Random Coding Research Articles

Interplays between classical and quantum entanglement-assisted communication scenarios

Prepare-and-measure scenarios (pm), in their many forms, can be seen as the basic building blocks of communication tasks. As such, they can be used to analyze a diversity of classical and quantum protocols—of which dense coding and random access codes are key examples—in a unified manner. In particular, the use of entanglement as a resource in pm scenarios have only recently started to be systematically investigated, and many crucial questions remain open. In this work, we explore such scenarios and provide answers to some seminal questions. More specifically, we show that, in scenarios where entanglement is a free resource, quantum messages are equivalent to classical ones with twice the capacity. We also prove that, in such scenarios, it is always advantageous for the parties to share entangled states of dimension greater than the transmitted message. Finally, we show that unsteerable states cannot provide advantages in classical communication tasks, thus proving that not all entangled states are useful resources in these scenarios.

A conformal coding metasurface for dual polarization conversion and radar cross section (RCS) reduction

Integrated meta-devices are the need of modern stealth application systems and have recently received a great deal of attention. Most studies have concentrated on the physics and structural design of planar metasurfaces, while conformal design that is suitable for arbitrarily curved surfaces has been rarely discussed. In this study, a conformal polarization-independent 1-bit coding metasurface (CM) is proposed. A fundamental element of a proposed CM is firstly designed which converts the linearly polarized incident electromagnetic wave into its orthogonal equivalent at 8.53–11.63 and 18.67–22.34 GHz with a polarization-conversion-ratio of more than 90%, and enables linear-to-circular polarization conversion from 12.40 to 17.56 GHz. Next, the basic element is rotated by 90° to generate another element with a phase difference of π between them. Both these elements are distributed in an array using a random aperiodic coding sequence to form 1-bit CM for radar cross section (RCS) reduction. More than 10 dB RCS reduction for arbitrarily polarized waves has been realized in dual frequency bands ranging 8.75–11.22 and 19.10–21.20 GHz, for planar as well as with conformal structures. A prototype is fabricated, and the experiments show a good agreement with simulated results. Potential applications of the proposed design include reflector antennas, radar, satellite communication, and stealth technology.

High performance multi-image encryption based on temporal-spatial double random ghost coding

A high performance multi-image encryption method is presented by using the temporal-spatial double random ghost coding (TSDRGC). As an original idea in optical encryption, TSDRGC employs the chaotic lasers as light sources instead of conventional lasers. The random fluctuations of the chaotic laser can be seen as the new temporal security keys introduced into the cryptosystem, except the random illumination patterns used usually in the conventional ghost imaging as a set of spatial security keys. In the encryption process, all the images can be optically encoded in parallel and the time consumed by the encryption process does not increase compared to encrypting an image alone. Meanwhile, a decryption algorithm is proposed to achieve high quality decryption of each image independently while reducing the bit-width requirement. The simulations demonstrate the excellent performance of the scheme in terms of security, capability and robustness.

Coded-Aperture imaging based on random code and Backpropagation neural network

Coded-aperture radiation imaging technology has important application value in the field of space radiation detection. A coded-aperture imaging algorithm based on random code and Backpropagation neural network (BPNN) is proposed for high quality imaging of x-rays and gamma rays in space environments. The experimental results show that BPNN can improve the signal-to-noise ratio of the reconstructed image when applied to the reconstruction process of coded-aperture imaging based on random code. Therefore, it has a good application prospect in the field of space coded-aperture imaging.

Efficient Communications in V2V Networks with Two-Way Lanes Based on Random Linear Network Coding.

Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.

Network Coding for Efficient File Transfer in Narrowband Environments

Achieving efficient end-to-end file transfer is challenging in a narrow-band communication environment with high latency and high packet loss rate. The traditional TCP-based scheme and the UDP-based automatic retransmission scheme have defects in the transmission performance, which cannot meet the increasing user demands. This paper proposes a high-efficiency file transfer scheme based on random linear network coding and the Kalman filtering algorithm to implement efficient end-to-end file transfer in narrow-band environment. The scheme predicts the link quality of file transmission through the Kalman filter algorithm and designs an adaptive coding strategy for file transfer through random linear network coding. Experimental results show that the proposed method outperforms traditional file transfer schemes.

Quantum Learning on Structured Code With Computing Traps for Secure URLLC in Industrial IoT Scenarios

Resilient and secure ultra-reliable low-latency communications (URLLC) over radio interface is expected to play a crucial role in next generation Industrial IoT scenarios. However, attacking wireless pilot signals has been a potential easy way to interrupt URLLC services. In this work, we propose a random structured code to encode and decode pilot signals on multidimensional physical resources, and also design a quantum learning framework to make this code secure and reliable. Specifically, the code suggests using random encoding with little structures to disperse the effect of attacks. We find that the decoding process can be modeled as a computing trap if the group spatial channel features are employed. The security problem is therefore transformed as a random computing with redundancy. We employ a quantum algorithm to learn the computing trap model such that the computing redundancy can be removed quickly while the dispersed attack can be eliminated. In this respect, we can prove the existence of the quantum black-box model corresponding to the computing trap, and derive a precise expression of computing performance. Based on the result, we can formulate novel analytical closed-form expressions of system failure probability to characterize the reliability of the URLLC. Numerical results show that the proposed system can maintain ultra-high reliability and low latency against attacks on wireless pilots.

Competitive learning to generate sparse representations for associative memory

One of the most well established brain principles, Hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned. Palm’s work implements this concept through multiple binary Willshaw associative memories, in a model that not only has a wide cognitive explanatory power but also makes neuroscientific predictions. Yet, Willshaw’s associative memory can only achieve top capacity when the stored vectors are extremely sparse (number of active bits can grow logarithmically with the vector’s length). This strict requirement makes it difficult to apply any model that uses this associative memory, like Palm’s, to real data. Hence the fact that most works apply the memory to optimal randomly generated codes that do not represent any information. This issue creates the need for encoders that can take real data, and produce sparse representations - a problem which is also raised following Barlow’s efficient coding principle. In this work, we propose a biologically-constrained network that encodes images into codes that are suitable for Willshaw’s associative memory. The network is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme. After conducting auto- and hetero-association experiments on two visual data sets, we can conclude that our network not only beats sparse coding baselines, but also that it comes close to the performance achieved using optimal random codes.

A Secure Cooperative Transmission of Image Super-Resolution in Wireless Relay Networks

The image transmission over wireless media experiences not only unavailable performance loss caused by the environment and hardware issues, but also information leakage to eavesdroppers who can overhear and attempt to recover the images. This paper proposes a secure cooperative relaying (SCR) protocol for the image communications in wireless relay networks (WRNs) where Alice sends high-resolution (HR) images to Bob with the assistance of a relaying user named Relay, and in the presence of an eavesdropper named Eve. In order to enhance the security of the image communications, random linear network coding (RLNC) is employed at both Alice and Relay to conceal the original images from Eve with RLNC coefficient matrices and reference images in the shared image datastore. Furthermore, the original HR images are downscaled at Alice to save transmission bandwidth and image super-resolution (ISR) is adopted at Bob due to its capability to recover the HR images from their low-resolution (LR) version, while still maintaining the image quality. In the proposed SCR protocol, Bob can decode both the original images transmitted from Alice over the direct link and the images forwarded by Relay over the relaying links. Simulation results show that the SCR protocol achieves a considerably higher performance at Bob than at Eve since Eve does not know the coefficient matrices and reference images used at Alice and Relay for the RLNC. The SCR protocol is also shown to outperform the counterpart secure direct transmission protocol without the relaying links and secure relaying transmission without the direct link. Additionally, an increased scaling factor can save the transmission bandwidth for a slight change in the image quality. Moreover, the impacts of direct, relaying and wiretap links are evaluated, verifying the effectiveness of the SCR protocol with the employment of Relay to assist the image communications between Alice and Bob in the WRNs.

Smoothing Codes and Lattices: Systematic Study and New Bounds

In this article we revisit smoothing bounds in parallel between lattices <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> codes. Initially introduced by Micciancio and Regev, these bounds were instantiated with Gaussian distributions and were crucial for arguing the security of many lattice-based cryptosystems. Unencumbered by direct application concerns, we provide a systematic study of how these bounds are obtained for both lattices <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> codes, transferring techniques between both areas. We also consider multiple choices of spherically symmetric noise distributions. We found that the best strategy for a worst-case bound combines Parseval’s Identity, the Cauchy-Schwarz inequality, and the second linear programming bound, and this holds for both codes and lattices and all noise distributions at hand. For an average-case analysis, the linear programming bound can be replaced by an expected value computation. This alone gives optimal results for spherically uniform noise over random codes and random lattices. This also improves prior Gaussian smoothing bounds for worst-case lattices, but surprisingly this provides even better results with uniform ball noise than for Gaussian (or Bernoulli noise for codes). This counterintuitive situation can be resolved by adequate decomposition and truncation of Gaussian and Bernoulli distributions into a superposition of uniform noise, giving further improvement for those cases, and putting them on par with the uniform cases.

Sliding Window Network Coding Enables NeXt Generation URLLC Millimeter-Wave Networks

Ultra-reliability and low-latency are pivotal requirements of the emerging 6th generation of communication systems (xURLLC). The transition in millimeter-wave (mmWave) technology, from omni-directional to highly directional antennas, has been seen as an enabler for high bandwidth communications, still susceptible to high loss and high latency variation. Classical error recovery approaches cannot close the rising gap between high throughput and low delay in such systems. In this work, we incorporate effective sliding window network coding solutions in mmWave communications. While legacy systems such as rateless codes improve the delay, cross-layer results show that they do not provide Low Latency Communications (LLC), due to the lossy behaviour of mmWave channel and the lower-layers’ retransmission mechanisms. On the other hand, fixed sliding window random linear network coding (RLNC) is able to achieve LLC, and even better, adaptive sliding window RLNC obtains Ultra-Reliable LLC (URLLC) in mmWave backhaul networks.

Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports

While radiologists can describe a fracture’s morphology and complexity with ease, the translation into classification systems such as the Arbeitsgemeinschaft Osteosynthesefragen (AO) Fracture and Dislocation Classification Compendium is more challenging. We tested the performance of generic chatbots and chatbots aware of specific knowledge of the AO classification provided by a vector-index and compared it to human readers. In the 100 radiological reports we created based on random AO codes, chatbots provided AO codes significantly faster than humans (mean 3.2 s per case vs. 50 s per case, p < .001) though not reaching human performance (max. chatbot performance of 86% correct full AO codes vs. 95% in human readers). In general, chatbots based on GPT 4 outperformed the ones based on GPT 3.5-Turbo. Further, we found that providing specific knowledge substantially enhances the chatbot’s performance and consistency as the context-aware chatbot based on GPT 4 provided 71% consistent correct full AO codes for the compared to the 2% consistent correct full AO codes for the generic ChatGPT 4. This provides evidence, that refining and providing specific context to ChatGPT will be the next essential step in harnessing its power.

A Universal Random Coding Ensemble for Sample-Wise Lossy Compression.

We propose a universal ensemble for the random selection of rate-distortion codes which is asymptotically optimal in a sample-wise sense. According to this ensemble, each reproduction vector, x^, is selected independently at random under the probability distribution that is proportional to 2-LZ(x^), where LZ(x^) is the code length of x^ pertaining to the 1978 version of the Lempel-Ziv (LZ) algorithm. We show that, with high probability, the resulting codebook gives rise to an asymptotically optimal variable-rate lossy compression scheme under an arbitrary distortion measure, in the sense that a matching converse theorem also holds. According to the converse theorem, even if the decoder knew the ℓ-th order type of source vector in advance (ℓ being a large but fixed positive integer), the performance of the above-mentioned code could not have been improved essentially for the vast majority of codewords pertaining to source vectors in the same type. Finally, we present a discussion of our results, which includes among other things, a clear indication that our coding scheme outperforms the one that selects the reproduction vector with the shortest LZ code length among all vectors that are within the allowed distortion from the source vector.

Rapid design of hybrid mechanism metasurface with random coding for terahertz dual-band RCS reduction.

In this paper, a hybrid mechanism metasurface (HMM) employing 1-bit random coding is proposed to achieve polarization-insensitive and dual-wideband monostatic/bistatic radar cross section (RCS) reduction under a wide range of incident angles. The anisotropic unit cell is designed by the combination of the multi-objective particle swarm optimization (MOPSO) algorithm and Python-CST joint simulation, which facilitates the rapid acquisition of the desired unit cell with excellent dual-band absorption conversion capability. The unit cell and its mirrored version are used to represent the units "0" and "1", respectively. In addition, the array distribution with random coding of the units "0" and "1" is optimized under different incident angles, polarizations and frequencies, which enables better diffusion-like scattering. Simulation results demonstrate that the proposed coding HMM can effectively reduce the monostatic/bistatic RCS by over 10 dB within the dual-band frequency ranges of 2.07-3.02 THz and 3.78-4.71 THz. Furthermore, the specular and bistatic RCS reduction performances remain stable at oblique incident angles up to 45° for both TE and TM polarizations.

AES as Error Correction: Cryptosystems for Reliable Communication

In this paper, we show that the Advanced Encryption Standard (AES) cryptosystem can be utilized as an error-correcting code to obtain reliability over noisy communication and data systems. Specifically, we show that by composing a simple padding followed by encrypting with AES, we can achieve error-correcting performance similar to random codes. We conduct an empirical comparison between our AES-based error correction approach, Random Linear Codes, and CA-Polar codes to assess their performance in practical scenarios. Our results show that the AES-based approach achieves nearly identical performance to the other well-established error-correcting codes.

Nonbinary LDPC Coded QAM Signals With Optimized Mapping: Bounds and Simulation Results

This paper studies specific properties of nonbinary low-density parity-check (NB LDPC) codes when used in coded modulation systems. The paper is focused on the practically important NB LDPC codes over the Galois extension fields GF(2m) with m ≤ 6 used with QAM signaling. Performance of NB LDPC coded transmission strongly depends on the mapping of nonbinary symbols to signal constellation points. We obtain a random coding bound on the maximum-likelihood decoding error probability for an ensemble of random irregular NB LDPC codes used with QAM signaling for specific symbol-to-signal point mappings. This bound is based on the ensemble average squared Euclidean distance spectra derived for these mappings. The simulation results for the belief-propagation decoding in the coded modulation schemes with the NB quasi-cyclic (QC)-LDPC codes under different mappings are given. Comparisons with the optimized binary QC-LDPC codes in the WiFi and 5G standards, as well as with the new bound, are performed.

RCS reduction based on double parabolic phased metasurface

This paper presents a novel metasurface design strategy to realize broadband radar cross section (RCS) reduction. The phase distribution across the metasurface aperture can be regarded as applying an additional parabolic phase upon periodically arranged parabolic subarrays. Such a design fully utilizes the diffusive scattering nature of the parabolic phase distribution. Since the proposed metasurface is governed by only two focal lengths, the optimization procedure is quite easy compared to metasurface with random coding sequence. Experimental results show that the proposed metasurface can achieve more than 10 dB RCS reduction from 7.52 GHz to 19.66 GHz with a fractional bandwidth of 89.3% under both linearly and circularly polarized normal incidences, and keeping a performance of more than 7 dB RCS reduction until the incident angle increases to 40° for both x-polarized and y-polarized incidences in the frequency range of 8–19.35 GHz. When the incident angle increases to 60°, 7 dB RCS reduction can still be obtained for x-polarized incidence from 7.9 GHz to 19.35 GHz with a fractional bandwidth of 84%.

Sufficient conditions for quantum advantage in random access code protocols with two-qubit states

Sufficient conditions for quantum advantage in random access code protocols with two-qubit states

Nested Hash Access With Post Quantum Encryption for Mission-Critical IoT Communications

Secure ultra-reliable low-latency communication (URLLC) has become a crucial requirement of mission-critical Internet of Things (IoT) applications and use cases including automotive driving, remote surgery, and many others. However, it is still challenging to protect initial access of massive IoT devices over wireless channels, especially when malicious quantum adversaries paralyse the initial access by overhearing and tampering critical wireless messages, i.e., preambles. We propose a nested hash access system with post-quantum encryption to solve this issue. The system performs random repetition coding and nested hash coding on multi-domain physical-layer resources to encode and decode preambles precisely and resiliently. Particularly, a subtle compression and encryption mechanism based on quasi-cyclic (QC)-moderate-density parity-check (MDPC) code is proposed between repetition and hashing operations to avoid passive eavesdropping during preamble encoding process. We show that the code information can be maintained at 128-bit or higher privacy level, depending on the length of repetition code. Besides, the preamble decoding process can be proved secure agaisnt active attacks with a tolerable loss of decoding errors. Then we formulate two non-convex integer programming problems, each problem corresponding to the minimization of upper bound of preamble decoding error in an example application scenario. Finally, we can derive the expressions of system failure probability to evaluate the reliability of URLLC system under mission-critical IoT scenarios. Simulation results show the effectiveness of our proposed scheme despite attack.

BAR: Blockwise Adaptive Recoding for Batched Network Coding.

Multi-hop networks have become popular network topologies in various emerging Internet of Things (IoT) applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch; BNC has much smaller computational and storage requirements at intermediate nodes compared with direct application of random linear network coding. In this paper, we discuss a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to fluctuations in channel conditions. Due to the low computational power of remote IoT devices, we focus on investigating practical concerns such as how to implement efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch sizes compared with existing baseline recoding schemes. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be used in practice as the exact channel model and its parameters could be unknown and subject to changes from time to time.