Physical-Layer Multicasting by Stochastic Transmit Beamforming and Alamouti Space-Time Coding
Xiaoxiao Wu, Wing-Kin Ma, and Anthony Man-Cho So

TL;DR
This paper introduces two novel physical-layer multicasting strategies for MISO downlink channels: stochastic beamforming and a beamformed Alamouti scheme, both improving multicast rates and SNR performance over traditional SDR-based methods.
Contribution
The paper proposes two new transceiver strategies—stochastic beamforming and a beamformed Alamouti scheme—that bypass or improve upon SDR-based beamforming for multicasting.
Findings
Stochastic beamforming achieves a rate gap no worse than 0.8314 bits/s/Hz.
Beamformed Alamouti scheme offers better SNR scaling than SDR-based beamforming.
Combining SBF and Alamouti yields a rate gap of 0.39 bits/s/Hz.
Abstract
Consider transceiver designs in a multiuser multi-input single-output (MISO) downlink channel, where the users are to receive the same data stream simultaneously. This problem, known as physical-layer multicasting, has drawn much interest. Presently, a popularized approach is transmit beamforming, in which the beamforming optimization is handled by a rank-one approximation method called semidefinite relaxation (SDR). SDR-based beamforming has been shown to be promising for a small or moderate number of users. This paper describes two new transceiver strategies for physical-layer multicasting. The first strategy, called stochastic beamforming (SBF), randomizes the beamformer in a per-symbol time-varying manner, so that the rank-one approximation in SDR can be bypassed. We propose several efficiently realizable SBF schemes, and prove that their multicast achievable rate gaps with respect…
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