This paper describes a novel wavelength aggregation scheme that we propose. The scheme aims to incrementally upgrade time and wavelength division multiplexing passive optical networks (TWDM-PONs), with the aim of enabling future networks to function effectively. Mobile base stations are split into central units/distributed units (CU/DUs) and remote units (RUs). The optical link between the CU/DUs and the RUs is known as mobile fronthaul (MFH). Future networks that rise above fifth generation networks will need to deploy a huge number of RUs. Thus, quite a few optical fibers will be needed. Accordingly, TWDM-PONs enable MFH to be constructed cost effectively. However, mobile systems tend to upgrade their data rate incrementally up to the standardized maximum data rate in response to user traffic demand. Thus, both upgraded and non-upgraded CU/DUs co-exist in a mobile system at the same time. Therefore, TWDM-PONs have to upgrade their maximum data rates to enable the CU/DUs to be upgraded without stopping any transmission from other CU/DUs that were not upgraded in the PON branch. For this purpose, we propose a wavelength-aggregation scheme with a delay-based frame distribution technique that satisfies the MFH latency requirement. Unlike other schemes, this scheme can upgrade TWDM-PON data rates without stopping any other traffic. Numerical simulations demonstrated that the scheme can provide seamless upgrading that satisfies the MFH latency requirement with a minimized number of wavelengths, one that is less than the upper limit defined in the TWDM-PON specification.

In future radio access systems, since the number of mobile base stations will increase to cope with the increasing mobile traffic, the number of mobile fronthaul (MFH) links will also have to increase. To reduce the MFH link cost, MFH networking has been attracting attention. In particular, the use of a time-division multiplexed passive optical network (TDM-PON) makes the MFH link cost effective. On the other hand, a TDM-PON has a huge latency when forwarding uplink traffic. In a typical dynamic bandwidth allocation (DBA) scheme, an optical network unit (ONU) has a very long transmission waiting time, e.g., several milliseconds. This transmission waiting time in the ONU is a critical problem since the latency requirement for the MFH link is very strict, e.g., less than 250 μs defined by the Third Generation Partnership Project. In this paper, we propose a novel statistical DBA scheme taking high-speed traffic fluctuation to reduce the transmission waiting time. Our proposed scheme allocates bandwidth based on the only ratio of the data amount of each distributed unit and detects the pattern of the transmission interval of the burst signal. We show the feasibility with a numerical simulation and experiments.

A time sensitive converged network that can aggregate fronthaul and machine-to-machine (M2M) streams is required for the fifth generation mobile communication system (5G) era. Recently, a time sensitive network (TSN) for fronthaul in IEEE 802.1CM has been attracting attention. A time aware shaper (TAS), which is a component technology of a TSN, is an effective way to realize a converged network. However, lower priority streams suffer from a lack of usable bandwidth and a huge latency when employing a TAS because the broad bandwidth is reserved for high priority streams in TAS schemes. In this paper, we propose a gate shrunk (GS-)TAS, where a GS-frame is added at the end of high priority streams. We evaluate bandwidth use efficiency by using a numerical simulation. We also simulate the cumulative distribution function (CDF) for the latency performance in a bridged node. We report that the latency reduction rate for the M2M stream is 55.2% for a CDF of 99.7%. In addition, the GS-TAS use does not affect the fronthaul streams.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) have attracted attention as the next step in relation to optical access systems. A major application of TWDM-PON is expected to be fronthaul streams in the centralized radio access network (C-RAN) architecture of mobile networks. In the 5G era, the upstream traffic that OLT receives becomes highly bursty, because of the variable data rate generated by the functional split between baseband unit and remote radio heads, and the global synchronization of data transmission with time-division duplex (TDD). To flexibly allocate upstream bandwidth via the dynamic wavelength and bandwidth allocation (DWBA) scheme has been a hot research topic for TWDM-PON. However, there is no existing DWBA scheme for TWDM-PON that is designed to satisfy the strict delay requirement for fronthaul with minimum number of active wavelength channels. Therefore, in this paper we propose a novel efficient DWBA algorithm that minimizes active wavelength channels considering the high burstiness of fronthaul data transmission. With the proposed algorithm, the allowable delay and constraint for allocation timing for each ONU is formulated based on the different propagation delay between the OLT and ONUs. Then, the wavelength channel and bandwidth is allocated with the simple allocation algorithm based on the descending sort by propagation delay. It was confirmed through computer simulations that the proposed algorithm can reduce the number of active wavelength by 50% by considering propagation delay.

A fronthaul bridged network has attracted attention as a way of efficiently constructing the centralized radio access network (C-RAN) architecture. If we change the functional split of C-RAN and employ time-division duplex (TDD), the data rate in fronthaul will become variable and the global synchronization of fronthaul streams will occur. This feature results in an increase in the queuing delay in fronthaul bridges among fronthaul flows. This paper proposes a novel low-latency routing scheme designed to satisfy the latency requirements in fronthaul networks with path-control protocols. The proposed scheme formulates the maximum queuing delay by defining competitive links and flows. It selects the set of paths that satisfy the latency requirements with the Markov chain Monte Carlo method using machine learning (MCMC-ML). The initial paths are selected from candidate paths using the learned solutions, and path-reselection is performed with the MCMC method. We confirmed with computer simulations that the proposed scheme can compute routes for all flows that satisfy the delay requirements. We also confirmed that the route computation is accelerated with the learned solutions, even if the flow distribution changes.