Joint Channel Coding Research Articles

Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding

Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding

Joint Learning and Channel Coding for Error-Tolerant IoT Systems Based on Machine Learning

In several machine learning (ML)-based Internet of Things (IoT) systems, data is captured by IoT devices and then transmitted over a wireless channel for remote processing. Since noise often appears on the channel (so causing data corruption and consequently an incorrect ML result), channel protection must be provided to guarantee an acceptable error rate for the transmitted data, especially in safety-critical applications. An often-used protection technique employs error correction codes (ECCs); however, even with some improved designs, the power dissipation required by an ECC implementation may still not meet the strict requirements of hardware-constrained platforms. To address this issue, a “joint learning and channel coding” (JLCC) scheme is proposed in this paper. In such a scheme, the ML model is retrained using two methods to tolerate some channel errors, such that the system requires an ECC with significantly lower protection capability. Since ML training is executed remotely, JLCC achieves a significant power reduction for ECC without introducing any additional overhead to the IoT device. An electrocardiogram (ECG) system is taken as a case study to illustrate the proposed JLCC scheme and evaluate its effectiveness. A low-density parity-check (LDPC) code is employed for protection of the system with/without JLCC; its analysis and implementation are presented. Simulation results show that, when employing JLCC with the proposed two retraining methods, an average reduction of 29.15% and 34.82% in the dissipated power is achieved for the ECG sensor when compared to the original system.

The Joint Channel Coding and Pre-Distortion Technique on the USRP-Based MIMO-OFDM System

Modern wireless communication systems use orthogonal frequency division multiplexing (OFDM), a multi-carrier modulation method that resists multipath channels and provides bandwidth efficiency. OFDM is generally used with a Multiple-Input Multiple-Output (MIMO) system to boost diversity gain and channel capacity. MIMO-OFDM has several advantages, but its high PAPR value is a drawback. A non-linear high-power amplifier (HPA) can distort signals with high PAPR values. This issue can be resolved by employing predistortion, which compensates for nonlinear HPA. In addition to PD, channel coding can be used to improve the quality of systems with high PAPR values by adding redundant bits to the bits to be sent. In this paper, we report the experimental evaluations of the joint channel coding and pre-distortion (PD) technique on a 2x2 MIMO OFDM system using USRP hardware. The experiments are conducted in two scenarios: line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The channel coding used in this scenario is convolutional code with code rates of 1/2, 2/3, and 3/4. From the results of the experiment, it can be seen that the system that uses PD combined with the convolution code produces better performance in the LOS and NLOS scenarios compared to the system without PD. In the LOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by approximately 58.74%, 75.97%, and 96.20%. In the NLOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by about 60.71%, 73.59%, and 71.84%. The measurement of the LOS scenario gives a better SNR value than the NLOS scenario, with a maximum SNR value of 30.86 dB, while the maximum SNR value of the NLOS scenario is 30.23 dB. This happened because the LOS scenario suffered minimal multipath fading compared to the NLOS scenario

Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.

Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

Procedure for generating and decoding codewords of a joint low-density source and channel code

Procedure for generating and decoding codewords of a joint low-density source and channel code

Channel-Adaptive Wireless Image Transmission With OFDM

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our approach is adaptive to channel-gain and noise-power variations by exploiting the estimated channel state information (CSI). Specifically, with the proposed dual-attention mechanism, our model can learn to map the features and allocate transmission-power resources judiciously based on the estimated CSI. Extensive numerical experiments verify that CA-JSCC achieves state-of-the-art performance among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel conditions and can better exploit the limited channel resources by transmitting critical features over better subchannels.

Double Polar Codes for Joint Source and Channel Coding

In this paper, we design a joint source and channel coding (JSCC) framework combining the source polar coding and the channel polar coding. The source is first compressed using a polar code (PC), and source check decoding is employed to construct an error set containing the index of all source decoding errors. Then, the proposed JSCC system employs another PC or systematic PC (SPC) to protect the compressed source and the error set against noise, which is called double PC (D-PC) or systematic double PC (SD-PC), respectively. For a D-PC JSCC system, we prove a necessary condition for the optimal mapping between the source PC and the channel PC. On the receiver side, by introducing the joint factor graph representation of the D-PC and SD-PC, we propose two joint source and channel decoders: a joint belief propagation (J-BP) decoder, and a systematic joint belief propagation (SJ-BP) decoder. In addition, a biased extrinsic information transfer (B-EXIT) chart is developed for various decoders as a theoretical performance evaluation tool. Both B-EXIT and simulation results show that the performance of the proposed JSCC scheme has no error floor and outperforms the turbo-like BP decoder.

Semantic Communication With Adaptive Universal Transformer

With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount of language texts. Accordingly, we can achieve semantic communication in terms of joint semantic source and channel coding over a noisy channel with the help of NLP. However, the existing method to realize this goal is to use a fixed transformer of NLP while ignoring the difference of semantic information contained in each sentence. To solve this problem, we propose a new semantic communication system based on Universal Transformer. Compared with the traditional transformer, an adaptive circulation mechanism is introduced in the Universal Transformer. Through the introduction of the circulation mechanism, the new semantic communication system can be more flexible to transmit sentences with different semantic information, and achieve better end-to-end performance under various channel conditions.

Joint Channel Estimation and Coding Over Channels With Memory Using Polar Codes

A joint channel estimation and channel coding scheme is presented for channels with memory using polar codes. Unlike the conventional approach of first estimating all channel parameters and then performing channel decoding separately, the proposed scheme incorporates a subset of reliable estimates of channel parameters into the decoding procedure and computes decoding metrics averaged over the statistical behavior of the channel. Specifically, decoding algorithms for finite-state Markov channels of any order, for the Gauss-Markov channel and for flat-fading channels are presented. Further, by adapting list decoding to identify reliably-decoded bits within a codeword, channel estimation and decoding steps are performed iteratively to boost the reliability of channel estimation as well as error correction. In order to improve the performance even further, a new pilot arrangement scheme is developed that utilizes the structure of polar codes and sends pilot symbols embedded within the polar codewords. This construction can be viewed as a new family of shortened polar codes that can be of independent interest. Simulation results demonstrate the benefit of the proposed approach compared to existing solutions.

Joint Source and Channel Coding Using Double Polar Codes

In this letter, a double polar code (D-Polar) for joint source and channel coding (JSCC) is proposed, in which the source compress is implemented by a polar code, the channel error correction is performed by a systematic polar code. Furthermore, a turbo-like belief propagation (TL-BP) decoder consisted of a channel BP decoder and a source BP decoder is proposed for joint source and channel decoding in the receiver. In this TL-BP decoder, the soft information is exchanged between the channel BP decoder and the source BP decoder so as to improve the efficiency of channel decoding in utilizing the source information residual in the compressed bits. Simulation results show that the performance of the proposed D-Polar JSCC scheme with TL-BP decoder is significantly improved compared with the existing source-channel joint polarization scheme.

Private Information Retrieval Over Gaussian MAC

Consider the problem of Private Information Retrieval (PIR), where a user wishes to retrieve a single message from N non-communicating and non-colluding databases (servers). All servers store the same set of M messages and they respond to the user through a block fading Gaussian Multiple Access Channel (MAC). The goal in this setting is to keep the index of the required message private from the servers while minimizing the communication overhead. This work provides joint privacy and channel coding retrieval schemes for the Gaussian MAC with and without fading. The schemes exploit the linearity of the channel while using the Compute and Forward (CF) coding scheme. Consequently, single-user encoding and decoding are performed to retrieve the required message. In the case of a channel without fading, the achievable retrieval rate is shown to outperform a separation-based scheme, in which the retrieval and the channel coding are designed separately. Moreover, this rate equals the channel capacity without privacy constraints for even N (hence the joint scheme is optimal) and has a negligible gap from the capacity for odd N as N grows. When the channel suffers from fading, the asymmetry between the servers' channels forces a more complicated solution, which involves a hard optimization problem. Nevertheless, we provide coding schemes and lower bounds on the expected achievable retrieval rate which are shown to have the same scaling laws as the channel capacity, both in the number of servers and the SNR.

Binary Joint Network-Channel Coding for Reliable Multi-Hop Wireless Networks

The high and variable packet loss in wireless networks due to the unreliable nature of wireless channels, interference, link or router failure, fading, and shadowing wireless link are the main problems and challenges in implementing a reliable wireless network. Packet loss probability is the most important performance measure for real-time applications. Packet loss is due to errors, congestion in the queue because of high traffic, collision, hidden nodes, and link failures. In this paper, we focus on the links failure problem. To address this problem, improve the performance of wireless networks, and enhance and make the network survivability and availability more reliable, a new efficient binary joint network and channel coding (B-JNCC) technique is proposed. B-JNCC combines low-complexity parity-check (LCPC) channel coding and the wireless network erasure correcting with router code to detect lost packets to reduce the bit error rate and increase the packet delivery ratio over a wireless multi-access channel. B-JNCC is applied at the receiver node. Simulation results show that the proposed B-JNCC outperforms the binary symbol-wise JNCC, non-binary JNCC (NB-JNCC), and NB separate network-channel coding.

Multi-Level Distribution Matching

A general distribution matching architecture based on a multi-level structure is presented. It allows to generate arbitrary symbol distributions using simple binary distribution matchers. This is particularly advantageous for large symbol alphabets, where non-binary distribution matching algorithms tend to have high complexity. Some examples for possible implementations are provided, and a specific realization based on polar codes, which enables joint shaping and channel coding for reliable data transmission with higher-order modulation, is discussed and evaluated in detail.

A Joint Encryption, Channel Coding and Modulation Scheme Using QC-LDPC Lattice-Codes

We propose a new nonlinear Rao-Nam like symmetric key encryption scheme. In our design, we employ a specific type of coded modulation schemes namely quasi-cyclic low-density parity-check (QC-LDPC) lattice-codes which have low-complexity encoding and decoding algorithms. Due to the application of coded modulation schemes in our design, the proposed scheme performs encryption, encoding and modulation simultaneously. Therefore, we regard the proposed scheme as a joint cryptosystem . The proposed joint cryptosystem withstands all variants of chosen plaintext attacks applied on Rao-Nam like cryptosystems due to its nonlinearity. Moreover, some conditions implying the uniformity of the ciphertexts distribution are introduced through our analysis. Our scheme is efficient and admits small key size. These features are obtained due to several reasons including the quasi-cyclic form of the generator and the parity-check matrices of QC-LDPC lattice-codes, and the simple hardware structure for generating the permutation matrix, the intentional error vector and the nonlinear functions used in our design. The QC-LDPC lattice-codes facilitate high-rate transmission which is suitable for bandlimited AWGN channels. Our simulations indicate that QC-LDPC lattice-codes outperform the error performance of high-order coded modulation schemes based on QAM modulations. Hence, our scheme provides secure, reliable and efficient data transmission in bandlimited AWGN channels.

Finite-Length Analyses for Source and Channel Coding on Markov Chains.

We derive finite-length bounds for two problems with Markov chains: source coding with side-information where the source and side-information are a joint Markov chain and channel coding for channels with Markovian conditional additive noise. For this purpose, we point out two important aspects of finite-length analysis that must be argued when finite-length bounds are proposed. The first is the asymptotic tightness, and the other is the efficient computability of the bound. Then, we derive finite-length upper and lower bounds for the coding length in both settings such that their computational complexity is low. We argue the first of the above-mentioned aspects by deriving the large deviation bounds, the moderate deviation bounds, and second-order bounds for these two topics and show that these finite-length bounds achieve the asymptotic optimality in these senses. Several kinds of information measures for transition matrices are introduced for the purpose of this discussion.

A Novel PAPR Reduction Scheme for Polar-Coded OFDM Systems

A novel joint channel coding and peak-to-average power ratio (PAPR) reduction technique for polar-coded orthogonal frequency-division multiplexing (OFDM) systems is proposed. The main concept is to introduce shaping indices to polar encoder, and generate multiple OFDM symbol candidates according to various shaping bit patterns and then select the one with the lowest PAPR as a transmitted signal. Since information and shaping bits can be jointly decoded using the conventional decoding algorithm of polar codes, transmission of side information is not required. The polar code design algorithm for efficient PAPR reduction is also discussed. It is demonstrated that the proposed shaping method offers a gain of more than 4.5dB in terms of PAPR performance without affecting the decoding complexity.

Joint Source, Channel, and Space-Time Coding of Progressive Bitstream in MIMO Channels

The optimization of joint source and channel coding for a sequence of numerous progressive packets has been a challenging problem. Further, the problem becomes more complicated if the space-time coding is also involved with the optimization in a multiple-input multiple-output (MIMO) system. This is because the number of ways of jointly assigning channel codes and space-time codes to progressive packets is much larger than that of solely assigning channel codes to the packets. To our knowledge, there is no complete and feasible solution for the optimization of joint source, channel, and space-time coding of progressive packets. This paper applies a parametric approach to address such a highly nonlinear optimization problem in MIMO systems. We use the parametric methodology and derive some relevant properties, which are exploited to propose an efficient optimization method where the joint assignment of channel codes and space-time codes to the packets can be optimized in a packet-by-packet manner. As a result, the computational complexity of the proposed method linearly increases in the number of packets, whereas that of the exhaustive search exponentially increases in the number of packets. The numerical results show that the proposed method significantly improves the peak-signal-to-noise ratio performance of the conventional rate-based optimal solution in a MIMO system.

Study on image transmission mechanism of ghost imaging based on joint source and channel coding

In this paper, through the detailed analysis of ghost imaging algorithm and joint source–channel coding transmission, it not only combines the efficient compression function of source code, and the error detection and error correction function of channel code, but also combines the accurate transmission function of ghost imaging, which finally completes the coding transmission of the image. In this paper, the image transmission mechanism of ghost imaging based on joint source–channel coding is studied, and the feasibility, bit error rate and robustness are analyzed respectively. The transmission mechanism of the image coding can improve the compressibility of the coded image and the difficulty of being deciphered by the attacker, reduce the bit error rate in the process of the transmission of the image coding, enhance the ability of anti-interference and anti-interception, realize the accurate image coding transmission, and it also solves the problems such as lack of feasibility, low reduction degree and poor imaging quality of current imaging algorithms.

Joint Channel Coding and Intrinsic Randomness

Joint Channel Coding and Intrinsic Randomness