Performance Evaluation of Coordinated Multi-Point Transmission Schemes with Predicted CSI

Coordinated multi-point (CoMP) transmission is considered as an efficient technique to improve cell-edge performance as well as system spectrum efficiency. In CoMP-enabled systems, a cluster of coordinated base stations (BSs) are typically assumed to be connected to a control unit (CU) via backhaul links, and the provided performance gain heavily relies on the quality of the channel state information (CSI) available at the CU side. In this paper, we consider the downlink of a CoMP cluster and compare three different CoMP transmission schemes: zero-forcing coherent joint transmission, non-coherent joint transmission and coordinated scheduling. Moreover, for each of the analyzed schemes, the performance in terms of average sum rate of the CoMP cluster is studied with predicted CSI, considering the effects of the feedback and backhaul latency, as well as the user mobility. Compared to zero-forcing coherent joint transmission, we show that non-coherent joint transmission and coordinated scheduling are more robust to channel uncertainly. In addition, depending on the latency, user mobility and user locations, different schemes would achieve the highest average sum rate performance. Hence, a system could switch between the transmission schemes to improve the sum rate

The Wireless IP Project

The optimization of resources in wireless packet data systems is challenging when users are mobile with timevarying link quality. Within the SSF PCC program, we have since year 2000 formed the Wireless IP project, which studies such issues. We perform research towards a flexible packet data system with wide area coverage for mobile users. This system should have performance equivalent to a 100Mbit/s Ethernet connection, with support for Quality of Service and fairness between wireless users. This will requir

Robust H_2 Filtering For Structured Uncertainty: The Performance Of Probabilistic And Minimax Schemes.

: A probabilistic approach to the robustification of Kalman filters is presented. It results in a higher order model, in which the uncertainty can be taken into account by simply modifying the noise covariance matrices. The proposed method provides a systematic way of performing this transformation. The performance of the robustified Kalman filter is compared to that of a recently proposed minimax H 2 scheme, based on two coupled Riccati equations and a one--dimensional numerical search. It is concluded that such methods should be used with care, since their guaranteed performance may be worse than that obtained by doing no filtering at all. 1 Introduction The aim of this paper is to discuss two recently proposed design techniques for robust filtering: 1. to minimize the worst case mean square error by utilizing two coupled Riccati equations, see e.g. [17]; 2. obtaining modified Wiener or Kalman filters by averaging over stochastic model uncertainties, as described in [16] and [13]. T..

Design and measurement based evaluations of coherent JT CoMP : a study of precoding, user grouping and resource allocation using predicted CSI

Coordinated multipoint (CoMP) transmission provides high theoretic gains in spectral efficiency with coherent joint transmission (JT) to multiple users. However, this requires accurate channel state information at the transmitter (CSIT) and also user groups with spatially compatible users. The aim of this paper is to use measured channels to investigate if significant CoMP gains can still be obtained with channel estimation errors. This turns out to be the case, but requires the combination of several techniques. We here focus on coherent downlink JT CoMP to multiple users within a cluster of cooperating base stations. The use of Kalman predictors is investigated to estimate the complex channel gains at the moment of transmission. It is shown that this can provide sufficient CSIT quality for JT CoMP even for long (> 20 ms) system delays at 2.66 GHz at pedestrian velocities or, for lower delays, at 500 MHz, at vehicular velocities. A user grouping and resource allocation scheme that provides appropriate groups for CoMP is also suggested. It provides performance close to that obtained by exhaustive search at very low complexity, low feedback cost and very low backhaul cost. Finally, a robust linear precoder that takes channel uncertainties into account when designing the precoding matrix is considered. We show that, in challenging scenarios, this provides large gains compared with zero-forcing precoding. Evaluations of these design elements are based on measured channels with realistic noise and intercluster interference assumptions. These show that high JT CoMP gains can be expected, on average over large sets of user positions, when the above techniques are combined - especially in severely intracluster interference limited scenarios

Robust Decision Feedback Equalizers

Design equations are presented for robust and realizable decision feedback equalizers, for IIR channels with coloured noise. Given a probabilistic measure of model uncertainty, the mean MSE, averaged over the whole class of possible models, is minimized. A robustification parameter, which trades off error propagation against theoretical performance, is also introduced. The resulting design equations define a large class of equalizers, with DFE's and linear equalizers based on nominal models being special cases. If data sequences fd(n)g are transmitted in the presence of intersymbol interference, they have to be reconstructed from the received sequences fy(n)g. Equalizers compute estimates ¯ d(n) on a symbol by symbol basis. Their main advantage, compared to the MLSE Viterbi detector, is a low computational complexity. If channels are slowly time-- varying, filter coefficients can be adjusted during known training sequences, and held fixed until the next training. For fast time--variat..

Wiener Filter Design Using Polynomial Equations

A simplified way of deriving of realizable and explicit Wiener filters is presented. Discrete time problems are discussed, in a polynomial equation framework. Optimal filters, predictors and smoothers are calculated by means of spectral factorizations and linear polynomial equations. A new tool for obtaining these equations, for a given problem structure, is described. It is based on evaluation of orthogonality in the frequency domain, by means of cancelling stable poles with zeros. Comparisons are made to previously known derivation methodology such as "completing the squares" for the polynomial systems approach and the classical Wiener solution. The simplicity of the proposed derivation method is particularly evident in multisignal filtering problems. To illustrate, two examples are discussed: a filtering and a generalized deconvolution problem. A new solvability condition for linear polynomial equations appearing in scalar problems is also presented. EDICS no. 4.2.2. Keywords: Wiene..