1、5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC the number of MIMO layers; the maximum supported modulation scheme; the number of transmit and receive antennas; and the use cases that need to be supported by the network. The distribution of radio access network (RAN) functions betwee
2、n the radio antenna site and central locations also plays a pivotal role in the transport requirements. These functions include radio frequency (RF) signal processing and other layers of the protocol stack, including: the physical (PHY); medium access control (MAC); radio link control (RLC); packet
3、data convergence protocol (PDCP); and radio resource control (RRC) layers. Figure 2 shows the relationship between the RAN functions and the 5G core network (5GC) and end user equipment (UE). In 2, the Third Generation Partnership Project (3GPP) defined a next generation RAN (NG-RAN) architecture wh
4、ere 5G NR base station (gNB) functionality is split between two logical units: a central unit (CU) and a distributed unit (DUs). In the 3GPP model, the CU is connected to the 5G core (5GC) via the NG interface and the CU is connected to the DU via the F1 interface, as shown below in Figure 3. Figure
5、 2 - Radio access network functions Figure 3 - 3GPP NG-RAN Architecture The 3GPP studied several different functional splits between the CU and DU in 2. In total, 8 possible split options were considered, including 5 high level split (HLS) options and 3 low level split (LLS) options. The different s
6、plit options are shown in Figure 4 below. Figure 4 - Functional split between central and distributed units 5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC (b) IAB node using EN-DC (NSA mode) Figure 13 - IAB CU/DU architecture 2.2.1 IAB-donor As shown in Figure 11, an IAB-donor is a
7、gNB that provides network access to UEs via a network of backhaul and access links and consists of an IAB-donor-CU and one or more IAB-donor-DUs. The IAB-donor-CU and IAB-donor-DU communicate with each other via the F1 interface. The IAB-donor connects to the IAB-node using the 5G New Radio (NR) acc
8、ess interface and is connected to the Core Network. All functions specified for a gNB-DU are equally applicable for an IAB-donor-DU and all functions specified for a gNB-CU are equally applicable for an IAB-donor-CU. A Backhaul Adaptation Protocol (BAP) layer has been added above the Radio Link Cont
9、rol (RLC) layer in order to include routing information and allow for hop-by-hop forwarding. Details of BAP layer is mentioned in section 2.2.3. 5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC how many children and descendant nodes each node has to serve; and the strategies adopted f
10、or procedures such as network formation, route selection, and resource allocation. 2.5.1.1 IAB Node Integration The IAB node integration procedure is performed in three phases. The overall procedure for IAB node integration is shown in Figure 27 below (from 3GPP TS 38.401). In the first phase, the I
11、AB node mobile terminal (MT) connects to the network as a normal UE. In doing so, the MT of an IAB node makes use of the synchronization signals transmitted by the already integrated nodes to estimate the channel and select its potential parents. A potential parent is identified based on an over-the
12、-air indication from either the IAB nodes or IAB-donor-DUs, which is, for example, transmitted in the system information block (SIB). It identifies a parent node (another IAB node or an IAB donor) by performing Reference Signal Received Power (RSRP) / Reference Signal Received Quality (RSRQ) Radio R
13、esource Management (RRM) measurements. The MT then performs random access and transmits a Radio Resource Control (RRC) connection setup request to the central unit (CU) via the parent node. Following that, the backhaul Radio Link Control (RLC) channel for carrying Control Plane (CP) traffic to and f
14、rom the IAB node is established. In phase two, a routing update is performed, which includes configuration of BAP routing identifiers and updating of routing tables of the IAB donor DU and all IAB nodes on the path to the IAB node. This contains: the configuration of the BAP address on the newly int
15、egrated IAB node; the routing identifiers for the downstream direction on the IAB-donor-DU; and the BAR routing identifiers in the upstream direction on the newly integrated IAB nodes MT functionality. In phase three, which is the IAB DU setup phase, the DU functionality of the newly integrated IAB
16、node is configured. This consists of the transport network layer establishment and the F1-C connection setup between the IAB node and the IAB donor CU. Once this is completed, the IAB node can provide service to UEs. 5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC ii) the detachment
17、of an IAB node from the topology; iii) the detection of backhaul link overload; iv) deterioration of the backhaul link quality or link failure; and v) other events such as blockage or congestion. During topology adaptation, the network needs to determine an updated topology and then activate or deac
18、tivate links to achieve it. To this end, the following tasks are performed: Information collection over a sufficiently large area of the IAB topology, e.g., information on backhaul link quality, load, and signal strengths Topology determination: deciding on the best topology based on the information
19、 collected Topology reconfiguration: adjusting topology based on the result of topology determination through establishing new connections, releasing other connections, changing routes, etc. 2.5.1.3 IAB routing mechanisms When the IAB network assumes a directed acyclic graph (DAG) topology, multiple
20、 routes can exist between two nodes in the network. This multi-connectivity or route redundancy can be used for back-up purposes, in case of backhaul link failure or node congestion. It is also possible that the redundant routes between the 5G Americas | Innovations in 5G Backhaul Technologies: IAB,
21、 HFC however, such repetition-based schemes are not spectrally efficient. In this context, linear network coding 11 can be considered as a solution to improve the end-to-end latency and reliability performance for IAB networks, taking advantage of the more complex network topologies. Network coding
22、consists of adding redundancy at the upper layers and could be a superior alternative to the packet repetition schemes in such multi-hop, multi-route networks. Each data packet at the source is partitioned into equal-sized segments, to which network coding (linear combination of segments) is applied
23、. At the destination, as long as the number of received linearly independent network encoded segments is larger than or equal to the number of segments in the original partition, the original packet can be correctly recovered, regardless of which segments are actually received. 5G Americas | Innovat
24、ions in 5G Backhaul Technologies: IAB, HFC second, we can make use of multi- route diversity, by sending network-coded segments of a packet via multiple routes to a given destination, thereby treating the multiple routes jointly as a single data pipe. Events such as blockage/congestion of a single r
25、oute results in a “narrowing of the pipe” but not a complete stoppage of data transfer. If the destination receives enough encoded segments, the packet can be recovered via network decoding. It is also more spectrally efficient than PDCP duplication since the packet recovery criterion for network co
26、ding is much easier to satisfy than packet duplication. That is, network coding needs to transmit much less redundancy than PDCP duplication to achieve the same reliability target. 2.6.2.1 Machine learning and artificial intelligence Recently, machine learning (ML) and artificial intelligence (AI) h
27、ave attracted significant interest due to the increase in the size of data collected and cheap computational resources. In general, ML and AI offer an efficient alternative when conventional domain knowledge-based engineering cost and development time are high, and the problem is too complex to mode
28、l and analyze. In addition, ML has the advantage of exploiting new data (change in environment) to enable easy to install, self- configuring and high performing IAB networks. In light of the above promises, ML can be useful for the design and intersection of different protocol stacks to satisfy qual
29、ity of service requirements in single and multi-hop scenarios when mixed traffic flows coexist. In addition, radio resource management (e.g., in-band and out-of-band spectra, beam management) in IAB networks between the access and backhaul links can be handled by ML to avoid congestion and interfere
30、nce and improve load balancing, end-to-end latency and throughput. In addition, ML/AI assisted topology adaptation (e.g., path selection, dual connectivity, multi-hop networking) methods can be designed to exploit and forecast changes in the environment (e.g., link failure, blockage) and user behavi
31、or (e.g., mobility, social events etc.). 2.6.2.2 Fully digital beamforming for mmWave One of the main challenges of the mmWave IAB networks is the design of low power mmWave devices with multiple antennas. In the literature, hybrid phased array architectures with a small number of RF-chains is consi
32、dered as a solution to enable multi-user/multi-directional beamforming, but its performance (e.g., beamforming and beam tracking capabilities) is limited by the number of RF-chains. In contrast, fully digital mmWave architectures bring all the advantages of the digital beamforming in terms of fast b
33、eam management and beamforming optimality. Although a fully digital architecture can provide optimal performance, it has the highest ADC power consumption for a given bit resolution and sampling rate. Furthermore, power dissipation at the input/output (I/O) interface between RFIC and BBIC increases
34、linearly with the number of ADCs and their bit resolution. Finally, a fully digital architecture also has the highest baseband processor power consumption as the complexity of the channel estimation and multiple-input multiple-output (MIMO) processing increases linearly with the number of the RF- ch
35、ains. Therefore, an efficient digital beamforming architecture with low-bit processing and spatial compression 12 can be considered in the future to have faster beam tracking (e.g., automatic pointing) and management. This architecture will enable blockage resilience, high throughput, and adaptive I
36、AB network deployment. 5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC or a converged cable operator who owns both cable and mobile operations and carries its own mobile traffic; LLX provides a means to significantly lower the latency of all traffic coming from the UE to levels compa
37、rable to fiber. Different traffic flows include signaling, IMS voice (data and signaling), low latency applications (mobile gaming), video conferencing applications (such as Zoom, Cisco WebEx and Apple FaceTime), and URLLC for 5G. LLX has the following fundamental components: scheduler pipelining us
38、ing the bandwidth report (BWR) message, a common QoS framework that matches the DOCSIS QoS to the mobile network QoS, and a grant sharing mechanism that allows the CM to perform real-time scheduling. In addition, there needs to be system level configuration and operation to align the DOCSIS and mobi
39、le systems. 3.1.4 LLX Scheduler Pipelining Scheduler pipelining is a very unique and inventive aspect of LLX and the heart of what creates a low latency transport. In a nutshell, LLX uses the decisions made by the mobile scheduler to inform the CMTS what is about to happen next. Normally, mobile (LT
40、E and 5G) and DOCSIS operate as two independent systems. As such, in a backhaul or xhaul situation, the end-to-end latency experienced by the mobile traffic is the sum of the two system latencies. With scheduler pipelining, however, the end-to-end latency is reduced significantly by initiating sched
41、uling requests in parallel. This is shown in Figure 36. 5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC xHaul architectures 5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC and Radio-Over-Ethernet (RoE). While the details differ, the underlying principles of both encap
42、sulation methods are similar, and both require low delay and packet delay variation (PDV). As noted in Section 1.1, the eCPRI specification was developed by the CPRI cooperation group to address some of the limitations of the previous CPRI specification. Unlike CPRI, the bandwidth requirements for e
43、CPRI scale proportionally with user traffic and, as such, eCPRI is roughly 10 x more efficient than CPRI. eCPRI is a Layer-3 (and above) protocol that relies on Ethernet MAC and PHY functions. The protocol stack is shown in Figure 49. The synchronization and control and management (C structure aware
44、; and native mode. Native mode contains two sub-mapping methods for different functional splits. Structure aware and agnostic modes were defined to ease the evolution towards packet-based fronthaul by allowing RoE to be used in existing CPRI-based systems without any modifications to the BU or RU. T
45、he native modes, on the other hand, require changes to the hardware but will result in a more efficient fronthaul. A key requirement for both encapsulation methods is strict latency and packet delay variation (PDV) control. For example, the one-way delay requirement for eCPRI is 100 s, including the
46、 fiber propagation delay. Figure 49 - eCPRI protocol stack over IP / Ethernet 6 The next section describes recent advances in TSN that address the requirement for deterministic latency and PDV in Ethernet networks. 3.3.3 Time Sensitive Networking The IEEE TSN Task Group has published a new standard
47、(IEEE 802.1CM) that addresses TSN for fronthaul networks 26. The purpose of this standard is to enable the transport of time-sensitive fronthaul streams in Ethernet bridged networks. The standard defines profiles that select features, options, configurations, defaults, protocols and procedures of br
48、idges, stations and LANs that are necessary to build networks that are capable of transporting fronthaul streams, which are time sensitive. Ethernet networks complying with IEEE 802.1CM will provide deterministic transport of eCPRI and RoE streams by controlling traffic scheduling, timing synchroniz
49、ation, and system reliability. Since Ethernet networks are a shared medium, it is important to prioritize fronthaul packets over other lower-priority packets. The TSN Task Group has addressed this need with a standard (IEEE 802.1Qbu) that enables express 5G Americas | Innovations in 5G Backhaul Technologies: IAB, HFC & Fiber 51 packets to preempt lower priority packets. For example, fronthaul traffic will be able to preempt other “best effort” traffic on the same Ethernet port even after transmission has started. Separate flows are also assigned to fronthaul and sy
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