Showing papers on "Bit error rate published in 2019"

Wireless communications with programmable metasurface: Transceiver design and experimental results

Abstract: Metasurfaces have drawn significant attentions due to their superior capability in tailoring electromagnetic waves with a wide frequency range, from microwave to visible light. Recently, programmable metasurfaces have demonstrated the ability of manipulating the amplitude or phase of electromagnetic waves in a programmable manner in real time, which renders them especially appealing in the applications of wireless communications. In this paper, we present the fundamental principle of applying programmable metasurface as transmitter for wireless communications. Then, we establish a prototype system of meta-surface-based transmitter to conduct several experiments and measurements over the air, which practically demonstrate the feasibility of using programmable metasurfaces in future communication systems. By exploiting the dynamically controllable property of programmable metasurface, the design, implementation and experimental evaluation of the proposed metasurface-based wireless communication system are presented with the prototype, which realizes single carrier quadrature phase shift keying (QPSK) transmission over the air. In the developed prototype, the phase of the reflected electromagnetic wave of programmable metasurface is directly manipulated in real time according to the baseband control signal, which achieves 2.048 Mbps data transfer rate with video streaming transmission over the air. In addition, experimental result is provided to compare the performance of the proposed metasurface-based architecture against the conventional one. With the slight increase of the transmit power by 5 dB, the same bit error rate (BER) performance can be achieved as the conventional system in the absence of channel coding. Such a result is encouraging considering that the metasurface-based system has the advantages of low hardware cost and simple structure, thus leading to a promising new architecture for wireless communications.

Programmable metasurface-based RF chain-free 8PSK wireless transmitter

Abstract: In this Letter, a wireless transmitter using the new architecture of programmable metasurface is presented. The proposed transmitter does not require any filter, nor wideband mixer or wideband power amplifier, thereby making it a promising hardware architecture for cost-effective wireless communications systems in the future. Using experimental results, the authors demonstrate that a programmable metasurface-based 8-phase shift-keying (8PSK) transmitter with 8 × 32 phase adjustable unit cells can achieve 6.144 Mbps data rate over the air at 4.25 GHz with a comparable bit error rate performance as the conventional approach without channel coding, but with less hardware complexity.

100 m/500 Mbps underwater optical wireless communication using an NRZ-OOK modulated 520 nm laser diode

Abstract: In this paper, we proposed and experimentally demonstrated a long-distance high-speed underwater optical wireless communication (UOWC) system in a laboratory environment by using a low-cost green laser diode (LD) and power-efficient non-return-to-zero on-off keying (NRZ-OOK) modulation. The system successfully achieved a data rate of 500 Mbps through a 100 m tap-water channel by using a pigtailed single-mode fiber 520 nm green LD. The tap water was measured to have an attenuation coefficient comparable to pure seawater. The measured system bit error rate (BER) value of 2.5 × 10-3 was below the forward error correction (FEC) limit of 3.8 × 10-3 with 7% overhead. The distance can be extended if the received optical power is allowed to reduce to the minimum power to meet the data rate requirement. Based on the measured minimum required power and the power decay model in the water channel, the transmission performance was predicted to be 146 m/500 Mbps and 174 m/100 Mbps.

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks

Abstract: This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer. ORACLE trains a convolutional neural network (CNN) that balances computational time and accuracy, showing 99% classification accuracy for a 16-node USRP X310 SDR testbed and an external database of >100 COTS WiFi devices. Our work makes the following contributions: (i) it studies the hardware-centric features within the transmitter chain that causes IQ sample variations; (ii) for an idealized static channel environment, it proposes a CNN architecture requiring only raw IQ samples accessible at the front-end, without channel estimation or prior knowledge of the communication protocol; (iii) for dynamic channels, it demonstrates a principled method of feedback-driven transmitter-side modifications that uses channel estimation at the receiver to increase differentiability for the CNN classifier. The key innovation here is to intentionally introduce controlled imperfections on the transmitter side through software directives, while minimizing the change in bit error rate. Unlike previous work that imposes constant environmental conditions, ORACLE adopts the ‘train once deploy anywhere’ paradigm with near-perfect device classification accuracy.

Performance Characterization of Underwater Visible Light Communication

Abstract: In this paper, we investigate the performance limits of underwater visible light communication (UVLC) systems. We first develop a closed-form path loss expression as a function of transceiver parameters and water type. We then utilize this new expression to determine the maximum achievable link distance for UVLC systems in pure sea, clear ocean, coastal water, and harbor water. Our results demonstrate that the maximum achievable distance is limited to a few tens of meters. This necessitates the deployment of relay-assisted UVLC systems to extend the transmission range. We consider both detect-and-forward and amplify-and-forward relaying. For each relaying method, we first consider a dual-hop UVLC system and determine optimal relay placement to minimize the bit error rate (BER). Then, we consider a multi-hop system with equidistant relays and determine the maximum achievable distance for a given number of hops to satisfy a targeted end-to-end BER.

Performance Analysis of UAV Relay Assisted IoT Communication Network Enhanced With Energy Harvesting

Abstract: This paper investigated the multiple unmanned aerial vehicle (UAV) relays' assisted network in the Internet of Things (IoT) systems enhanced with energy harvesting in order to overcome the large-scale fading between source and sink as well as achieve the green cooperative communications, where time switch (TS) and power splitting (PS) strategies were typically applied for UAV relays to implement energy harvesting transmission, which was also selected via signal to noise ratio (SNR) maximization criterion so that the terminal node can obtain the optimal signal. Meanwhile, it was worth noting that the terminal node may be disturbed by aggregated interference caused by dense network signaling interaction in the future 5G/B5G systems. Therefore, after TS and PS protocols designing and utilizing, the closed-form expressions of outage probability and bit error rate (BER) for UAV relay assisted IoT systems suffered from aggregated interference were derived in detail. In addition, the throughput and delay limited state of UAV relay assisted transmission were also analyzed thoroughly. The derivations and analysis results showed that the proposed multi-parameter joint optimization of transmitting power, scaling factor, and UAV relay selection could effectively improve the system throughput and reduce the system outage probability and BER. The simulation experiments verified the effectiveness of the proposed schemes and the correctness of theoretical analysis.

Novel Receiver for Superparamagnetic Iron Oxide Nanoparticles in a Molecular Communication Setting.

Abstract: Superparamagnetic iron oxide nanoparticles (SPIONs) have recently been introduced as information carriers in a testbed for molecular communication (MC) in duct flow. Here, a new receiver for this testbed is presented, based on the concept of a bridge circuit. The capability for a reliable transmission using the testbed and detection of the proposed receiver was evaluated by sending a text message and a 80 bit random sequence at a bit rate of 1/s, which resulted in a bit error rate of 0 %. Furthermore, the sensitivity of the device was assessed by a dilution series, which gave a limit for the detectability of peaks between 0.1 to 0.5 mg/mL. Compared to the commercial susceptometer that was previously used as receiver, the new detector provides an increased sampling rate of 100 samples/s and flexibility in the dimensions of the propagation channel. Furthermore, it allows to implement both single-ended and differential signaling in SPION-bases MC testbeds.

Arbitrarily routed mode-division multiplexed photonic circuits for dense integration.

Abstract: On-chip integrated mode-division multiplexing (MDM) is an emerging technique for large-capacity data communications. In the past few years, while several configurations have been developed to realize on-chip MDM circuits, their practical applications are significantly hindered by the large footprint and inter-mode cross talk. Most importantly, the high-speed MDM signal transmission in an arbitrarily routed circuit is still absent. Herein, we demonstrate the MDM circuits based on digitized meta-structures which have extremely compact footprints. 112 Gbit/s signals encoded on each mode are arbitrarily routed through the circuits consisting of many sharp bends and compact crossings with a bit error rate under forward error correction limit. This will significantly improve the integration density and benefit various on-chip multimode optical systems. On-chip mode-division multiplexing has many challenges including crosstalk, losses, and footprint. Here the authors use a nanohole metastructure to create multiplexed bends and crossings for photonic data communications circuit routing with high density that combats these challenges.

Resource Allocation for Wireless-Powered IoT Networks With Short Packet Communication

Abstract: Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users’ individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.

Rydberg-atom-based digital communication using a continuously tunable radio-frequency carrier.

Abstract: Up to now, the measurement of radio-frequency (RF) electric field achieved using the electromagnetically-induced transparency (EIT) of Rydberg atoms has proved to be of high-sensitivity and shows a potential to produce a promising atomic RF receiver at resonance between two chosen Rydberg states. In this paper, we study the extension of the feasibility of digital communication via this quantum-based antenna over a continuously tunable RF-carrier at off-resonance. Our experiment shows that the digital communication at a rate of 500 kbps can be performed reliably within a tunable bandwidth of 200 MHz near a 10.22 GHz carrier. Outside of this range, the bit error rate (BER) increases, rising to, for example, 15% at an RF-detuning of ±150 MHz. In the measurement, the time-varying RF field is retrieved by detecting the optical power of the probe laser at the center frequency of RF-induced symmetric or asymmetric Autler-Townes splitting in EIT. Prior to the digital test, we studied the RF-reception quality as a function of various parameters including the RF detuning and found that a choice of linear gain response to the RF-amplitude can suppress the signal distortion. The modulating signal can be decoded at speeds up to 500 kHz in the tunable bandwidth. Our test consolidates the physical basis for reliable communication and spectral sensing over a wider broadband RF-carrier, which paves a way for the concurrent multi-channel communications founded on the same pair of Rydberg states.

One-Bit OFDM Receivers via Deep Learning

Abstract: This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver—specifically, an autoencoder—jointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.

Layered Orthogonal Frequency Division Multiplexing With Index Modulation

Abstract: In this paper, we propose a novel scheme termed layered orthogonal frequency division multiplexing with index modulation (L-OFDM-IM) to increase the spectral efficiency (SE) of OFDM-IM systems. In L-OFDM-IM, all subcarriers are first divided into multiple layers, each determining the active subcarriers and their modulated symbols. The index modulation (IM) bits are carried on the indices of the active subcarriers of all layers, which are overlapped and distinguishable with different signal constellations so that the number of the IM bits is larger than that in traditional OFDM-IM. A low-complexity detection is proposed to alleviate the high burden of the optimal maximum-likelihood detection at the receiver side. A closed-form upper bound on the bit error rate, the achievable rate, and diversity order are derived to characterize the performance of L-OFDM-IM. To enhance the diversity performance of L-OFDM-IM, we further propose coordinate interleaving L-OFDM-IM (CI-L-OFDM-IM), which interleaves the real and imaginary parts of the modulated symbols over two different subchannels. Computer simulations verify the theoretical analysis, and results show that L-OFDM-IM outperforms the conventional OFDM-IM scheme. Moreover, it is also confirmed that CI-L-OFDM-IM obtains an additional diversity order in comparison with L-OFDM-IM.

Error Probability Analysis of Non-Orthogonal Multiple Access Over Nakagami- $m$ Fading Channels

Abstract: Non-orthogonal multiple access (NOMA) is currently considered as a promising technology for the next-generation wireless networks. In this paper, the error rate performance of NOMA systems is investigated over Nakagami- $m$ fading channels, while considering imperfect successive interference cancelation. In particular, this paper focuses on the pairwise error probability (PEP) analysis, where exact PEP expressions are derived to characterize the performance of all users under different fading conditions. The obtained PEP expressions are then used to derive an exact union bound on the bit error rate (BER). Through the derived PEP expressions, the asymptotic PEP analysis is presented to investigate the maximum achievable diversity gain of NOMA users. Moreover, using the derived BER bound, the power allocation problem for all users in NOMA systems is considered under average power and users BER constraints, which allows realizing the full potential of NOMA. Monte Carlo simulation and numerical results are presented to corroborate the derived analytical expressions and give valuable insights into the error rate performance of each user and the achievable diversity gain.

Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection

Abstract: This paper considers a low-complexity iterative linear minimum mean square error (LMMSE) multiuser detector for the multiple-input and multiple-output system with nonorthogonal multiple access (MIMO-NOMA), where multiple single-antenna users simultaneously communicate with a multiple-antenna base station (BS). While LMMSE being a linear detector has a low complexity, it has suboptimal performance in multiuser detection scenario due to the mismatch between LMMSE detection and multiuser decoding. Therefore, in this paper, we provide the matching conditions between the detector and decoders for MIMO-NOMA, which are then used to derive the achievable rate of the iterative detection. We prove that a matched iterative LMMSE detector can achieve the optimal capacity of symmetric MIMO-NOMA with any number of users, the optimal sum capacity of asymmetric MIMO-NOMA with any number of users, all the maximal extreme points in the capacity region of asymmetric MIMO-NOMA with any number of users, and all points in the capacity region of two-user and three-user asymmetric MIMO-NOMA systems. In addition, a kind of practical low-complexity error-correcting multiuser code, called irregular repeat-accumulate code, is designed to match the LMMSE detector. Numerical results shows that the bit error rate performance of the proposed iterative LMMSE detection outperforms the state-of-art methods and is within 0.8 dB from the associated capacity limit.

Deep Learning-Based Detector for OFDM-IM

Abstract: This letter presents the first attempt of exploiting deep learning (DL) in the signal detection of orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. Particularly, we propose a novel DL-based detector termed as DeepIM, which employs a deep neural network with fully connected layers to recover data bits in an OFDM-IM system. To enhance the performance of DeepIM, the received signal and channel vectors are pre-processed based on the domain knowledge before entering the network. Using datasets collected by simulations, DeepIM is first trained offline to minimize the bit error rate (BER) and then the trained model is deployed for the online signal detection of OFDM-IM. Simulation results show that DeepIM can achieve a near-optimal BER with a lower runtime than existing hand-crafted detectors.

End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks

Abstract: We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find that this sliding window BRNN (SBRNN), based on end-to-end deep learning of the communication system, achieves a significant bit-error-rate reduction at all examined distances in comparison to previous block-based autoencoders implemented as feed-forward neural networks (FFNNs), leading to an increase of the transmission distance. We also compare the end-to-end SBRNN with a state-of-the-art IM/DD solution based on two level pulse amplitude modulation with an FFNN receiver, simultaneously processing multiple received symbols and approximating nonlinear Volterra equalization. Our results show that the SBRNN outperforms such systems at both 42 and 84 Gb/s, while training fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber channel.

1-Tb/s Millimeter-Wave Signal Wireless Delivery at D-Band

Abstract: We experimentally demonstrate the photonics-aided wireless transmission of 4 × 4 multiple-input multiple-output probabilistic shaping 64-ary quadrature-amplitude-modulation (PS 64QAM) millimeter-wave signals at D-band (110–170 GHz) over 3.1-m distance with a total bit rate of 1.056 Tb/s and a bit-error ratio under 4 × 10−2. The employment of advanced digital-signal-processing techniques, including probabilistic shaping, the Nyquist shaping, and look-up-table predistortion, significantly improves the transmission capacity and distance as well as the system performance. To the best of our knowledge, this is the first time to realize >1-Tb/s millimeter-wave signal wireless delivery.

Packet Size Optimization for Lifetime Maximization in Underwater Acoustic Sensor Networks

Abstract: Recently, underwater acoustic sensor networks (UASNs) have been proposed to explore underwater environments for scientific, commercial, and military purposes. However, long propagation delays, high transmission losses, packet drops, and limited bandwidth in underwater propagation environments make realization of reliable and energy-efficient communication a challenging task for UASNs. To prolong the lifetime of battery-limited UASNs, two critical factors ( i.e. , packet size and transmission power) play vital roles. At one hand, larger packets are vulnerable to packet errors, while smaller packets are more resilient to such errors. In general, using smaller packets to avoid bit errors might be a good option. However, when small packets are used, more frames should be transmitted due to the packet fragmentation, and hence, network overhead and energy consumption increases. On the other hand, increasing transmission power reduces frame errors, but this would result in unnecessary energy consumption in the network. To this end, the packet size and transmission power should be jointly considered to improve the network lifetime. In this study, an optimization framework via an integer linear programming (ILP) has been proposed to maximize the network lifetime by joint optimization of the transmission power and packet size. In addition, a realistic link-layer energy consumption model is designed by employing the physical layer characteristics of UASNs. Extensive numerical analysis through the optimization model has been also performed to investigate the tradeoffs caused by the transmission power and packet size quantitatively.

Exact BER Performance Analysis for Downlink NOMA Systems Over Nakagami-m Fading Channels

Abstract: In this paper, the performance of a promising technology for the next generation wireless communications, non-orthogonal multiple access (NOMA), is investigated. In particular, the bit error rate (BER) performance of downlink NOMA systems over Nakagami-m flat fading channels, is presented. Under various conditions and scenarios, the exact BER of downlink NOMA systems considering successive interference cancellation (SIC) is derived. The transmitted signals are randomly generated from quadrature phase shift keying (QPSK) and two NOMA systems are considered; two users' and three users' systems. The obtained BER expressions are then used to evaluate the optimum power allocation for two different objectives, achieving fairness and minimizing average BER. The two objectives can be used in a variety of applications such as satellite applications with constrained transmitted power. Numerical results and Monte Carlo simulations perfectly match with the derived BER analytical results and provide valuable insight into the advantages of optimum power allocation which show the full potential of downlink NOMA systems.

Linear Precoding With Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink

Abstract: We consider the downlink of a massive multiuser (MU) multiple-input multiple-output (MIMO) system in which the base station (BS) is equipped with low-resolution digital-to-analog converters (DACs). In contrast to most existing results, we assume that the system operates over a frequency-selective wideband channel and uses orthogonal frequency division multiplexing (OFDM) to simplify equalization at the user equipments (UEs). Furthermore, we consider the practically relevant case of oversampling DACs. We theoretically analyze the uncoded bit error rate (BER) performance with linear precoders (e.g., zero forcing) and quadrature phase-shift keying using Bussgang’s theorem. We also develop a lower bound on the information-theoretic sum-rate throughput achievable with Gaussian inputs, which can be evaluated in closed form for the case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet accurate, expressions for the distortion caused by low-precision DACs, which can be used to establish the lower bounds on the corresponding sum-rate throughput. Our results demonstrate that, for a massive MU-MIMO-OFDM system with a 128-antenna BS serving 16 UEs, only 3–4 DAC bits are required to achieve an uncoded BER of ${10}^{-{4}}$ with a negligible performance loss compared to the infinite-resolution case at the cost of additional out-of-band emissions. Furthermore, our results highlight the importance of considering the inherent spatial and temporal correlations caused by low-precision DACs.

Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Abstract: Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.

The Engagement of Hybrid Dispersion Compensation Schemes Performance Signature for Ultra Wide Bandwidth and Ultra Long Haul Optical Transmission Systems

Abstract: This paper presents the performance signature of the engagement of hybrid symmetrical hybrid compensation techniques for ultra wide bandwidth and ultra long haul optical transmission systems. These schemes that are namely optigrating, ideal dispersion compensation fiber Bragg Grating (IDCFBG), and dispersion compensation fiber (DCF). The combination of mixing these techniques together which is called hybrid symmetrical dispersion compensation techniques in that case. The employment of these mixing schemes is in symmetrical configuration with the presence of Erbium doped fiber amplifiers in order to upgrade optical fiber system capacity to reach transmission distance up to 432 km and transmission data rate up to 320 Gb/s. Maximum signal quality factor, minimum bit error rate (BER), output optical signal to noise ratio, electrical received power after APD photodetector, noise figure, and gain are the major interesting performance parameters for measuring the system operation efficiency. It is observed that the optimum case for maximum quality factor and minimum BER is achieved with 15 m EDFA amplifier length and 150 mW EDFA pump power.

Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems

Abstract: Machine learning-based channel estimation and equalization methods may improve the robustness and bit error rate (BER) performance of communication systems. However, the implementation of these methods has been blocked by some limitations, mainly including channel model-based offline training and high-computational complexity for training deep neural network (DNN). To overcome those limitations, we propose an online fully complex extreme learning machine (C-ELM)-based channel estimation and equalization scheme with a single hidden layer feedforward network (SLFN) for orthogonal frequency-division multiplexing (OFDM) systems against fading channels and the nonlinear distortion resulting from an high-power amplifier (HPA). Computer simulations show that the proposed scheme can acquire the information of channels accurately and has the ability to resist nonlinear distortion and fading without pre-training and feedback link between receiver and transmitter. Furthermore, the robustness of the proposed scheme is well investigated by extensive simulations in various fading channels, and its excellent generalization ability is also discussed and compared with the DNN.

Hierarchical Distribution Matching for Probabilistically Shaped Coded Modulation

Abstract: The implementation difficulties of combining distribution matching (DM) and dematching (invDM) for probabilistic shaping (PS) with soft-decision forward error correction (FEC) coding can be relaxed by reverse concatenation, for which the FEC coding and decoding lies inside the shaping algorithms. PS can seemingly achieve performance close to the Shannon limit, although there are practical implementation challenges that need to be carefully addressed. We propose a hierarchical DM (HiDM) scheme, having fully parallelized input–output interfaces and a pipelined architecture that can efficiently perform the DM/invDM without the complex operations of previously proposed methods such as constant composition DM. Furthermore, HiDM can operate at a significantly larger post-FEC bit error rate (BER) for the same post-invDM BER performance, which facilitates simulations. These benefits come at the cost of a slightly larger rate loss and required signal-to-noise ratio at a given post-FEC BER.

A Statistical Learning Approach to Ultra-Reliable Low Latency Communication

Abstract: Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to 10−9. Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper, we treat this fundamental problem in the simplest possible communication-theoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; and (iii) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems.

Joint Transceiver Optimization for Wireless Communication PHY Using Neural Network

Abstract: Deep learning has a wide application in the area of natural language processing and image processing due to its strong ability of generalization. In this paper, we propose a novel neural network structure for jointly optimizing the transmitter and receiver in communication physical layer under fading channels. We build up a convolutional autoencoder to simultaneously conduct the role of modulation, equalization, and demodulation. The proposed system is able to design different mapping scheme from input bit sequences of arbitrary length to constellation symbols according to different channel environments. The simulation results show that the performance of neural network-based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate under fading channels. The proposed system can also be combined with other coding techniques to further improve the performance. Furthermore, the proposed system network is more robust to channel variation than traditional communication methods.

A Wide-Area Coverage 35 Gb/s Visible Light Communications Link for Indoor Wireless Applications.

Abstract: Visible Light Communications (VLC) can provide both illumination and communications and offers a means to alleviate the predicted spectrum crunch for radio-frequency wireless communications. In this paper, we report a laser diode based white-light communications link that operates over a wide area and supports high data rates. The proposed system is a four-colour multiplexed high-speed VLC system that uses a microelectromechanical system (MEMS) mirror-based beam-steering. The system operates at record data-rates of more than 35 Gb/s (Bit Error Rate(BER) < 3.8 × 10−3) with a coverage area of 39 m2 at a link distance of 4 m. To the best of our knowledge this is the fastest VLC demonstration reported thus far. The paper also addresses issues of eye-safety, showing data rates of more than 10 Gb/s are feasible.

Memory-controlled deep LSTM neural network post-equalizer used in high-speed PAM VLC system.

Abstract: Linear and nonlinear impairments severely limit the transmission performance of high-speed visible light communication systems. Neural network-based equalizers have been applied to optical communication systems, which enables significantly improved system performance, such as transmission data rate and distance. In this paper, a memory-controlled deep long short-term memory (LSTM) neural network post-equalizer is proposed to mitigate both linear and nonlinear impairments in pulse amplitude modulation (PAM) based visible light communication (VLC) systems. Both 1.15-Gbps PAM4 and 0.9Gbps PAM8 VLC systems are successfully demonstrated, based on a single red-LED with bit error ratio (BER) below the hard decision forward error correction (HD-FEC) limit of 3.8 x 10−3. Compared with the traditional finite impulse response (FIR) based equalizer, the Q factor performance is improved by 1.2dB and the transmission distance is increased by one-third in the same experimental hardware setups. Compared with traditional nonlinear hybrid Volterra equalizers, the significant complexity and system performance advantages of using a LSTM-based equalizer is demonstrated. To the best of our knowledge, this is the first demonstration of using deep LSTM in VLC systems.

Tractable Optical Channel Modeling Between UAVs

Abstract: More recently, the research on potential use of free-space optical (FSO) link as a powerful communication link between unmanned aerial vehicles (UAVs) has created much interest in academia and industry. Due to the need of higher number of UAV-based FSO links relative to the conventional ground-based FSO links, the optimum design of UAV-based FSO system parameters (such as optimum values for beam divergence angle, photodetector size, receiver lens radius, and transmit power, among many others) is much more needful and challenging relative to the ground-based counterpart. Moreover, to avoid the time consumed in Monte-Carlo simulations, existence of a simple and tractable channel model is very important and necessary. To address this need, in this paper, for the weak turbulence conditions, we construct a novel channel model for the considered system model under the effect of log-normal atmospheric turbulence channel. Then, for moderate to strong turbulence conditions, a novel closed-form statistical channel model is derived for Gamma-Gamma turbulence channel. The provided channel models, despite being simple and tractable, include the combined effects of atmospheric turbulence as well as the pointing errors. These also include the effects of receiver field-of-view limitation and inherent position, and orientation deviations of UAVs. Subsequently, for better performance analysis, the closed-form expressions for the outage probability and bit error rate (BER) are derived. Finally, the validity of the proposed novel channel models as well as the closed-form expressions for the outage probability and BER are confirmed by employing Monte-Carlo simulations. The developed results can therefore be applied as a benchmark for finding the optimal tunable parameters of UAV-based FSO links under different channel conditions and different levels of UAV instability without resorting to time-consuming Monte-Carlo simulations.

Leveraging quantum annealing for large MIMO processing in centralized radio access networks

Abstract: User demand for increasing amounts of wireless capacity continues to outpace supply, and so to meet this demand, significant progress has been made in new MIMO wireless physical layer techniques. Higher-performance systems now remain impractical largely only because their algorithms are extremely computationally demanding. For optimal performance, an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user is often required. The base station's computational capacity is thus becoming one of the key limiting factors on wireless capacity. QuAMax is the first large MIMO centralized radio access network design to address this issue by leveraging quantum annealing on the problem. We have implemented QuAMax on the 2,031 qubit D-Wave 2000Q quantum annealer, the state-of-the-art in the field. Our experimental results evaluate that implementation on real and synthetic MIMO channel traces, showing that 10 µs of compute time on the 2000Q can enable 48 user, 48 AP antenna BPSK communication at 20 dB SNR with a bit error rate of 10-6 and a 1,500 byte frame error rate of 10-4.