Ultra-reliable Communication Research Articles

Multivariate Extreme Value Theory Based Rate Selection for Ultra-Reliable Communications

Multivariate Extreme Value Theory Based Rate Selection for Ultra-Reliable Communications

Microservice-Based Vehicular Network for Seamless and Ultra-Reliable Communications of Connected Vehicles

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks.

An Analytical Reliability Performance Assessment of Cell-Free Massive MIMO Systems for 5G and Beyond Cellular Networks

In today‟s realm, the 5G cellular networks offer a new era of fully digital environment by connecting people anywhere anytime. This era requires inspiring demands to the networks in terms of spectral and energy efficiencies, low-latency and ultra-reliable communications. A major development to deal with these unparalleled demands is the introduction of Cell- Free Massive MIMO (CF mMIMO) systems to the cellular network. In recent years, mathematical foundations have been laid for a CF mMIMO communication system to evaluate the spectral and energy efficiencies given the number of access points. Various existing literatures have adopted mathematical models to formulate closed-form expressions for these efficiencies using uncorrelated Rayleigh fading for simple linear processing detectors. In this paper, we try to address a compelling and interesting question which has not received much attention lately. The question is how to evaluate the reliability of a connected user terminal in both a CF mMIMO and conventional mMIMO system? We answer this question by first analyzing the signal models for both systems assuming exact channel estimates and consider an uncorrelated Rayleigh fading channel model. The exact Bit Error Rate, as a reliability assessment, in closed-form are then derived based on strong mathematical foundations, which are simple to numerically evaluate in Matlab R2022, for MPSK and MQAM modulation schemes. The mathematical models presented in this work can be used in 5G and Beyond 5G networks to evaluate the distributions of error rates for a user given the number of Access points or Base Station antennas and modulation scheme. Machine Learning algorithms can be applied to the analytical expressions with a view to predict the error performance of a user terminal given the fading characteristic of the propagation environmen

Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications

Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications

The Evolution of Mobile Communication: A Comprehensive Survey on 5G Technology

This comprehensive survey meticulously navigates the evolution of mobile communication, focusing on the revolutionary strides introduced by 5G technology. The study elucidates primary objectives, research methodology, pivotal findings, and implications inherent in the trajectory of mobile communication. It aims for a detailed examination of the technological landscape, outlining features that distinguish 5G from its predecessors. The research methodology involves an exhaustive literature review, culminating in a comprehensive overview of 5G technology. Key findings highlight 5G's transformative capacities, spanning unparalleled data transfer speeds, low-latency communication, and applications like IoT, augmented reality, and autonomous vehicles. The study underscores 5G's far-reaching impact across various sectors. Serving as both a retrospective analysis and a valuable resource, the survey guides researchers, industry professionals, and policymakers through the intricate landscape of 5G technology. Addressing the imperative of accommodating increasing mobile data traffic, the 5G network strives for improved spectrum utilization, energy efficiency, and QoS parameters. Notable innovations include Massive MIMO, promising high spectral efficiency. The focus on efficacy, accuracy, and channel estimation techniques is evident. The article explores specific requirements of 5G wireless communication systems, addressing challenges faced by earlier mobile generations. The advent of 5G emerges as a pivotal solution, aiming for a 1000-fold capacity gain and assuring quality-of-service, higher spectral efficiency, ultra-reliable communication, and facilitating massive machinetype communication. Challenges in traffic load and resource consumption necessitate innovative approaches. The research reviews emerging wireless technologies, including an analysis of Massive MIMO as an efficient solution. The paper promises to be a valuable resource, contributing to the seamless development of state-of-the-art 5G and B5G networks, recognizing the importance of continued research and innovation in the dynamic landscape of mobile communication technologies.

Energy-efficient anchor activation protocol for non-cooperative localization of Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoT) necessitates low-latency and ultra-reliable communication to support various applications. Besides, the data generated in IIoT must be geo-tagged to take location-aware timely actions. Therefore, this paper proposes an accurate and energy-efficient localization scheme for localizing the devices in IIoT. We introduce a novel anchor activation protocol for the non-cooperative localization of IIoT devices. The proposed method is sequential, distributed, and error adaptive, that first performs distributed scheduling followed by adaptive selection. This distributed localization scheme is well-suited for IIoT due to the non-cooperative nature of the devices; however, at the same time, it is more challenging because no centralized node can disseminate the anchors’ network information. Therefore, the anchors rely only on the local information from other anchors in their vicinity. The distributed approach can significantly benefit such networks because the centralized node can be a system’s bottleneck. Besides the distributed nature, the decision process of anchor selection for localization considers the transmission energy of the anchors to make it energy efficient. Our results show the effectiveness of the proposed method both in terms of localization accuracy and energy efficiency.

Radio environment maps for indoor visible light communications aided by machine learning

VLC has been proposed as reliable high-capacity wireless optical communication system for indoor access networks. This technology comprises a high bandwidth unlicensed solution and has been suggested for ultra-reliable environments like hospitals. In this study, Machine Learning is used to investigate indoor wireless optical channels where ultra-reliable communications and signal coverage are required. Specifically, we assess the precision of different Decision Trees ML algorithms based on their mean absolute error metric in comparison to the anticipated simulated outcomes for optical channel estimation in various rooms that could be used in medical care. We demonstrate that Decision Trees models are capable of making fast and accurate predictions. It is shown that the Stack model is the most appropriate Decision Trees-based method, from the ones we studied, for predicting indoor VLC RSS values and constructing the corresponding REMs. These models are 300–65,000 times faster than traditional calculation methods, depending on the number of reflections bounces considered, and deliver accurate predictions, with less than 0.015 mean absolute percentage error, of the Radio Environment Map of a communication system that supports VLC. This is the first implementation of constructing optical REMs for Indoor VLC systems to the best knowledge of the authors.

Assessing the Spectrum Needs for Network-Wide Ultra-Reliable Communication With Meta Distributions

Identifying and assessing the spectrum needs for ultra-reliable communication are key to enabling ultra-reliable communication services in the next generation of mobile networks. This article proposes a framework to assess the spectrum required to support ultra-reliable communication services network-wide. Using meta distributions, we derive expressions that reveal a simple relationship between spectrum needs and reliability requirements. Broadly speaking, each “nine” in reliability (e.g., five-nines in 99.999%) maps onto one order of magnitude of additional bandwidth. Numerical evaluations indicate that the bandwidth required for ultra-reliable communication can be up to hundreds of gigahertz, beyond what is available in today’s networks.

Multi-Technology Cooperative Driving: An Analysis Based on PLEXE

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version of PLEXE, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naive approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments.

AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications

Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.

Low-Rate and Short-Block-Length Random Permutation-Coded Modulations Achieve Finite-Length Bounds

Ultra-reliable low-latency communication (URLLC) is an important feature brought by 5G New Radio (NR). By using a block code, the low-latency requirement in general requires a short block length in order to reduce the latency of transmitting the entire code block. The ultra-reliable communication requires a low-rate code to achieve high decoding reliability even under low signal-to-noise ratios (SNRs). This paper proposes a new class of coding and modulation schemes, termed permutation-coded modulations, for low-rate and short-block-length applications, such as URLLC. We show that permutation-coded modulations under maximum-likelihood (ML) decoding have remarkable performance levels that achieve the dispersion bounds with normal approximation (NA) for short block lengths. However, their encoding and ML-decoding complexities are prohibitive if the number of message bits transmitted per code block increases. To reduce the encoding and decoding complexities, we propose a trapezoidal permutation-coded modulation scheme which can be decoded efficiently by successive cancellation list (SCL) decoders. We also show that the trapezoidal permutation-coded modulations under SCL decoding can achieve the dispersion bounds with NA for short block lengths.

Resource Allocation for IRS-Assisted Uplink URLLC Systems

We study the resource allocation optimization in an intelligent reflecting surface (IRS)-assisted uplink ultra reliable and low-latency communication (URLLC) system. The transmit power of users, the receive beamformer at the base station (BS) and the IRS reflection phase shifts are jointly optimized to maximize the achievable sum finite blocklength (FBL) rate, which turns out to be a complicated non-convex problem. To tackle this problem, we develop a low-complexity minorization-maximization (MM)-based algorithm, which only involves calculating closed-form expressions in each iteration. Compared with an existing quadratic transform based scheme, the proposed design achieves better performance while with reduced complexity.

On the Required Radio Resources for Ultra-Reliable Communication in Highly Interfered Scenarios

Future wireless systems are expected to support mission-critical services demanding higher and higher reliability. In this letter, we dimension the radio resources needed to achieve a given failure probability target for ultra-reliable wireless systems in high interference conditions, assuming a protocol with frequency hopping combined with packet repetitions. We resort to packet erasure channel models and derive the minimum amount of resource units in the case of receiver with and without collision resolution capability, as well as the number of packet repetitions needed for achieving the failure probability target. Analytical results are numerically validated, and can be used as benchmark for realistic system simulations.

Outage Performance of RIS-Assisted V2I Communications With Inter-Cell Interference

With the ability of enhancing desired signals, Reconfigurable Intelligent Surface (RIS) is expected to be applied to vehicular networks which require ultra-reliable communications. In this paper we propose a promising RIS-assisted vehicle-to-infrastructure (V2I) network model in highway environment. Then under Rayleigh fading channels, our goal is to derive the downlink outage probability (OP) of the proposed V2I system with considering the inter-cell interference, i.e., interference from neighbor road side units (RSUs) and RISs. To achieve this intractable derivation, the probability density functions (PDFs) of desired signal power and interference-plus noise power are calculated by leveraging Gauss-Chebyshev quadrature and moment matching. Numerical results verify the accuracy of our derivation and it is shown that RISs can enhance the desired signal with causing little interference to neighbor cells, so that the OP of V2I communications can be greatly reduced.

Midhaul Performance Modelling Using Commodity Hardware C-RAN Testbed

Midhauls introduce new characteristics to wireless networks. We study implementation performance behaviour to make appropriate network and algorithm design decisions soft–real-time midhaul-based radio access network implementations. A model is developed based on data obtained using a testbed, which can be used to estimate midhaul latency as a function of the number of communicating nodes. The testbed comprises one central unit (CU) controlling a variable number of distributed units (DU) synchronized over Precision Time Protocol (PTP). Average reporting latencies of 266~upmu hbox {s} were observed with 16 DUs. Typical jitter performance was 99.99 % of values below 471.75~upmu hbox {s} but maximum values up to an order of magnitude larger. Variations in performance of up to 16.2 times more deadline misses were observed between best and worst performing DUs. A model fitted to the obtained data estimates the latency and jitter of CU-DU communication as a function of the number DUs. Results indicate suitability for application with moderate synchronization requirements, for example positioning, but insufficient for the most stringent uses case such ultra-reliable communication.

Millimeter-wave channel modeling in a VANETs using coding techniques.

The Vehicular ad-Hoc Network (VANET) is envisioned to ensure wireless transmission with ultra-high reliability. In the presence of fading and mobility of vehicles, error-free information between Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) requires extensive investigation. The current literature lacks in designing an ultra-reliable comprehensive tractable model for VANET using millimeter wave. Ultra-reliable communication is needed to support autonomous vehicular communication. This article aims to provide a comprehensive tractable model for VANET over millimeter waves using Space-Time-Block-Coding (STBC) concatenated with Reed Solomon (RS) coding. The designed model provides the fastest way of designing and analyzing VANET networks on 60 GHz. By using the derived BER expressions and Reed Solomon coded doppler expression ultra-reliable vehicular networks can be build meeting the demands of massive growing volume of traffic. The performance of the model is compared with previous BER computational techniques and existing VANET communication systems, i.e., IEEE 802.11bd and 3rd generation partnership project vehicle to everything (3GPP V2X). The findings show that our proposed approach outperforms IEEE 802.11bd and the results are comparable with V2X NR. Packet Error Rate (PER), Packet Reception Ratio (PRR) and throughput are used as performance metrics. We have also evaluated the model on higher velocities of vehicles. Further, the simulation and numerical findings show that the proposed system surpass the existing BER results comprising of various modulation and coding techniques. The simulation results are verified by the numerical results there-by, showing the accuracy of our derived expressions.

Study of Uplink Resource Allocation for 5G IoT Services by Using Reinforcement Learning

&lt;p&gt;In order to support real time IoT services, the ultra Reliable and Low Latency Communications (uRLLC) was proposed in 5G wireless communication network. Different from the grant based access in 4G, the grant free technique is proposed in 5G to reduce the random access delay of uRLLC-required applications. This paper proposes the dedicated resource for exclusive access of individual UE and the shared resource pool for the contention of multiple UEs by adopting the reinforcement learning approach. The objective of this paper is to accomplish the uplink successful rate above 99.9% under certain transmission error probability. The proposed Prediction based Hybrid Resource Allocation (PHRA) scheme allocates the access resource in a heuristic manner by referring to the activity of UEs. The dedicated resource is mainly allocated to the high activity UEs and the initial transmission of UEs with medium activity while the shared resource pool is allocated for the re-transmission of medium activity UEs and low activity UEs by using the reinforcement learning model. The burst traffic model was applied during the exhaustive experiments. And the simulation results show that the proposed scheme achieves higher uplink packet delivery ratio and more effective resource utilization than the other schemes.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Energy and MCS Optimization in HARQ Protocol for Ultrareliable Regime With Maximized Throughput

In this article, we propose a cross-layer approach to maximize the throughput efficiency of the hybrid automatic repeat request (HARQ) protocol while guaranteeing ultrareliable communication (URC). The study combines adaptive modulation and coding and power control, at the physical layer, with automatic repeat request (ARQ)/HARQ at the data-link layer. The proposed optimization approach, based on genetic algorithms, allows a judicious allocation of the available energy budget and a suitable modulation and coding scheme (MCS) choice while keeping the same energy budget. For a given signal-to-noise ratio (SNR), a vector of energy and an MCS are modeled as one individual. The individuals with the highest throughput efficiency are selected to be used in generating the next population through crossover of the survivals and mutation. The individual with the highest throughput efficiency in the last population presents the solution offering the optimized parameters used in the packet transmission. We compare our solution to an existing one that exploits Lagrange multipliers. The results show that the proposed genetic algorithm based solution ensures URC with packet erasure probability less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-5}$</tex-math></inline-formula> from SNR values of 7 and 13 dB in static and fading channels, respectively.

A Learning-Based Context-Aware Quality Test System in B5G-Aided Advanced Manufacturing

The booming of the industrial Internet of Things (IIoT) brings an exponential increase in industrial devices, calling for more flexible and low-cost communications. The fifth generation and beyond (B5G) communication technologies provide a dedicated solution by supporting two industry-targeted technologies: Massive machine-type communications (mMTC) and ultra reliable low-latency communications (URLLC). In this article, we design a B5G-aided quality test system in advanced manufacturing, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short length commands and small size feedback to each other, respectively, via URLLC. We formulate a long-term optimization problem to improve the product qualification rate by maximizing the expected average reward with limited testing capacity and changing configurations. To address this problem, we develop a novel context-aware combinatorial quality test (CC-QT) algorithm based on bandit learning (BL), which integrates contextual information to predict the product quality, and a combinatorial method to decrease the complexity of the BL process. Furthermore, we derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

Learning to Broadcast for Ultra-Reliable Communication With Differential Quality of Service via the Conditional Value at Risk

Learning to Broadcast for Ultra-Reliable Communication With Differential Quality of Service via the Conditional Value at Risk