# Spectrum Sensing and Resource Allocation for 5G Heterogeneous Cloud Radio Access Networks

## TL;DR

This paper investigates spectrum sharing in 5G C-RAN systems, proposing an iterative optimization approach to enhance low-priority user throughput while ensuring high-priority user quality of service.

## Contribution

It introduces a novel low-complexity iterative method for joint spectrum sensing and resource allocation in 5G C-RAN, addressing a non-convex NP-hard problem.

## Key findings

- Adjusting sensing time improves spectrum utilization.
- Balancing sensing and throughput is crucial for system performance.
- Iterative optimization effectively manages resource allocation in complex networks.

## Abstract

In this paper, the problem of opportunistic spectrum sharing for the next generation of wireless systems empowered by the cloud radio access network (C-RAN) is studied. More precisely, low-priority users employ cooperative spectrum sensing to detect a vacant portion of the spectrum that is not currently used by high-priority users. The design of the scheme is to maximize the overall throughput of the low-priority users while guaranteeing the quality of service of the high-priority users. This objective is attained by optimally adjusting spectrum sensing time with respect to imposed target probabilities of detection and false alarm as well as dynamically allocating and assigning C-RAN resources, i.e., transmit powers, sub-carriers, remote radio heads (RRHs), and base-band units. The presented optimization problem is non-convex and NP-hard that is extremely hard to tackle directly. To solve the problem, a low-complex iterative approach is proposed in which sensing time, user association parameters and transmit powers of RRHs are alternatively assigned and optimized at every step. Numerical results are then provided to demonstrate the necessity of performing sensing time adjustment in such systems as well as balancing the sensing-throughput tradeoff.