Eric Ojard

Subspace Beamforming for Near-Capacity MIMO Performance

A subspace beamforming method is presented that decomposes a multiple-input multiple-output (MIMO) channel into multiple pairs of subchannels. The pairing is done based on singular values such that similar channel capacity is obtained between different subchannel pairs. This new capacity balancing concept is key to achieving high performance with low complexity. We apply the subspace idea to geometric mean decomposition (GMD) and maximum-likelihood (ML) detection. The proposed subspace GMD scheme requires only two layers of detection/decoding, regardless of the total number of subchannels, thus alleviating the latency issue associated with conventional GMD. We also show how the subspace concept makes the optimization of ML beamforming and ML detection itself feasible for any K timesK MIMO system. Simulation results show that subspace beamforming performs nearly as well as optimum GMD performance, and to within only a few decibels of the Shannon bound.

Improved MU-MIMO performance for future 802.11 systems using differential feedback

Multiuser MIMO (MU-MIMO) is a new technology adopted in IEEE 802.11ac for enhancing downlink throughput. 802.11ac has an explicit feedback mechanism that enables very high quality channel feedback to be provided to the transmitter, but the overhead can be quite substantial. We propose a differential feedback method that significantly reduces this overhead by using a simple lossless, variable-length compression technique that exploits the slowly time varying wireless channel.

Subspace beamforming for near-capacity MIMO performance

A subspace beamforming method is presented that decomposes a MIMO channel into multiple pairs of subchannels. The pairing is done based on singular values such that similar channel capacity is obtained between different subchannel pairs. This new capacity balancing concept is key to achieving high performance with low complexity. We apply the subspace idea to geometric mean decomposition (GMD) and maximum likelihood (ML) detection. The proposed subspace GMD scheme requires only two layers of detection/decoding, regardless of the total number of subchannels, thus alleviating the latency issue associated with conventional GMD. We also show how the subspace concept makes the optimization of ML beamforming and ML detection itself feasible for any KtimesK MIMO system. Simulation results show that subspace beamforming performs nearly as well as optimum GMD performance, and to within only a few dB of the Shannon bound.

Subspace beamforming with capacity-balancing channel decomposition

A subspace beamforming method is presented that decomposes a MIMO channel into multiple pairs of subchannels. The pairing is done based on singular values such that similar channel capacity is obtained between different subchannel pairs. This new capacity balancing concept is key to achieving high performance with low complexity. We apply the subspace idea to geometric mean decomposition (GMD) and maximum likelihood (ML) detection. The proposed subspace GMD scheme requires only two layers of detection/decoding, regardless of the total number of subchannels, thus alleviating the latency issue associated with conventional GMD. We also show how the subspace concept makes the optimization of ML beamforming and ML detection itself feasible for any K times K MIMO system. Simulation results show that subspace beamforming performs nearly as well as optimum GMD performance, and to within only a few dB of the Shannon bound.

A 2×2 MIMO baseband for high-throughput wireless local-area networking (802.11n)

This article consists of a collection of slides from the authors conference presentation on a 2X2 MIMO basedband system for high throughput wireless local area networking. Some of the specific topics discussed include: the demand for wireless LAN, 2000-2011; schemas of multipath channels; exploiting multipath processing for high rates and the impact on energy capacity planning; system architecture for MIMO-ODFM; system processing capabilities and performance summaries; the need for flexible transceivers; and block diagrams and system topologies.