AMMSE Optimization for Multiuser MISO Systems with Imperfect CSIT and Perfect CSIR

In this paper, we consider the design of robust linear precoders for MU-MISO systems where users have perfect Channel State Information (CSI) while the BS has partial CSI. In particular, the BS has access to imperfect estimates of the channel vectors, in addition to the covariance matrices of the estimation error vectors. A closed-form expression for the Average Minimum Mean Square Error (AMMSE) is obtained using the second order Taylor Expansion. This approximation is used to formulate two fairness-based robust design problems: a maximum AMMSE-constrained problem and a power-constrained problem. We propose an algorithm based on convex optimization techniques to address the first problem, while the second problem is tackled by exploiting the close relationship between the two problems, in addition to their monotonic natures.Comment: IEEE Global Communications Conference (GLOBECOM) 201

Robust Transmission in Downlink Multiuser MISO Systems: A Rate-Splitting Approach

We consider a downlink multiuser MISO system with bounded errors in the Channel State Information at the Transmitter (CSIT). We first look at the robust design problem of achieving max-min fairness amongst users (in the worst-case sense). Contrary to the conventional approach adopted in literature, we propose a rather unorthodox design based on a Rate-Splitting (RS) strategy. Each user's message is split into two parts, a common part and a private part. All common parts are packed into one super common message encoded using a public codebook, while private parts are independently encoded. The resulting symbol streams are linearly precoded and simultaneously transmitted, and each receiver retrieves its intended message by decoding both the common stream and its corresponding private stream. For CSIT uncertainty regions that scale with SNR (e.g. by scaling the number of feedback bits), we prove that a RS-based design achieves higher max-min (symmetric) Degrees of Freedom (DoF) compared to conventional designs (NoRS). For the special case of non-scaling CSIT (e.g. fixed number of feedback bits), and contrary to NoRS, RS can achieve a non-saturating max-min rate. We propose a robust algorithm based on the cutting-set method coupled with the Weighted Minimum Mean Square Error (WMMSE) approach, and we demonstrate its performance gains over state-of-the art designs. Finally, we extend the RS strategy to address the Quality of Service (QoS) constrained power minimization problem, and we demonstrate significant gains over NoRS-based designs.Comment: Accepted for publication in IEEE Transactions on Signal Processin

Exploiting Known Interference as Green Signal Power for Downlink Beamforming Optimization

We propose a data-aided transmit beamforming scheme for the multi-user multiple-input-single-output (MISO) downlink channel. While conventional beamforming schemes aim at the minimization of the transmit power subject to suppressing interference to guarantee quality of service (QoS) constraints, here we use the knowledge of both data and channel state information (CSI) at the transmitter to exploit, rather than suppress, constructive interference. More specifically, we design a new precoding scheme for the MISO downlink that minimizes the transmit power for generic phase shift keying (PSK) modulated signals. The proposed precoder reduces the transmit power compared to conventional schemes, by adapting the QoS constraints to accommodate constructive interference as a source of useful signal power. By exploiting the power of constructively interfering symbols, the proposed scheme achieves the required QoS at lower transmit power. We extend this concept to the signal to interference plus noise ratio (SINR) balancing problem, where higher SINR values compared to the conventional SINR balancing optimization are achieved for given transmit power budgets. In addition, we derive equivalent virtual multicast formulations for both optimizations, both of which provide insights of the optimal solution and facilitate the design of a more efficient solver. Finally, we propose a robust beamforming technique to deal with imperfect CSI, that also reduces the transmit power over conventional techniques, while guaranteeing the required QoS. Our simulation and analysis show significant power savings for small scale MISO downlink channels with the proposed data-aided optimization compared to conventional beamforming optimization

QoS constrained power minimization in the multiple stream MIMO broadcast channel

This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/ licenses/by-nc-nd/4.0/. This version of the article: González-Coma, J. P., Joham, M., Castro, P. M., & Castedo, L. (2018). 'QoS constrained power minimization in the multiple stream MIMO broadcast channel', has been accepted for publication in Signal Processing, 143, 48–55. The Version of Record is available online at: https://doi.org/10.1016/ j.sigpro.2017.08.015.[Abstract]: This work addresses the design of optimal linear transmit filters for the Multiple Input-Multiple Output (MIMO) Broadcast Channel (BC) when several spatial streams are allocated to each user. We further consider that the Channel State Information (CSI) is perfect at the receivers but is only partial at the transmitter. A statistical model for the partial CSI is assumed and exploited for the filter design. The relationship between average rate and average Mean Square Error (MSE) is studied to determine the optimal way to distribute the per-user rates among the streams. Finally, the feasible average sum-MSE (sMSE) region is studied and the impact of the CSI uncertainty over the overall system performance is evaluated.This work was funded by Xunta de Galicia (ED431C 2016-045, ED341D R2016/012, ED431G/01), AEI of Spain (TEC2013-47141-C4-1-R, TEC2015-69648-REDC, TEC2016-75067-C4-1-R), and ERDF funds (AEI/FEDER, EU).Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED341D R2016/012Xunta de Galicia; ED431G/0