# Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks

## TL;DR

This paper investigates joint beamforming and layered division multiplexing (LDM) for unicast and broadcast in cellular networks, optimizing power and rate constraints to enhance multimedia streaming efficiency.

## Contribution

It introduces a novel joint beamforming and power allocation framework for LDM-based unicast and broadcast transmission, including robust solutions under practical imperfections.

## Key findings

- LDM improves unicast throughput and broadcast coverage.
- Proposed optimization achieves near-optimal power efficiency.
- Distributed implementation facilitates practical deployment.

## Abstract

Limited bandwidth resources and higher energy efficiency requirements motivate incorporating multicast and broadcast transmission into the next-generation cellular network architectures, particularly for multimedia streaming applications. Layered division multiplexing (LDM), a form of NOMA, can potentially improve unicast throughput and broadcast coverage with respect to traditional orthogonal frequency division multiplexing (FDM) or time division multiplexing (TDM), by simultaneously using the same frequency and time resources for multiple unicast or broadcast transmissions. In this paper, the performance of LDM-based unicast and broadcast transmission in a cellular network is studied by assuming a single frequency network (SFN) operation for the broadcast layer, while allowing arbitrarily clustered cooperation among the base stations (BSs) for the transmission of unicast data streams. Beamforming and power allocation between unicast and broadcast layers, the so-called injection level in the LDM literature, are optimized with the aim of minimizing the sum-power under constraints on the user-specific unicast rates and on the common broadcast rate. The effects of imperfect channel coding and imperfect CSI are also studied to gain insights into robust implementation in practical systems. The non-convex optimization problem is tackled by means of successive convex approximation (SCA) techniques. Performance upper bounds are also presented by means of the $\rm{S}$-procedure followed by semidefinite relaxation (SDR). Finally, a dual decomposition-based solution is proposed to facilitate an efficient distributed implementation of LDM where the optimal unicast beamforming vectors can be obtained locally by the cooperating BSs. Numerical results are presented, which show the tightness of the proposed bounds and hence the near-optimality of the proposed solutions.