Sébastien Aubert

Parallel QR Decomposition in LTE-A systems

The QR Decomposition (QRD) of communication channel matrices is a fundamental prerequisite to several detection schemes in Multiple-Input Multiple-Output (MIMO) communication systems. Herein, the main feature of the QRD is to transform the non-causal system into a causal system, where consequently efficient detection algorithms based on the Successive Interference Cancellation (SIC) or Sphere Decoder (SD) become possible. Also, QRD can be used as a light but efficient antenna selection scheme. In this paper, we address the study of the QRD methods and compare their efficiency in terms of computational complexity and error rate performance. Moreover, a particular attention is paid to the parallelism of the QRD algorithms since it reduces the latency of the matrix factorization.

Complexity Gain of QR Decomposition Based Sphere Decoder in LTE Receivers

It has been widely shown that the Sphere Decoding can be used to find the Maximum Likelihood (ML) solution with an expected complexity that is roughly cubic in the dimensions of the problem. However, the computational complexity becomes prohibitive if the Signal-to-Noise Ratio is too low and/or if the dimension of the problem is too large. That is why another technique denoted as Fixed-complexity Sphere Decoder (FSD) is an interesting approach. This algorithm needs a preprocessing step, and in this paper the QR-Decomposition-based prepro- cessing technique, which is not inconsequential, will be studied. Two different techniques are exposed, including the classical Gram Schmidt orthonormalization process. Their computational complexities and their impacts on the FSD computational com- plexity are studied. In the LTE context, the overall computational complexities of the two detection techniques are quantified and are shown to be dependent on the constellation size. Index Terms—MIMO detection, Sphere Decoder, QR Decom- position, Householder, LTE.

On the implementation of MIMO-OFDM schemes using perturbation of the QR decomposition: Application to 3GPP LTE-a systems

Consider a flat-fading Multiple-Input Multiple-Output (MIMO) system. The near-optimum detection problematic has been shown to be efficiently solved through QR Decomposition (QRD)-based detectors. However, this step is demanding, and leads to wasting a large part of the system resource in case of a straightforward multi-carrier extension. In this paper, a solution that provides the QRD output - by advantageously considering the correlation between adjacent channels - is proposed. Originally, it relies on the matrix perturbation theory and induces a strong complexity reduction. Simulation results show that the proposed solution significantly reduces the implementation complexity while maintaining good performance.

From Linear Equalization to Lattice-Reduction-Aided Sphere-Detector as an Answer to the MIMO Detection Problematic in Spatial Multiplexing Systems

Employing multiple antennas at both the transmitter and the receiver linearly boosts the channel capacity by min (nT , nR), where nT and nR are the number of transmit and receive antennas, respectively A. Telatar (1999). Multiple-Input Multiple-Output (MIMO) technologies are classified into three categories: (i) MIMO diversity, (ii) MIMO Spatial Multiplexing (MIMO-SM) and (iii) beamforming that will not be addressed here since it particularly deals with transmitter algorithms. MIMO diversity techniques are deployed to increase the reliability of communications by transmitting or receiving multiple copies of the same signal at different resource entities of the permissible dimensions, i.e., time, frequency, or space. In contrast, the target of MIMO-SM is to increase the capacity of the communication channel. To this end, independent symbols are transmitted simultaneously from the different transmit antennas. Due to its attracting implementation advantages, Vertical Bell Laboratories Layered Space-Time (V-BLAST) transmitter structure is often used in the practical communication systems P. Wolniansky, G. Foschini, G. Golden, and R. Valenzuela (1998). In 3GPP Long Term Evolution-Advanced (3GPP LTE-A) 3GPP (2009), the challenge of de-multiplexing the transmitted symbols via SM techniques, i.e. detection techniques, stands as one of the main limiting factors in linearly increasing system’s throughput without requiring additional spectral resources. The design of detection schemes with high performance, low latency, and applicable computational complexity is being a challenging research topic due to the power and latency limitations of the mobile communication systems M. Mohaisen, H.S. An, and K.H.Chang (2009).