Scalable mode division multiplexed transmission over a 10-km ring-core fiber using high-order orbital angular momentum modes

We propose and demonstrate a scalable mode division multiplexing scheme based on orbital angular momentum modes in ring core fibers. In this scheme, the high-order mode groups of a ring core fiber are sufficiently de-coupled by the large differential effective refractive index so that multiple-input multiple-output (MIMO) equalization is only used for crosstalk equalization within each mode group. We design and fabricate a graded-index ring core fiber that supports 5 mode groups with low inter-mode-group coupling, small intra-mode-group differential group delay, and small group velocity dispersion slope over the C-band for the high-order mode groups. We implement a two-dimensional wavelength- and mode-division multiplexed transmission experiment involving 10 wavelengths and 2 mode groups each with 4 OAM modes, transmitting 32 GBaud Nyquist QPSK signals over all 80 channels. An aggregate capacity of 5.12 Tb/s and an overall spectral efficiency of 9 bit/s/Hz over 10 km are realized, only using modular 4x4 MIMO processing with 15 taps to recover signals from the intra-mode-group mode coupling. Given the fixed number of modes in each mode group and the low inter-mode-group coupling in ring core fibres, our scheme strikes a balance in the trade-off between system capacity and digital signal processing complexity, and therefore has good potential for capacity upscaling at an expense of only modularly increasing the number of mode-groups with fixed-size (4x4) MIMO blocks

Mode division multiplexing based on ring core optical fibers

The unique modal characteristics of ring core fibers (RCFs) potentially enable the implementation of mode-division multiplexing (MDM) schemes that can increase optical data transmission capacity with either low-complexity modular multi-input multi-output (MIMO) equalization or no MIMO equalization. This paper attempts to present a comprehensive review of recent research on the key aspects of RCF-based MDM transmission. Starting from fundamental fiber modal structures, a theoretical comparison between RCFs and conventional step-index and graded-index multi-mode fibers in terms of their MDM capacity and the associated MIMO complexity is given first as the underlining rationale behind RCF-MDM. This is followed by a discussion of RCF design considerations for achieving high-mode channel count and low crosstalk performances in either MIMO-free or modular MIMO transmission schemes. The principles and implementations of RCF mode (de-)multiplexing devices are discussed in detail, followed by RCF-based optical amplifiers culminating in MIMO-free or modular-MIMO RCF-MDM data transmission schemes. A discussion on further research directions is also given

Design and Optimization of Optical Devices Using Artificial Intelligence Techniques

Over the last decade, there has been a growing interest in utilizing novel photonic and optical devices for a diverse range of applications. For the next generation of wireless communication networks, the development of new and optimal optical devices is inevitable. Existing optical network infrastructure cannot meet the stringent requirements of next-generation data networks (such as a 1000-fold increase in bandwidth demand, very low latency, better spectral and energy efficiency, etc.). In other words, the physical layer of the communication network must be revolutionized to provide the proper foundation for these emerging technologies. Optical networks are based on propagating light. Light propagation in realistic settings is usually a complicated phenomenon. When it comes to the context of optical devices and its propagation in the new devices, the complexity of the problem becomes much higher. In other words, the relations between the light propagation characteristics and the structural parameters of the new devices are mostly unknown. Therefore, the conventional method for designing such devices in the absence of a clear analytic description is usually based on a trial and error process. This method has many disadvantages, being time-consuming, inefficient, and the designed device is usually far from an optimized one. Also, the designing process needs intensive human involvement. Therefore, to fill this gap, we have utilized artificial intelligence (AI) techniques to design, analyze, and optimize several different optical devices. More specifically, we have proposed several optimization frameworks for designing orbital angular momentum (OAM) fibers, large mode area photonic crystal (PhC) fibers, waveguide-based LP01 to LP0m mode converter, PhC filters, PhC sensors, and PhC-enhanced light-emitting diodes (LEDs). In all of these devices, we are dealing with a complicated system in which the relationships between the structural parameters and the output performance merit factors are very complicated. Such problems have a long simulation runtime, so it is not viable to employ an exhaustive optimization algorithm, which evaluates all of the possible combinations of the parameters to find the optimal one. Therefore, we consider our problem as a black box and use the AI optimization algorithm to find the optimal solution. Eventually, the proposed optimization frameworks open up an effective way to design high-performance optical devices for a diverse range of applications and pave the way for the development of next-generation optical devices for next-generation optical networks