer clusters. The parameters of the individual distributions as well as the mixture proportions are estimated. The large sample performance of the estimator is studied by deriving the Cramer-Rao lower bound and comparing the variance of the estimates to it. The simulations show that the the proposed estimator has asymptotically optimal performance since it attains the Cramer-Rao lower bound for relatively small sample sizes.

Cramer-Rao bound for angular propagation parameter estimation in MIMO systems

Realistic channel models are crucial in developing future wireless systems. Channel sounding and consequently propagation parameter estimation are key tasks in creating such models. In this paper we present an estimator for the angular distribution of the diffuse scattering component that is frequently observed in channel sounding measurements. Finding the Cramer-Rao lower bound and comparing the variances study the large sample performance of the estimator. The simulations show that the variance of the proposed estimation technique closely reaches the Cramer-Rao lower bound for small sample size and for any number of antennas.

Detecting Specular Propagation Paths in the Presence of Distributed Scattering in Angle and Delay Domains

In this paper, we derive a method for the detection and estimation of specular propagation paths that takes the correlation in both, angle and delay domain, of the distributed scattering into account. The method is based on an iterative procedure that alternates between the estimation of specular paths and of distributed scattering. The computational complexity is reduced compared to deterministic methods, which are based on the estimation of parameters of a large number of specular paths. The results show that the proposed method is able to detect and estimate the propagation parameters of specular paths that have low power, which would not be distinguished from distributed scattering by existing techniques.