Cloud-RAN platform for LSA in 5G networks — Tradeoff within the infrastructure

Abstract

The focus of this paper is to provide a unique perspective on new network architectures supporting novel spectrum sharing mechanisms. A Cloud-based MD-MIMO RAN (Massive Distributed Multiple-Input Multiple-Output Radio Access Network) is proposed for exploitation of Licensed Shared Access (LSA) spectrum sharing concept. In order to deliver a particular service, the virtual network operator leases spectrum and antennas from the LSA spectrum pool and the Cloud-RAN platform following the pay-per-use model. The spectrum is turned into a consumable resource along with the infrastructure resources and there is a tradeoff in the system between using more antennas or more bandwidth. In this paper we propose a physical platform that can consume these resources dynamically.

Resource sharing in heterogeneous cloud radio access networks

“…From the first scenario, let us assume a density of 10,000 UE/km 2 with 50 small cells deployed, and average spectrum efficiency of 2.434 b/s/Hz, which notionally represents the infrastructure of an operator that leases infrastructure (receiver) from another (donor). According to LSA methodology [2], shared cells must prioritize the communications of UEs that belong to the donor (i.e., primary UEs) rather than UEs from the receiver. In addition, we must consider that the donor leased both spectrum and infrastructure resources, allowing its BBUs to exchange and process the receiver workload freely through CC-CRRM and CC-CSON [1].…”

“…This improvement suggests a trade-off between the number of cooperating cells and spectrum [2]. The impact of the increasing number of pico cells and antennas is clearer when considering a virtual network operator obtaining antennas and spectrum from a pool, and configuring the network on the fly.…”

“…2, the trade-off is depicted through the optimal number of antennas and spectrum (megahertz) required to satisfy a minimum rate constraint of 60 MB/s and signal-to-interference-plus-noise ratio (SINR) of 10 dB as a function of the ratio of the antenna to spectrum costs. The study in Gomez-Miguelez et al [2] outlines scenarios for Antennas/user MHz/user which more infrastructure and less bandwidth use is better and vice versa. The inherent flexibility enabled by H-CRANs supports the dynamic trade-off between infrastructure and spectrum.…”

“…C-RANs introduce digital functional units, called baseband processing units (BBUs), to handle computing workloads, such as signal processing and resource management. Moreover, C-RAN also inserts radio functional units, called remote radio heads (RRHs), to handle RF translation [2]. Within the BBU, a centralized processing entity called a central processor computes workloads while accounting for quality of service (QoS) requirements (e.g., low round-trip time and high bandwidth) as well as resource constraints (e.g., power, time, and frequency bands).…”

Simultaneous Spectrum Sensing and Data Reception for Cognitive Spatial Multiplexing Distributed Systems

“…To this end, training-based signaling, which is, in principle, utilized for primary cellular configurations can be used from the cognitive/secondary system to perform spectrum sensing and/or acquire important statistics, such as channel gains and transmission powers of primary nodes/users. Besides, long-term evolution (LTE) has initiated a CR-based operation quite recently [25], under the concept of licensedassisted access/licensed-shared access (LAA/LSA), which operates with the aid of training (pilot) signaling [26]- [28].…”