Asymptotic Analysis of SU-MIMO Channels With Transmitter Noise and Mismatched Joint Decoding

Hardware impairments in radio-frequency components of a wireless system cause unavoidable distortions to transmission that are not captured by the conventional linear channel model. In this paper, a 'binoisy' single-user multiple-input multiple-output (SU-MIMO) relation is considered where the additional distortions are modeled via an additive noise term at the transmit side. Through this extended SU-MIMO channel model, the effects of transceiver hardware impairments on the achievable rate of multi-antenna point-to-point systems are studied. Channel input distributions encompassing practical discrete modulation schemes, such as, QAM and PSK, as well as Gaussian signaling are covered. In addition, the impact of mismatched detection and decoding when the receiver has insufficient information about the non-idealities is investigated. The numerical results show that for realistic system parameters, the effects of transmit-side noise and mismatched decoding become significant only at high modulation orders.Comment: 16 pages, 7 figure

Analysis of the Local Quasi-Stationarity of Measured Dual-Polarized MIMO Channels

It is common practice in wireless communications to assume strict or wide-sense stationarity of the wireless channel in time and frequency. While this approximation has some physical justification, it is only valid inside certain time-frequency regions. This paper presents an elaborate characterization of the non-stationarity of wireless dual-polarized channels in time. The evaluation is based on urban macrocell measurements performed at 2.53 GHz. In order to define local quasi-stationarity (LQS) regions, i.e., regions in which the change of certain channel statistics is deemed insignificant, we resort to the performance degradation of selected algorithms specific to channel estimation and beamforming. Additionally, we compare our results to commonly used measures in the literature. We find that the polarization, the antenna spacing, and the opening angle of the antennas into the propagation channel can strongly influence the non-stationarity of the observed channel. The obtained LQS regions can be of significant size, i.e., several meters, and thus the reuse of channel statistics over large distances is meaningful (in an average sense) for certain algorithms. Furthermore, we conclude that, from a system perspective, a proper non-stationarity analysis should be based on the considered algorithm

Multi-user Linear Precoding for Multi-polarized Massive MIMO System under Imperfect CSIT

The space limitation and the channel acquisition prevent Massive MIMO from being easily deployed in a practical setup. Motivated by current deployments of LTE-Advanced, the use of multi-polarized antennas can be an efficient solution to address the space constraint. Furthermore, the dual-structured precoding, in which a preprocessing based on the spatial correlation and a subsequent linear precoding based on the short-term channel state information at the transmitter (CSIT) are concatenated, can reduce the feedback overhead efficiently. By grouping and preprocessing spatially correlated mobile stations (MSs), the dimension of the precoding signal space is reduced and the corresponding short-term CSIT dimension is reduced. In this paper, to reduce the feedback overhead further, we propose a dual-structured multi-user linear precoding, in which the subgrouping method based on co-polarization is additionally applied to the spatially grouped MSs in the preprocessing stage. Furthermore, under imperfect CSIT, the proposed scheme is asymptotically analyzed based on random matrix theory. By investigating the behavior of the asymptotic performance, we also propose a new dual-structured precoding in which the precoding mode is switched between two dual-structured precoding strategies with 1) the preprocessing based only on the spatial correlation and 2) the preprocessing based on both the spatial correlation and polarization. Finally, we extend it to 3D dual-structured precoding.Comment: accepted to IEEE Transactions on Wireless Communication

Quantized Multimode Precoding in Spatially Correlated Multi-Antenna Channels

Multimode precoding, where the number of independent data-streams is adapted optimally, can be used to maximize the achievable throughput in multi-antenna communication systems. Motivated by standardization efforts embraced by the industry, the focus of this work is on systematic precoder design with realistic assumptions on the spatial correlation, channel state information (CSI) at the transmitter and the receiver, and implementation complexity. For spatial correlation of the channel matrix, we assume a general channel model, based on physical principles, that has been verified by many recent measurement campaigns. We also assume a coherent receiver and knowledge of the spatial statistics at the transmitter along with the presence of an ideal, low-rate feedback link from the receiver to the transmitter. The reverse link is used for codebook-index feedback and the goal of this work is to construct precoder codebooks, adaptable in response to the statistical information, such that the achievable throughput is significantly enhanced over that of a fixed, non-adaptive, i.i.d. codebook design. We illustrate how a codebook of semiunitary precoder matrices localized around some fixed center on the Grassmann manifold can be skewed in response to the spatial correlation via low-complexity maps that can rotate and scale submanifolds on the Grassmann manifold. The skewed codebook in combination with a lowcomplexity statistical power allocation scheme is then shown to bridge the gap in performance between a perfect CSI benchmark and an i.i.d. codebook design.Comment: 30 pages, 4 figures, Preprint to be submitted to IEEE Transactions on Signal Processin

Linear Beamforming for the Spatially Correlated MISO broadcast channel

A spatially correlated broadcast setting with M antennas at the base station and M users (each with a single antenna) is considered. We assume that the users have perfect channel information about their links and the base station has only statistical information about each user's link. The base station employs a linear beamforming strategy with one spatial eigen-mode allocated to each user. The goal of this work is to understand the structure of the beamforming vectors that maximize the ergodic sum-rate achieved by treating interference as noise. In the M = 2 case, we first fix the beamforming vectors and compute the ergodic sum-rate in closed-form as a function of the channel statistics. We then show that the optimal beamforming vectors are the dominant generalized eigenvectors of the covariance matrices of the two links. It is difficult to obtain intuition on the structure of the optimal beamforming vectors for M > 2 due to the complicated nature of the sum-rate expression. Nevertheless, in the case of asymptotic M, we show that the optimal beamforming vectors have to satisfy a set of fixed-point equations.Comment: Published in IEEE ISIT 2010, 5 page

MIMO Transmission with Residual Transmit-RF Impairments

Physical transceiver implementations for multiple-input multiple-output (MIMO) wireless communication systems suffer from transmit-RF (Tx-RF) impairments. In this paper, we study the effect on channel capacity and error-rate performance of residual Tx-RF impairments that defy proper compensation. In particular, we demonstrate that such residual distortions severely degrade the performance of (near-)optimum MIMO detection algorithms. To mitigate this performance loss, we propose an efficient algorithm, which is based on an i.i.d. Gaussian model for the distortion caused by these impairments. In order to validate this model, we provide measurement results based on a 4-stream Tx-RF chain implementation for MIMO orthogonal frequency-division multiplexing (OFDM).Comment: to be presented at the International ITG Workshop on Smart Antennas - WSA 201