Paula Maria Castro

Optimized CSI Feedback for Robust THP Design

In the broadcast channel (BC), the signals of the multiple users can be separated by means of preceding. For the design of precoding, channel state information (CSI) is necessary at the transmitter side. In many cases (e.g., for frequency division duplex (FDD) systems), the transmitter cannot estimate this information and the CSI has to be communicated from the receivers to the transmitter via a feedback channel that is assumed to be error-free but introduces a delay. Every user estimates the channel and reduces it to a low-dimensional representation for data compression that is possible due to the channel correlations. Before the feedback, the CSI is quantized and only the index of the codebook entry is sent to the transmitter, since the data rate of the feedback channel is limited. We propose a joint MSE optimization of the channel estimation and the rank reduction basis, where the quantizer is modeled as a data independent additive noise source. Based on the fed back codebook index, robust multi-user precoding schemes, viz., linear precoding and Tomlinson Harashima precoding (THP), are designed that clearly outperform non-robust schemes.

MMSE optimal feedback of correlated CSI for multi-user precoding

For the separation of the signals for multiple users in the vector broadcast channel (BC), channel state information (CSI) is necessary at the transmitter. Since the transmitter has no access to this information in many cases, the CSI must be fed back from the receivers to the transmitter. Before the feedback, the receivers estimate the CSI and apply a rank reduction possible due to the channel correlations. We propose a joint optimization of the estimation, the rank reduction, and the codebook used for the feedback. Interestingly, the estimator and the rank reduction resulting from this monolithic formulation are independent of the used codebook which can be computed with the generalized Lloyd algorithm. Applying the proposed feedback design to a system with multi-user precoding based on CSI feedback shows the clear superiority of the optimized codebook compared to previous designs.

Power minimization and QoS feasibility region in the multiuser MIMO broadcast channel with imperfect CSI

The aim of this work is to jointly achieve individual rate requirements and minimum total transmit power in a Multi User Multiple Input Multiple Output (MU-MIMO) Broadcast Channel (BC). Data streams are transmitted from a multi-antenna base station to several independent and non-cooperative multi-antenna users. Perfect Channel-State-Information (CSI) is known at the receivers and it is fed back to the transmitter, where partial CSI is used for the design of linear transmit filters. Employing a duality between Multiple Access Channel (MAC) and BC w.r.t. the average Mean Square Error (MSE) and identifying standard interference functions, we propose an algorithmic joint solution for the transmit filter design and the power allocation. Additionally, we describe the feasibility region in the average MMSE domain. This result allows for checking the convergence of the algorithm for given Quality of Service (QoS) constraints.

Power minimization in the multiuser downlink under user rate constraints and imperfect transmitter CSI

The aim of this work is to jointly achieve individual rate requirements and minimum total transmit power in the vector Broadcast Channel (BC). Data streams are transmitted from a multi-antenna base station to several non-cooperative single-antenna receivers having perfect Channel-State-Information (CSI). Partial CSI, e.g., obtained via feedback, is used for the design of linear transmit filters at the transmitter. Employing a duality between Multiple Access Channel (MAC) and BC rate regions and the so-called standard interference functions, we propose an algorithmic joint solution for the transmit filter design and the power allocation in this work.

Robust Precoding With Limited Feedback Design Based on Precoding MSE for MU-MISO Systems

For the separation of the signals in the vector broadcast channel (BC), some information about the channel state is necessary at the transmitter. In many cases, this channel state information (CSI) must be fed back from the receivers to the transmitter. We jointly design the channel estimators and the quantizers at the receivers together with the precoder at the transmitter based on a precoder-centric criterion, i.e., the minimization of a mean square error (MSE) metric appropriate for the precoder design. This is in contrast to our previous works, where the quantizer design was based on a CSI MSE metric, i.e., based on the minimization of the MSE between the true channel and the channel recovered by the transmitter using a feedback channel. Interestingly, the estimators resulting from this joint formulation are independent of the used codebook. The codebook entries are the employed precoders. Therefore, each receiver feeds back the index of a set of precoders and the intersection of the sets gives the appropriate precoder. Since the quantizers of the different receivers have to work separately, the metric for the computation of the partition cells cannot be expressed as a simple squared error depending on the quantizer output. The proposed system based on a joint optimization clearly outperforms previous designs with separate optimization of feedback and precoding.

Robust MMSE linear precoding for Multiuser MISO systems with limited feedback and channel prediction

In this paper we investigate the design of a robust MMSE linear precoding Multiuser Multiple Input Single Output (MU MISO) system with limited feedback that exploits multiple feedback vectors to improve the quality of the available CSI at transmission. We explain how to appropriately exploit this additional past channel information to design the channel estimator, rank basis reduction and the quantizer parameters.

Testbed-Assisted Learning for Digital Communications Courses

We introduce testbed‐assisted learning as an effective means for teaching digital communications. Laboratory teaching activities of digital communications courses benefit very much from utilizing a hardware testbed, since it greatly facilitates the understanding of very important effects introduced by real‐world transceivers. We overcome the main drawback of communications hardware, that is, the cumbersome low‐level programming interfaces provided by hardware manufacturers, by introducing a distributed multilayer software architecture. This architecture provides different abstraction levels to access hardware testbeds, releasing students from the low‐level interaction with the hardware. Also, the distributed nature of this architecture results in a high flexibility of operation. This way, students can focus on learning communications topics without devoting any time to low‐level programming, that is usually out of the scope of digital communications courses. Thanks to testbed‐assisted learning, they are able to perform illustrative experiments to understand digital communications concepts (e.g., source coding, modulation, space‐time coding, etc.) and to test algorithms without developing a new program from scratch, speeding up both the implementation and the debugging tasks. However, those students interested in hardware implementations can use the software architecture to access and interact with lower programming levels until they are as close as possible to the hardware.

Robust Precoding With Bayesian Error Modeling for Limited Feedback MU-MISO Systems

We consider the robust precoder design for multiuser multiple-input single-output (MU-MISO) systems where the channel state information (CSI) is fed back from the single antenna receivers to the centralized transmitter equipped with multiple antennas. We propose to compress the feedback data by projecting the channel estimates onto a vector basis, known at the receivers and the transmitter, and quantizing the resulting coefficients. The channel estimator and the basis for the rank reduction are jointly optimized by minimizing the mean-square error (MSE) between the true and the rank-reduced CSI. Expressions for the conditional mean and the conditional covariance of the channel are derived which are necessary for the robust precoder design. These expressions take into account the following sources of error: channel estimation, truncation for rank reduction, quantization, and feedback channel delay. As an example for the robust problem formulation, vector precoding (VP) is designed based on the expectation of the MSE conditioned on the fed-back CSI. Our results show that robust precoding based on fed-back CSI clearly outperforms conventional precoding designs which do not take into account the errors in the CSI.

Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.

Impact of transmit impairments on multiuser MIMO non-linear transceivers

This paper analyzes the impact of residual transmit impairments on the performance of multiuser Multiple-Input Multiple-Output (MIMO) systems. We focus on the uplink with Decision Feedback (DF) non-linear receivers and the downlink with Tomlinson-Harashima non-linear precoders. We show that transmit noise severely affects the performance of both non-linear transmission schemes, specially for the downlink with THP. Nevertheless, this degradation can be significantly alleviated when including the transmit noise into the transceiver design.