1. Introduction
The 5G-NR-based non-public networks are becoming increasingly popular especially for industrial applications. This is owing to superior performance characteristics, mobility support, scalability and security aspects of 5G NR compared to other existing wireless technologies, e.g., Wi-Fi or 4G/LTE. Due to challenging requirements of industrial applications, these technologies do not adequetly meet the desired communication demands [
1]. There are various 5G NR non-public network deployement options [
2], either using spectrum licensed for public operator networks or locally licensed spectrum available in some markets. For instance, the 3.7–3.8 GHz spectrum is locally licensed for industrial use in Germany by the spectrum management authority (BNetzA) [
3]. The increasing density of non-public network deployments in the local licensed and shared spectrum give rise to a cochannel coexistence problem, which has been discussed in the research community [
4].
The performance characteristics of a non-public network may deteriorate due to spectral interference from coexisting networks. This performance impact depends upon a number of factors such as proximity of the interfering transmitter to the receiver of the non-public network and antenna directionality aspects, power levels of the transmitting node and that of the interfering entity, signal quality level at the receiver due to propagation characteristics, etc. While enhanced mobile broadband application use-cases target high data rates and peak throughputs in the best effort manner, industrial use-cases are typically characterized by mission-critical communication requirements of high reliablity and low-latency, albeit support for the best effort background traffic is also required [
5,
6]. Given that there is available system capacity for packet retransmissions, the overall throughput characteristics might remain unaffected even when packets need to be retransmitted due to interference from a coexisting network. However, the interference caused from the coexisting network may still lead to instantaneous link outages which are rectified by the error control mechanisms in the 5G NR protocol stack. These error correction mechanisms lead to instantenous latency spikes (e.g., due to Hybrid automatic repeat request (HARQ retransmissions), which can be intolerable for mission-critical industrial traffic.
There exists various analytical and simulation studies on the performance analysis of mission-critical services due to interference from coexisting networks [
7,
8]. However, so far there is no other systematic coexistence performance evaluation for a 5G NR non-public network based on real measurements. It is highly important to empircially quantify the impact of interference on throughput as well as latency, in both uplink and downlink directions, with different UE positions and under various traffic load conditions in realistic deployment scenarios. There is naturally a need to mitigate interference effects to guarantee the desired performance characteristics for mission-critical services.
In this article, we present detailed over-the-air performance evaluation results obtained on an industrial shopfloor considering realistic deployment scenarios of a 5G NR non-public indoor network coexisting in the same spectrum as a 5G NR outdoor network. The throughput and latency characteristics of the indoor network in various realistic interference scenarios due to a coexisting outdoor network are compared with their respective baselines without the presence of interference from the coexisting outdoor network. We present the empirical performance results for synchronized, as well as different unsynchronized, TDD patterns used by the 5G NR indoor non-public network. In this article, the term unsynchronized TDD patterns refers to a scenario where the indoor and outdoor networks are using different TDD patterns. However, also in the “unsynchronized” scenario, the two networks have a common time reference and their respective TDD patterns are slot-aligned. In such scenario, all transmission slots will be aligned, even if in opposite directions, so any interference from a given slot is confined to a single slot. Earlier research has shown that coexistence of different TDD patterns for cochannel and adjacent channel networks have a significant role in the interference characteristics [
7,
8]. We also investigate coexistence interference mitigation aspects in our measurement-based study.
The rest of the article is organized as follows: In
Section 2, we give an overview on related works on 5G NR coexistence analysis.
Section 3, describes the cochannel coexistence problem and illustrates the different types of interference situations encountered in a practical cochannel deployment setup. In
Section 4, we describe our systematic measurement methodology and deployment setup. We also give an overview of the networks used in our empirical evaluation in this section.
Section 5 presents the detailed performance results and their analysis. Finally,
Section 6 concludes the article and outlines our current and future work directions.
2. Related Work
The coexistence issue for different technologies is widely studied. The survey in [
9] presents inter-technology techniques of spectrum sharing in wireless technologies. The survey describes the inter-technology coexistence (in shared spectrum bands) for technologies with a hierarchical and flat regulatory framework with equal access rights. Furthermore, it briefly presents the inter-technology coexistence for different access rights in the hierarchical regulatory framework and for the integration of technologies in different spectrum bands. The complexity of testing interference and the coexistence of wireless systems in critical infrastructure has been reported in [
10]. The report highlights the need for studying test methods and performance metrics that allow designers, manufacturers, and customers of new smart communications systems to understand and predict the ability of a device or system to resist interference and coexist within a particular radio frequency environment. According to the type of the spectrum, whether it is dedicated or shared with other technologies, different coexistence problems might emerge for 5G NR deployments. The coexistence in the unlicensed bands is studied in [
11,
12], with results of the coexistence between 5G NR and Wi-Fi in the 6 GHz frequency band available for unlicensed use. Related to the multiple licensed spectrum bands allocated to 5G NR technologies, multiple studies analyzed the coexistence issue with inter- and intra-technologies. Authors in [
13] demonstrated on an experimental setup the efficient spectrum sharing for 5G NR, LTE-A Pro and NB-IoT aiming to take advantage of the 700 MHz propagation aspects. Similarly, a field trial study in [
14] presents the coexistence between LTE and NR based on a frequency division duplexing (FDD) system under dynamic spectrum sharing (DSS). Furthermore, authors in [
15] and [
16] study the coexistence between the 5G system and fixed satellite communications in the mid- (3.4 GHz–3.6 GHz) and high-frequency bands (40 GHz), respectively. The results from the coexistence of fixed satellites and 5G in mid-band, based on simulations, and lab and field tests, demonstrated that 5G systems could be deployed on a large scale for commercial deployments. The coexistence aspects for FSS (fixed-satellite services) and FS (fixed services) are discussed in [
17]. For the mmWave band, it was demonstrated that 5G IoT systems can meet the interference protection criteria of the FSS from at least several hundred base stations and thousands of IoT terminals simultaneously. Additionally, related simulation-based studies considered the coexistence between different 5G networks in the mid-band spectrum as well. For 5G system operating in the mmW range, mitigation of cochannel interference using multiple beam formations, including directivity of beams and the distance between the base station and terminal device, is proposed in [
18] with mathematical analysis and simulations.
In [
7], cochannel and adjacent channel performance evaluation in the mid-band frequency range of 3.5 GHz between an enhanced mobile broadband (eMBB) macro network and an ultra-reliable low latency communication (URLLC) factory network demonstrated that macro downlink interference results in reduction in uplink and downlink capacity and service availability for certain synchronized and unsynchronized TDD patterns. The conclusion of the study highlights that a local factory can coexist with wide-area network when certain isolation is guaranteed to protect the URLLC traffic in the worst-case scenario. In a similar way in [
8], the authors highlight that the coexistence of the public macro network and the non-public network is very difficult, unless the isolation between the networks is sufficiently large. These studies also point out the issue of the cross-link interference between public and non-public UEs. This is highlighted by authors in [
19], where cross-link interference mitigation techniques from different authors from the literature are compiled. The proposals are divided into three sections: inter-cell coordination, advanced receiver and sensing techniques. For instance, clustering, scheduling and resource allocation, power control, beamforming, UL/DL configurations are solutions proposed based on inter-cell coordination. Advanced receiver and sensing solutions proposed are interference suppression, maximum likelihood, interference cancellation and listen-before-talk (LBT) techniques. The paper of [
19] also deals with a survey of specifications based on signaling for Cross Link Interference (CLI) mitigation. Authors in [
20] present enhancements of 5G NR TDD operation and performance to mitigate CLI between neighboring cells. A reinforcement learning algorithm adjusts the TDD configuration for both macro and indoor deployments considering URLLC and eMBB traffic coexistence based on the new features introduced in 3GPP NR Release-16 [
21] to manage CLI, i.e., CLI-RSSI (received signal strength indicator), CLI SRS-RSRP (sounding reference signal-reference signal received power) and RIM (remote interference management). More recently, authors in [
22] highlight the importance of UL traffic for industry use cases. Based on the coexistence issue and the traffic requirements of private networks, potential solutions are presented related to the coexistence issue, e.g., UL MIMO (multiple-input and multiple-output), carrier aggregation, full-duplex operation, or symbol blanking. Other recommendations are to implement different TDD configurations to minimize possible interference and the introduction of balanced or UL oriented TDD patterns.
Most of the existing coexistence studies are based on theoretical and simulation analysis, especially in the context of cellular networks. Despite several deployment scenarios envisioned for 5G NR and the increasing popularity of 5G non-public networks, no systematic experimental investigations on coexistence behavior have been conducted that quantify over-the-air performance impact in real deployments. In [
2], we describe our measurement methodology and initial results on the cochannel coexistence performance between an indoor non-public 5G NR network and an outdoor 5G NR network. In this article, we present a deep analysis of the cochannel coexistence results. Moreover, we present results on mitigation through link robustness of the observed performance degradation caused due to the coexistence issue.
3. The Cochannel Coexistence Problem
The cochannel coexistence problem between two or more networks in 5G deployments is constrained to regional regulations. In this article, the coexistence scenario analysis is based on locally deployed networks for indoor industrial applications operating on the same spectrum as other neighborhood networks. As described in Chapter 5 in [
2], there are three different scenarios that could be addressed in this coexistence study: (i) a local non-public network may have a license to operate in the same spectrum as other public 5G services in the wide-area, (ii) an indoor non-public network might make use of a local 5G license in close vicinity with another non-public network within the same spectrum with its own local license, and (iii) a local license may include multiple factory buildings but also an outdoor network section on the same industrial environment area. In the third scenario, the outdoor network requires coordination among other outdoor networks on the same or adjacent channels, thus the TDD configuration needs to be aligned with the public 5G networks.
As per [
23], the indoor and outdoor networks have a common clock reference. Hence, the TDD pattern start is always synchronized in our measurements. Then, when the indoor and outdoor network use the same TDD pattern, the only present interference is the so-called near–far interference. Such interference situation occurs when both networks are either in DL or UL transmission mode at the same time. From the network perspective, the DL near–far interference arises when the coexisting base station is transmitting to its UE and creates interference to the neighbor UE. Hence, the UL near–far interference is the interference perceived on the base station due to the transmission of the neighbor UE. However, indoor network deployments might not be constrained to the harmonized outdoor TDD pattern and might choose a more balanced or UL-oriented TDD pattern in order to meet the system requirements of the use-cases within the non-public network. For instance, in
Figure 1, the indoor and outdoor network TDD patterns are referred to as DDDU and DDDDU, respectively, where D and U represent downlink and uplink time slots, respectively. As already mentioned, the interference impact of the DL-to-DL and UL-to-UL slots is measured in the near–far interference. Moreover, due to the nature of the unsynchronized TDD pattern (indoors: DDDU, outdoors: DDDDU) the DL-to-UL and UL-to-DL interference is called the cross-link interference. This interference might arise when the interference network is transmitting in the opposite direction of the victim network.
4. Experimental Methodology
In order to systematically carry out the coexistence measurements, we developed a tool for configurable traffic generation and carrying out accurate one-way (DL/UL) latency and throughput measurements. We have first measured the baseline latency and throughput, i.e., without interference, and later measured latency and throughput in the presence of interference. The baseline peak throughput performance depends upon the system bandwidth configured. Higher peak throughput is achieved with a larger bandwidth and vice-versa. The amount of interference level and its direction (UL/DL) determines the performance loss compared to the respective baseline. Various aspects of interfernce impact on the performance of the indoor network has been evaluated.
Our over-the-air coexistence measurements have been performed in the 3.7–3.8 GHz 5G N78 TDD band, which is allocated with a local license for industrial application use in Germany. Our measurements have been carried out at the 5G Industry Campus Europe (5G ICE) in Aachen [
24], which has an outdoor network deployment covering an area of approximately 1 km
2, with four outdoor macro sites colored in yellow in
Figure 2a. As part of 5G ICE, we have selected the indoor network deployment on the shopfloor of the Fraunhofer Institute for Production Technology (IPT), shown as a green rectangle with an area of ca. 2700 m
2. The power levels for the indoor and outdoor networks are in the nominal operating range. During the empirical study, both the indoor and outdoor networks were exclusively used and we ensured no other device was operating in the two networks.
The coexistence measurements were conducted with four different UE locations, two indoor and two outdoor UE locations. The four scenarios are as described in
Table 1 according to
Figure 2b. The first scenario is the worst-case of interference, when the indoor and outdoor UE are close by to each other at a distance of around 50 cm from the window of the shopfloor. In the second scenario, the outdoor UE is in the same location and the indoor UE is moved at distance of 10 m from the window, close to a robot cell. Then, in the third scenario the indoor UE is moved to a location next to the factory shopfloor window, while the outdoor UE is moved away from the shopfloor to a distance of 15 m. Finally, the indoor UE was placed close to the robot cell, while keeping the outdoor UE away at a distance of 15 m from the shopfloor.
As described in
Table 2, both indoor and outdoor networks are based on a mid-band 5G non-stand alone (NSA) system with independent baseband and core networks. The 4G anchor cell of both networks is on the 2300 MHz B40 frequency band while the 5G leg of the NSA deployment uses the full 100 MHz bandwidth available within the n78 band of the 3.7–3.8 GHz spectrum, which fulfills the requirement for cochannel networks.
For our measurements, we used one test UE for the indoor network and one test UE for the outdoor network. Both the UEs are identical and based on Qualcomm x55 modem chipset. We ensured that no other devices were active besides the test devices when performing measurements.
6. Conclusions and Future Work
In this article, we have presented our detailed empirical 5G NR cochannel coexistence results. We have systematically analyzed the impact of a coexisting outdoor network, which could resemble to any type of 5G network, on the latency and throughput performance of the indoor network for an industrial shopfloor in several different scenarios. These include the performance impact on the UL and the DL transmissions of a factory shopfloor network when there is interference from an outdoor network in UL and DL directions. Moreover, we have studied the impact of the TDD pattern used in the indoor network, and the indoor and outdoor UE deployment positions. Our results indicate that cochannel interference from an outdoor network can downgrade the performance of the indoor network, even for the same TDD pattern used indoors and outdoors, when the outdoor UE is close to the indoor shopfloor deployment. However, the interference effects gradually subside when the outdoor interfering UE moves away from the indoor deployment premises, and eventually disappears. While the performance loss is significant due to cross-link interference, when unsynchronized TDD patterns are used indoors for deployments of outdoor UEs close to the indoor shopfloor premises, there are certain TDD patterns which inherently mitigate the cross-link interference and thereby allow a better UL ratio in the TDD pattern UL/DL split. Detailed knowledge of the cochannel coexistence behavior may be an enabler for tailoring TDD configurations to the needs of industry users. Considering that industrial 5G use cases are typically characterized by a high demand for uplink capacity, appropriate TDD patterns should be considered. We have also observed, in our experimental results, that appropriate link adaptation for high link robustness provides resilience against interference from coexisting networks.
As part of our ongoing and future work, we are empirically evaluating the adjacent channel coexistence effects, where key performance indicators (KPIs) are studied for both indoor and outdoor networks due to adjacent channel coexistence. We plan to publish our detailed adjacent channel coexistence results and the key findings in a future article.