dB Performance Improvement Research Articles

Performance improvement of non-orthogonal multiple access with a 3D constellation and a 2D IFFT modulator.

In this paper, we propose a performance improvement of non-orthogonal multiple access (NOMA) with a three-dimensional (3D) constellation and a two-dimensional Inverse Fast Fourier Transform IFFT modulator (2D-IFFT) for the passive optical network (PON). Two kinds of 3D constellation mapping are designed for the generation of a three-dimensional NOMA (3D-NOMA) signal. Higher-order 3D modulation signals can be obtained by superimposing signals of different power levels by pair mapping. Successive interference cancellation (SIC) algorithm is implemented at the receiver to remove interference from different users. Compared with the traditional two-dimensional NOMA (2D-NOMA), the proposed 3D-NOMA can increase the minimum Euclidean distance (MED) of constellation points by 15.48%, which enhances the bit error rate (BER) performance of the NOMA. The peak-to-average power ratio (PAPR) of NOMA can be reduced by 2 dB. A 12.17 Gb/s 3D-NOMA transmission over 25 km single-mode fiber (SMF) is experimentally demonstrated. The results show that at the bit error rate (BER) of 3.8 × 10-3, the sensitivity gain of the high-power signals of the two proposed 3D-NOMA schemes is 0.7 dB and 1 dB compared with that of 2D-NOMA under the condition of the same rate. Low-power level signal also has 0.3 dB and 1 dB performance improvement. Compared with 3D orthogonal frequency-division multiplexing (3D-OFDM), the proposed 3D-NOMA scheme could potentially expand the number of users without obvious performance degradation. Due to its good performance, 3D-NOMA is a potential method for future optical access systems.

Clustering Algorithm-Based Quantization Method for Massive MIMO CSI Feedback

Deep learning-based channel state information (CSI) feedback can provide high downlink throughput for massive multiple-input multiple-output (MIMO) system in frequency division duplex mode. The compression and quantization of CSI greatly reduce the feedback overhead. However, existing quantization methods design quantization levels without matching the distribution of feedback codewords, which impairs the reconstruction accuracy of CSI. In this letter, we propose a k-means clustering algorithm-based quantizer, named KMQBlock, which can find the appropriate quantization levels according to the distribution of feedback codewords by using scalar and vector quantization. To illustrate the efficacy of KMQBlock, we firstly propose a deep learning-based CSI feedback network MRNet to improve the CSI reconstruction accuracy. Then, we apply KMQBlock to MRNet to quantize the feedback codewords. Experimental results show that KMQBlock can provide at most 8.20 dB performance improvement for MRNet compared with conventional quantization methods.

Adaptive protograph-based BICM-ID relying on the RJ-MCMC algorithm: a reliable and efficient transmission solution for plasma sheath channels

For reentry communication, owing to the influence of the highly dynamic plasma sheath (PS), the parasitic modulation effect can occur and the received phase shift keying (PSK) signal constellation can be severely rotated, leading to unacceptable demodulation performance degradation. In this work, an adaptive non-coherent bit-interleaved coded modulation with iterative decoding (BICM-ID) system with binary PSK (BPSK) modulation and protograph low-density parity-check under the PS channel is proposed. The proposed protograph-based BICM-ID (P-BICM-ID) system can achieve joint processing of demodulation and decoding, where the soft information is adaptively estimated by reversible-jump Markov chain Monte Carlo (RJ-MCMC) algorithms. Simulation results indicate that compared to existing algorithms, the proposed system can adapt well to the dynamic characteristics of the PS channel and can obtain a 5 dB performance improvement at a bit error rate of 10−6.

Combined Neural Network and Adaptive DSP Training for Long-Haul Optical Communications

Machine Learning (ML) algorithms have shown to complement standard digital signal processing (DSP) tools in mitigating fiber nonlinearity and improving long-haul transmission performance. However, dynamic transmission impairments such as polarization effects and carrier phase noise corrupt the training data and conventional cost functions for neural network (NN) training become unsuitable. Simple cascade of ML and standard adaptive DSP blocks will also result in suboptimal transmission performance or require impractical training methodologies in presence of such dynamic transmission impairments. We show how the adaptive DSP blocks can be treated as extra stateful NN layers and be combined with the main NN so that standard backpropagation-like training algorithms in ML can be applied. In this case, the adaptive filters are viewed as NN states which are updated in the forward pass of the backpropagation. We study the combined training of linear and nonlinear parameters in the digital backpropagation (DBP) algorithm for fiber nonlinearity compensation (named generalized DBP (GDBP) hereafter), residual impairments, polarization effects, frequency offsets and carrier phase noise compensation filters as a single NN in a 7 × 288 Gb/s polarization multiplexed (PM)-16QAM transmission experiment over 1125 km. We derived the complete set of backpropagation-like gradients and state update equations for the static and dynamic parameters of the combined NN. We further proposed and open-sourced a JAX-based coding framework for their easy and practical implementation. GDBP is more generalized than other DBP variants proposed in literature and for a given total number of steps, GDBP is the first experimental demonstration of optimal single-channel DBP based-fiber nonlinearity compensation algorithm. In addition, for complexity constrained situations with shortened filter taps, GDBP enables a 1 dB performance improvement over other DBP variants.

Probabilistic shaping and forward error correction scheme employing uneven segmentation mapping for data center optical communication.

The combination of probabilistic shaping (PS) technology and forward error correction (FEC) technology can significantly boost the performance of a transmission system. In this paper, we propose a probabilistic shaping distribution matching algorithm employing uneven segmentation for data center optical networks, while keeping extremely low computational complexity for both encoding and decoding. Based on the proposed probabilistic shaping distribution matching algorithm, we develop a novel integrated scheme of PS and FEC coding that lifts the restrictions on the use of FEC technology and increases the use of interleaver. An experiment used to evaluate the probabilistically shaped data transmission is successfully conducted over a 25 km standard single-mode fiber (SSMF) with 16 quadrature amplitude modulation (16-QAM). Simultaneously, we use a simulation software to analyze the bit error rate performance at higher resolution. The results show that the joint coding scheme can achieve a 0.4dB performance improvement compared with the single FEC system.

Probabilistic Shaping in Time-Frequency-Packed Terabit Superchannel Transmission

To combine the individual benefits of probabilistic-shaping (PS) and time-frequency-packing (TFP), we consider for the first time PS-TFP wavelength-division multiplexing (WDM) superchannels. However, TFP introduces inter-symbol interference (ISI) and/or inter-carrier interference (ICI). Moreover, the presence of reconfigurable optical add-drop multiplexers in the fiber links may further degrade the system performance. In this letter, we efficiently handle such challenges to present PS-TFP superchannels enabling Terabit-per-second data rates. For this, we perform a joint ISI and ICI channel estimation in tandem with turbo equalization to mitigate the interference. We investigate optimizing the parameters in the shaping and packing dimensions to achieve a desired target spectral efficiency. We show through our numerical results that such an optimized PS-TFP transmission leads up to 1.2 dB performance improvement over an unshaped Nyquist WDM system under similar conditions.

Power-Efficient Single-Sideband Transmission With Clipped Iterative SSBI Cancellation

Distributed data-center networks rely on power and cost-efficient high-speed fiber optical connections over distances up to 80 km which can be densely wavelength-division multiplexed (WDM) in the C-band. Recently, single-sideband (SSB) direct detection (DD) has been considered as an attractive transmission scheme for achieving data rates beyond 100 Gb/s per channel over 80 km. The advantages of SSB DD transmission include its simple transceiver architecture and its capability of electronic dispersion compensation. However, SSB transmissions require a carrier-to-signal power ratio (CSPR) of around 10 dB, even when signal-signal beat interference (SSBI) cancellations are applied. This high CSPR value reduces the power efficiency of the system and limits the total number of WDM channels and accordingly the total system capacity due to power limitation of optical amplifiers. In this article, we propose an iterative SSBI cancellation scheme, which can be effectively applied without digital upsampling at low CSPR values. Using this technique, we have experimentally demonstrated a 30 Gbaud 128 QAM SSB direct detection transmission over 80 km with a low CSPR of 5 dB, showing 4.6 dB performance improvement compared to the Kramers–Kronig scheme operated without digital upsampling.

MSE-Based Downlink and Uplink Joint Beamforming in Dynamic TDD System Based on Cloud-RAN

In this paper, we consider a dynamic time division duplex system based on a cloud radio access network (RAN) to support the ultimate goal of efficient resource utilization. To optimize the performance of both remote radio heads (RRHs) and user equipments (UEs) which have multiple antennas, we propose an iterative scheme consisting of four types of beamforming, namely downlink (DL) transmit beamforming, DL receive beamforming, uplink (UL) transmit beamforming, and UL receive beamforming, in an attempt to minimize mean squared error (MSE). The proposed beamforming scheme makes it possible to allow DL and UL transmission be supported simultaneously by managing interference between RRHs and UEs. Our simulation results show that the MSE and the bit error rate (BER) are greatly improved under the proposed beamforming algorithm compared to conventional schemes. From intensive system level simulations, the proposed algorithm points out more than 3 dB performance improvement in terms of the required signal-to-noise-ratio (SNR) to achieve the BER less than $10^{-3}$ compared to conventional schemes.

Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Multiple-Input Multiple-Output (MIMO) systems are characterised by increased capacity and improved performance compared to the single-input single-output (SISO) systems. One of the main challenge in the design of MIMO systems is the detection of the transmitted signals due to the interference caused by the multiple simultaneously transmitted symbols from the multiple transmit antennas. Several detection techniques have been proposed in the literature in order to reduce the detection complexity, while maintaining the required quality of service. Among these low-complexity techniques is the Lattice Reduction (LR), which can provide good performance and significantly lower complexity compared to Maximum Likelihood (ML) detector. In this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better Bit Error Ratio (BER) performance and lower complexity than the conventional Lenstra, Lanstra, and Lovasz (LLL) LR-aided detection. Additionally, we propose a hardware implementation for the ELR-aided K-Best detector for a MIMO system equipped with four transmit and four receive antennas. The ELR-aided K-Best detector requires an extra 18% increase in power consumption and an extra 20% in area overhead compared to a regular K-Best detector dispensing with ELR, where this increase in the hardware requirements is needed in order to achieve a 2 dB performance improvement at a bit error rate of 10−5.

Outage analysis of relay-assisted underwater wireless optical communication systems

In this paper, we theoretically evaluate the outage probabilities of underwater wireless optical communication (UWOC) systems. Our derivations are general as the channel model under consideration takes into account all of the channel degrading effects, namely absorption, scattering, and turbulence-induced fading. We numerically show that the UWOC systems, due to the severe channel impairments, cannot typically support longer link ranges than 100 m. Therefore, in this paper, in order to increase the transmission reliability and hence extend the viable communication range of UWOC systems, we apply decode-and-forward (DF) relay-assisted communications either in the form of multi-hop transmission, where multiple intermediate relays are serially employed between the source and destination, or parallel relaying in which multiple DF relays are distributed among the source-to-destination path to cooperate in the end-to-end transmission. Our numerical results reveal that multi-hop transmission, owing to the distance-dependency of all of the channel degrading effects, can tremendously improve the end-to-end outage probability and increase the accessible link ranges to hundreds of meter. For example, a dual-hop transmission in a 45 m coastal water link can provide up to 41 dB performance improvement at the outage probability of 10−9.

Low-Complexity Belief Propagation Detection for Correlated Large-Scale MIMO Systems

In this paper, belief propagation (BP) detection in real domain for large-scale multiple-in multiple-out (MIMO) systems is proposed. The mathematical analysis of message updating rules for independent identically distributed (i.i.d.) and correlated fading MIMO channels are given in detail. The damped BP with damping on the a priori probability vector is employed to improve the performance for the uplink large-scale MIMO systems with correlation among transmitting antennas or loading factor ρ = 1. Based on the convergence analysis, the method of selecting message damping factor δ is presented also. In addition, the adaptive message updating for BP detection is first proposed to provide a good trade-off between performance and complexity. Simulation results have shown that, for 16 × 16 MIMO with quadrature phase shift keying (QPSK) modulation, this approach can show 1 dB performance improvement at BER of 10−2, compared to complex domain single-edge based BP (SE-BP). For 8 × 32 MIMO with correlation among transmitting and receiving antennas, where 16-Quadrature Amplitude Modulation (16-QAM) is employed, simulation results have shown that the proposed adaptive BP detection achieves a complexity reduction of 50% compared to the general BP detection with negligible performance loss. For i.i.d. and correlated fading channels with various antennas configurations, advantages of the proposed approach over existing BP detections as well as MMSE approach have been demonstrated by thorough simulations. Hence, the proposed BP detection is suitable for large-scale MIMO systems, especially for those with high-order modulations. Furthermore, the adaptive BP detection together with message damping is expected to be a good choice for low complexity detection.

Performance Studies of Underwater Wireless Optical Communication Systems With Spatial Diversity: MIMO Scheme

In this paper, we analytically study the performance of multiple-input multiple-output underwater wireless optical communication (UWOC) systems with ON–OFF keying modulation. To mitigate turbulence-induced fading, which is amongst the major degrading effects of underwater channels on the propagating optical signal, we use spatial diversity over UWOC links. Furthermore, the effects of absorption and scattering are considered in our analysis. We analytically obtain the exact and an upper bound bit error rate (BER) expressions for both optimal and equal gain combining. In order to more effectively calculate the system BER, we apply Gauss-Hermite quadrature formula as well as approximation to the sum of lognormal random variables. We also apply the photon-counting method to evaluate the system BER in the presence of shot noise. Our numerical results indicate an excellent match between the exact and upper bound BER curves. Also, a good match between the analytical results and numerical simulations confirms the accuracy of our derived expressions. Moreover, our results show that spatial diversity can considerably improve the system performance, especially for channels with higher turbulence, e.g., a $3\times 1$ multiple-input single-output transmission in a 25 m coastal water link with a log-amplitude variance of 0.16 can introduce 8 dB performance improvement at the BER of 10−9.

Multiple-Symbol Differential Detection for Spatial Modulation

Approximately 3 dB Signal-to-Noise Ratio (SNR) loss is always paid with conventional Differential Spatial Modulation (DSM) as compared to coherent Spatial Modulation (SM). In this paper, a Multiple-Symbol Differential Detection (MSDD) technique is proposed for DSM systems to mitigate the loss due to differential detection. The new scheme can greatly narrow the 3 dB performance gap by extending the observation interval for differential decoding. The technique uses maximum-likelihood sequence detection instead of traditional symbol-by-symbol detection, and is carried out on the slow, flat Rayleigh fading channel. A generalized decision metric is derived for an observation interval of arbitrary length. It is shown that for a moderate number of symbols, MSDD provides approximately 1.5 dB performance improvement over the conventional differential detection. In addition, a closed-form pairwise error probability and approximate bit error probability (BEP) are derived for multiple-symbol differential spatial modulation. Results show that the theoretical BEP matches well the simulated one. The BEP is shown to converge asymptotically with the number of symbols in the observation interval to that of the differential scheme with coherent detection.

Design of Joint Sparse Graph for OFDM System

Low density signature orthogonal frequency division multiplexing (LDS-OFDM) and low density parity-check (LDPC) codes are multiple access and forward error correction (FEC) techniques, respectively. Both of them can be expressed by a bipartite graph. In this paper, we construct a joint sparse graph combining the single graphs of LDS-OFDM and LDPC codes, namely joint sparse graph for OFDM (JSG-OFDM). Based on the graph model, a low complexity approach for joint multiuser detection and FEC decoding (JMUDD) is presented. The iterative structure of JSG-OFDM receiver is illustrated and its extrinsic information transfer (EXIT) chart is researched. Furthermore, design guidelines for the joint sparse graph are derived through the EXIT chart analysis. By offline optimization of the joint sparse graph, numerical results show that the JSG-OFDM brings about 1.5–1.8 dB performance improvement at bit error rate (BER) of $10^{-5} $ over similar well-known systems such as group-orthogonal multi-carrier code division multiple access (GO-MC-CDMA), LDS-OFDM, and turbo structured LDS-OFDM.

Microgrid state estimation and control for smart grid and Internet of Things communication network

Given the significant concerns regarding carbon emissions from fossil fuels, global warming and energy crises, the renewable distributed energy resources (DERs) are having to be integrated in the smart grid (SG). The SG can spread intelligence of the energy distribution and control system from the central unit to long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, a novel approach for SG SE is proposed based on concatenated coding structures, where the grid state is treated as a dynamic outer code and the recursive systematic convolutional code is seen as a concatenated inner code for protecting and redundancy in the system state. Furthermore, Kalman filter (KF)-based online DERs SE and a discrete-time linear quadratic regulation method are proposed to control these state deviations for the SG and the Internet of Things (IoTs) communication network, which can exploit the far-reaching connectivity and privacy of DERs messages. Simulation results show that the proposed algorithm achieves 3 dB performance improvement compared with the existing KF.

Analysis of adaptive optics-based telescope arrays in a deep-space inter-planetary optical communications link between Earth and Mars

Earth-based telescope array receivers employing optical communications have the potential to fulfill the communication needs of technologically sophisticated, deep-space exploration missions. Atmospheric turbulence is the chief restrictive factor in an optical deep-space channel (ODSC). In this paper, investigation and design of adaptive optics (AO) subsystems are presented for the compensation of the coupled effects of optical turbulence and background noise in telescope array receivers. An end-to-end simulation platform for an ODSC between Mars and Earth is implemented, which incorporates pulse-position modulation (PPM), direct-detection receivers, and detectors with the capability of detection of single photon. The extreme conditions of atmospheric turbulence and background noise are also modeled in the analysis. AO subsystems are incorporated at individual telescopes in the array receiver to mitigate the turbulence effects. The performance of array receivers is evaluated in terms of the probability of error and communication throughput. The analysis in this research depicts that in worst-case turbulence and background noise conditions, inclusion of AO systems results in 8.5dB performance improvement in communication data rates. The performance improvement of 5.6dB is achievable in moderate channel conditions. Comparison of performance of array receivers with that of a large monolithic telescope shows that incorporation of AO systems is more feasible in arrays comprising telescopes with relatively smaller diameters.

BER-Aware ADC-Based 2<formula formulatype="inline"> <tex Notation="TeX">$\,\times\,$</tex></formula>1 MIMO Blind Receiver for High Speed Broadband Communication Links

An analog-to-digital-converter (ADC)-based bit-error rate (BER)-aware blind receiver is presented to maximize the BER performance under a given low ADC resolution. The adaptation engine simultaneously adjusts the equalizer coefficients to compensate for the channel dispersion and calibrates the reference levels of the ADC to maximize the BER performance of the receiver. BER-aware adaptation is achieved through selective updates using the steepest-descent least-mean-square (LMS) algorithm. A time-interleaved 2 $\,\times $ -oversampling ADC followed by a 2 $\,\times\,$ 1 multiple-input multiple-output (MIMO) equalizer improves the BER performance without sampling phase dependency. Simulation results reveal that the proposed design achieves a 4 dB performance improvement at a BER of 1 $\, \times 10^{-5}$ over conventional designs in a 27” backplane channel at a data rate of 28 Gb/s.

Retinex based visual identicalness detection for videos corrupted by imaging noise

Detecting the visually identical regions among successive frames for noisy videos, called visual identicalness detection (VID) in this paper, is a fundamental tool in video applications for lower power consumption and higher efficiency. In this paper, instead of performing VID on the original video signal or on the de-noised video signal, a Retinex based VID approach is proposed to perform VID on the Retinex signal to eliminate the noise influence introduced by imaging system. Several Retinex output generation approaches are compared, within which the proposed Cohen–Daubechies–Feauveau wavelet based approach is demonstrated to have better efficiency in detection and higher adaptability to the video content and noise severity. Compared with approaches performing detection in the de-noised images, the proposed algorithm presents up to 4.78 times higher detection rate for the videos with moving objects and up to 30.79 times higher detection rate for the videos with static scenes, respectively, at the same error rate. Also, an application of this technique is provided by integrating it into an H.264/AVC video encoder. Compared with compressing the de-noised videos using the existing fast algorithm, an average of 1.7dB performance improvement is achieved with up to 5.47 times higher encoding speed. Relative to the reference encoder, up to 32.47 times higher encoding speed is achieved without sacrificing the subjective quality.

On Receiver Design for Uplink Low Density Signature OFDM (LDS-OFDM)

Low density signature orthogonal frequency division multiplexing (LDS-OFDM) is an uplink multi-carrier multiple access scheme that uses low density signatures (LDS) for spreading the symbols in the frequency domain. In this paper, we introduce an effective receiver for the LDS-OFDM scheme. We propose a framework to analyze and design this iterative receiver using extrinsic information transfer (EXIT) charts. Furthermore, a turbo multi-user detector/decoder (MUDD) is proposed for the LDS-OFDM receiver. We show how the turbo MUDD is tuned using EXIT charts analysis. By tuning the turbo-style processing, the turbo MUDD can approach the performance of optimum MUDD with a smaller number of inner iterations. Using the suggested design guidelines in this paper, we show that the proposed structure brings about 2.3 dB performance improvement at a bit error rate (BER) equal to 10-5 over conventional LDS-OFDM while keeping the complexity affordable. Simulations for different scenarios also show that the LDS-OFDM outperforms similar well-known multiple access techniques such as multi-carrier code division multiple access (MC-CDMA) and group-orthogonal MC-CDMA.

Improved transmitted reference pulse cluster scheme for ultra wideband communication systems

In this study, from a more practical point of view, the authors develop an improved transmitted reference pulse cluster (iTRPC) scheme. Based on a recently proposed transmitted reference pulse cluster (TRPC) structure, each pulse cluster is broken into reference pulse clusters (RPCs) and data pulse clusters (DPCs). It is observed that by inserting a number of zeros at the end of each RPC and each DPC, inter pulse interference can be dramatically reduced. This also results in more multi-path delayed power being captured for data detection. The performance of the proposed iTRPC scheme is analysed and compared with that of traditional TR and the original TRPC (oTRPC) system. Simulations verify that this iTRPC scheme, with quite low-complexity and not extra energy requirements, achieves more than 7 dB performance improvement over the oTRPC scheme.