General Rank Multiuser Downlink Beamforming With Shaping Constraints Using Real-valued OSTBC

TL;DR

This paper introduces a novel multiuser downlink beamforming method that leverages real-valued OSTBC and semidefinite relaxation to handle numerous quadratic shaping constraints efficiently, guaranteeing optimal solutions under specific rank conditions.

Contribution

It combines high-dimensional real-valued OSTBC with convex optimization to significantly increase the number of feasible shaping constraints in beamforming design, ensuring optimality beyond traditional rank-one methods.

Findings

Can incorporate up to 79 shaping constraints with guaranteed optimality.

Outperforms conventional methods by handling more constraints.

Simulation confirms increased flexibility and effectiveness.

Abstract

In this paper we consider optimal multiuser downlink beamforming in the presence of a massive number of arbitrary quadratic shaping constraints. We combine beamforming with full-rate high dimensional real-valued orthogonal space time block coding (OSTBC) to increase the number of beamforming weight vectors and associated degrees of freedom in the beamformer design. The original multi-constraint beamforming problem is converted into a convex optimization problem using semidefinite relaxation (SDR) which can be solved efficiently. In contrast to conventional (rank-one) beamforming approaches in which an optimal beamforming solution can be obtained only when the SDR solution (after rank reduction) exhibits the rank-one property, in our approach optimality is guaranteed when a rank of eight is not exceeded. We show that our approach can incorporate up to 79 additional shaping constraints for which an optimal beamforming solution is guaranteed as compared to a maximum of two additional constraints that bound the conventional rank-one downlink beamforming designs. Simulation results demonstrate the flexibility of our proposed beamformer design.