Beamforming training for IEEE 802.11ad

1. Beamforming training for IEEE 802.11ad Authors: Date: 2010-05-17 Changsoon Choi, IHP microelectronics

2. Abstract • The performance of 60-GHz wireless LAN can be significantly enhanced if the receiver beamforming is capable of interference mitigation. • In order to do this, beamforming training mechanism should allow for estimation of the CSI (channel state information) matrix. • This proposal addresses the number of beamforming training sequence repetition necessary to achieve this, and demonstrates the performance improvement that can be obtained. Changsoon Choi, IHP microelectronics

3. Beamforming for interference mitigation • Important to manage mutual interference among different 60-GHz devices /networks. • Even within TGad networks, interference is a main concern for efficient spatial reuse. • Beamforming (BF) needs interference mitigation capability. • IEEE 802.15.3c BF is NOT capable of it due to the nature of codebook approach • In order to achieve interference mitigation, there should be a mechanism in 802.11ad for the channel matrix to be estimated e.g. IEEE 802.15.3c AP Interference STA Changsoon Choi, IHP microelectronics

4. Digital baseband Digital baseband Digital baseband Digital baseband Digital baseband Weighting vector calculation Beamforming for < 6-GHz and 60-GHz 60-GHz < 6-GHz • 60-GHz BF transceivers would be based on analog beamforming • Baseband does not know the received signals on each antenna individually because they are combined in analog domain prior to digital baseband elements of MIMO channel matrix cannot be estimated directly Analog phase-shifter Changsoon Choi, IHP microelectronics

5. BF training proposal • For BF training of an N-element receiver STA, a transmit STA will send N-repetitions of BF training sequences for one Tx beam. • Receiver STA can estimate channel state information (CSI) in various ways (e.g. LS, MMSE). For N-element beamforming receiver Changsoon Choi, IHP microelectronics

6. BF model for performance evaluation • Consider SIMO channel. • This reflects the usage case where one mobile terminal (e.g. smart phone) transmits data to an access point with beamforming capability. Beamforming capable Non- beamforming capable Changsoon Choi, IHP microelectronics

7. Example: BF training with codebook approach • Transmit STA sends N-repetitions of a BF training sequence while the receiver cycles through different beamforming vectors from codebook matrix • Codebook matrix (n-element, k-beam) defined as: • Received baseband signals for k-th beamforming vector • Collect all baseband signals (or channel estimates) for n-repetition BF training sequences • Estimation of CSI on each antenna matrix matrix, n = k for matrix inversion Changsoon Choi, IHP microelectronics

8. Channel and antenna models 60-GHz NLOS residential model (CM2.3) with AoA information (used in IEEE 802.15.3c) 100 channel realizations and averaged results. Each channel normalized to unit power 90-degree Gaussian beam pattern HPBW (half-power beamwidth) for receiver antenna. No backside emission assumed. Constant total gain from beamformers assumed BF codebook matrix (C) from IEEE 802.15.3c std System simulation model for BF evaluation Time response Angular response Antenna (90-degree HPBW) Beam pattern for codebook (C) Changsoon Choi, IHP microelectronics

9. Maximum signal-to-interference plus noise (SINR) beamformer is used for this work. IEEE 802.15.3c beamformer is included for comparison Improved beamforming gain is obtained with full MIMO CSI BF performance with full CSI(no interference) Beamforming gain vs. number of RX antennas CSI covariance matrix Interference covariance Changsoon Choi, IHP microelectronics

10. BF performance with full CSI(with co-channel interference) Array factors for full CSI beamforming and codebook Output SINR vs. Input SNR • Co-channel interference • Assume that angle of arrival (AoA) of co-channel interference was ideally estimated in receiver • Random signals (AWGN-like) with random AoA were generated for co-channel interference. • Beamforming provides efficient interference nulling with full MIMO CSI. • Higher SINR can be expected with the help of interference mitigation. • No interference mitigation capability in IEEE 802.15.3c codebook BF. Changsoon Choi, IHP microelectronics

11. Optimization of Tx and Rx beamforming vectors (1) – SIMO and MISO channels • Method for estimating SIMO channel can be used for MISO channel. • Tx has M elements, Rx has N elements • Find best beams (BF vectors) for Tx and Rx by switching different beams • For fixed Tx BF vectors • Tx transmits N repetitions of training sequence • For each repetition, receive STA uses a different beamforming vector from codebook • Optimize Rx BF vectors using the estimated SIMO channel matrix • Optimization algorithm (e.g. Max SINR, MMSE) is implementation-dependent • For optimized Rx BF vectors (through above-mentioned process) • Tx transmits M repetitions of training sequence • For each repetition, transmit STA uses a different beamforming vector from codebook • Estimated CSIs for different Tx BF vectors are fed back to Tx • Optimized Tx BF vectors using the estimated SIMO channel matrix • repetition of training sequences • This procedure can be repeated in multiple times for maximizing SINR

12. Optimization of Tx and Rx beamforming vectors (2) – Full MIMO channels • Method for estimating SIMO channel can be extended to MIMO • Transmit STA sends: • repetitions of training sequence • for each repetition, receive STA uses a different beamforming vector from codebook matrix • where the above repetitions are repeated times • for each repetition, transmit STA uses a different beamforming vector from codebook matrix • Codebook matrices should be orthogonal • Complex received signal on subcarrier i for each repetition placed in corresponding element of matrix • Full MIMO channel state information • repetition of BF training is required. • Maximum performance but higher complexity Changsoon Choi, IHP microelectronics

13. Conclusion • This proposal addresses required number of beamforming training sequences for channel matrix estimation (SIMO, MISO). • It gives us possibility to adaptively mitigate co-channel interference, which is also advantageous for spatial reuse. Changsoon Choi, IHP microelectronics

14. Acknowledgement • This work has been supported by the European Community’s Seventh Framework Programs referred to as MIMAX and OMEGA Changsoon Choi, IHP microelectronics