# Interference-aware User Grouping Strategy in NOMA Systems with QoS Constraints

## TL;DR

This paper proposes an interference-aware user grouping strategy for NOMA systems that minimizes power consumption while satisfying QoS constraints, using a graph-based approach and algorithms like Bellman-Ford.

## Contribution

It introduces a novel PCE function for user grouping in NOMA, extending it to multi-user scenarios and developing algorithms to efficiently minimize power consumption.

## Key findings

- Proposed algorithms significantly reduce total power consumption.
- The graph-based approach effectively accounts for interference and QoS constraints.
- Simulation results outperform existing user grouping strategies.

## Abstract

To meet the performance and complexity requirements from practical deployment of non-orthogonal multiple access (NOMA) systems, several users are grouped together for NOMA transmission while orthogonal resources are allocated among groups. User grouping strategies have significant impact on the power consumption and system performance. However, existing related studies divide users into groups based on channel conditions, where diverse quality of service (QoS) and interference have not been considered. In this paper, we focus on the interference-aware user grouping strategy in NOMA systems, aiming at minimizing power consumption with QoS constraints. We define a power consumption and externality (PCE) function for each user to represent the power consumption involved by this user to satisfy its QoS requirement as well as interference that this user brings to others in the same group. Then, we extend the definition of PCE to multi-user scenarios and convert the user grouping problem into the problem of searching for specific negative loops in the graph. Bellman-Ford algorithm is extended to find these negative loops. Furthermore, a greedy suboptimal algorithm is proposed to approach the solution within polynomial time. Simulation results show that the proposed algorithms can considerably reduce the total power consumption compared with existing strategies.