# Low Complexity Resource Allocation for Massive Carrier Aggregation

## TL;DR

This paper introduces a scalable iterative resource allocation algorithm for massive carrier aggregation in wireless networks, achieving near-optimal performance with linear complexity and proven convergence properties.

## Contribution

It presents a novel iterative algorithm based on successive geometric programming approximations for joint resource allocation in massive CA, scalable to large problem sizes.

## Key findings

- Algorithm converges to integer solutions with high probability.
- Performance surpasses common heuristic methods.
- Complexity scales linearly with problem size.

## Abstract

Optimal resource allocation (RA) in massive carrier aggregation scenarios is a challenging combinatorial optimization problem whose dimension is proportional to the number of users, component carriers (CCs), and OFDMA resource blocks per CC. Towards scalable, near-optimal RA in massive CA settings, an iterative RA algorithm is proposed for joint assignment of CCs and OFDMA resource blocks to users. The algorithm is based on the principle of successive geometric programming approximations and has a complexity that scales only linearly with the problem dimension. Although its derivation is based on a relaxed formulation of the RA problem, the algorithm is shown to converge to integer-valued RA variables with probability 1 under mild assumptions on the distribution of user utilities. Simulations demonstrate improved performance of the proposed algorithm compared to commonly considered heuristic RA procedures of comparable complexity.