Darren Phillip Mcnamara

On Generating Soft Outputs for Lattice-Reduction-Aided MIMO Detection

Lattice reduction (LR) aided MIMO detection has been shown to provide near-optimal hard outputs. However soft outputs are required in practical systems to fully exploit gains from forward error correction. Due to the non-linear nature of LR aided MIMO detection, generating soft information on the estimates it provides is not trivial. Consequently this problem has not been treated much in the literature. In this paper we present two related algorithms for computing soft outputs on estimates of LR-aided MIMO detection. It is shown through computer simulations that the proposed soft output generation algorithms allow LR-aided MIMO detection to achieve near- optimal performance for coded systems.

Analysis of frequency-offset tracking in MIMO OFDM systems

This paper presents an analysis of frequency-offset estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Tracking of the frequency offset between a transmitter and a receiver is often aided by transmitting pilots embedded in the data payload of a packet. The extension to multiple-antenna OFDM systems means that spatial beamforming will occur when transmitting pilots from all antennas simultaneously, which can reduce the robustness of frequency-offset estimation. This paper presents a thorough analysis of the spatial beamforming problem, and shows how the spatial correlation of the MIMO channel impacts performance. Simulation results are presented for both synthetic and measured channels, which agree well with the theoretical results

Complexity Study of Lattice Reduction for MIMO Detection

This paper analyses lattice reduction for MIMO decoding and investigates how different parameters affect the complexity. Lattice reduction is a promising technique and a common approach to implementing it is to use the LLL algorithm. In this paper we show how the complexity of the LLL algorithm depends on such parameters as the number of antennas, SNR, channel correlation and channel matrix column sorting. Both real- and complex-valued versions are considered and complexity-performance trade-offs are also studied.

Analog MIMO detection on the basis of Belief Propagation

In this article we present the design of an analog detector for multiple-input multiple-output (MIMO) wireless systems, based on the well known belief propagation (BP) algorithm. BP has been shown to obtain excellent results when solving inference problems in sparsely connected factor graphs. Unfortunately, MIMO detection is an example of inference in a densely connected graph, so other techniques need to be applied. We show that BP in conjunction with simulated annealing can offer near optimal performance when there is enough diversity in the receiver. Interestingly, this algorithm can be easily mapped into analog circuitry, thus leading to potentially low-power area-efficient MIMO detectors. A small analog detector based on this work has been designed and laid out in a 0.25 mum BiCMOS process.

Frequency offset tracking for MIMO OFDM systems using pilots

This paper presents an analysis of frequency offset estimation in MIMO OFDM systems. Tracking of frequency offsets is often aided by pilots transmitted in the data payload of a packet. The extension to multiple antenna OFDM systems means that spatial beamforming will occur when transmitting pilots from all antennas simultaneously, which can reduce the robustness of frequency offset estimation. We present a thorough analysis of the spatial beamforming problem and show how the spatial correlation of the MIMO channel impacts the performance. Simulation results are presented for measured channels recorded in an office environment, which agree well with the theoretical results.