Higher Order Modulation Schemes Research Articles

Exploiting Fine-grained Dimming with Improved LiFi Throughput

Optical wireless communication (OWC) shows great potential due to its broad spectrum and the exceptional intensity switching speed of LEDs. Under poor conditions, most OWC systems switch from complex and more error prone high-order modulation schemes to more robust On-Off Keying (OOK) modulation defined in the IEEE OWC standard. This paper presents LiFOD, a high-speed indoor OOK-based OWC system with fine-grained dimming support. While ensuring fine-grained dimming, LiFOD remarkably achieves robust communication at up to 400 Kbps at a distance of 6 meters. This is the first time that the data rate has improved via OWC dimming in comparison to the previous approaches that consider trading off dimming and communication. LiFOD makes two key technical contributions. First, LiFOD utilizes Compensation Symbols (CS) as a reliable side-channel to represent bit patterns dynamically and improve throughput. We firstly design greedy-based bit pattern mining. Then we propose 2D feature enhancement via YOLO model for real-time bit pattern mining. Second, LiFOD synchronously redesigns optical symbols and CS relocation schemes for fine-grained dimming and robust decoding. Experiments on low-cost Beaglebone prototypes with commercial LED lamps and the photodiode (PD) demonstrate that LiFOD significantly outperforms the state-of-the-art system with 2.1× throughput on the SIGCOMM17 data-trace.

Effects of diagram plane on neural network based modulation recognition

Modulation recognition using deep learning presents challenges in effectively distinguishing high-order modulation schemes while maintaining a balance between complexity and recognition accuracy. In this study, we curate a comprehensive dataset in the rθ plane, encompassing eight distinct modulation schemes. Leveraging hyperparameter optimization and transfer learning, we explore the capabilities of various CNN-based architectures, including MobileNetV2, ResNet50V2, ResNet101V2, InceptionV3, ResNet152V2, Xception, and InceptionResNetV2, for the classification of modulation schemes. The simulation results demonstrate that with signal-to-noise ratio (SNR) values exceeding 5 dB, all models exhibit classification accuracies surpassing 50% and approach near-perfect accuracy at an SNR value of 20 dB. However, under low SNR conditions, such as 5 dB, the recognition accuracies of all models, except for ResNet152V2 and InceptionV3, show minimal variation. As the SNR increases by 5 dB from −5 dB to 20 dB, ResNet152V2 and InceptionV3 demonstrate remarkable classification accuracy improvements, exceeding 40%, 30%, 30%, 10%, and 15%, respectively. In contrast, the other models do not exhibit such robust responsiveness in accuracy enhancements. The remarkable performance improvements are achieved by fine-tuning pre-trained models through these processes.

Spectrum sensing for dynamic primary user with multiple transitions under Rayleigh distributed channel attenuation in additive Laplacian noise

We propose a real-time scenario of dynamic behavior of the primary user (PU) with multiple transitions under the Rayleigh distributed channel attenuation (RDCA) for spectrum sensing. We assume Multiple access interference (MAI) as an additive noise which is modeled using Laplacian noise model. Apart from this, we consider higher order modulation scheme such as Quadrature Amplitude Modulation (QAM) with its modulation order M. The Markov chain models the transitions of the PU using transition probabilities (TPs) which are functions of the Markov Chain parameters (MCPs) ψ and ξ. The MCPs are assumed to be known at the receiver. In improved absolute value cumulation detection (i-AVCD) scheme, decision statistic is obtained when the absolute value of received observation at the cognitive radio (CR) is raised to an exponent P with P∈(0,2]. The AVCD and ED (energy detection) are special cases of i-AVCD at P=1 and P=2, respectively. The TPs are used with i-AVCD to get a modified detection scheme, referred to as Mr: i-AVCD. The Mr refers to the term ‘modified’ under the effect of MCPs. Special cases, referred to as Mr: AVCD and Mr: ED are formed for the Mr: i-AVCD scheme at P=1 and P=2, respectively. Further, we consider the case of a multiple antenna system at the receiver. We derive closed-form expressions of the detection probability (PD) and false alarm probability (PF). Finally, we present the results using receiver operating characteristics (ROC) and PD vs. average SNR (γ). Our analytical findings are corroborated using a close match between simulations and analytical results. Besides this, the modified schemes have been compared with the conventional schemes. In conventional schemes, no information on MCPs is available at the receiver a priori. We find that the modified detection schemes (MDS) outperform conventional schemes. Further, we observe that performance of MDS degrades as we increase the value of M. We also observe that the spatial diversity improves the performance of the MDS.

GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification

Automatic modulation identification (AMI) is a technique to detect the modulation type and order of a received signal, which has the potential to enhance cognitive radio capabilities for future generations of communication devices. However, AMI classifiers traditionally have exhibited low efficiency in low signal-to-noise ratio (SNR) environments. Hence, to address this problem we present our novel Gated Graph Convolutional Neural Network (GGCNN) classifier for feature-based AMI. This architecture includes a robust feature extraction stage to extract deep correlative patterns about the received symbols. Not only does this feature extraction stage use the temporal characteristics of the received symbols, but it also takes advantage of embedded signaling features from the received signal. In the proposed classifier, the received constellations are treated as a graph, allowing it to outperform state-of-the-art classifiers due to its strong performance in graph classification. This is observed clearly in the visualization of the extracted features, even for high-order modulation schemes. In this paper, we present our systematic research conducted for maximizing the efficiency obtainable by our classifier. Extensive simulation results demonstrate a significant accuracy improvement of 18.44 percentage points, and an efficiency increase by 60.78% for our GGCNN-AMI classifier compared to state-of-the-art classifiers in low-SNR environments.

Performance Analysis of GFDM System in 5G Cellular Networks

Orthogonal Frequency Division Multiplexing (OFDM) is employed in the current 4G cellular technologies. Because of its resistance to multipath propagation channels, OFDM is a popular multicarrier modulation technique. Although OFDM is one of the effective approaches for a 4G network, out-of-band radiation, inter-symbol interference (ISI), and inter-carrier interference make this method unsuitable for 5G. (ICI). A new approach or next-generation cellular network, i.e., very high data rate, low latency, greater throughput, more device connectivity, and improved device-to-device communication, should be used to deliver effective and dependable communication. The system introduces generalized frequency division multiplexing (GFDM). In GFDM, a single cyclic prefix is utilized throughout the frame and an adjustable pulse-shaping filter can be used to monitor the transmitted signal’s out-of-band emission. Using this ISI, the multipath channel can be controlled. In the proposed system, with large signal constellations, higher-order modulation schemes, and advanced receiver structure, a very high data rate can be achieved. By using various iterative receiver structures the performance of the GFDM system in 5G cellular networks can be analyzed.

Brain inspired optoelectronic integrated receiver chip for wireless optical communication

Inspired by the mechanism of visual attentional selection, a wireless optical communication (WOC) Integrated receiver consisting of a vertical double junction photodetector (VDJ-PD) and an attentional selection circuit (ASC) is presented. The whole receiver is fabricated in standard CMOS technology. The VDJ-PD can realize optical signal detection and wavelength identification. The attentional selection circuit activates the competition among the two PN junctions in VDJ-PD to select the signal with significant feature in real-time while discarding non-significant signals. A higher-order signal modulation scheme is applied by the proposed receiver to obtain a high transfer rate, and all data are decoded using color features. The optical testing results show that parallel and real-time communication of the red and blue light can be achieved, and the rise time is 4.47 μs. To measure the anti-interference performance of the receiver, a water tank is used to simulate a harsh communication environment. Results indicate that the real-time and reliable communication requirements in a harsh channel condition are satisfied. When the percentage ratio between the intensity of stray light relative to the light with maximum intensity is 84%, an accurate information decoding can still be achieved. Moreover, the whole processing procedure does not require the participation of ADC, CPU and memory, consequently avoiding the von Neumann bottleneck.

A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems.

In this paper, we revisit a two-user space-time line coded uplink non-orthogonal multiple access (STLC-NOMA) system for Internet-of-things (IoT) networks and propose a novel low-complexity STLC-NOMA system. The basic idea is that both IoT devices (stations: STAs) employ amplitude-shift keying (ASK) modulators and align their modulated symbols to in-phase and quadrature axes, respectively, before the STLC encoding. The phase distortion caused by wireless channels becomes compensated at the receiver side with the STLC, and thus each STA’s signals are still aligned on their axes at the access point (AP) in the proposed uplink STLC-NOMA system. Then, the AP can decode the signals transmitted from STAs via a single-user maximum-likelihood (ML) detector with low-complexity, while the conventional uplink STLC-NOMA system exploits a multi-user joint ML detector with relatively high-complexity. We mathematically analyze the exact BER performance of the proposed uplink STLC-NOMA system. Furthermore, we propose a novel expectation-maximization (EM)-based blind energy estimation (BEE) algorithm to jointly estimate both transmit power and effective channel gain of each STA without the help of pilot signals at the AP. Somewhat interestingly, the proposed BEE algorithm works well even in short-packet transmission scenarios. It is worth noting that the proposed uplink STLC-NOMA architecture outperforms the conventional STLC-NOMA technique in terms of bit-error-rate (BER), especially with high-order modulation schemes, even though it requires lower computation complexity than the conventional technique at the receiver.

A Deep Learning-Based Robust Automatic Modulation Classification Scheme for Next-Generation Networks

Due to stochastic wireless environment, the process of modulation classification has become a challenging task. Because of its powerful feature extraction ability and promising performance over the conventional schemes, deep learning (DL) models are employed to automatic modulation classification (AMC) problems. Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. In this work, different scaling factors are selected for the generation of [Formula: see text]-QAM frames. The combination of scaling factors, which maximize the classification accuracy is chosen. A convolutional neural network (CNN) with six stages is employed for AMC. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes.

Blind nonlinearity equalization by machine-learning-based clustering for QAM-based quantum noise stream cipher transmission

In quantum noise stream cipher (QNSC) systems, it is difficult to compensate fiber nonlinearity by digital signal processing (DSP) due to interactions between chromatic dispersion (CD), amplified spontaneous emission (ASE) noise from erbium-doped fiber amplifier (EDFA) and Kerr nonlinearity. Nonlinearity equalizer (NLE) based on machine learning (ML) algorithms have been extensively studied. However, most NLE based on supervised ML algorithms have high training overhead and computation complexity. In addition, the performance of these algorithms have a lot of randomness. This paper proposes two clustering algorithms based on Fuzzy-logic C-Means Clustering (FLC) to compensate the fiber nonlinearity in quadrature amplitude modulation (QAM)-based QNSC system, including FLC based on subtractive clustering (SC) and annealing evolution (AE) algorithm. The performance of FLC-SC and FLC-AE are evaluated through simulation and experiment. The proposed algorithms can promptly obtain suitable initial centroids and choose optimal initial centroids of the clusters to achieve the global optimal initial centroids especially for high order modulation scheme. In the simulation, different parameter configurations are considered, including fiber length, optical signal-to-noise ratio (OSNR), clipping ratio and resolution of digital to analog converter (DAC). Furthermore, we measure the Q-factor of transmission signal with different launched powers, DAC resolution and laser linewidth in the optical back-to-back (BTB) experiment with 80-km single mode fiber. Both simulation and experimental results show that the proposed techniques can greatly mitigate the signal impairments.

Combined Classifier-Demodulator Scheme Based on LSTM Architecture

When it comes to studies on smart receiver designs, using machine learning and deep learning techniques for the development of automatic modulation classifiers as well as demodulators which require little to no information about the transmitted signal or the channel state is an area of interest. Through this study, we have proposed a combined classifier-demodulator system that is entirely deep learning-based and one that is focused on higher-order quadrature amplitude modulation (QAM) schemes such as 64QAM and 256QAM that can be used in next-generation mobile technologies. The system was developed by training a bidirectional long-short-term memory (BiLSTM) and long-short-term memory (LSTM) network for the classifier and demodulator, respectively, using randomly generated data, demodulated using binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), 16QAM, 64QAM, and 256QAM transmitted through a simulated additive white Gaussian noise (AWGN) channel of varying signal to noise ratio (SNR) levels. The classifier was then tested for its prediction accuracy while the demodulator models were tested against traditional mathematical models while calculating the effective capacity. The results showcased that the classifier worked extremely well for the QAM schemes across all SNR levels and less so with the PSK models. Considering the demodulator models’ performance, all schemes except the 256QAM demodulator were able to reach a zero or near zero bit error rate (BER) level within minimum acceptable SNR ranges.

Design of Optical Fiber Communication System Under Carrier Photodiode/Compact PCI Bus and Signal Data Modulation

This study was developed to design a high-speed and high-power uni-traveling-carrier photodiode (UTC-PD). Gaussian doping is adopted in the absorption layer, and a thin-doped cliff layer is introduced above the collection layer. The effects on the high-speed performance of UTC-PD are analyzed, and the thickness and concentration of absorption layer, cliff layer, and collection layer are optimized. Then, the carrier optical detector is used in optical fiber communication system, and a small pluggable SFP fiber module is used to realize the conversion of photoelectric signal. Based on the test requirements, the test platform of optical fiber communication system is built, and the CPCI bus based on PCI9054 is designed to communicate with the industrial computer. Meanwhile, a multi-polarization and high-order modulation scheme is designed for optical signal. The effects of transmission power, transmission distance, and optical signal-to-noise ratio (OSNR) on the transmission performance of orthogonal amplitude modulation (DP-16QAM, DP-32QAM) signals are analyzed by simulation. Simulation analysis shows that the optimized UTC-PD has improved its high speed and saturation, whose 3 dB bandwidth is up to 56 GHz, and responsiveness up to 0.28 A/W. In high-speed serial differential data transmission, the optical fiber communication system based on carrier optical detector/CPCI bus receives and sends reliably, and the error code value is always 0. Based on the signal modulation scheme in this study, the increase of OSNR can reduce the bit error rate (BER), and the bit error rate of 2A8P-16QAM and 4A8P-32QAM will decrease, that is, the signal transmission needs a higher optical signal-to-noise ratio.

On the performance of a relay assisted hybrid RF-NLOS UVC system with imperfect channel estimation

Non-line-of-sight (NLOS) ultraviolet communication (UVC) is emerging as an attractive optical wireless communication technology that enables wireless connectivity in radio-frequency (RF) prohibited areas with no LOS availability. NLOS UVC, however, suffers from a very high path loss, thereby restricting its usage to smaller link distances. In this paper, we address the challenge of providing long-distance wireless connectivity to RF prohibited areas by mixing NLOS UVC with RF communication using a decode-and-forward relay. The RF link is modeled using Rayleigh distribution, and the NLOS UV link is modeled using lognormal distribution under weak turbulence conditions. A framework for analytical expressions of the outage probability and probability density function (PDF) of the end-to-end signal-to-noise ratio is presented by considering the practical scenario of imperfect channel state information (CSI) at the receiver. Subsequently, a PDF based novel closed-form analytical expression of the average symbol error rate is deduced for spectrally efficient higher-order modulation schemes, including rectangular quadrature modulation (RQAM), square QAM (SQAM), cross-QAM (XQAM), and hexagonal QAM (HQAM). Numerical investigations are conducted, and the impact of CSI imperfections on the system performance is evaluated. It is shown that the RF link is more vulnerable to channel estimation error (CEE) than the NLOS UV link. Further, it is illustrated that for constellation sizes greater than four, HQAM always performs better than the RQAM, SQAM, and XQAM schemes, irrespective of the amount of CEE present. Furthermore, it is shown that an elevation angle of 70° or less in the NLOS UVC link results in better outage performance. Correctness of the derived analytical expressions is supported through extensive Monte Carlo simulations.

Towards InAs/InP Quantum-Dash Laser-Based Ultra-High Capacity Heterogeneous Optical Networks: A Review

The unprecedented increase in internet traffic witnessed in the last few years has pushed the community to explore heterogeneous optical networks, including free-space-optics (FSO) communication, radio-over-fiber (RoF), wireless (WL), and their convergent hybrid technologies. This stems from the possibility of seamless integration with the existing fiber-optic backhaul networks while providing much-needed flexibility and scalability. Moreover, efficient optical transceivers, particularly light sources in the form of semiconductor laser diodes, are key in meeting the future demands of the next generation green optical access networks. In this respect, InAs/InP Quantum-dash (Qdash) nanostructure-based lasers have shown tremendous progress in the past few years with applications spanning from a wavelength division multiplexed (WDM) optical communication to recently, millimeter-wave (MMW) over fiber networks. This waveguide-based laser diode (LD) has demonstrated a rule-changing broad multi-wavelength lasing emission, thanks to the inherent and wavelength-tunable wide gain profile offered by the Qdash active-gain region covering S- to U-band regions. Moreover, exploiting mode-locking and optical injection locking assisting techniques, highly coherent InAs/InP Qdash comb laser source at 1550 nm and 1610 nm have been respectively realized, thus opening a new paradigm for their potential applications in green optical and 5G access networks. In this work, progress on utilizing InAs/InP Qdash LD, supporting several sub-carriers, in energy-efficient ~1550 nm and ~1610 nm WDM heterogeneous optical networks with single-mode-fibers (SMF) has been highlighted, exhibiting aggregate data rates of 11 – 12 Tb/s by employing a single device, and accommodating PAM4 and higher-order 32-QAM modulation schemes. Moreover, the recent deployment of this LD in the RoF domain, targeting MMW frequency band, for convergent fiber-wireless networks, is also summarized, with demonstrated 25 to >100 GHz MMW beat-tone frequency generates and transmission of up to 24 Gb/s.

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However, MMW spectrums with high-order modulation schemes drive the power amplifier (PA) at significant back-off, causing severe nonlinear distortions, thus deteriorating the transceiver’s (TRXs) modulation process. Typically, the TRX efficacy is quantified with standardized linearization matrices, which take advantage of different predistortion (PD) schemes to handle deep compression of the PA. In this regard, TRX baseband signals are mostly linearized in the digital domain, where digitally controlled linearization needs higher sampling rates due to increasing MMW bandwidths to compensate for intermodulation (IMD) products, resulting in increased system cost and power consumption. Alternately, the digitally controlled analog-based linearization, i.e., the hybridization of digital predistortion (DPD) and analog predistortion (APD), is highly productive and cost-effective for fulfilling the linearized energy-efficient design vision of MMW networks. Therefore, this paper puts an extensive spotlight on the progress in PD-based linearization for 5G and beyond communications. It first provides background information on the advancements of PD schemes through recent surveys, then classifies the general roadmap of PD waveform processing across the TRX system models as preliminary. After this, we present three prominent PD architectures and their design approaches with intrinsic performance metrics. Finally, we explore four case studies encompassing PD operation under certain nonlinear constraints of different communication schemes. We examine the suitability of PD-based linearization solutions, both existing and proposed till the first quarter of 2022, and identify the potential prospects in this domain.

Design and Feasibility Verification of 6G Wireless Communication Systems with State of the Art Technologies

Frequencies above 100 GHz are the promising frequency bands for 6G wireless communication systems because of the abundant unexplored and unused spectrum. The increasing global demand for ultra-high spectral efficiencies, data rates, speeds and bandwidths in next-generation wireless networks motivates the exploration of peak capabilities of massive MIMO (Multi–Input–Multi–Output) wireless access technology at THz bands (0.1–10 THz). The smaller wavelengths (order of microns) of these frequencies give an advantage of making high gain antennas with smaller physical dimensions and allows massive spatial multiplexing. This paper presents the design of ultra-massive MIMO (ultra-mMIMO) hybrid beamforming system for multi users and its feasibility to function at THz frequency bands. The functionality of the proposed system is verified at higher order modulation schemes to achieve higher spectral efficiencies using performance metrics that includes error vector magnitude, symbol constellations, and antenna array radiation beams. The performance results suggest to use a particular mMIMO antenna configuration based on number of independent data streams per user and strongly recommended to use higher number of data streams per user in order to achieve higher throughputs that satisfy the needs of 6G wireless systems. Also the performance of the proposed system at 0.14 THz is compared with mmWave systems that operate at 28 GHz and 73 GHz bands to justify the feasibility of the proposed work.

A Performance Comparison Between Quantum Dash and Quantum Well Fabry-Pérot Lasers

We directly compare the performance of 1.55 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Fabry-Pérot lasers using quantum well (QW) and quantum dash gain regions. Other than the gain medium the designs were identical, grown in the same growth system, and processed at the same time. The static laser performance (light-current and spectral) were similar for both gain materials but the intensity and phase noise characteristics of the dash based lasers were significantly better. This was attributed to the self mode-locking of the dash lasers that was not observed for the QW lasers. This reduction in noise allowed individual lasing lines from the Fabry-Pérot dash lasers to be used for data transmission using high order modulation schemes, with quadrature phase-shift keying (QPSK) demonstrated at a symbol rate of 32GBaud. This performance could not be achieved using the equivalent QW based laser.

M-ary quadrature amplitude modulation order optimization for terahertz wireless communications over dispersive channels

Highly accurate atmospheric models, based on molecular resonance information contained within the HITRAN database, were used to simulate the propagation of high capacity single-carrier quadrature amplitude modulated signals through the atmosphere for various modulation orders. For high-bandwidth signals such as those considered in this work, group velocity dispersion caused by atmospheric gases distorts the modulated waveform, which may produce bit errors. This leads to stricter Signal-To-Noise Ratio requirements for error-free operation, and this effect is more pronounced in high-order modulation schemes. At the same time, high-order modulation schemes are more spectrally efficient, which reduces the bandwidth required to maintain a given data rate, and thus reduces the total group velocity dispersion in the link, resulting in less distortion and better performance. Our work with M-ary quadrature amplitude modulated signals shows that optimal selection of modulation order can minimize these conflicting effects, resulting in decreased error rate, and reducing the performance requirements placed on any equalizers, other dispersion-compensating technologies, or signal processing hardware.

A Literature Survey on Algorithms and Hardware Architectures of Max-Log-MAP Demapping

Emergent wireless communication standards, which are employed in different transmission environments, support various modulation schemes. High-order constellations are targeted to achieve high bandwidth efficiency. However, the complexity of the symbol-by-symbol Maximum A Posteriori (MAP) algorithm increases dramatically for these high-order modulation schemes. In order to reduce the hardware complexity, the suboptimal Max-Log-MAP, which is the direct transformation of the MAP algorithm into logarithmic domain, is alternatively implemented. In the literature, a great deal of research effort has been invested into Max-Log-MAP demapping. Several simplifications are presented to meet with specific constellations. In addition, the hardware implementations dedicated for Max-Log-MAP demapping vary greatly in terms of design choices, supported flexibility and performance criteria, making them a challenge to compare. This paper explores the published Max-Log-MAP algorithm simplifications and existing hardware demapper designs and presents an extensive review of the current literature. In-depth comparisons are drawn amongst the designs and different key performance characteristics are described, namely, achieved throughput, hardware resource requirements and flexibility. This survey should facilitate fair comparisons of future designs, as well as opportunities for improving the design of Max-Log-MAP demappers.

Amplitude, phase, and quadrant (APQ) modulation for indoor visible light communications

The main challenge in visible light communications (VLC) is the low modulation bandwidth of light-emitting diodes (LEDs). This forms a barrier towards achieving high data rates. Moreover, the implementation of high order modulation schemes is restricted by the requirements of intensity modulation (IM) and direct detection (DD), which demand the use of real unipolar signals. In this paper, we propose a novel amplitude, phase and quadrant (APQ) modulation scheme that fits into the IM/DD restrictions in VLC systems. The proposed scheme decomposes the complex and bipolar symbols of high order modulations into three different symbols that carry the amplitude, phase and quadrant information of the intended symbol. The constructed symbols are assigned different power levels and are transmitted simultaneously, i.e. exploiting the entire bandwidth and time resources. The receiving terminal performs successive interference cancellation to extract and decode the three different symbols, and then uses them to decide the intended complex bipolar symbol. We evaluate the performance of the proposed APQ scheme in terms of symbol-error-rate and achievable system throughput for different setup scenarios. The obtained results are compared with generalized spatial shift keying (GSSK). The presented results show that APQ offers a higher reliability compared to GSSK across the simulation area, while providing lower hardware complexity.

High-Throughput Visual MIMO Systems for Screen-Camera Communications

Screen-camera communications, using a liquid crystal display (LCD) screen and camera image sensors, have been attractive variants of visible light communications (VLC) since any external light-emitting modules and photo detectors are required for recent mobile devices, which are usually equipped with display and camera. A major issue in screen-camera communications is a performance loss in transmission rate due to nonlinear channel impairments with ambient noise. To improve transmission rates, we investigate the impact of nonlinear channel equalization, nonbinary channel coding, probabilistic shaping, and nonlinear precoding for high-order modulation schemes. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.8–3.3 times higher transmission rates compared to existing schemes for a communication distance of 60–160 cm.