Abstract
Physical layer multicasting with opportunistic user selection and optimized space-time transmission is examined in this work. We consider a multi-antenna downlink scenario where a single base-station, equipped with multiple antennas, transmits common information to a given set of users. We first study the case where each user has a single receive antenna, that is, the multi-user MISO scenario. Although the multicast capacity is known, the complexity of achieving it is very high. In previous works, pratical multi-antenna multicasting solutions have been restricted to spatial multiplexing and transmit beamforming. Here, we incorporate the recently proposed opportunistic multicast scheduling (OMS) scheme into these techniques to improve the performance. The messages in OMS are source coded using fountain codes and only an optimal subset of users is scheduled at each time slot. Capitalizing on extreme value theory, we derive analytical expressions for the average throughput of OMS aided systems. These expressions are also utilized to obtain the optimal user selection ratio for both OMS aided spatial multiplexing and transmit beamforming scenarios. Both OMS aided spatial multiplexing and transmit beamforming impose stringent constraints on the channel input covariance matrix. And the benefits of having channel state information (CSI) at the transmitter is not fully exploited. To solve this problem, we further propose an optimized space-time transmission (OST) scheme where the constraints on the channel input covariance matrix are removed. The proposed OST subsumes both transmit beamforming and spatial multiplexing and, thus, outperforms both conventional schemes. Since the OST problem is in general full rank optimal, we can reduce coding complexity by adding an rank constraint on the channel input covariance matrix. However, the optimization problem becomes non-convex. We then proposed an heuristic dimension selection algorithm which avoids solving the non-convex optimization problem. Simulation results verifies the superiority of this algorithm compared to the OMS aided spatial multiplexing and transmit beamforming scenarios. To relax the system overhead to feedback the full CSI needed by the OMS aided OST scheme, we also propose a method to reduce the channel feedback requirement. Finally, these concepts are further extended to the MIMO scenario. We show that the MIMO OST problem can be solved via convex optimization with full CSI at the transmitter. The channel feedback reduction algorithm is also extended to the MIMO scenario.