Hybrid Automatic Repeat Request Research Articles

Reliability and Security Performances Analysis of Rate Splitting Based VLC System With HARQ

This work first attempts to investigate the reliability and physical layer security (PLS) performances of a rate splitting (RS) based visible light communication (VLC) system with one trusted user and one untrusted user. Specifically, the hybrid automatic repeat request (HARQ) protocol is adopted at the trusted user and the movement of users is assumed to be subject to the random way point (RWP) model with four decoding strategies considered at receiver side. The exact closed-form expressions of outage probability (OP) for both users are then obtained, and the analytical expressions of secrecy outage probability (SOP), reliable and secure transmission probability (RSP), and effective secrecy throughput (EST) of trusted user are also mathematically derived. Through simulations, it is found that to guarantee the reliability and PLS performances, the trusted user should first decode the untrusted user’s private stream and the untrusted user should first decode the trusted user’s private stream. Besides, the reliability and PLS performances of RS-VLC system could be enhanced with the appropriate increase in the maximum transmission round of HARQ with the best decoding strategy. Moreover, they could be further enhanced by the proper common stream power allocation coefficient. This work will benefit the research and development of indoor VLC system.

Performance Analysis of Uplink Rate-Splitting Multiple Access With Hybrid ARQ

Performance Analysis of Uplink Rate-Splitting Multiple Access With Hybrid ARQ

Hybrid Automatic Repeat Request for Downlink Rate-Splitting Multiple Access

Hybrid Automatic Repeat Request for Downlink Rate-Splitting Multiple Access

A Channel-Sensing-Based Multipath Multihop Cooperative Transmission Mechanism for UE Aggregation in Asymmetric IoE Scenarios

With the continuous progress and development of technology, the Internet of Everything (IoE) is gradually becoming a research hotspot. More companies and research institutes are focusing on the connectivity and transmission between multiple devices in asymmetric networks, such as V2X, Industrial Internet of Things (IIoT), environmental monitoring, disaster management, agriculture, and so on. The number of devices and business volume of these applications have rapidly increased in recent years, which will lead to a large load of terminals and affect the transmission efficiency of IoE data transmission. To deal with this issue, it has been proposed to perform data transmission via multipath cooperative transmission with multihop transmission. This approach aims to improve transmission latency, energy consumption, reliability, and throughput. This paper designs a channel-sensing-based cooperative transmission mechanism (CSCTM) with hybrid automatic repeat request (HARQ) for user equipment (UE) aggregation mechanism in future asymmetric IoE scenarios, which ensures that IoE devices data can be transmitted quickly and reliably, and supports real-time data processing and analysis. The main contents of this proposed method include strategies of cooperative transmission and redundancy version (RV) determination, a joint combination of decoding process at the receiving side, and a design of transmission priority through ascending offset sort (AOS) algorithm based on channel sensing. In addition, multihop technology is designed for the multipath cooperative transmission strategy, which enables cooperative nodes (CN) to help UE to transmit data. As a result, it can be obtained that CSCTM provides significant advancements in latency and energy consumption for the whole system. It demonstrates improvements in enhanced coverage, improved reliability, and minimized latency.

Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss

Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by using the adaptive learning capabilities of IS. Hybrid Automatic Repeat Request (HARQ), an error recovery method, is utilized to detect network errors in order to correct and retransmit data to the destination during error-prone network conditions. Queue learning action sets are used to discover the finest path for efficient data transmission. The OMNeT++ simulator is used to assess the performance of the proposed BIP-GANs protocol. The performance results are compared with the prevailing data transmission protocols, such as EAP-IFBA, DNN-CSO, and ALO-DHT. Findings: The suggested BIP-GANs data transmission protocol outperforms with promising results, with an energy consumption rate of 6%, 8 seconds data transmission speed, 6 second less delay rate, 98% robustness (security and confidentiality), 98.5% alive nodes, and 99% network life span, which is higher than the prevailing methods. Novelty: This research study provides a comprehensive solution to secure and efficient data transmission by integrating the AI-based bio-inspired BIP-GANs method to address the security and data transmission challenges of existing methods such as EAP-IFBA, DNN-CSO, and ALO-DHT in terms of energy consumption rate, transmission speed, delay rate, life span of the network, robustness, and number of alive nodes. Keywords: Artificial Intelligence, Generative Adversarial Networks, Error Recovery, Secured Data Transmission, Deep Learning, Bio­Inspired Algorithm

Resource-Efficient Multicast URLLC Service in 5G Systems.

Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity.

Simulation and analysis of C-V2X data transmission in different traffic scenarios based on MAC layer scheduling algorithms

With the rapid development of communication technology, vehicles are becoming more and more intelligent, and Vehicle to Vehicle (V2V) communication has become an important cornerstone for the future intelligence of vehicles. Because of the high-reliability and low-latency characteristics of 5G networks, many applications can be realized by using Vehicle to Network (V2N) communications. Different applications have different requirements for Quality of Service (QoS), which should be met by the network, and the base station can use different scheduling methods to satisfy these different requirements. This paper focuses on the scheduler at the MAC layer in 5G base stations. The scheduler must handle both video signals and other communication signals transmitted by the vehicle, and must also consider Hybrid Automatic Repeat reQuest (HARQ) retransmissions. We performed a C-V2X signaling network transmission simulation by using OMNeT++. We considered urban and motorway, two public transportation scenarios. Different types of data are set to be transmitted in two scenarios. We used different scheduling schemes for data scheduling, then investigated the performance of these scheduling schemes for transmitting data. The methods and simulation experiments mentioned in this paper are important for practical generalization.

Post-Cancellation-Based LLR Refining for MIMO Multiple ARQ Systems

In multiple-input multiple-output (MIMO) multiple automatic repeat request (ARQ) systems, multiple streams with independent hybrid ARQ (HARQ) processes can be simultaneously sent. Thus, the interference from other streams can affect future retransmissions of a packet as well as the current transmission, and proper management of interference at the receiver is required. Therefore, in this paper, a post-cancellation-based log-likelihood ratio (LLR) refining scheme is proposed for MIMO multiple ARQ (MMARQ) systems. In the proposed scheme, after the end of the entire conventional reception procedure for packet decoding, LLR refining is performed for the non-terminated packets that will be sent during the next transmission time interval. For LLR refining, the packet cancellation is performed to cancel only the successfully decoded packets. Thus, the LLRs of the non-terminated packets are refined without any error propagation, including the inter-transmission error propagation. Consequently, the proposed scheme can compensate for the interference problem in MMARQ systems and improve system performance. In order to utilize the error detection results of decoded codewords, the proposed scheme should be performed after the end of the entire reception procedure for packet decoding. Therefore, as the post-processing scheme, the proposed scheme can be employed to any existing LLR-level combining-based MMARQ receiver without changing the original procedure. Simulation results verify that the proposed scheme can significantly improve the error performance and throughput of MMARQ systems, especially for hard-decision interference cancellation-based receivers and high-order modulation. In addition, compared with the conventional reception procedure, the proposed scheme requires a smaller computational complexity in most of the simulated SNR region. Therefore, the proposed LLR refining scheme can be considered as an effective and practical post-processing scheme for an MMARQ receiver.

A Hybrid Millimeter-Wave and Free-Space-Optics Communication Architecture with Adaptive Diversity Combining and HARQ Techniques

We propose and demonstrate a hybrid communication architecture that combines millimeter-wave (MMW) in the radio frequency (RF) domain and free-space-optics (FSO) technologies using adaptive combining and hybrid automatic repeat request (HARQ) techniques. At the receiving end, we employed joint signal processing with an adaptive diversity combining technique (ADCT) based on a maximum ratio combining (MRC) algorithm. We derived closed-form expressions for the outage probability and throughput of the hybrid RF and FSO (RF/FSO) system, considering various characteristics of atmospheric turbulence in the FSO link. Experimental testing with 10-Gbaud quadrature phase shift keying (QPSK) data was conducted under different simulated atmospheric turbulence intensities, FSO and MMW speed-ratios, and forward error correction (FEC) overheads. Additionally, we validated improvements in terms of bit error ratio (BER), outage probability, and throughput performance.

An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning

V2X communication is susceptible to attenuation and fading caused by external interference. This interference often leads to bit error and poor quality and stability of the wireless link, and it can easily disrupt packet transmission. In order to enhance communication reliability, the 3rd Generation Partnership Project (3GPP) introduced the Hybrid Automatic Repeat Request (HARQ) technology for both 4G and 5G systems. Nevertheless, it can be improved for poor communication conditions (e.g., heavy traffic flow, long-distance transmission), especially in advanced or cooperative driving scenarios. In this paper, we propose an Adaptive Hybrid Automatic Repeat Request (A-HARQ) scheme that can reduce the average block error rate, the average number of retransmissions, and the round-trip time (RTT). It adapts the Q-learning model to select the timing and frequency of retransmission to enhance the transmission reliability. We also design some transmission schemes—K-repetition, T-delay and [T, K]-overlap—which are used to shorten latency and avoid packet collision. Compared with the conventional 5G HARQ, our simulation results show that the proposed A-HARQ scheme decreases the system’s average BLER, the number of retransmissions, and the RTT to 5.55%, 1.55 ms, and 0.97 ms, respectively.

Highly-Efficient Low-Latency HARQ Built on NOMA for URLLC: Radio Resource Allocation and Transmission Rate Control Aspects

Hybrid automatic repeat request (HARQ) is an essential technology that efficiently reduces the transmission error rate. However, for ultra-reliable low latency communications (URLLC) in the 5th generation mobile communication systems and beyond, the increase in latency due to retransmission must be minimized in HARQ. In this paper, we propose a highly-efficient low-latency HARQ method built on non-orthogonal multiple access (NOMA) for URLLC while minimizing the performance loss for coexisting services (use cases) such as enhanced mobile broadband (eMBB). The proposed method can be seen as an extension of the conventional link-level non-orthogonal HARQ to the system-level protocol. This mitigates the problems of the conventional link-level non-orthogonal HARQ, which are decoding error under poor channel conditions and an increase in transmission delay due to restrictions in retransmission timing. In the proposed method, delay-sensitive URLLC packets are preferentially multiplexed with best-effort eMBB packets in the same channel using superposition coding to reduce the transmission latency of the URLLC packet while alleviating the throughput loss in eMBB. This is achieved using a weighted channel-aware resource allocator (scheduler). The inter-packet interference multiplexed in the same channel is removed using a successive interference canceller (SIC) at the receiver. Furthermore, the transmission rates for the initial transmission and retransmission are controlled in an appropriate manner for each service in order to deal with decoding errors caused by error in transmission rate control originating from a time varying channel. We show that the proposed method significantly improves the overall performance of a system that simultaneously provides eMBB and URLLC services.

Performance Analysis and Optimal Design of HARQ-IR-Aided Terahertz Communications

Terahertz (THz) communications are envisioned to be a promising technology for 6G thanks to its broad bandwidth. However, the large path loss, antenna misalignment, and atmospheric influence of THz communications severely deteriorate its reliability. To address this, hybrid automatic repeat request (HARQ) is recognized as an effective technique to ensure reliable THz communications. This paper delves into the performance analysis of HARQ with incremental redundancy (HARQ-IR)-aided THz communications in the presence/absence of blockage. More specifically, the analytical expression of the outage probability of HARQ-IR-aided THz communications is derived, with which the asymptotic outage analysis is enabled to gain meaningful insights, including diversity order, power allocation gain, modulation and coding gain, etc. Then the long term average throughput (LTAT) is expressed in terms of the outage probability based on renewal theory. Moreover, to combat the blockage effects, a multi-hop HARQ-IR-aided THz communication scheme is proposed and its performance is examined. To demonstrate the superiority of the proposed scheme, the other two HARQ-aided schemes, i.e., Type-I HARQ and HARQ with chase combining (HARQ-CC), are used for benchmarking in the simulations. In addition, a deep neural network (DNN) based outage evaluation framework with low computational complexity is devised to reap the benefits of using both asymptotic and simulation results in low and high outage regimes, respectively. This novel outage evaluation framework is finally employed for the optimal rate selection, which outperforms the asymptotic based optimization.

Saving Energy and Spectrum in Enabling URLLC Services: A Scalable RL Solution

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Diversity techniques and power control are the main approaches to reduce latency and enhance the reliability of wireless communications at the expense of redundant transmissions and excessive resource usage. Focusing on the application layer reliability key performance indicators (KPIs), we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize these KPIs. Furthermore, to address the scalability issue that emerges in the per-device orchestration problem, we develop a new branching soft actor-critic framework in which a separate branch represents the action space of each industrial device. Our orchestrator enables near real-time control and can be implemented in the edge cloud. We test our solution with a 3GPP-compliant and realistic simulator for factory automation scenarios. Compared to the state-of-the-art, our solution offers significant scalability gains in terms of computational time and memory requirements. Our extensive experiments show significant improvements in our target KPIs, over the state-of-the-art, especially for 5th percentile user availability. To achieve these targets, our framework requires substantially less total energy or spectrum, thanks to our scalable RL solution.

Raptor-like Coded Broadcasting for Efficient V2X Communications

Broadcasting is a critical feature in V2X communication, allowing for the simultaneous dissemination of safety-critical messages to all nearby vehicles. However, the requirement for low latency in information dissemination and the need for reliable and efficient data transmission pose significant challenges to broadcasting in V2X communication systems. In this paper, we present a novel raptor-like coded broadcasting (RLCB) scheme for low-latency V2X communications. Firstly, we introduce feedback into a concatenated fountain code, and adjust its precoding and coding structure to achieve effective data deliverance under a limited number of retransmissions for low-latency transmission. Then, based on the raptor-like encoding and decoding structure, we propose a mutual exclusion-based network encoding (MENC) algorithm to enable retransmission in broadcasting scenarios. We also conduct a complexity analysis on the encoding and decoding process of our proposed scheme. Numerical results demonstrate the superior performance of our proposed scheme in reducing the packet error rate (PER) and improving spectral efficiency compared to the R10 code and hybrid automatic repeat request (HARQ) scheme.

On Coordinated Scheduling of Radio and Computing Resources in Cloud-RAN

Cloud Radio Access Network is a promising mobile network architecture based on centralizing the baseband processing of many cellular base stations in a BBU (BaseBand Unit) pool. Such architecture has many advantages. However, computing resources are shared among the base stations connected to the BBU pool. It is challenging to schedule the processing of users’ data, especially on overloaded BBU pools, while respecting the time constraints imposed by the Hybrid Automatic Repeat Request (HARQ) mechanism. Given that the processing time of users’ data and the computing requirement depends on the radio parameters such as the Modulation and Coding Scheme (MCS), we propose to enable the coordination between radio and computing resources schedulers; such coordination makes the selection of MCS dependent on the availability of radio and computing resources and on the ability to process data while respecting the HARQ-deadline. In this context, we propose and evaluate three Integer Linear Programming (ILP)-based schemes and three low-complexity heuristics, demonstrating their ability to reduce the wasted transmission power. Moreover, we evaluate the performance of the coordination under a multi-services scenario consisting of two services having heterogeneous requirements, enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC).

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

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

Age of error information and throughput for truncated and layer‐coded HARQ‐based satellite‐IoT systems

SummaryThe work focuses on the simultaneous improvement on the throughput and information freshness of the promising satellite‐based Internet of Things (S‐IoT). To this end, we first propose a method based on the adaptive modulation coding (AMC) and truncated layer‐coded hybrid automatic repeat request (L‐HARQ) strategies by especially considering the difference in the channel state information (CSI) between the transmitter and receiver. On account of the large signal delay of the S‐IoT systems, this paper establishes the channel model based on the correlation of the signal‐to‐noise ratio (SNR) under the outdated channel and the ideal channel. Secondly, considering the fact that the improved backtrack decoding is used in the L‐HARQ, the classical definition of age of information (AoI) is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack‐decoded status update. Thirdly, we characterize the statistical descriptions of the maximum wait time of the successfully recovered status updates and the interdeparture time between the two consecutive status update packets that are lastly successfully received. Then, the closed‐form expression of average AoEI is derived under the assumption of independent and identically distributed fading channels. Finally, the Markov decision process (MDP) framework is used to optimize the packet parameters in each round of L‐HARQ process under the general fading scenario. Thereby, the optimized transmission scheme that minimizes AoI with throughput constraint is solved. The simulations investigate the effect of system parameters on the average AoEI.

HARE: Hybrid ARQ-Based Adaptive Retransmission Control Scheme for Synchronous Multi-Link in Wireless LANs

As the resolution of video becomes high and the number of mobile devices proliferates, the demand for video traffic is exploding. New traffic types, such as augmented reality (AR) and virtual reality (VR), which require high-throughput and low-latency, are also emerging. Two of the promising techniques discussed in increasing throughput and lowering latency are multi-link transmission and hybrid automatic repeat request (HARQ). However, in most cases of a device using a low-cost chipset, there are potential limitations to using HARQ on multi-link. This paper proposes a HARQ-based adaptive retransmission control scheme (HARE), which determines the optimal retransmission policy by casting it as a Markov decision process (MDP) for the devices undergoing the aforementioned limitations. Through this, we can decide whether to execute HARQ and select the adequate modulation and coding rate when a retransmission occurs. Through extensive analysis, we found that HARE can greatly increase throughput compared to the ARQ-only and HARQ-only retransmission methods with a small amount of buffer occupancy. This technique expects high-resolution video and brand-new services such as AR/VR to be enjoyed even by the devices with low-cost chipsets.

Polar-Coded Cross-Packet HARQ Based On Polarizing Matrix Extension

In this letter, polar-coded cross-packet hybrid automatic repeat request (XP-HARQ) using multilevel coded modulation (MLCM) to improve throughput performance is investigated. Firstly, a novel polar-coded XP-HARQ framework combining polar codes, MLCM, and level-independent XP-HARQ by using polarizing matrix extension is proposed. Then, a two-step throughput-based code design method is developed to generate optimal code rates and corresponding bit protect pairs and new information bit sets in extended codes. Simulation results demonstrate that the proposed scheme outperforms level-independent Turbo-coded XP-HARQ and state-of-art hybrid automatic repeat request (HARQ) using polar-coded modulation.

FSO-Based Space-Air-Ground Integrated Vehicular Networks: Cooperative HARQ With Rate Adaptation

Space-air-ground integrated vehicular networks (SAGIVN) have been widely envisioned as a promising network architecture for 6 G to support the Internet of Vehicles (IoV). In SAGIVN, free-space optics (FSO)-based, high altitude platform (HAP) relay-assisted low Earth orbit (LEO) satellite systems have recently attracted research efforts worldwide. Critical challenges in designing and implementing FSO-based SAGIVN include atmospheric turbulence, weather conditions, and pointing misalignment. This paper offers a comprehensive cross-layer design framework of error-control protocols with rate adaptation for FSO burst transmissions in HAP-aided SAGIVN. Remarkably, we propose a design of link-layer cooperative incremental redundancy (IR) hybrid automatic repeat request (HARQ)-based sliding window mechanism. An analytical channel model for HAP-aided LEO satellite to emerging unmanned aerial vehicles (UAVs) FSO links is provided. At the same time, the analysis can also be applied to other kinds of vehicles. The queuing behavior induced by both the cooperative IR-HARQ protocol and the rate adaptation scheme is analyzed with a Markov model. Several performance metrics are analytically obtained, including the average round-trip frame delay, throughput, and energy efficiency. The results quantitatively demonstrate the impact of atmospheric turbulence and weather conditions on the system performance and support the optimal selection of system parameters. Additionally, the effectiveness of the proposed system is numerically confirmed. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.