André L. F. de Almeida

Space-time spreading-multiplexing for MIMO wireless communication systems using the PARATUCK-2 tensor model

In this paper, we present a new space-time spreading-multiplexing model for multiple-input multiple-output (MIMO) wireless communication systems relying on a tensor modeling of the transmitted and received signals. At the transmitter, we exploit the core of a PARATUCK-2 tensor model composed of a precoding matrix and two allocation matrices that allow to control the spreading and multiplexing of the data streams across the space dimension (transmit antennas) and time-dimension (time-slots). Different MIMO schemes combining space-time multiplexing and diversity can be derived from the proposed model. The identifiability and uniqueness of the PARATUCK-2 tensor model for the received signal are discussed and subsequently exploited for a joint blind channel estimation and symbol detection. The bit-error-rate performance of different transmit schemes derived from the proposed tensor model is evaluated by means of computer simulations.

Multiuser MIMO system using block space-time spreading and tensor modeling

In this paper, we consider a point-to-multipoint downlink multiuser wireless communication system, where a multiple-antenna base station simultaneously transmits data to several users equipped with multiple receive antennas. The transmit antenna array is partitioned into transmission blocks, each one being associated with a given user. Space-time spreading is performed within each block using a transmit antenna subset. We formulate block space-time spreading using a tensor modeling. We show that the tensor-based block space-time spreading model has the distinguishing feature of modeling a multiuser space-time transmission with different spatial spreading factors (diversity gains) as well as different multiplexing factors (code rates) for the users. The space-time spreading structure is chosen to allow a deterministic multiuser interference (MUI) elimination by each user. A block-constrained tensor model is then presented for the received signal, which is characterized by fixed constraint matrices that reveal the overall space-time spreading pattern. At each receiver, blind joint channel and symbol recovery is performed using an alternating least squares algorithm. Simulation results illustrate the performance of the proposed transceiver model in terms of bit-error-rate, channel/symbol estimation accuracy and link-level throughput.

Blind identification of underdetermined mixtures based on the hexacovariance and higher-order cyclostationarity

Static linear mixtures with more sources than sensors are considered. Blind identification (BI) of underdetermined mixtures is addressed by taking advantage of sixth order (SixO) statistics and the virtual array (VA) concept. Surprisingly, identification methods solely based on the hexacovariance matrix succeed well, despite their expected high estimation variance; this is due to the inherently good conditioning of the problem. A computationally simple but efficient algorithm, named BIRTH (Blind Identification of mixtures of sources using Redundancies in the daTa Hexacovariance matrix), is proposed and enables the identification of the steering vectors of up to P=N/sup 2/-N+1 sources for arrays of N sensors with space diversity only, and up to P=N/sup 2/ for those with angular and polarization diversities. Five numerical algorithms are compared.

Space-time spreading MIMO-CDMA downlink systems using constrained tensor modeling

This paper considers the downlink of a multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) system based on space-time spreading (STS) using multiuser/multicode transmission. A constrained tensor model is proposed for the STS transmitted signal. The new STS model generalizes existing ones by allowing multiuser spatial multiplexing with different code reuse/multiplexing patterns for each user. This model is parametrized by two allocation matrices with variable structure, which allow to control, respectively, the allocation of multiple data streams and spreading codes to the transmit antennas. A methodology is proposed to design these allocation matrices leading to finite sets of feasible STS schemes with guaranteed blind symbol recovery. The construction of feasible STS schemes for 2, 3 and 4 transmit antennas is presented as an illustration of this methodology. A blind receiver based on the alternating least squares algorithm is used for symbol recovery. The bit-error-rate performance of this receiver is tested and compared to some existing STS-based downlink transceivers.

Trilinear Space-Time-Frequency Codes for broadband MIMO-OFDM systems

A new multi-antenna coding framework is proposed for space-time-frequency (STF) transmissions over broadband multiple input multiple output (MIMO) systems based on orthogonal frequency division multiplexing (OFDM). A tensor decomposition known as PARAFAC is used as the core of a multi- stream space-time-frequency coder that jointly multiplex and spreads several input streams over space (transmit antennas), time (symbol periods) and frequency (subcarriers). We coin the term trilinear STF codes since each input symbol is coded over a space-time-frequency grid by a triple product code: each trilinearly coded symbol is interpreted as an element of a third-order tensor, which can be decomposed using PAFAFAC analysis. Trilinear STF codes are designed for an arbitrary number of transmit and receive antennas. They afford a variable degree of multiplexing-spreading over each one of the three signal dimensions while providing full diversity gain for each multiplexed stream. At the receiver, a direct blind decoding based on a relatively simple linear processing is made possible thanks to the PARAFAC modeling of the received signal. Computer simulation results are provided for performance assessment of the proposed codes in a variety of configurations.