Abstract
The radio stripe (RS) system is a practical implementation of cell-free mMIMO, in which a set of multi-antenna access points (APs) serves at the same time-frequency resources the user equipment (UE) in the network. The APs are sequentially connected in a stripe, sharing the same fronthaul link to the central processing unit. This work considers an uplink power optimization problem that aims to enhance the network spectral efficiency (SE) by considering two metrics—the max–min fairness and the max–sum rate. We employ a meta-heuristic based on the differential evolution algorithm to solve the bi-objective optimization problem. The SE performances of the full power along with the single-objective and multiple-objective scenarios are analyzed and compared for the optimal sequential linear processing detection scheme. The bi-objective approach is able to unveil the trade-offs to identify solution balancing the SE distribution resulting from the optimization of the max–min fairness and the max–sum rate objective functions.
Highlights
The conventional massive multiple-input–multiple-output fifth-generation (5G) communication systems are cell-centric, in which a single base station (BS) contains the electronic components of all antennas while serving the user equipment (UE) in a cell
The existing work in the literature has focused in developing sequential uplink (UL) processing algorithms to be employed in the access points (APs) over the fronthaul that are capable of increasing the UE spectral efficiency (SE) [10,11]
This paper focuses on the UL and the frame format is based on the time-divisionduplex protocol with two phases: a channel estimation phase leveraging on a pilot training sequence of τp samples and a payload data sequence of τc − τp samples [12]
Summary
The conventional massive multiple-input–multiple-output (mMIMO) fifth-generation (5G) communication systems are cell-centric, in which a single base station (BS) contains the electronic components of all antennas while serving the user equipment (UE) in a cell This configuration has low-capacity requirements in the fronthaul along with the possibility of covering large areas. The main bottleneck for a practical implementation of a CF mMIMO network is the need for a the large fronthaul capacity and signaling requirements along with a high network implementation cost (with a huge density of long cables) of the connections from APs to the central processing unit (CPU) This way, practical CF mMIMO systems with decentralized processing algorithms have been recently proposed [10–12]. The APs are sequentially located inside the same cable, providing synchronization, data transfer and power supply via a shared link [12], thereby avoiding the need for dedicated fronthaul links between each AP and the corresponding CPU
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
