Reducing the Computational Complexity of Multicasting in Large-Scale Antenna Systems

Abstract

In this paper, we study the physical layer multicasting to multiple co-channel groups in large-scale antenna systems. The users within each group are interested in a common message and different groups have distinct messages. In particular, we aim at designing the precoding vectors solving the so-called quality of service (QoS) and weighted max-min fairness (MMF) problems, assuming that the channel state information is available at the base station (BS). To solve both problems, the baseline approach exploits the semidefinite relaxation (SDR) technique. Considering a BS with N antennas, the SDR complexity is more than O(N 6 ), which prevents its application in large-scale antenna systems. To overcome this issue, we present two new classes of algorithms that, not only have significantly lower computational complexity than existing solutions, but also largely outperform the SDR based methods. Moreover, we present a novel duality between transformed versions of the QoS and the weighted MMF problems. The duality explicitly determines the solution to the weighted MMF problem given the solution to the QoS problem, and vice versa. Numerical results are used to validate the effectiveness of the proposed solutions and to make comparisons with existing alternatives under different operating conditions.Index Terms-Physical layer multicasting, large-scale antenna systems, massive MIMO multicasting, computational complexity.

Multigroup Multicast Precoding in Massive MIMO

“…In [15], a computationally efficient precoder is proposed for multigroup massive MIMO multicasting, that significantly outperforms SDR-based methods. It employs a two-layered precoder, where the outer layer cancels the inter group interference and the inner layer is designed to maximize the minimum SINR of the system while satisfying the power constraint at the BS.…”

“…However, it assumes 1) perfect CSI is available at both BS and UTs, and 2) focuses on instantaneous SINR rather than SE. These simplifying assumptions have also been used in the literature, e.g., [5], [6], [13]- [15], however 1) channels are unknown and need to be estimated, and 2) as SE accounts for the CSI acquisition overhead, it is more practical to consider the SE than the SINR as the performance metric.…”

“…Here, we present a two-layered precoding scheme based on [15] and then derive an achievable SE that addresses both of the aforementioned issues. Our proposed precoding matrix is an N × G matrix W = [w 1 , .…”

“…As massive MIMO systems provide high energy and spectral efficiencies [7]- [12], recent works on multicasting have tried to address the computational complexity of massive MIMO multicasting [13]- [15]. Particularly, [13] presents a successive convex approximation technique for single-group single-cell multicasting of large-scale antenna arrays which reduces the computational complexity to O(N 3.5 ).…”

Joint Unicast and Multi-Group Multicast Transmission in Massive MIMO Systems

“…methods in [7]- [9] require perfect CSI to be available at the BS and UTs, while imperfect CSI is the case of practical interest.…”