Traffic-Estimation-Based Low-Latency XGS-PON Mobile Front-Haul for Small-Cell C-RAN Based on an Adaptive Learning Neural Network  
[PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, No. 800, Road Dongchuan, Shanghai 200240, China; [EMAIL_ADDRESS] (S.B.H. );[EMAIL_ADDRESS] (A.S.)  
2  
[PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, No. 800, Road Dongchuan, Shanghai 200240, China; [EMAIL_ADDRESS] (S.B.H. );[EMAIL_ADDRESS] (A.S.)  
2  
[PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, No. 800, Road Dongchuan, Shanghai 200240, China; [EMAIL_ADDRESS] (S.B.H. );[EMAIL_ADDRESS] (A.S.)  
2  
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reported in the literature that are compatible with both XG-PON and 10G-EPONs standards such as the Mobile-DBA proposal [4; 5], in which the mobile uplink scheduling information is utilized to schedule the grants for the ONUs to minimize the upstream latency of TDM-PON-based mobile front-haul. Another example is the Mobile-PON proposal [6], which focuses on eliminating the conventional PON scheduling by unifying the TDM-PON scheduler and long-term evolution (LTE) mobile scheduler (i.e., physical layer functional split-based resource block scheduler). However, these proposals require some modifications to current TDM-PONs and BBU processing and protocol; for example, Mobile-DBA requires an additional interface between the baseband unit (BBU) and OLT to exchange the mobile scheduling information, whereas, Mobile-PON requires a modification on current TDM-PON system regarding ONUs synchronization. The only works that are fully compatible with XG-PON standard are: (1) Group-Assured Giga-PON Access Network DBA (gGIANT DBA) introduced in [7], which has been proposed to optimize XG-PON average upstream delay to support mobile backhaul; however, the delay requirement for mobile front-haul is much lower than in backhaul case; (2) Our pervious proposal, Optimized Round Robin DBA (Optimized-RR DBA), was introduced in [8] to optimize XG-PON to transport MAC-PHY split-based front-haul traffic in small-cell C-RAN architecture. In [8], a front-haul latency as low as 300 \(\upmu\)s was achieved with Optimized-RR DBA; however, taking into account the new emerging 5G QS requirements, front-haul transmission latency should not exceed 250 \(\upmu\)s, as recommended by the Next-Generation Mobile Networks Alliance (NGMA) in [9]. Motivated by the above facts, to achieve front-haul transmission latency similar to that recommended by NGMA without modifying current the TDM-PONs and BBU systems' processing and protocols, this paper proposes a traffic-estimation-based low-latency XGS-PON mobile front-haul to support small-cell-based cloud radio access network architecture. In this paper, we consider the small-cell-based cloud radio access network architecture depicted in Figure 1, where the Physical layer split (PHY split) architecture reported in [6] (i.e., the split between the wireless resource de-mapping and the modulation decoding) is considered the split point between BBU and RRU functional entities of small-cell baseband (note: three functional entities are currently defined for 5G baseband: Centralized Unit (CU), Distributed Unit (DU), and RRU. The CU provides the non-real-time functionalities while the DU provides physical layer and the real-time functions [10]). In the C-RAN architecture (Figure 1), the instantaneous actual LTE system traffic load, in the form of antenna symbols, is mapped into bit-streams. Then these bit-streams are encapsulated into an Ethernet packet and transmitted between RRU and BBU via XGS-PON mobile front-haul.