Analog Joint Source Channel Coding Research Articles

High-Resolution Data Acquisition and Joint Source-Channel Coding in Underwater IoT

Reliable and persistent water monitoring is a challenging problem in smart Underwater Internet of Things (UW IoT) due to its harsh, unexplored, and unpredictable nature. Given the need for high-resolution spatio-temporal sensing in such environments, traditional digital sensors are not suitable due to their high cost, high power consumption, and non-biodegradable nature. Further, reliable and low latency communication techniques that avoid data packet retransmissions, if the feedback is available, are crucial for reconstructing the phenomenon being monitored in a timely manner at the fusion center, such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that enable persistent sensing and continually transmitting data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing analog Joint Source-Channel Coding (JSCC), a low-complexity compression-communication technique, using biodegradable Field Effect Transistors (FETs). We then propose a correlation-aware Hybrid Automatic Repeat Request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages JSCC and the redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus saving energy and time.

Signal Recovery Performance Analysis in Wireless Sensing With Rectangular-Type Analog Joint Source-Channel Coding

The signal recovery performance of the rectangular-type Analog Joint Source-Channel Coding (AJSCC) is analyzed in this work for high and medium/low Signal-to-Noise Ratio (SNR) scenarios in a wireless sensing system with the Doppler channel. The analytical formulations of the Mean Square Error (MSE) performance are derived based on the geometry of the rectangular-type AJSCC on analog sensing and system with Doppler effects. The comprehensive listing of all cases in the three-dimensional geometric signal mapping curve is provided to obtain the theoretical formulations for the medium/low SNR scenario. Evaluation results indicate that there are optimal parameters in the rectangular-type AJSCC to minimize the signal recovery MSE for analog sensing and system with Doppler effects in both high and medium/low SNR scenarios. The analysis of the work provides insights on the optimization of the rectangular-type AJSCC in practical wireless sensing systems.

Low-Delay Analog Joint Source-Channel Coding With Deep Learning

We consider the design of low-delay joint source-channel coding (JSCC) schemes for the transmission of discrete-time analog sources over noisy channels based on deep neural networks. The design problem is addressed as optimization of an autoencoder model, and several scenarios are discussed. For point-to-point communication of independent and identically distributed (i.i.d) Gaussian sources and Gauss-Markov sources over additive-white Gaussian noise (AWGN) channels, the encoder and decoder are constructed using recurrent neural networks (RNNs). With minimum prior knowledge used for design, the performance of these RNNs-based models is optimized using fine tuning techniques during training. Sinusoidal representation networks (SIRENs)-based models are proposed and optimized for three JSCC problems namely, transmitting multivariate Gaussian sources over AWGN channels, transmitting i.i.d Gaussian sources with side information at the decoder, and for communicating correlated sources over orthogonal Gaussian channels. We show that these deep learning-based JSCC schemes perform comparably or better than state-of-the-art (SOTA) traditional schemes. The proposed scheme can extend flexibly to different pairs of source and channel dimensions. Moreover, the spontaneously learned encoder mappings exhibit structured patterns that are interpretable.

Optimizing Low-Complexity Analog Mappings for Low-Power Sensors With Energy Scheduling Capabilities

Power consumption is a major challenge for a massive deployment of wireless sensors in Internet of Things (IoT) networks. This article studies the use of analog joint source-channel coding (AJSCC) mappings in low-power sensing schemes. In particular, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">triangular</i> mapping geometry as a low-complexity dimension reduction mapping. The proposed triangular mapping is employed for analog compression of multiple sensor readings into one signal and, thus, limits the need for power-hungry analog-to-digital conversion and processing at the sensing nodes. A comprehensive performance analysis of the proposed triangular mapping in terms of the mean squared error (MSE) performance is provided analytically and verified numerically. The problem of mapping adaptation to different source distributions is also studied. Moreover, the proposed triangular mapping is adopted in an energy scheduling problem in which the sensing nodes schedule their use of the received powers at different time instants and adjust the mapping parameters accordingly with the goal of minimizing the sum distortion at the receiver. We present a fast low-complexity algorithm for optimal energy scheduling and verify its performance in comparison with commercial convex optimization solvers. It is shown that the proposed mapping provides a very good MSE performance compared to the AJSCC benchmarks despite having a much lower complexity circuit implementation.

Energy-Efficient Analog Sensing for Large-Scale and High-Density Persistent Wireless Monitoring

The research challenge of current wireless sensor networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single power charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities is proposed. Sensing is exclusively the responsibility of analog substrate-consisting of low-power, low-cost all-analog sensors-that sits beneath the traditional WSN comprising of digital nodes, which does all the processing of the sensor data received from analog sensors. A low-power and low-cost solution for substrate sensors has been proposed using analog joint source-channel coding (AJSCC) realized via the characteristics of metal-oxide-semiconductor field-effect transistor (MOSFET). Digital nodes (receiver) also estimate the source distribution at the analog sensors (transmitter) using machine learning techniques so as to find the optimal parameters of AJSCC that are communicated back to the analog sensors to adapt their sensing resolution as per the application needs. The proposed techniques have been validated via simulations from MATLAB and LTSpice to show promising performance and indeed prove that our framework can support large-scale high density and persistent WSN deployment.

Analog Mappings for Non-Linear Channels With Applications to Underwater Channels

This paper discuses the application of Analog Joint Source Channel Coding (JSCC) to Non-Linear Channels, including underwater acoustic channels. Different from traditional digital communication systems, which utilize a source code followed by a channel code, Analog JSCC systems perform coding via a single mapping. We will focus on a particular class of analog codes called space filling curves. The underwater acoustic channel is typically non-linear with Inter-Symbol Interference (ISI). We will first study a simplified version assuming no ISI, developing a scheme to adapt space filling curves to this environment, and studying the theoretical limits. Then, we will extend the analysis to the end-to-end acoustic channel (including ISI), discussing the proposed system in this case. Finally, we will present simulation results of the proposed communication system for the simplified (no ISI) and end-to-end channels.

Transmission of Still Images Using Low-Complexity Analog Joint Source-Channel Coding.

An analog joint source-channel coding (JSCC) system designed for the transmission of still images is proposed and its performance is compared to that of two digital alternatives which differ in the source encoding operation: Joint Photographic Experts Group (JPEG) and JPEG without entropy coding (JPEGw/oEC), respectively, both relying on an optimized channel encoder–modulator tandem. Apart from a visual comparison, the figures of merit considered in the assessment are the structural similarity (SSIM) index and the time required to transmit an image through additive white Gaussian noise (AWGN) and Rayleigh channels. This work shows that the proposed analog system exhibits a performance similar to that of the digital scheme based on JPEG compression with a noticeable better visual degradation to the human eye, a lower computational complexity, and a negligible delay. These results confirm the suitability of analog JSCC for the transmission of still images in scenarios with severe constraints on power consumption, computational capabilities, and for real-time applications. For these reasons the proposed system is a good candidate for surveillance systems, low-constrained devices, Internet of things (IoT) applications, etc.

Control Over Gaussian Channels With and Without Source–Channel Separation

We consider the problem of controlling an unstable linear plant with Gaussian disturbances over an additive white Gaussian noise channel with an average transmit power constraint, where the signaling rate of communication may be different from the sampling rate of the underlying plant. Such a situation is quite common since sampling is done at a rate that captures the dynamics of the plant and that is often lower than the signaling rate of the communication channel. This rate mismatch offers the opportunity of improving the system performance by using coding over multiple channel uses to convey a single control action. In a traditional, separation-based approach to source and channel coding, the analog message is first quantized down to a few bits and then mapped to a channel codeword whose length is commensurate with the number of channel uses per sampled message. Applying the separation-based approach to control meets its challenges: first, the quantizer needs to be capable of zooming in and out to be able to track unbounded system disturbances, and second, the channel code must be capable of improving its estimates of the past transmissions exponentially with time, a characteristic known as anytime reliability. We implement a separated scheme by leveraging recently developed techniques for control over quantized-feedback channels and for efficient decoding of anytime-reliable codes. We further propose an alternative, namely, to perform analog joint source–channel coding, by this avoiding the digital domain altogether. For the case where the communication signaling rate is twice the sampling rate, we employ analog linear repetition as well as Shannon–Kotel’nikov maps to show a significant improvement in stability margins and linear-quadratic costs over separation-based schemes. We conclude that such analog coding performs better than separation, and can stabilize all moments as well as guarantee almost-sure stability.

Toward Wireless Health Monitoring via an Analog Signal Compression-Based Biosensing Platform.

Wireless all-analog biosensor design for the concurrent microfluidic and physiological signal monitoring is presented in this paper. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by the analog joint source-channel coding(AJSCC). Two circuit designs are discussed, including the stacked-voltage-controlled voltage source(VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of printed circuit board realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.

Improved Circuit Design of Analog Joint Source Channel Coding for Low-Power and Low-Complexity Wireless Sensors

To enable low-power and low-complexity wireless monitoring, an improved circuit design of Analog Joint Source Channel Coding (AJSCC) is proposed for wireless sensor nodes. This innovative design is based on Analog Divider Blocks (ADB) with tunable spacing between AJSCC levels. The ADB controls the switching between two types of Voltage Controlled Voltage Sources (VCVS). LTSpice simulations were performed to evaluate the performance of the circuit, and the power consumption and circuit complexity of this new ADB-based design were compared with our previous parallel-VCVS design. It is found that this improved circuit design based on ADB outperforms the design based on parallel VCVS for a large number of AJSCC levels (>= 16), both in terms of power consumption as well as circuit complexity, thus enabling persistent and higher temporal/spatial resolution environmental sensing.

Distributed and Adaptive Analog Coding for Video Broadcast in Wireless Cooperative System

Recently, some analog joint source-channel coding (AJSCC) schemes have been proposed to deal with cliff effect in wireless video broadcasting system. And wireless video broadcast with user cooperation tends to be a promising way to improve broadcast video quality in the near future. In this paper, we introduce a distributed and adaptive analog coding scheme called ACVC (adaptive cooperative video coding) based on AJSCC and with the concept of coset coding in distributed source coding, to improve the overall video broadcast quality in wireless cooperative system. Particularly, an adaptive packet discarding module is introduced to the framework to avoid video quality deterioration under severe channel conditions. And a model for quantization step selection of coset coding is built to minimize the redundancy in the cooperative signal and improve the anti-noise ability of the video. The experimental results show that, ACVC has stronger adaptability and thus obtains higher quality of broadcasted video than existing wireless cooperative schemes in the literature under different channel conditions.

Analog Transmission of Correlated Sources Over BC With Distortion Balancing

Analog transmission of correlated information over Gaussian broadcast channels (BCs) using analog joint source–channel coding (JSCC) is addressed. This communication strategy has attractive advantages, such as low complexity, minimal delay, graceful degradation, and adaptation to time-varying environments. In this paper, we focus on the optimization of parametric continuous mappings that satisfy individual quality of service requirements over Gaussian BCs. This optimization is based on the balancing of the user distortions to ensure the feasibility of the resulting optimization problems. An analysis of the overall distortion corresponding to the considered mappings is carried out to design algorithms that determine the optimal values for the mappings parameters. Results show that the proposed analog JSCC scheme provides near optimal performance and an adequate balancing of the individual distortions.

Compensating Spectral Efficiency Loss of Wireless RF Energy Transfer With Analog Joint Source Channel Coding Compression

We investigate the use of discrete-time analog joint source channel coding (JSCC) in a wireless sensor network where the source of information is wirelessly powered by the destination. Wireless energy transfer and information transmission are multiplexed via a time-switching protocol. As a fraction of the time slot is spent for energy transfer, the source samples are saved and compressed using either parametric or non-parametric $N$ : $K$ dimension compression analog JSCC to transmit the information in the remaining fraction of the time slot. We analyze both matched and unmatched source and channel bandwidths. Moreover, we investigate the time-sharing parameter that optimizes the analog system performance and show that the proposed analog scheme can outperform a fully digital system.

Evaluation of Analog Joint Source-Channel Coding Systems for Multiple Access Channels

We address the evaluation of low-complexity analog Joint Source Channel Coding (JSCC) methods for the transmission of discrete-time analog symbols over Multiple-Input Multiple-Output (MIMO) Multiple Access Channels (MAC). Analog JSCC is employed to encode the source information at each transmitter prior to be directly input to the MAC access scheme. Three channel access methods are considered to ensure the receiver is able to recover the user information: Code Division Multiple Access (CDMA), linear MMSE access codes and opportunistic access. CDMA allows the orthogonal transmission of the user data requiring only Channel State Information (CSI) at reception. On the other hand, linear MMSE access codes exploit CSI knowledge at transmission and exhibit better performance. Finally, opportunistic access also exploits CSI at transmission and allocates all MAC resources to the user with the strongest channel. This latter access scheme exhibits the best performance in terms of sum distortion although it may lead to unfair rate distributions among users.

Neural Approximations of Analog Joint Source-Channel Coding

An estimation setting is considered, where a number of sensors transmit their observations of a physical phenomenon, described by one or more random variables, to a sink over noisy communication channels. The goal is to minimize a quadratic distortion measure (Minimum Mean Square Error - MMSE) under a global power constraint on the sensors' transmissions. Linear MMSE encoders and decoders, parametrically optimized in encoders' gains, Shannon-Kotel'nikov mappings, and nonlinear parametric functional approximators (neural networks) are investigated and numerically compared, highlighting subtle differences in sensitivity and achievable performance.

Analog Joint Source Channel Coding for Wireless Optical Communications and Image Transmission

An analog joint source channel coding (JSCC) system is developed for wireless optical communications. Source symbols are mapped directly onto channel symbols using space filling curves and then a non-linear stretching function is used to reduce distortion. Different from digital systems, the proposed scheme does not require long block lengths to achieve good performance reducing the complexity of the decoder significantly. This paper focuses on intensity-modulated direct-detection (IM/DD) optical wireless systems. First, a theoretical analysis of the IM/DD wireless optical channel is presented and the prototype communication system designed to transmit data using analog JSCC is introduced. The nonlinearities of the real channel are studied and characterized. A novel technique to mitigate the channel nonlinearities is presented. The performance of the real system follows the simulations and closely approximates the theoretical limits. The proposed system is then used for image transmission by first taking samples of a set of images using compressive sensing and then encoding the measurements using analog JSCC. Both simulation and experimental results are shown.

Analog joint source-channel coding over MIMO channels

Analog joint source-channel coding (JSCC) is a communication strategy that does not follow the separation principle of conventional digital systems but has been shown to approach the optimal distortion-cost tradeoff over additive white Gaussian noise channels. In this work, we investigate the feasibility of analog JSCC over multiple-input multiple-output (MIMO) fading channels. Since, due to complexity constraints, directly recovering the analog source information from the MIMO channel output is not possible, we propose the utilization of low-complexity two-stage receivers that separately perform detection and analog JSCC maximum likelihood decoding. We study analog JSCC MIMO receivers that utilize either linear minimum mean square error or decision feedback MIMO detection. Computer experiments show the ability of the proposed analog JSCC receivers to approach the optimal distortion-cost tradeoff both in the low and high channel signal-to-noise ratio regimes. Performance is analyzed over both synthetically computer-generated Rayleigh fading channels and real indoor wireless measured channels.

Low-Complexity Near-Optimal Decoding for Analog Joint Source Channel Coding Using Space-Filling Curves

Analog Joint Source-Channel Coding (JSCC) is a communication strategy that does not follow the separation principle of conventional digital systems but approaches the optimal distortion-cost tradeoff over AWGN channels. Conventional Maximum Likelihood (ML) analog JSCC decoding schemes suffer performance degradation at low Channel Signal to Noise Ratio (CSNR) values, while Minimum Mean Square Error (MMSE) decoding presents high complexity. In this letter we propose an alternative two step decoding approach which achieves the near-optimal performance of MMSE decoding at all CSNR values while maintaining a low complexity comparable to that of ML decoding. An additional advantage of the proposed analog JSCC decoding approach is that it can also be used in Multiple Input Multiple Output (MIMO) fading channels.

Spatial Diversity Using Analog Joint Source Channel Coding in Wireless Channels

We consider the use of spatial diversity to improve the performance of analog joint source-channel coding in wireless fading channels. The communication system analyzed in this paper consists of discrete-time all-analog-processing joint source-channel coding where Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are employed. By assuming a fast-fading Rayleigh channel, we show that MMSE performs much better than ML at high Channel Signal-to-Noise Ratios (CSNR) in single-antenna wireless systems. However, such performance gap can be significantly reduced by using multiple receive antennas, thus making low complexity ML decoding very attractive in the case of receive diversity. Moreover, we show that the analog scheme can be considerably robust to imperfect channel estimation. In addition, as an alternative to multiple antennas, we also consider spatial diversity through cooperative communications, and show that the application of the Amplify-and-Forward (AF) protocol with single antenna nodes leads to similar results than when two antennas are available at the receiver and Maximal Ratio Combining (MRC) is applied. Finally, we show that the MMSE implementation of the analog scheme performs very close to the unconstrained capacity of digital schemes using scalar quantization, while its complexity is much lower than that of capacity-approaching digital systems.

Multiple Description Analog Joint Source-Channel Coding to Exploit the Diversity in Parallel Channels

The complexity and delay introduced by efficient digital coding strategies may be a barrier in some real-time communications. In this sense, these last years, joint source-channel coding schemes based on analog mappings have gained prominence precisely for their simplicity and their implicit low delay. In this work, analog mappings originally designed for point-to-point communications are adapted to the case of parallel channels by following the multiple description strategy traditionally used in source coding. In principle, the coding scheme is designed to transmit over parallel AWGN on-off channels, which are characterized by the possibility of having failures. We also show that our scheme performs satisfactorily over slow Rayleigh fading parallel channels.