With the global proliferation of autonomous driving technology, the advancement of sensor technology is essential to ensure its safety. In particular, sensor fusion plays a crucial role in autonomous driving systems. However, equipping vehicles with multiple sensors leads to increased costs, making it necessary to have a single sensor that can perform multiple roles. This study focuses on event cameras, exploring their potential among various sensor technologies. Event cameras possess characteristics such as high dynamic range, low latency, and high temporal resolution, and they can also leverage visible light communication. This enables high visibility in low-light and backlit environments, as well as excellent performance in detecting pedestrian movements and acquiring traffic information between traffic lights and vehicles. These characteristics are particularly beneficial for autonomous driving systems. Additionally, if distance estimation functionality can be provided by the event camera, it allows a single sensor to perform multiple roles, offering significant advantages in terms of cost efficiency. In this study, we achieved distance estimation based on triangulation using an event camera and two points on an LED bar installed along a road. Furthermore, by employing the phase-only correlation method, we achieved sub-pixel precision in estimating the distance between two points on the LED bar, enabling even more accurate distance estimation. This approach performed monocular distance estimation in outdoor driving environments at distances ranging from 20 to 60 meters, achieving a success rate of over 90 % with errors of less than 0.5 meters. We are considering implementing position estimation in the future, with the current distance estimation technology forming the foundation for this. By achieving high-precision distance estimation, the vehicle's position relative to surrounding ITS smart poles can be accurately determined, enabling more precise position estimation. This will allow autonomous vehicles to know their exact position in real-time and select the optimal driving route based on surrounding traffic conditions and road conditions. Ultimately, we believe that this technology can contribute to the development of a smart transportation system in the city.

In the fields of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD), sensors that serve as the “eyes” for sensing the vehicle's surrounding environment are essential. Traditionally, image sensors and LiDAR have played this role. However, a new type of vision sensor, event cameras, has recently attracted attention. Event cameras respond to changes in the surrounding environment (e.g., motion), exhibit strong robustness against motion blur, and perform well in high dynamic range environments, which are desirable in robotics applications. Furthermore, the asynchronous and low-latency principles of data acquisition make event cameras suitable for optical communication. By adding communication functionality to event cameras, it becomes possible to utilize I2V communication to immediately share information about forward collisions, sudden braking, and road conditions, thereby contributing to hazard avoidance. Additionally, receiving information such as signal timing and traffic volume enables speed adjustment and optimal route selection, facilitating more efficient driving. In this study, we construct a vehicle visible light communication system where event cameras are receivers, and multiple LEDs are transmitters. In driving scenes, the system tracks the transmitter positions and separates densely packed LED light sources using pilot sequences based on Walsh-Hadamard codes. As a result, outdoor vehicle experiments demonstrate error-free communication under conditions where the transmitter-receiver distance was within 40 meters and the vehicle's driving speed was 30 km/h (8.3 m/s).

In bit-interleaved coded modulation (BICM) systems, the conventional demodulation assumes equal a priori probabilities for all constellation points, inherently leading to performance degradation. To enhance the performance of BICM, BICM with iterative decoding (BICM-ID) was developed. We aim to apply neural networks to BICM to achieve joint modulation and decoding and overcome the performance degradation, with the goal of surpassing the performance of BICM-ID. In this paper, we propose a neural demodulator that incorporates an additional probability layer to mitigate performance degradation in BICM systems. Furthermore, we introduce Joint Model-1, which integrates this neural demodulator with a belief propagation (BP) decoder for 16-QAM polar-coded BICM. To further improve performance, we use Joint Model-1 as a pre-trained model and extend it by adding an additional dense layer, resulting in Joint Model-2. Experimental results show that Joint Model-2 surpasses both BICM and BICM-ID systems under Ungerboeck labeling.

The eBCH-polar codes is a powerful way to construct polar codes. With high minimum Hamming distance, it outperforms ordinary Polar codes under successive cancellation list (SCL) decoder. However, the absence of cyclic redundancy check (CRC) bits in eBCH-polar codes makes it challenging to adopt SCL-flip (SCLF) decoding scheme. In this research, we propose a flip scheme activated by a Normalized Path Metric (PM) range and a mismatch criterion, which respectively leverage the properties of SCL decoding and dynamic frozen bits of eBCH-polar codes. This work also improves the selection the flipping bits by modifying metric to account for the structure of dynamic frozen bits and reduce the search space. Based on these modifications, we proposed SCLF-1 and SCLF-2 decoding algorithms for eBCH-polar codes. Simulation results show that both of the proposed decoding schemes achieve superior performance compared to other contemporary decoding algorithms for CRC aided polar (CA-polar) codes, demonstrating enhanced error rates performance within the same decoding attempt.

Grant-free random access (GF-RA) has recently emerged to support massive random access. Due to the absence of access grant in GF-RA, the base station (BS) has to first identify each active user. However, the state-of-the-art user-activity detection (UAD) solution, i.e., covariance-based maximum-likelihood detection (CB-MLD) is still subject to some critical deficiencies. Specifically, the update step size in each CB-MLD iteration relies on an asymptotically large antenna number, which may cause convergence issues in practice. In addition, the hard-decision threshold for UAD remains to be fine-tuned in complicated scenarios. Both deficiencies are hard to address via analytical methods. Thus, we propose a hybrid-driven UAD network (HyD-UADNet), where a model-driven network is constructed to learn the proper update step size, and a data-driven network is designed to learn the soft decision on user activity. Furthermore, we construct a dynamic configuration-adaptive mixture-of-expert network (CA-MoENet). This CA-MoENet can adaptively produce weighting coefficients for different expert HyD-UADNets, so as to enhance the UAD robustness against varying configurations. Finally, simulations show the superior UAD accuracy of the HyD-UADNet, and reveal the robustness of the CA-MoENet even if the testing configuration is never seen by any expert during training.

Low earth orbit (LEO) satellite-based Internet of Things (IoT) has great potential to provide seamless global coverage, but the large propagation delay and severe Doppler shift in terrestrial-satellite link (TSL) will become the most challenging problem. To handle these challenges and facilitate massive grant-free random access, we propose an orthogonal time frequency space-based scramble-division multiple access (OTFS-SDMA) scheme, where the scrambling technique is used to tackle the correlated TSL channels between neighboring devices. At the receiver, we first propose a user activity detection (UAD) method based on capturing the dominant line-of-sight (LoS) path, without relying on the assumption of a static TSL. To facilitate accurate channel estimation (CE) against severe Doppler shifts, we exploit prior information about satellite velocity to detect the angles of arrival (AoAs) of active devices with the two-dimensional multiple signal classification (2D-MUSIC) algorithm, and further estimate the Doppler shifts. Building on the Doppler estimation, the orthogonal matching pursuit (OMP) algorithm is used to estimate the sparse TSL channel in the time-delay (TD) domain. In accordance with the OTFS-SDMA scheme, we propose a cross-domain elementary signal estimator (CD-ESE) for multi-user detection (MUD). In the CD-ESE MUD structure, both bit-level and symbol-level scrambling sequences help to distinguish neighboring active devices with correlated TSL channels, and the channel decoder works in conjunction with the CD-ESE to enhance MUD accuracy. Simulation results are provided to demonstrate the superior performance of the proposed OTFS-SDMA scheme over the state-of-the-art solutions.

This research addresses challenges in visible light communication (VLC) systems that use two orthogonally aligned rolling shutters (RS) image sensors as receivers and an LED array as a transmitter. The study focuses on overcoming burst signal loss caused by unsensed periods between frames in RS image sensors. To improve VLC performance, we propose and compare three different schemes. The first is a conventional approach that superimposes a Barker code synchronization signal on the transmission signal using pulse width modulation (PWM). The second introduces a new method of spatial synchronization by dedicating specific LEDs in an array for synchronization signals. The third scheme extends this spatial approach by incorporating a 4 -level PWM for information transmission to increase data rates. The study aims to evaluate and compare the error rate characteristics of these three schemes, assessing their effectiveness in mitigating burst errors and enhancing overall VLC system performance. This research has potential applications, including vehicle communication, Internet of Things devices, and smartphones.

This paper proposes a novel transmitter that rearranges the 16-QAM (Quadrature Amplitude Modulation) constellation for a 1-bit ADC (Analogue-to-Digital Converter) receiver. A 1-bit ADC receiver is more eco-friendly and lower-cost than receivers with a high-resolution ADC. However, in conventional 1-bit ADC receivers, severe degradation of SER (Symbol Error Rate) in high Es/N0poses a critical problem. This degradation is due to the lack of noise power in high Es/N0, which prevents the receiver from sufficiently benefiting from stochastic resonance. To solve this issue, we propose a constellation rearrangement scheme for 16-QAM based on the probability density function of noise. The constellation rearrangement scheme mitigates the nonlinearity of a 1-bit ADC, enabling reliable demodulation of multilevel signals in high Es/N0conditions. Numerical examples demonstrate that the proposed transmitter achieves superior SER performance across a wide range of Es/N0, even when a 1-bit ADC receiver is employed, compared to conventional transmitters.

In the unsourced random access (U-RA), only a portion of users in a large number of users are active during the same time slot. Each user employs the same codebook, and the task of the decoder is to recover a list of transmitted messages, regardless of the user's identity. A concatenated coding approach, referred to as a coded compressed sensing scheme, decreases the computational complexity. However, there is a limitation that the inner CS decoding only decodes the support of a sparse vector, which leads to each user at the same sub-slot must send a different message, and the maximum tolerable active user number is low. In this paper, we consider an inner CS decoding scheme that initially decodes the amplitudes of a sparse vector and quantifies them to determine the number of active users choosing the same columns. This eliminates the constraint of requiring each user to send different messages in the same sub-slot, thereby increasing the maximum tolerable number of active users. We also show the maximum tolerable active user number with various codelengths. Next, we improve the survival probabilities' upper and lower bounds of the outer tree encoder.

In recent years, neural network decoding of polar codes, such as neural belief propagation (BP), has been intro-duced. These methods use deep learning to transform the factor graph into a neural network model by unfolding the decoding iterations, thereby enhancing the accuracy of traditional decoding processes. However, current prevalent methodologies for loss function calculation only take into account the output of the final layer. In our analysis, we found that when calculating the loss function using only the output from the last layer, the convergence speed of the decoder significantly decreases, especially when the number of unfolded iterations is higher. In this paper, we incorporate the output of all iterations into the loss function in the original neural BP structure. Additionally, we optimize the loss function by assigning different weights to losses at different iterations. As a result, the weighted loss function not only provides a lower Bit Error Rate (BER) compared to the original neural BP decoder at lower SNR, but also accelerates convergence speed.

Polar codes are provably capacity-achieving errorcorrecting codes suitable for error correction and source coding, constrained coding, and multiple access channels. Many decoding schemes have been proposed for Polar codes, including CRCaided successive cancellation list decoding (CA-SCL), known for its superior error-correcting performance. Belief propagation (BP) techniques have also been explored for decoding polar codes. BP decoding with partially permuted factor graphs (PPFG) exhibits good performance despite being inferior to CA-SCL decoding. Guess Random Additive Noise Decoding (GRAND) is a general decoding scheme for linear codes. Soft-GRAND (SGRAND) exhibits excellent performance for Polar codes despite its high time complexity. We introduce a novel decoding scheme that combines BP decoding with PPFG and SGRAND, advancing the state-of-the-art in Polar code decoding. Our proposed scheme, which fuses BP decoding with PPFG and SGRAND, demonstrates performance similar to that of CA-SCL decoding, representing a significant step forward in Polar code decoding.

User identification (UI) and channel estimation (CE) are essential in wireless networks with numerous users. Signature-code-based UI and CE schemes are widely used owing to their high spectral efficiency. Traditional signature code uses a discrete sensing matrix as a dictionary to generate codewords. Subsequently, the sparse vector recovery algorithm is used to recover the user state and channel state information in the received signal to complete the UI and CE. In this study, we propose an end-to-end machine-learning-aided signature code scheme under a multiple-access Rayleigh fading channel called machine-learning signature code (ML-SC). The ML-SC consists of a binarized-neural-networks-based (BNNs-based) trainable encoder and a trainable-iterative-soft-threshold-algorithm-based (TISTA-based) trainable decoder. To improve the accuracy, the dictionary is optimized by minimizing the mean squared error between the original and recovered information. Our proposed scheme achieved better performance and efficiency than the conventional schemes in the simulation. Moreover, it was confirmed that the dictionary generated by the ML-SC is suitable for various conventional decoders. Finally, by analyzing the results of the simulations, we found that ML-SC improves the restricted isometric constants and coherence of the dictionary.

The user identification scheme for a multiple-access fading channel based on the binary signature code is considered. In previous works, the signature code was used over a noisy multiple-access adder channel, and only the status of uses was decoded by the signature decoder. In this study, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. To improve the efficiency of the decoding process, we proposed an iterative deep neural network (DNN)-based decoder. Our simulation results show that for the binary signature code, our proposed DNN-based decoder requires less computing time to achieve higher active user detection accuracy and channel estimation accuracy than the classical signal recovery algorithm used in compressed sensing.

Two dimensional magnetic recording systems are investigated extensively to achieve higher recording density. Since the recording density is very high, a small physical defect may cause many two dimensional errors. Hence many two dimensional signal processing of magnetic recording systems are also proposed and investigated. Matcha et al. have proposed a two dimensional LDPC code and investigated the error correcting performance of the code. In this paper we propose a code which is a variant of their code but we can show that the theoretical lower bound of the burst erasure correction capability of our code is better than that of Matcha's code.

A T -user UD k-ary codes for MAAC is proposed. First, a coding scheme for a Tf+g-user UD k-ary code with code length f + g is proposed that is constructed from a Tf-user UD k-ary code and a Tg-user UD (2k - 1)-ary difference set. In fact, the Tg-user UD (2k - 1)-ary difference set is associated with Tg-user UD (2k - 1)-ary code. Second, originally from the multi-user UD (2i(k - 1) + 1)-ary (i = 0, 1, 2,..., m) codes with unitary code length, by recursively employing the coding scheme, 2m+1-user k-ary code with code length 2mis obtained. Finally, by recursively employing the coding scheme, 2n-user UD k-ary code with arbitrary code length n from the codes with length 2i(i = 0, 1,..., ⌊log2n⌋) is given. Since introducing the high-order multiary difference sets, the total rates of the proposed codes are higher those of conventional codes.

A recursive construction of k-ary uniquely decodable multiuser codes is proposed for use in a noiseless multiple-access adder channel. The code rates of the proposed codes are higher than those of previous uniquely decodable multiuser codes. A recursive decoding algorithm is also proposed.

The frame error rate (FER) of spatially-coupled irregular low-density parity-check (SC-iLDPC) code ensemble in error floor region is estimated. First, the number of codewords of each weight is calculated by the permutations of all the sub-codewords at their coupling positions. Second, the FER is estimated by these numbers of the codewords of each weight. Numerical results show that the FER performances in the error floor regions of the SC-iLDPC code ensembles are superior to the conventional irregular LDPC code ensembles at almost identical belief-propagation (BP) thresholds.

In this paper, spatial coupling technique is used to improve the error-correcting performance of finite length polar codes. Polar codes are considered as base codes of coupling. Two types of spatial-coupling methods are investigated. For the first coupling method, some message bits of a polar base code at each coupling position are used as frozen bits of one-side adjacent polar base code. For the second spatial-coupling method, multiple polar codes are associated by combining message bit blocks of adjacent coupling positions with modular-two addition. Simulation results show that, with a little extra iteration complexity, the two types of spatially coupled polar codes can provide better error-correcting performance than original polar codes. When setting the coupling ratio at 0.33 and coupling pattern as Uncertain-Certain, the first spatial-coupling method provides the best performance output. The second spatial-coupling method with coupling width 3 provides better performance than that with coupling width 2. Furthermore, the first spatial-coupling method with short message block length provides better decoding performance, and the second method with large message block length performs better.

Permutation codes with various metrics have recently been studied for flash memory applications. In this work, we derive a tighter upper bound on the generalized Cayley distance using the block permutation distance proposed by Yang, Schoeny, and Dolecek. Furthermore, we employ our upper bound to derive a tighter upper bound on the optimal rate for codes with the generalized Cayley distance when the code length is relatively small.

A kind of multiuser k-ary codes for the multiple-access adder channel is proposed. Given any Tf-user and Tg-user k-ary uniquely decodable (UD) codes with code lengths f and g, respectively, a (Tf+ Tg+ 1)-user k-ary UD code with code length f + g is obtained. The proposed scheme has no restriction on the code structure, e.g., affine, and can be constructed from any arbitrary two UD codes. Moreover, a recursive construction of a kind of k-ary UD codes with arbitrary code length is given. The proposed codes have the higher code rate and the shorter code length than those of the previous codes for a fixed number of users.

Recent years have seen increasing efforts to improve the input/output performance of multilevel flash memory. In this regard, we propose a coding scheme for two-page unrestricted-rate parallel random input-output (P-RIO) code, which enables different code rates to be used for each page of multilevel memory. On the second page, the set of cell-state vectors for each message consists of two complementary vectors with length n. There are a total of 2n-1sets that are disjoint to guarantee that they are uniquely decodable for 2n-1messages. On the first page, the set of cell-state vectors for each message consists of all weight-u vectors with their non-zero elements restricted to the same (2u-1) positions, where the non-negative integer u is less than or equal to half of the code length. Finding cell-state vector sets such that they are disjoint on the first page is equivalent to the construction of constant-weight codes, and the number of disjoint sets is the best-known number of code words in the constant-weight codes. Our coding scheme is constructive, and the code length is arbitrary. The sum rates of our proposed codes are higher than those of previous work.

Index-less indexed flash code (ILIFC) is a coding scheme for flash memories in which one bit of a data sequence is stored in a slice consisting of several cells but the index of the bit is stored implicitly. Although several modified ILIFC schemes have been proposed, in this study we consider an ILIFC with inversion cells (I-ILIFC). The I-ILIFC reduces the total number of cell level changes at each write request. Computer simulation is used to show that the I-ILIFC improves the average performance of ILIFC in many cases. This paper presents our derivation of the lower bound on the number of write operations by I-ILIFC. Additionally, we consider another lower bound thereon and show that the threshold of the code length that determines whether the I-ILIFC improves the worst-case performance of the ILIFC is smaller than that in the first lower bound. Lastly, we analyze the asymptotic performance of the I-ILIFC in the worst case.

An asymmetric multiple access channel (AMAC) is a multiple-access channel where a portion of the users can observe the messages of other users. We first propose three 2-user uniquely decodable (UD) codes for two-user AMAC, which are shown to achieve higher sum-rate than the previous 2-user codes. Then, we consider multiuser error correcting codes for T-user noisy AMAC. A theorem shows that given a Ta-user δa-decodable k-ary code A and a Td-subset δa-decodable difference set D a priori, a larger error-correcting T-user code C is obtained by Hadamard matrices. For practical construction, we give 2-user difference sets based on the 2-user UD codes, and obtain multiuser correcting codes for multiuser AMAC. The proposed correcting codes have increasing sum-rate and error-correcting capability with an increasing code length.

Random input/output (RIO) code is a coding scheme that enables reading of one logical page using a single read threshold in multilevel flash memory. The construction of RIO codes is equivalent to the construction of WOM codes. Parallel RIO (P-RIO) code is a RIO code that encodes all pages in parallel. In this paper, we utilize coset coding with Hamming codes to construct P-RIO codes. Coset coding is a technique that constructs WOM codes using linear binary codes. We leverage the information on the data of all pages to encode each page. Our constructed codes store more pages than RIO codes constructed via coset coding.

A family of rate‐compatible capacity‐approaching codes is obtained by spatially coupling multiple identical copies of rate‐compatible base codes. The base code consists of a given regular low‐density parity‐check (LDPC) code and a parameter‐adjustable repeat‐accumulate (RA) extension. The RA‐extension repeatedly accumulates all of the variable nodes of the given LDPC code by q + 1 times to generate q + 1 blocks of accumulated nodes, in which all of the accumulated variable nodes in the first q blocks and the α ‐fractional accumulated variable nodes in the last block are transmitted. The spatially coupled versions of the base codes, called RA‐extended spatially coupled LDPC (SC‐LDPC) codes, achieve arbitrary rates by simply adjusting parameters q and α , and thus they are rate‐compatible. The potential thresholds of the base code ensembles are calculated to predict the iterative decoding performance of the proposed RA‐extended SC‐LDPC codes. Numerical results and simulations show that the authors’ proposed rate‐compatible codes are capacity‐approaching over binary erasure channels.

A coding scheme of (k+1) -ary error-correcting signature codes for a noisy multiple-access adder channel is proposed. Given a signature matrix A and a difference matrix D=D+- D-a priori, a larger signature matrix is obtained by replacing each element in the Hadamard matrix with A , or D+, or D-depending on the values of the elements and their locations in the Hadamard matrix. The set of rows in the proposed matrix gives an error-correcting signature code. Introducing a difference matrix makes it possible to construct an error-correcting signature code whose sum rate is increased with an increase in the order of the Hadamard matrix. Either binary or non-binary signature codes are constructed when the pairs of matrices A and D are given.

We propose a generalized construction scheme of error-correcting signature code. We form a signature matrix whose rows become the non-zero codewords of the signature code. In the coding scheme, a signature matrix is obtained from a Hadamard matrix by replacing every element by an initial signature matrix or its associated matrix depending on the element's binary value. The proposed code has longer length, higher decodability, and larger cardinality. In this coding scheme, the initial signature matrix is in a general form and can be a signature matrix of any initial signature code. Different initial matrices provide different error-correcting signature codes, including conventional codes. This general form makes it possible to obtain error-correcting signature codes with a higher sum rate than conventional codes.