Partial Response Equalization Research Articles

Suppressed Noise Enhancement Equalization Combined With Nonlinear-MLSE Techniques for Bandwidth-Limitation IM/DD System

Bandwidth limitation introduced inter-symbol interference (ISI) is the major impediment for high baud rate intensity modulation and direct detection (IM/DD) system with commercial devices. Least mean square (LMS) criterion based feedforward equalizer (FFE) is the most common method to eliminate ISI in single-carrier system. However, there is a tradeoff between the residual ISI and enhanced noise when the system bandwidth is severely limited. Although a post filter (PF) followed by maximum likelihood sequence estimation (MLSE) can suppress the enhanced noise effectively, the residual ISI left by post-FFE will degrade the system performance as the subsequent MLSE only handles ISI introduced by PF. In this paper, an adaptive partial response equalization realized by a channel-shortening filter (CSF) is investigated, which is combined with MLSE. As the target of CSF is a partial response signal with desired low-pass feature, the noise will not be enhanced in this scheme. And the residual ISI with determined response can be fully solved by MLSE. The performance of CSF based MLSE (CSF-MLSE) is evaluated and compared to PF based MLSE (PF-MLSE) in 60/70/75Gbaud four-level pulse amplitude modulation (PAM-4) system with 10-dB bandwidth of 16.2GHz. The experimental results show that CSF-MLSE has a better capability of ISI elimination compared to PF-MLSE. The bit error rate (BER) can be reduced from 7.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> to 4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and from 3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> to 1×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for 70Gbaud and 75Gbaud in optical back-to-back (OBTB) transmission. To further compensate the system nonlinear impairments, several nonlinear MLSE (NL-MLSE) techniques assisted by look-up table (LUT) or Volterra filter are proposed and investigated in this paper. The BER of 75Gbuad PAM-4 can be below 3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> after using NL-MLSE schemes.

Convolutional Neural Network-Based Media Noise Prediction and Equalization for TDMR Turbo-Detection With Write/Read TMR

This article considers a turbo-detection system that includes a convolutional neural network (CNN)-based equalizer, a Bahl-Cocke-Jelinek-Raviv (BCJR) trellis detector, a CNN-based media noise predictor (MNP), and a low-density parity-check (LDPC) channel decoder for two-dimensional magnetic recording (TDMR) in the presence of track misregistration (TMR). The input readings are passed to a 2-D partial response (PR) equalizer, which is either linear or CNN-based. The equalized waveforms are inputs to a 2-D BCJR detector, which generates log-likelihood-ratio (LLR) outputs. The CNN MNP is provided with BCJR LLRs to estimate signal-dependent media noise samples and feed them back to the BCJR. A second pass through the BCJR produces LLRs, which are decoded by an LDPC decoder; achieved areal density (AD) is computed from the LDPC code rate. Spatially varying read- and write-TMR models are developed. We investigate the performance of the proposed system on simulated TDMR readback waveforms generated by grain-switching probabilistic (GSP) simulations. We have two types of GSP datasets. Dataset #1 includes two 10 nm bit length (BL) datasets with 18 and 24 nm track pitch (TP). Dataset #2 has 11 nm BL and 15 nm TP. The comparison baseline is a 1-D BCJR detector with pattern-dependent noise prediction (PDNP) and soft intertrack interference (ITI) subtraction, referred to as 1-D PDNP with LLR exchange. The write-TMR and read-TMR are modeled as cross-track-independent downtrack-correlated random processes. In the presence of joint write- and read-TMR, the proposed turbo-detection system achieves 8.34% and 0.70% AD gain over 1-D PDNP with LLR exchange for TP 18 and 24 nm dataset #1, respectively, and is more robust to TMR compared to the baseline.

PR-NN: RNN-Based Detection for Coded Partial-Response Channels

In this paper, we investigate the use of recurrent neural network (RNN)-based detection of magnetic recording channels with inter-symbol interference (ISI). We refer to the proposed detection method, which is intended for recording channels with partial-response equalization, as Partial-Response Neural Network (PR-NN). We train bi-directional gated recurrent units (bi-GRUs) to recover the ISI channel inputs from noisy channel output sequences and evaluate the network performance when applied to continuous, streaming data. The computational complexity of PR-NN during the evaluation process is comparable to that of a Viterbi detector. The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code with an E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PR4 partial-response channel target. Experimental results with ideal PR signals show that the performance of PR-NN detection approaches that of Viterbi detection in additive white gaussian noise (AWGN). Moreover, the PR-NN detector outperforms Viterbi detection and achieves the performance of Noise-Predictive Maximum Likelihood (NPML) detection in additive colored noise (ACN) at different channel densities. A PR-NN detector trained with both AWGN and ACN maintains the performance observed under separate training. Similarly, when trained with ACN corresponding to two different channel densities, PR-NN maintains its performance at both densities. Experiments confirm that this robustness is consistent over a wide range of signal-to-noise ratios (SNRs). Finally, PR-NN displays robust performance when applied to a more realistic magnetic recording channel with MMSE-equalized Lorentzian signals.

Deep Neural Network Media Noise Predictor Turbo-Detection System for 1-D and 2-D High-Density Magnetic Recording

This article presents a concatenated Bahl–Cocke–Jelinek–Raviv (BCJR) detector, low-density parity-check (LDPC) decoder, and deep neural network (DNN) architecture for a turbo-detection system for 1-D and 2-D magnetic recording (1DMR and TDMR). The input readings first are fed to a partial response (PR) equalizer. Two types of the equalizer are investigated: a linear filter equalizer with a 1-D/2-D PR target and a convolutional neural network (CNN) PR equalizer that is proposed in this work. The equalized inputs are passed to the BCJR to generate the log-likelihood-ratio (LLR) outputs. We input the BCJR LLRs to a CNN noise predictor to predict the signal-dependent media noise. Two different CNN interfaces with the channel decoder are evaluated for TDMR. Then, the second pass of the BCJR is provided with the estimated media noise, and it feeds its output to the LDPC decoder. The system exchanges LLRs between BCJR, LDPC, and CNN iteratively to achieve higher areal density. The simulation results are performed on a grain flipping probabilistic (GFP) model with 11.4 Teragrains per square inch (Tg/in2). For the GFP data with 18 nm track pitch (TP) and 11 nm bit length (BL), the proposed method for TDMR achieves 27.78% areal density gain over the 1-D pattern-dependent noise prediction (PDNP). The presented BCJR-LDPC-CNN turbo-detection system obtains 3.877 Terabits per square inch (T/bin2) areal density for 11.4 Tg/in2 GFP model data, which is among the highest areal densities reported to date.

Deep Neural Network-Based Detection and Partial Response Equalization for Multilayer Magnetic Recording

To increase the storage capacity limit of magnetic recording channels, recent studies proposed multilayer magnetic recording (MLMR): the vertical stacking of magnetic media layers. MLMR readback waveforms consist of the superposition of signals from each layer recovered by a read head placed above the upper layer. This article considers the problem of equalization and detection for MLMR comprising two layers. To this end, we use MLMR waveforms generated using a grain switching probability (GSP) model that is trained on realistic micromagnetic simulations. We propose three systems for equalization and detection. The first is a convolutional neural network (CNN) equalizer followed by an MLMR Viterbi algorithm (VA) for detection. We show that this system outperforms the traditional 2-D linear minimum mean squared error (2-D-LMMSE) equalizer. The second system uses CNNs for equalization and separation of signals from each layer, which is followed by a regular VA. The third system contains CNNs trained to directly provide soft bit estimates. By interfacing the CNN detector with a channel decoder, we show that an areal density gain of 16.2% can be achieved by a two-layer MLMR system over a one-layer system.

Convolutional Neural Network Based Symbol Detector for Two-Dimensional Magnetic Recording

Data detection in magnetic recording (MR) channels can be viewed as an image processing problem, proceeding from the 2-D image of readback bits, to higher level abstractions of features using convolutional layers that finally allow classification of individual bits. In this work, convolutional neural networks (ConvNets) are employed in place of the typical partial response equalizer and maximum-likelihood detector with noise prediction to directly process the un-equalized readback signals and output soft estimates. Several variations of ConvNets are compared in terms of network complexity and performance. The best performing ConvNet detector with two convolutional layers provides a data storage density of up to 3.7489 Terabits/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> on a low track pitch two-dimensional MR channel simulated with a grain-flipping-probability (GFP) model. An alternate ConvNet architecture reduces the network complexity by about 74%, yet results in only a 2.09% decrease in density compared to the best performing detector.

Deep Neural Network a Posteriori Probability Detector for Two-Dimensional Magnetic Recording

In two-dimensional magnetic recording (TDMR) channels, intersymbol interference (within and between tracks) and pattern-dependent media noise are impediments to reaching higher areal density. We propose a novel deep neural network (DNN)-based a posteriori probability (APP) detection system with parallel multi-track detection for TDMR channels. The proposed DNN-based APP detector replaces the trellis-based Bahl–Cocke–Jelinek–Raviv (BCJR) or Viterbi algorithm and pattern-dependent noise prediction (PDNP) in a typical TDMR scenario, in which it directly outputs log-likelihood ratios of the coded bits and iteratively exchanges them with a subsequent channel decoder to minimize bit error rate (BER). We investigate three DNN architectures—fully connected DNN, convolutional neural network (CNN), and long short-term memory (LSTM) network. The DNN’s complexity is limited by employing linear partial response (PR) equalizer pre-processing. The best performing DNN architecture, CNN, is selected for iterative decoding with a channel decoder. Simulation results on a grain-flipping-probability (GFP) media model show that all three DNN architectures yield significant BER reductions over a recently proposed 2D-PDNP system and a previously proposed local area influence probabilistic (LAIP)-BCJR system. On a GFP model with 18 nm track pitch and 11.4 Teragrains/in2, the CNN detection system achieves an information areal density of 3.08 Terabits/in2, i.e., a 21.72% density gain over a standard BCJR-based 1D-PDNP; the CNN-based system also has $3\times $ the throughput of 1D-PDNP, yet requires only 1/10th the computer run time.

Single-reader dual-track readback scheme for synchronized interlaced magnetic recording

In this study, multi-level joint-track readback scheme is proposed for interlaced magnetic recording (IMR) with write synchronization. IMR typically provides an areal density capability (ADC) gain by pre-fixing the recording orders as interlaced and customizing the write configurations for neighboring tracks, such that narrow and wide tracks are interlaced. On the other hand, due to the unbalanced track widths in IMR, readback waveforms by a single wide reader covering two adjacent tracks can be decomposed into four distinguishable levels, when tracks are recorded in a synchronous manner. By taking advantage of the four level (4L) detecting capability, the single reader based dual-track readback (SR-DTR) IMR has a potential to provide a doubled data rate (2X) retrieval. For the feasibility test of the SR-DTR IMR, numerical simulations are conducted for bit-error rate (BER) evaluations by the micro-pixelated magnetic recording channel model. Partial response equalizer and Viterbi detector are modified for 4L readback waveforms, and are investigated with cross-track read offsets under various track and linear densities. BER bathtub investigation results show the feasibility of 2X retrievals by the SR-DTR IMR approach.

User Areal Density Optimization for Conventional and 2-D Detectors/Decoders

The magnetic recording industry is perennially trying to grow the areal density by optimizing the head/media, the coding/signal processing, and the bit length (BL) and track pitch (TP) of the data written on the medium. Writing data at smaller BL or TP on the medium lead to loss of SNR during readback, which can be corrected by the coding and signal processing subsystem at the cost of the code rate $R$ . The optimization of the BL, TP, and code rate for a given head and media is the task of the system integrator. In this paper, we make use of an advanced channel model: the grain-flipping probability model, to perform an equivalent optimization together with a software recording channel that is able to include the impact of the generalized partial response equalizer, soft-output Viterbi algorithm detector, and low density parity check decoder on the final performance. In this paper, we vary the BL, TP, and code rate $R$ in the simulations to search for the highest user areal density achievable for a given head/medium setup. We investigate the effect of having a constant reader width (RW) as well as with RW proportionate to TP in our channel model. We also investigate the effect of more advanced 2-D coding and detection schemes on the final system performance.

Polar Channel Coding Schemes for Two-Dimensional Magnetic Recording Systems

This paper proposes new two-dimensional magnetic recording (TDMR) systems using polar channel coding as practical error correction coding. It is known that the time and space complexities of the encoding/decoding algorithms based on polar channel coding are $\mathcal {O}(N \log N)$ , where $N$ is the codeword (block) length. If we compare the error-correction performance of a polar code with that of a low-density parity-check (LDPC) code in the same rate, it is known that the polar code has a longer length and its decoder still has a lower implementation complexity than the LDPC decoder. Therefore, relatively low-complexity coding schemes are preferable for any TDMR systems under high rates and relatively long codeword lengths. In this paper, the proposed TDMR system serially concatenates a two-dimensional (2-D) modulation code with one-dimensional (1-D) polar codes in each down-track direction. These element polar codes are designed on the fundamentals of the channel polarization theory, which are applied for channels with memory. Actually, it evaluates the performance of the signal processing scheme with concatenated coding and generalized partial response equalization for the proposed TDMR system using bit-patterned media by computer simulations. As a result, it shows that the block error rate performance of the proposed TDMR system with the 2-D modulation and polar channel coding schemes is superior to that of the 1-D system with the conventional 1-D high rate modulation and LDPC coding schemes.

Joint Timing Recovery and Signal Detection for Two-Dimensional Magnetic Recording

Two-dimensional magnetic recording (TDMR) is a promising technology for boosting areal densities using sophisticated signal processing algorithms within a systems framework. The channel impairments comprise of 2-D intersymbol interference (ISI), 2-D synchronization errors along with media, and electronic noise sources, making it challenging for designing optimum algorithms and architectures for read/write channels. In this paper, we propose a novel iterative joint timing and signal detection algorithm to handle synchronization errors along with the 2-D ISI for TDMR channels. Our work is novel in two aspects: 1) we develop a discrete timing error model using 2-D random walk by incorporating correlated timing drifts due to phase and frequency offsets and 2) we develop a 2-D joint timing and signal detection engine for handling 2-D ISI constraints along with timing errors toward near maximum likelihood detection. The proposed 2-D joint timing-detection algorithm can be iteratively configured and observed to achieve a significant signal-to-noise ratio gain of > 1.2 dB in bit error rate performance (translating to similar to 10% increase in areal densities around the 1 Tb/in(2) regime with the grain sizes of 10 nm and the bit sizes of 25x25 nm) at the output of 2-D soft-output Viterbi algorithm (SOVA) as compared with a standalone timing loop coupled to a 2-D detector in an open-loop configuration. The efficacy of our proposed algorithm and system architecture is evaluated via simulations for TDMR configurations comprising of a 2-D generalized partial response equalization along the 2-D SOVA with data-dependent noise prediction capability over the Voronoi media model.

An FIR Filter Using Segmented Photodiodes for Silicon Photodiode Equalization

This letter presents a finite-impulse response (FIR) filter implemented using segmented photodiodes, which provides a bandwidth enhancement or a partial frequency response equalization for a silicon photodiode. The FIR filter utilizes an $LC$ ladder network for its delay cells, where each segmented photodiode constitutes the ladder network. The measurement results show that the three-tap high-pass FIR filter incorporating segmented photodiodes designed for maximum bandwidth equalization achieves a bandwidth improvement of 7.4 times compared with that of a photodiode without segmentation and filtering. The upper bound of the equalization frequency of the three-tap high-pass FIR filter designed in this letter for maximum peaking frequency equalization is 14 times of the photodiode bandwidth.

Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR

Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in2 using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in2 with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give $\sim$ 10% gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.

Novel Joint Detection and Decoding Schemes for TDMR Channels

Two-dimensional magnetic recording (TDMR) relies on advanced signal processing and coding techniques to enable storage of data at higher densities. Various detection and decoding schemes have been proposed in the literature. In this paper, a recently proposed joint detection and decoding scheme known as the joint Viterbi detector decoder (JVDD) is presented for TDMR channels. The JVDD operates on a trellis and attempts to find a sequence in the trellis that belongs to a legal codeword with minimum metric. It does so by using a metric thresholding and parity checking technique over the same trellis structure. The iterative detector/decoder consisting of a soft-output Viterbi algorithm detector and low-density parity check decoder is used as a performance benchmark for the JVDD. In this paper, we perform the comparison with various equalization/detection techniques consisting of: 1) a 1-D general partial response equalization scheme followed by a 1-D-detector as used in today’s Partial Response Maximum Likelihood Detection (PRML) channels; 2) a 2-D equalization scheme that equalizes to a 1-D target [fully equalizing away the intertrack interference (ITI)], followed by a series of parallel 1-D-detectors to handle the intersymbol interference (ISI); and 3) a 2-D-equalization scheme followed by 2-D detector to combat both ITI and ISI.

Iterative Detection-Decoding of Interleaved Hermitian Codes for High Density Storage Devices

Traditionally, Reed-Solomon (RS) codes have been employed in magnetic data storage devices due to their effectiveness in correcting random errors and burst errors caused by thermal asperities and inter-symbol interference (ISI). However, as storage densities increase the effect of ISI becomes more severe and much longer RS codes are needed, but this requires significantly increasing the size of the finite field. A possible replacement for RS codes are the one-point Hermitian codes, which are a class of algebraic-geometric (AG) code that have larger block sizes and minimum Hamming distances over the same finite field. In this paper, we present a novel iterative soft detection-decoding algorithm for interleaved Hermitian codes. The soft decoding employs a joint adaptive belief propagation (ABP) algorithm and Koetter-Vardy (KV) list decoding algorithm. It is combined with a maximum a posteriori (MAP) partial response (PR) equalizer and likelihoods from the output of the KV or the ABP algorithm are fed back to the equalizer. The proposed scheme's iterative detection-decoding behavior will be analyzed by utilizing the Extrinsic Information Transfer (ExIT) chart. Our simulation results demonstrate the performance gains achieved by iterations and Hermitian codes' performance advantage over RS codes.

Improved partial response maximum likelihood method combining modulation code for signal waveform modulation multi-level disc

In this paper, we describe an improved adaptive partial response maximum likelihood (PRML) method combining modulation code for signal waveform modulation multi-level disc. This improved adaptive PRML method employs partial response equalizer and adaptive viterbi detector combining modulation code. Compared with the traditional adaptive PRML detector, the improved PRML detector additionally employs illogical sequence detector and corrector. Illogical sequence detector and corrector can avoid the appearance of illogical sequences effectively, which do not follow the law of modulation code for signal waveform modulation multi-level disc, and obtain the correct sequences. We implement the improved PRML detector using a DSP and an FPGA chip. The experimental results show good performance. The higher efficient and lower complexity can be obtained by using the improved PRML method than by using the previous PRML method. Meanwhile, resource utilization of the improved PRML detector is not changed, but the bit error rate (BER) is reduced by more than 20%.

Design and Implementation of a Low-Complexity Symbol Detector for Sparse Channels

In this paper, we present a low-complexity symbol detector for communication channels which have long spanning durations but a sparse multipath structure. Traditional maximum-likelihood sequence estimation using the Viterbi algorithm can provide optimal error performance for eliminating the multipath effect, but the hardware complexity grows exponentially with channel length and it is not practical for long sparse channels. We implement a near-optimal algorithm and its architecture by cascading an adaptive partial response equalizer (PRE) with an iterative belief propagation (BP) detector. A sparse channel is first equalized by a PRE to a target impulse response (TIR) with only a few nonzero coefficients remaining. The residual intersymbol interference is then canceled by a BP detector whose complexity is solely dependent on the number of nonzero coefficients in the TIR. Moreover, we present a pipeline high-throughput implementation of the detector for channel length 30 with quadrature phase-shift keying modulation. The detector can achieve a maximum throughput of 206 Mb/s with an estimated core area of 3.162 mm2 using 90-nm technology node. At a target frequency of 515 MHz, the dynamic power is about 1.096 W.

信号波形调制多阶光盘信道部分响应多项式与均衡器的设计

The multi-level disc,which uses the signal waveform modulation method for modulation and recording,employs the partial response maximum likelihood detection techniques for the readout channel recovery.With improving the recording density,the complexity and nonlinearity of the optical disc channel modeling will also increase.Therefore,a optimized design method of the partial response polynomial and the corresponding equalizer design were introduced to solve it.By finding the critical frequency point between signal frequency band and noisy frequency band in the power spectral density curve of the disc readout signal,the optimized parameters of the partial response polynomial and the partial response equalizer were designed,thus effectively improving the optical readout signal to noise ratio,reducing the error rate of the signal recognition.And this method can also be applied to the partial response maximum likelihood detection system of blu-ray and other high-density baseband transmission channel.

Combined utilization of partial-response coding and equalization for high-speed WDM-PON with centralized lightwaves

In this paper, we investigate the transmission impairments in a high-speed single-feeder wavelength-division-multiplexed passive optical network (WDM-PON) employing low-bandwidth upstream transmitter. A 1-GHz reflective semiconductor optical amplifier (RSOA) is operated at the rates of 10 Gb/s and 20 Gb/s in the proposed WDM-PON. Since the system performance is seriously limited by its uplink in both capacity and reach owing to inter-symbol interference and reflection noise, we present a novel technique with simultaneous capability of spectral efficiency enhancement and transmission distance extension in the uplink via coding and equalization that exploit the principles of partial-response (PR) signal. It is experimentally demonstrated that the proposed system supports the delivery of 10 Gb/s and 20 Gb/s upstream signals over 75-km and 25-km bidirectional fiber, respectively. The configuration of PR equalizer is optimized for its best performance-complexity trade-off. The reflection tolerance of 10 Gb/s and 20 Gb/s channels is improved by 8 dB and 6 dB, respectively, with PR coding. The proposed cost-effective signal processing scheme has great potential for the next-generation access networks.

High-speed and long-reach WDM networks using low-bandwidth transmitter

Reflective semiconductor optical amplifier (RSOA) has been widely employed in wavelength division multiplexed passive optical networks (WDM-PONs). In this paper, we propose a novel signal processing technique based on partial response (PR) equalization to enable high-speed and long-reach RSOA-based upstream transmission overcoming the bandwidth limitation from the RSOA and fiber channel. The selection of the target PR signal for the equalizer and its corresponding working range for the RSOA-based uplink are extensively studied. Our investigation reveals that the same PR equalizer can be applied on a range of distances, making the proposed technique practical for access networks. Moreover, experiments are carried out to demonstrate 10Gb/s transmission over 125km and 20Gb/s transmission over 30km using a 1GHz RSOA using the proposed equalizer design in WDM access networks.