Artificial Neural Network Equalizer Research Articles

Enhanced Performance of Artificial-Neural-Network-Based Equalization for Short-Haul Fiber-Optic Communications

This work proposes an efficient and easy-to-implement single-layer artificial neural network (ANN)-based equalizer with improved compensation performance. The proposed equalizer is used for effectively mitigating the distortions induced in the short-haul fiber-optic communication systems based on intensity modulation and direct detection (IMDD). The compensation performance of the ANN equalizer is significantly improved, exploiting an introduced advanced training scheme. The efficiency and robustness of the proposed ANN equalizer are illustrated through 10- and 28-Gbaud short-reach optical-fiber communication systems. Compared to the efficient but computationally expensive maximum likelihood sequence estimator (MLSE), the proposed ANN equalizer not only significantly reduces its computational equalization cost and storage memory requirements, but it also outperforms its bit error rate performance.

PRBS orders required to train ANN equalizer for PAM signal without overfitting.

Artificial neural network (ANN)-based nonlinear equalizers (NLEs) have been gaining popularity as powerful waveform equalizers for intensity-modulation (IM)/direct-detection (DD) systems. On the other hand, the M-ary pulse amplitude modulation (PAM-M) format is now widely used for high-speed IM/DD systems. For the training of ANN-NLE in PAM-M IM/DD systems, it is common to employ pseudorandom binary sequences (PRBSs) for the generation of PAM-M training sequences. However, when the PRBS orders used for training are not sufficiently high, the ANN-NLE might suffer from the overfitting problem, where the equalizer can estimate one or more of the constituent PRBSs from a part of PAM-M training sequence, and as a result, the trained ANN-NLE shows poor performance for new input sequences. In this paper, we provide a selection guideline of the PRBSs to train the ANN-NLE for PAM-M signals without experiencing the overfitting. For this purpose, we determine the minimum PRBS orders required to train the ANN-NLE for a given input size of the equalizer. Our theoretical analysis is confirmed through computer simulation. The selection guideline is applicable to training the ANN-NLE for the PAM-M signals, regardless of symbol coding.

8.75 Gbps visible light communication link using an artificial neural network equalizer and a single-pixel blue micro-LED.

Visible light communication (VLC) beyond 10 Gbps is an important characteristic that can support the future 6 G high-capacity requirements. On the one hand, in order to break the electro-optics (E-O) bandwidth limitation of the light-emitting diodes (LEDs), we fabricated two high-bandwidth (>gigahertz) single wetting layer micro-LEDs with 50 and 75 µm active regions. On the other hand, for mitigating the nonlinear effects of the VLC system, an artificial neural network equalizer is designed and implemented in offline digital signal processing. Both efforts lead to a breakthrough of data rates at a single VLC link. Using quadrature phase shift keying orthogonal frequency division multiplexing, the data rate of a 1.025 GHz VLC link based on a 75 µm micro-LED can reach 8.75 Gbps with a bit-error-ratio (BER) of 2.73×10-3. In addition, the E-O characteristics and communication performances of micro-LED with two diameters are compared and discussed. To the best of our knowledge, the data rate of 8.75 Gbps is the highest record of the VLC link based on a single-pixel blue micro-LED with a BER within the forward error correction citation of 3.8×10-3.

The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications.

In this paper, we propose and validate an artificial neural network-based equalizer for the constant power 4-level pulse amplitude modulation in an optical camera communications system. We introduce new terminology to measure the quality of the communications link in terms of the number of row pixels per symbol , which allows a fair comparison considering the progress made in the development of the current image sensors in terms of the frame rates and the resolutions of each frame. Using the proposed equalizer, we experimentally demonstrate a non-flickering system using a single light-emitting diode (LED) with of 20 and 30 pixels/symbol for the unequalized and equalized systems, respectively. Potential transmission rates of up to 18.6 and 24.4 kbps are achieved with and without the equalization, respectively. The quality of the received signal is assessed using the eye-diagram opening and its linearity and the bit error rate performance. An acceptable bit error rate (below the forward error correction limit) and an improvement of ~66% in the eye linearity are achieved using a single LED and a typical commercial camera with equalization.

Two-stage artificial neural network-based burst-subcarrier joint equalization in nonlinear frequency division multiplexing systems.

We propose an artificial neural network (ANN)-based scheme to improve the performance of nonlinear frequency division multiplexing (NFDM) optical transmission systems in both time and frequency domain. Through two-stage ANN equalization at the receiver side, time-domain distortions between adjacent bursts and frequency-domain cross talk between neighboring subcarriers can be jointly mitigated. Burst ANN and Subcarrier ANN equalizers are characterized and validated by numerical simulations of a dual-polarization NFDM transmission system. Compared with the basic detection scheme, the proposed two-stage ANN achieves a Q-factor gain of 3.01 dB for an NFDM system with 256 Gb/s gross data rate transmitting over 960 km standard single-mode fiber (SSMF). The two-stage ANN approach offers an effective way to jointly equalize the signal in multiple dimensions.

Reduced-Complexity Artificial Neural Network Equalization for Ultra-High-Spectral-Efficient Optical Fast-OFDM Signals

Digital-based artificial neural network (ANN) machine learning is harnessed to reduce fiber nonlinearities, for the first time in ultra-spectrally-efficient optical fast orthogonal frequency division multiplexed (Fast-OFDM) signals. The proposed ANN design is of low computational load and is compared to the benchmark inverse Volterra-series transfer function (IVSTF)-based nonlinearity compensator. The two aforementioned schemes are compared for long-haul single-mode-fiber-based links at 9.69 Gb/s direct-detected optical Fast-OFDM signals. It is shown that an 80 km extension in transmission-reach is feasible when using ANN compared to IVSTF. This occurs because ANN can tackle stochastic nonlinear impairments, such as parametric noise amplification. Using ANN, the dynamic parameters requirements of the sub-ranging quantizers can also be relaxed compared to linear equalization, such as the reduction of the optimum clipping ratio and quantization bits by 2 dB and 2-bits, respectively, and by 2 dB and 2 bits when compared to the IVTSF equalizer.

FBMC-based dispersion compensation using artificial neural network equalization for long reach-passive optical network

This paper presents a Filter Bank Multicarrier (FBMC), a viable waveform candidate for fifth generation (5G) communications using Staggered-Modulated Multitone (SMT). FBMC is preferred in optical communication because of its ability to work without Cyclic Prefix (CP). In any case, the operation of FBMC in optical access systems with Artificial Neural Networks (ANNs) has not been broadly explored either downstream or upstream. This work presents an advanced Nonlinear Feed-Forward Equalizer (NFFE) that makes use of multilayer ANN for dispersion compensation. ANN is trained to act as a filter with an extensive equalizer training which has the ability to mitigate dispersion and increase the performance of the system. The simulation work is used to study the performance of intensity modulated FBMC system with direct detection in Long Reach-Passive Optical Networks (LR-PONs).The transmission data rate is varied between 8 and 10[Formula: see text]Gbps with the optical fiber length from 30 to 90[Formula: see text]km of Standard Single Mode Fiber (SSMF). The obtained result suggests that FBMC system with ANN-NFFE equalizer fundamentally builds the resilience to the Chromatic Dispersion (CD) distortion, and a CP-less transmission is possible upto 90[Formula: see text]km.

A nonlinear ANN equalizer with mini-batch gradient descent in 40Gbaud PAM-8 IM/DD system

Abstract We propose and validate a novel nonlinear artificial neural network (ANN) equalizer for PAM-8 transmission in IM/DD system. Mini-batch gradient descent is introduced to efficiently train ANN equalizer. Using the proposed ANN equalizer, we successfully transmit a 40Gbaud PAM-8 signal over 4-km SMF with BER under the threshold of 3.8 × 10−3 and over 10-km SMF with BER under the threshold of 1 × 10−2. We also elaborately compare the proposed ANN equalizer with other methods including LMS equalizer, Volterra equalizer and look-up table (LUT). Experimental results indicate that ANN achieves the best performance that is slightly superior to Volterra equalizer with computational complexity half reduced. To the best of our knowledge, this is the first time to adopt mini-batch gradient descent to train ANN equalizer in IM/DD system.

Wavelength-Multiplexed Polymer LEDs: Towards 55 Mb/s Organic Visible Light Communications

We present recent progress on visible light communication systems using polymer light-emitting diodes as the transmitters and a commercial silicon photodetector as the receiver. In this paper, we use transmitters at red, green, and blue wavelengths to investigate the maximum on-off keying link performance of each device type as the first steps toward a wavelength-division multiplexed link. We show that a total transmission speed of 13 Mb/s is achievable when considering the raw bandwidth of each of the RGB PLEDs. Such a rate represents a 30% gain over previously demonstrated systems. Further capacity improvement can be achieved using high performance artificial neural network equalizer offering a realistic prospect for transmission speeds up to 54.9 Mb/s.

Visible Light Communications: 170 Mb/s Using an Artificial Neural Network Equalizer in a Low Bandwidth White Light Configuration

In this paper, we experimentally demonstrate for the first time an on off keying modulated visible light communications system achieving 170 Mb/s using an artificial neural network (ANN) based equalizer. Adaptive decision feedback (DF) and linear equalizers are also implemented and the system performances are measured using both real time (TI TMS320C6713 digital signal processing board) and offline (MATLAB) implementation of the equalizers. The performance of each equalizer is analyzed in this paper using a low bandwidth (4.5 MHz) light emitting diode (LED) as the transmitter and a large bandwidth (150 MHz) PIN photodetector as the receiver. The achievable data rates using the white spectrum are 170, 90, 40 and 20 Mb/s for ANN, DF, linear and unequalized topologies, respectively. Using a blue filter to isolate the fast blue component of the LED (at the cost of the power contribution of the yellowish wavelengths) is a popular method of improving the data rate. We further demonstrate that it is possible to sustain higher data rates from the white light with ANN equalization than the blue component due to the high signal-to-noise ratio that is obtained from retaining the yellowish wavelengths. Using the blue component we could achieve data rates of 150, 130, 90 and 70 Mb/s for the same equalizers, respectively.

Visible light communications: 375 Mbits/s data rate with a 160 kHz bandwidth organic photodetector and artificial neural network equalization [Invited

This paper presents an experimental demonstration of a visible light communications link with an light emitting diode and a low-bandwidth organic photodetector as transmitter and receiver, respectively, that achieves sub 4 Mbits/s speeds. An artificial neural network (ANN) equalizer is required in order to achieve such high data rates because of the influence of intersymbol interference. The digital modulation formats tested in this paper are nonreturn-to-zero on–off keying (OOK), and fourth-order pulse position modulation (4-PPM). Without equalization, data rates of 200 and 300 kbits/s can be achieved for 4-PPM and OOK, respectively. With ANN equalization, data rates of 2.8 and 3.75 Mbits/s can be achieved for the first time for OOK and 4-PPM, respectively.

Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel

This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.