In this paper, a novel integrated control and communication (ICC) framework is proposed for vehicle platoons, ensuring platoon stability while optimizing energy consumption for communications. To address the high signaling overhead and long delays caused by multi-link competition in radio-frequency-based vehicle-to-vehicle communications, visible light communications (VLCs) are employed, allowing adjacent vehicles to establish collision-free links and simultaneously transmit motion status information (MSI) for platoon control. Besides, age of information (AoI) is utilized to quantify the heterogeneous timeliness of the MSI. Building upon this, a distributed linear control strategy is designed for vehicle platoons with a multi-predecessor-leader-multi-following information topology (IT) and heterogeneous AoIs. Afterward, through stability analysis, the maximum allowable AoI threshold is obtained to guarantee both internal stability and input-state string stability under external disturbances. Accordingly, taking the AoI threshold as a constraint, a long-term energy minimization problem is further formulated to optimize the energy consumed by multi-source, multi-hop, and multicast intra-platoon VLC while maintaining platoon stability. To solve this problem, an online distributed information scheduling policy based on Lyapunov optimization is proposed. Finally, simulation results reveal that the proposed ICC framework effectively maintains platoon stability across various ITs, while significantly reducing the energy consumption for communications to no more than 40% of that achieved by multiple baselines, thus highlighting its substantial potential in vehicle platoons.

In multicell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and intercell interference, leading to unfairness among vehicles. In this article, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application.

Ultraviolet (UV) communication research has prioritized channel modeling for its critical role in system optimization. Current non-line-of-sight (NLoS) UV modeling mainly addresses obstacle-free scenarios and single-obstacle situations: the former manifests constrained applicability at small transceiver elevation angles with obstacle susceptibility, while the latter suffers from high modeling complexity and can only handle one-obstacle scenarios, which pose critical challenges for Internet of Things applications. To overcome these limitations, we propose a single-collision model for short-range NLoS UV channels incorporating both scattering and reflection effects. Initially, the impact of air scattering on the received pulse energy is presented for diverse obstacle situations, where an obstacle-boundary approximation method (OBAM) is developed to reduce the modeling complexity. Besides, the dimensions, coordinates, shapes, orientation angles, and number of obstacles are considered to emulate practical environments. Subsequently, the impact of obstacle reflection on the received pulse energy is investigated for single, double, and multiple obstacle situations. On this basis, we account for certain scenarios where obstacle surfaces comprise multiple sub-regions, each characterized by distinct reflection coefficients attributed to their varying material compositions. Moreover, we verify the proposed model by comparing it with the Monte-Carlo photon-tracing (MCPT) model and the obstacle-free integral model via simulations. These results demonstrate that the path loss curves obtained by the proposed model exhibit close alignment with those simulated by the MCPT model, while its calculation time is less than 10% that of the MCPT model. Additionally, when obstacle reflection is prominent, the assessment error of the proposed OBAM can be ignored in estimating the path loss of NLoS UV channels containing obstacles.

As transceiver elevation angles increase from small to large, existing ultraviolet (UV) non-line-of-sight (NLoS) models encounter two challenges: 1) cannot estimate the channel characteristics of UV NLoS communication scenarios when there exists an obstacle in the overlap volume between the transmitter beam and the receiver field-of-view (FoV), and 2) cannot evaluate the channel path loss for the wide beam and wide FoV scenarios with existing simplified single-scattering path loss models. To address these challenges, a UV NLoS scattering model incorporating an obstacle was investigated, where the obstacle’s orientation angle, coordinates, and geometric dimensions were taken into account to approach actual application environments. Then, a UV NLoS reflection model was developed combined with specific geometric diagrams. Further, a simplified single-scattering path loss model was proposed with a closed-form expression. Finally, the proposed models were validated by comparing them with the Monte-Carlo photon-tracing model, the exact single-scattering model, and the latest simplified single-scattering model. Numerical results show that the path loss curves obtained by the proposed models agree well with those attained by related NLoS models under identical parameter settings, and avoiding obstacles is not always a good option for UV NLoS communications. Moreover, the accuracy of the proposed simplified model is superior to that of the existing simplified model for all kinds of transceiver FoV angles.

Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering and obstacle reflection on UV signals were both taken into account. To validate the proposed model, we compared it to the related Monte-Carlo photon-tracing (MCPT) model that had been verified by outdoor experiments. Numerical results manifest that the path loss curves obtained by the proposed model agree well with those determined by the MCPT model, while its computation complexity is lower than that of the MCPT model. This work discloses that obstacle reflection can effectively reduce the channel path loss of UV NLoS communication systems.

Motivated by the ability of optical intelligent reflective surface (OIRS) to alter optical channels, the effectiveness of OIRS on aggregated visible light communication (VLC)-radio frequency (RF) systems is explored, and the system model is established in detail. With the aim of improving the energy efficiency (EE) of the aggregated system with OIRS, the EE maximization problem is formulated with diverse constraints, followed by the proposed block coordinate descent (BCD)-based resource allocation algorithm. Particularly, the EE maximization problem is partitioned into two subproblems, which are OIRS configuration and joint power allocation, and then are solved in the iteration process. Additionally, the substantial enhancement in EE brought by OIRS for the aggregated VLC-RF system, as well as the convergence and efficacy of the proposed BCD-based algorithm are demonstrated by comprehensive simulation results.

Electric vehicles (EVs) are crucial to the reduction of carbon emissions. However, their charging poses a threat to power system networks. Hence, EV charging control strategies are developed to curb this challenge, using charging prices to incentivize EV drivers to choose EV charging stations (EVCS) favourable to the grid's stability. The challenge of this strategy is the likelihood of EV drivers accepting EVCS suggestions. To increase the probability of accepting EVCS suggestions, we introduce a two-tailed incentive pricing (TTIP) scheme in an EV charging coordination model, where incentives are offered as charging prices and parking time. We formalized the EV charging problem as a multiobjective Markov decision process and proposed a deep deterministic policy gradient (DDPG) to solve it. To tackle the challenge of continuous action space that leads to the dimensionality curse, the proposed DDPG models the action space using a metaheuristic-based technique. The proposed scheme implements a multiple reward system to generate Pareto optimal solutions and a decision-making technique to choose the compromise reward. Using real-world electricity prices and the IEEE 33-bus distribution network, numerical simulations show that our proposed TTIP scheme yields an average of 18% improvement in grid stability than the sustainable policy following, random, and price-greedy algorithms. It also improves the EV charging profit margins by an average of 28%.

Intelligent reflecting surface (IRS) alters the wireless channel by dynamically adjusting the propagation of wireless signals, thereby having the ability to enhance communication performance. Due to its low power consumption, IRS is expected to play an important role in improving energy efficiency (EE). In this paper, both optical IRS (OIRS) and radio frequency (RF) IRS are employed to assist aggregated visible light communication (VLC)-RF systems, and then a resource allocation algorithm is proposed to improve EE. To this end, a model for the IRS-assisted aggregated VLC-RF system is first established, followed by the formulated EE maximization problem. Furthermore, the EE maximization problem is decomposed into four subproblems, focusing on RF IRS configuration, optical subchannel assignment, OIRS arrangement, and joint power allocation. By employing block coordinate descent (BCD), the four subproblems are solved iteratively. Moreover, simulation results show the significant EE improvement brought by the IRS for aggregated VLC-RF systems. In addition, the convergence and effectiveness of the proposed BCD-based resource allocation algorithm, as well as the influence of key system parameters on EE are also demonstrated.

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity$\mathcal {O}(n^{N})$with$n$denoting the dimension of action space and$N$denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to$\mathcal {O}(n^{2})$while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

In this article, discrete memory channel models of a low-cost, visible light communication (VLC) and a hybrid amplify-and-forward (AF) powerline communication (PLC) and VLC channel are derived using data obtained from an underground mining testbed. The testbed is set up close to the mining face, where blasting occurs. Then, the noise distributions as a result of the connection of a blasting equipment and the mains switchboard supply on the powerline channel are modeled. The baseband signals are communicated over software-defined radios, using multicarrier modulation in the VLC and hybrid AF PLC-VLC channels. Aided by a Fritchman 3-state semi-hidden Markov model (SHMM) for channel state classification, the channels are modeled using a first-order (FO) Markov process, which considers only one previous state in the channel. In addition, the block diagonal (BD) Markov model, which groups similar successive states of the channel, is implemented in order to reduce the training complexity. The models for the error distribution are then derived for both VLC and hybrid PLC-VLC channels. Results show that the probability of error obtained from the trained models closely matches the probability of error obtained from the channel measurements, validating the suitability of the SHMM for modeling VLC and hybrid PLC-VLC channels. In addition, the modified BD Markov model, which groups the channel states shows better performance with reduced complexity when compared with the FO Markov model.

Connected vehicles (CV) and automated vehicles (AV) are promising technologies for reducing road accidents and improving road efficiency. Significant advances have been achieved for AV and CV technologies, but they both have inherent shortcomings such line of sight sensing for AV. Connected autonomous vehicles (CAV) has been proposed to address the problems through sharing sensing and cooperative driving. While the focus of the research on CAV has been on the vehicles so far, cooperative and connected smart road infrastructure can play a critical role to enhance CAV and safe driving. In this paper we present an investigation of connected smart road infrastructure and AVs (CRAV). We discuss the potentials and challenges of CRAV, then propose a scalable simulation framework for the CRAV to facilitate fast, economic and quantitative study of CRAV. A case study of CRAV on smart road side unit (RSU) assisted vulnerable road users (VRU) collision warning is conducted, where the identification of VRU such as pedestrians on the road by the AVs is compared with and without RSU assistance. The impact of the location of RSUs on avoiding potential collisions is evaluated for vehicles with different sensor configurations. Preliminary simulation results show that with the support of smart RSUs, the CAVs could be notified of the existence of the VRUs on the road by the RSUs much earlier than they can detect with their own onboard sensors, and collisions with VRUs can be reduced. This study demonstrates the effectiveness of the proposed CRAV simulation framework and the great potentials of CRAV.

Recently, the use of unmanned aerial vehicles (UAVs) as a relay node has been envisaged as an enabling technology in the upcoming wireless communication era. Thus, in this paper, we consider a full-duplex (FD) cooperative communication system with a source and a destination, where UAV serves as a mobile relay. Here, the transmission power cost is debited to energy harvested using simultaneous wireless information and power transfer (SWIPT) and self-interference energy harvesting (EH) via power-splitting (PS) protocol. In poor channel conditions, UAV uses a soft angular modulation scheme to perceive the soft information. In this proposed system, we present the outage probability over the Nakagami-m fading channels. A closed-form solution for the outage probability is derived. In addition, we formulate an optimization problem to minimize end-to-end outage probability subject of the UAV's power profile. The KKT conditions have been used to obtain a closed-form solution of the proposed optimization problem. Finally, numerical results are provided to evaluate the proposed system under various setups.

In this study, we focused on the optimal coordinated charging of electric vehicles in a centralized charging model, based on a special multi-objective model. The objective functions are charging cost minimization, load variance minimization, and power loss minimization. The multi-objective problem has been solved by the Whale Optimization Algorithm (WOA). In the proposed approach, the probability density function was used to characterize a single EV (e.g. driving distance). For each time step, the optimal load variance is calculated. The WOA was compared to other optimization algorithms, and was tested on the IEEE 33-bus test distribution network. The simulation results suggest that the proposed framework can through the objective function, improve grid performance and simultaneously encourage EV drivers to participate in a centralized coordinated charging system. The WOA gives a superior performance in both grid performance and economic benefits. Compared to the uncoordinated charging system, the WOA outperforms other tested algorithms with a 90% minimization of charging cost. The WOA also reduces the real power loss more than the BAT, GWO, GWOCS, and PSOGWO.

Surface damage on concrete is important as the damage can affect the structural integrity of the structure. This paper proposes a two-step surface damage detection scheme using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The CNN classifies given input images into two categories: positive and negative. The positive category is where the surface damage is present within the image, otherwise the image is classified as negative. This is an image-based classification. The ANN accepts image inputs that have been classified as positive by the ANN. This reduces the number of images that are further processed by the ANN. The ANN performs feature-based classification, in which the features are extracted from the detected edges within the image. The edges are detected using Canny edge detection. A total of 19 features are extracted from the detected edges. These features are inputs into the ANN. The purpose of the ANN is to highlight only the positive damaged edges within the image. The CNN achieves an accuracy of 80.7% for image classification and the ANN achieves an accuracy of 98.1% for surface detection. The decreased accuracy in the CNN is due to the false positive detection, however false positives are tolerated whereas false negatives are not. The false negative detection for both CNN and ANN in the two-step scheme are 0%.

In this paper, we present two low complexity algorithms to decode non-binary LDPC codes. The proposed decoding algorithms update iteratively the hard decision received vector to search for a valid codeword in the vector space of Galois field (GF). The selection criterion for the position of unreliable symbols is based on failed checks and the information from the Galois field structure. In the first proposed algorithm, the flipping function is calculated for all symbols of the received sequence and multiple symbols are flipped in each iteration while in the second proposed algorithm, a single symbol is flipped per iteration. In the second method, unreliable positions are short-listed by using a majority voting scheme, and then the flipping function is computed to predict candidate symbols from the set of symbols in GF(q) while not violating the field order q. The proposed methods reduce the decoding complexity and memory use. The results of the algorithms show appealing tradeoffs between complexity and bit error rate performance for non-binary LDPC codes.

In this paper, an enhanced asymmetrically clipped DC biased optical OFDM (ADO-OFDM) with adaptive subcarrier allocation is proposed to accommodate the requirements of multi-users in downlink multiple access. The optimal optical power allocation is investigated to minimize the overall bit error rate (BER). Moreover, to meet the dimming demands for illumination, enhanced negative ADO-OFDM is investigated and combined with enhanced ADO-OFDM for multiplexing transmission. Furthermore, a dimming control scheme is proposed by changing the proportion of these two signals to a proper value, with full using of the entire dynamic range of light-emitting diodes. Simulation results show that the proposed scheme can achieve better BER performance at the same signal to noise ratio, while wide dimming range for illumination can be ensured compared to the conventional counterparts.

Recently, the index modulation technique is introduced into the visible light communication (VLC) system to convey information bits in a more energy-efficient and spectral-efficient way. However, in practice, the index modulation aided VLC system should be carefully designed to be compatible with dimming control. In this paper, a novel hybrid dimming scheme is proposed for the index modulation aided VLC system, which combines the frequency-domain and time-domain dimming strategies to maximize the channel capacity and maintain the desired illumination level. Simulation results validate the superior performance of the proposed hybrid dimming scheme.

Visible light communication (VLC) is regarded as one of the promising candidate to complement the conventional radio frequency communication as it can potentially perform communication and illumination simultaneously. Recently, VLC is combined with generalized spatial modulation (GenSM) technology to improve spectral and energy efficiencies. In order to provide high speed communication and flexible dimming control simultaneously, in this paper, a spectral-efficient GenSM based hybrid dimming scheme, which combines the spatial-domain and time- domain dimming strategies, is proposed for VLC. The channel capacity of the proposed scheme is analyzed and a closed-form expression of the upper bound of the channel capacity is derived. According to the required illumination level, the spatial-domain and time-domain strategies are realized based on the maximization of the channel capacity. Simulation results substantiate the superior performance of the proposed dimming scheme over state-of-art dimming schemes.

Recently, the technique of multiple input multiple output (MIMO) is becoming more and more widespread in the communication systems in order to significantly improve the system capacity. The channel estimation is still one of the most tricky problems because of the large amount of coefficients to be estimated for one receive antenna. This paper focuses on designing and optimizing the time-domain training sequences for MIMO systems and proposes a novel sparse channel estimation scheme taking advantage of the spatial correlation among both the transmit antennas and the receive antennas. The structured compressive sensing (SCS) framework is set up, which is the main approach to recover the sparse channel for different antennas. It is illustrated in the simulation results that the proposed scheme has a higher probability to recovery the nonzero support of the channel than that of the traditional schemes, while the mean square error is also smaller, which means our scheme is expected to have a superior performance.

Correlated sources are present in communication systems where protocols ensure that there is some predetermined information for sources. Here correlated sources across an eavesdropped channel that incorporate a heterogeneous encoding scheme and their effect on the information leakage when some channel information and a source have been wiretapped is investigated. The information leakage bounds for this scenario are provided. Further, an implementation method using a matrix partition approach is described.

Codes are presented that can correct the deletion or the insertion of a predetermined number of adjacent bits greater than or equal to three. This extends the constructions of codes beyond those proposed by Levenshtein fifty years ago to correct one or two adjacent deletions or insertions.

The first-order moment of (d, k)-constrained codes is investigated in this paper. A generalized moment balancing template is proposed to encode a (d, k) sequence into a single insertion or deletion correcting codeword without losing the constraint property. By relocating 0's in moment balancing runs, which appear in a pairwise manner of a (d, k) sequence, the first-order moment of this sequence can be modified to satisfy the Varshamov-Tenengolts construction. With a reasonably large base in the modulo system introduced by the Varshamov-Tenengolts construction, this generalized moment balancing template can be applied to run-length limited sequences. The asymptotic bound of the redundancy introduced by the template for (d, k) sequences is of the same order as the universal template for random sequences and, therefore, the redundancy is small and suitable for long sequences of practical interest.

The first-order moment of (d, k) constrained codes is investigated in this paper. We propose a generalized moment balancing template to encode a (d, k) sequence into a single insertion or deletion correcting codeword without losing the constraint property. By relocating 0's in moment balancing runs of a (d, k) sequence, the first-order moment of this sequence can be manipulated to satisfy the Varshamov-Tenengolts construction. The moment balancing runs appear in a pairwise manner in this sequence. The lower bound of the number of balancing bits in the template is asymptotically of the same order as the universal template for random sequences, and is of a practical-interest small.

Linear and cyclic codes are typically used to combat substitution errors. However, synchronization errors, associated with the deletion and insertion of symbols, can cause severe performance degradation unless the coding scheme possesses the capability to recover from such errors. It is shown that linear codes of rate greater than 1/2 cannot correct deletion or insertion errors but there are linear codes of rate 1/2 that can correct these errors. Although cyclic codes, except for repetition codes, cannot correct deletion or insertion errors, two approaches are investigated to yield codes, based on cyclic codes, that can correct these errors. In the first approach, it is shown that a binary or nonbinary cyclic code of rate at most 1/3 or 1/2, respectively, can be extended by one symbol to make it capable of correcting synchronization errors. In the second approach, a cyclic code of rate at most 1/2 is expurgated by appropriately deleting codewords such that the expurgated code is capable of correcting synchronization errors. It is shown that deleting codewords costs at most two information bits if the code is binary and one information symbol if the code is nonbinary.

The generalized moment balancing templates for spectral null codes are investigated in this paper. A new approach based on the insertion of a determined number of vectors at determinate indices of a spectral null code word, is found to balance the moment of the code word. Moment balanced code words preserve the spectral null properties and can guarantee the correction of one insertion or one deletion error. As compared to the universal template for an uncoded sequence, the redundancy is determined by the structure of the original spectral null code, however, it can be kept small. The new approach involves the applications of the optimized moment balancing template and nonbinary enumeration of the balancing moment values.

Templates are constructed to extend arbitrary additive error correcting or constrained codes, i.e., additional redundant bits are added in selected positions to balance the moment of the codeword. The original codes may have error correcting capabilities or constrained output symbols as predetermined by the usual communication system considerations, which are retained after extending the code. Using some number theoretic constructions in the literature, insertion/deletion correction can then be achieved. If the template is carefully designed, the number of additional redundant bits for the insertion/deletion correction can be kept small-in some cases of the same order as the number of parity bits in a Hamming code of comparable length.

We investigate moment balancing templates for spectral shaping codes, e.g., dc-free codes, high-order spectral-null codes and spectral nulls code. A new approach by addingkgrouped bits at determinate indices of the code word is found to balance the moment of the code word. Moment balanced code words preserve the spectral-null properties and can guarantee to correct one insertion or deletion error. The redundancy, which is a constant factor greater than that of the universal template for uncoded sequence, can be kept small. The new approach also involves the application of the optimized moment balancing template and the non-binary enumeration of balancing moment values.

The use of linear and cyclic codes for correcting synchronization errors is investigated. These codes are typically required to have the capability to correct substitution errors, which are the most common type of errors in most communication and storage systems. However, synchronization errors, associated with the deletion and insertion of symbols, can also occur and may cause severe performance degradation unless the coding scheme possesses the capability to recover from such errors. We show that linear codes of rate greater than 1/2 cannot correct deletion or insertion errors. Although cyclic codes, except for repetition codes, cannot correct deletion or insertion errors, we show that adding one extra symbol to the codewords of a binary or nonbinary cyclic code of rate at most 1/3 or 1/2, respectively, yields a code capable of correcting a single deletion or insertion.

We investigate extending a chosen block or convolutional code which has additive error correction capability, as predetermined by the usual communication systems or coding considerations. Our extension involves constructing a template to add additional redundant bits in positions, selected to balance the moment of the code word. Using some number theoretic constructions in the literature, insertion/deletion correction can then be achieved. If the template is carefully designed, the number of additional redundant bits for the insertion/deletion correction can be kept small - in some cases of the same order as for Hamming codes. Our construction technique can also be used for the systematic encoding of number theoretic codes, and furthermore have implications for other coding techniques utilizing the moment function, such as codes correcting asymmetrical errors, spectral shaping codes, or constant weight codes.

A concatenated system is presented to achieve unequal error protection (UEP) on a channel with insertion/deletion/substitution (IDS) errors. By using a feedback-mapping encoder scheme, the path-pruning of a convolutional code is achieved to periodically generate a specific block code, which can be exploited to correct insertion or deletion errors. This paper presents a general means to obtain a path-pruned convolutional code with specific trellis structure by using the puncturing and pruning processes. One application combining the rate-compatible path-pruned convolutional code, partial interleaving, and Reed-Solomon code was implemented to evaluate its performance on the IDS channel.